diff --git a/.coveragerc b/.coveragerc
new file mode 100644
index 0000000000..dffe22a6f4
--- /dev/null
+++ b/.coveragerc
@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Generated by synthtool. DO NOT EDIT!
+[run]
+branch = True
+omit =
+  google/__init__.py
+  google/cloud/__init__.py
+
+[report]
+fail_under = 35
+show_missing = True
+exclude_lines =
+    # Re-enable the standard pragma
+    pragma: NO COVER
+    # Ignore debug-only repr
+    def __repr__
+    # Ignore abstract methods
+    raise NotImplementedError
+omit =
+  */gapic/*.py
+  */proto/*.py
+  */core/*.py
+  */site-packages/*.py
+  google/cloud/__init__.py
diff --git a/.flake8 b/.flake8
index 7826eceab6..87f6e408c4 100644
--- a/.flake8
+++ b/.flake8
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright 2020 Google LLC
+# Copyright 2023 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -18,8 +18,6 @@
 [flake8]
 ignore = E203, E231, E266, E501, W503
 exclude =
-  third_party/**
-
   # Exclude generated code.
   **/proto/**
   **/gapic/**
diff --git a/.github/.OwlBot.lock.yaml b/.github/.OwlBot.lock.yaml
new file mode 100644
index 0000000000..a3da1b0d4c
--- /dev/null
+++ b/.github/.OwlBot.lock.yaml
@@ -0,0 +1,17 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+docker:
+  image: gcr.io/cloud-devrel-public-resources/owlbot-python:latest
+  digest: sha256:3e3800bb100af5d7f9e810d48212b37812c1856d20ffeafb99ebe66461b61fc7
+# created: 2023-08-02T10:53:29.114535628Z
diff --git a/.github/.OwlBot.yaml b/.github/.OwlBot.yaml
new file mode 100644
index 0000000000..c379bd3092
--- /dev/null
+++ b/.github/.OwlBot.yaml
@@ -0,0 +1,18 @@
+# Copyright 2021 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+docker:
+  image: gcr.io/cloud-devrel-public-resources/owlbot-python:latest
+
+begin-after-commit-hash: 92006bb3cdc84677aa93c7f5235424ec2b157146
diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index cd904459a9..7686a50da6 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -4,6 +4,7 @@
 # For syntax help see:
 # https://help.github.com/en/github/creating-cloning-and-archiving-repositories/about-code-owners#codeowners-syntax
 # Note: This file is autogenerated. To make changes to the codeowner team, please update .repo-metadata.json.
+
 # @googleapis/yoshi-python @googleapis/api-bigquery-dataframe are the default owners for changes in this repo
 *     @googleapis/yoshi-python @googleapis/api-bigquery-dataframe
 
diff --git a/.github/header-checker-lint.yml b/.github/header-checker-lint.yml
index 3058bec338..6fe78aa798 100644
--- a/.github/header-checker-lint.yml
+++ b/.github/header-checker-lint.yml
@@ -2,14 +2,14 @@
  "allowedLicenses": ["Apache-2.0", "MIT", "BSD-3"],
  "ignoreFiles": ["**/requirements.txt", "**/requirements-test.txt", "**/__init__.py", "samples/**/constraints.txt", "samples/**/constraints-test.txt"],
  "sourceFileExtensions": [
- 	"ts",
- 	"js",
- 	"java",
- 	"sh",
- 	"Dockerfile",
- 	"yaml",
+ 	"ts", 
+ 	"js", 
+ 	"java", 
+ 	"sh", 
+ 	"Dockerfile", 
+ 	"yaml", 
  	"py",
  	"html",
  	"txt"
  ]
-}
+}
\ No newline at end of file
diff --git a/.github/sync-repo-settings.yaml b/.github/sync-repo-settings.yaml
index 5004e872b4..b7dae76ba3 100644
--- a/.github/sync-repo-settings.yaml
+++ b/.github/sync-repo-settings.yaml
@@ -15,7 +15,7 @@ branchProtectionRules:
     - 'unit (3.10)'
     - 'unit (3.11)'
     - 'cover'
-    # TODO(tswast): add Kokoro once we've enabled it
+    - 'Kokoro presubmit'
 permissionRules:
   - team: actools-python
     permission: admin
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index d2aee5b7d8..16d5a9e90f 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -12,7 +12,7 @@ jobs:
     - name: Setup Python
       uses: actions/setup-python@v4
       with:
-        python-version: "3.10"
+        python-version: "3.8"
     - name: Install nox
       run: |
         python -m pip install --upgrade setuptools pip wheel
diff --git a/.github/workflows/unittest.yml b/.github/workflows/unittest.yml
index bb268fe6c6..465199fc9a 100644
--- a/.github/workflows/unittest.yml
+++ b/.github/workflows/unittest.yml
@@ -41,7 +41,7 @@ jobs:
     - name: Setup Python
       uses: actions/setup-python@v4
       with:
-        python-version: "3.10"
+        python-version: "3.8"
     - name: Install coverage
       run: |
         python -m pip install --upgrade setuptools pip wheel
@@ -54,4 +54,4 @@ jobs:
     - name: Report coverage results
       run: |
         coverage combine .coverage-results/.coverage*
-        coverage report --show-missing --fail-under=40
+        coverage report --show-missing --fail-under=35
diff --git a/.gitignore b/.gitignore
index 2cb117c23a..b4243ced74 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,12 +26,9 @@ pip-log.txt
 
 # Unit test / coverage reports
 .coverage
-.coverage.*
 .nox
 .cache
-.mypy_cache
 .pytest_cache
-.pytype
 
 
 # Mac
@@ -53,7 +50,6 @@ docs.metadata
 
 # Virtual environment
 env/
-venv/
 
 # Test logs
 coverage.xml
@@ -65,6 +61,3 @@ system_tests/local_test_setup
 # Make sure a generated file isn't accidentally committed.
 pylintrc
 pylintrc.test
-
-# Notebook scratch
-.ipynb_checkpoints
diff --git a/.kokoro/build.sh b/.kokoro/build.sh
index 402ac0eb8c..f91c541c6c 100755
--- a/.kokoro/build.sh
+++ b/.kokoro/build.sh
@@ -16,13 +16,13 @@
 set -eo pipefail
 
 if [[ -z "${KOKORO_GOB_COMMIT}" ]]; then
-    PROJECT_SCM="github"
+    PROJECT_SCM="github/python-bigquery-dataframes"
 else
-    PROJECT_SCM="git"
+    PROJECT_SCM="git/bigframes"
 fi
 
 if [[ -z "${PROJECT_ROOT:-}" ]]; then
-    PROJECT_ROOT="${KOKORO_ARTIFACTS_DIR}/${PROJECT_SCM}/bigframes"
+    PROJECT_ROOT="${KOKORO_ARTIFACTS_DIR}/${PROJECT_SCM}"
 fi
 
 cd "${PROJECT_ROOT}"
diff --git a/.kokoro/continuous/continuous.cfg b/.kokoro/continuous/continuous.cfg
index 18a4c35325..8f43917d92 100644
--- a/.kokoro/continuous/continuous.cfg
+++ b/.kokoro/continuous/continuous.cfg
@@ -1 +1 @@
-# Format: //devtools/kokoro/config/proto/build.proto
+# Format: //devtools/kokoro/config/proto/build.proto
\ No newline at end of file
diff --git a/.kokoro/continuous/prerelease-deps.cfg b/.kokoro/continuous/prerelease-deps.cfg
new file mode 100644
index 0000000000..3595fb43f5
--- /dev/null
+++ b/.kokoro/continuous/prerelease-deps.cfg
@@ -0,0 +1,7 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+# Only run this nox session.
+env_vars: {
+    key: "NOX_SESSION"
+    value: "prerelease_deps"
+}
diff --git a/.kokoro/docker/docs/Dockerfile b/.kokoro/docker/docs/Dockerfile
index e8ee8191ee..8e39a2cc43 100644
--- a/.kokoro/docker/docs/Dockerfile
+++ b/.kokoro/docker/docs/Dockerfile
@@ -80,4 +80,4 @@ RUN wget -O /tmp/get-pip.py 'https://bootstrap.pypa.io/get-pip.py' \
 # Test pip
 RUN python3 -m pip
 
-CMD ["python3.9"]
+CMD ["python3.8"]
diff --git a/.kokoro/docs/common.cfg b/.kokoro/docs/common.cfg
index bd73988540..e6e409f29c 100644
--- a/.kokoro/docs/common.cfg
+++ b/.kokoro/docs/common.cfg
@@ -11,7 +11,7 @@ action {
 gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline"
 
 # Use the trampoline script to run in docker.
-build_file: "bigframes/.kokoro/trampoline_v2.sh"
+build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh"
 
 # Configure the docker image for kokoro-trampoline.
 env_vars: {
@@ -20,12 +20,18 @@ env_vars: {
 }
 env_vars: {
     key: "TRAMPOLINE_BUILD_FILE"
-    value: ".kokoro/publish-docs.sh"
+    value: "github/python-bigquery-dataframes/.kokoro/publish-docs.sh"
+}
+
+env_vars: {
+    key: "STAGING_BUCKET"
+    value: "docs-staging"
 }
 
 env_vars: {
     key: "V2_STAGING_BUCKET"
-    # Push google cloud library docs to the Cloud RAD bucket `docs-staging-v2`
+    # Push non-cloud library docs to `docs-staging-v2-staging` instead of the
+    # Cloud RAD bucket `docs-staging-v2`
     value: "docs-staging-v2"
 }
 
@@ -58,4 +64,4 @@ before_action {
       keyname: "docuploader_service_account"
     }
   }
-}
+}
\ No newline at end of file
diff --git a/.kokoro/docs/docs-presubmit-gerrit.cfg b/.kokoro/docs/docs-presubmit-gerrit.cfg
new file mode 100644
index 0000000000..1d0dc4b499
--- /dev/null
+++ b/.kokoro/docs/docs-presubmit-gerrit.cfg
@@ -0,0 +1,23 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "V2_STAGING_BUCKET"
+    value: "gcloud-python-test"
+}
+
+# We only upload the image in the main `docs` build.
+env_vars: {
+    key: "TRAMPOLINE_IMAGE_UPLOAD"
+    value: "false"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: ".kokoro/build.sh"
+}
+
+# Only run this nox session.
+env_vars: {
+    key: "NOX_SESSION"
+    value: "docfx"
+}
diff --git a/.kokoro/docs/docs-presubmit.cfg b/.kokoro/docs/docs-presubmit.cfg
index 1d0dc4b499..805cfd162b 100644
--- a/.kokoro/docs/docs-presubmit.cfg
+++ b/.kokoro/docs/docs-presubmit.cfg
@@ -1,5 +1,10 @@
 # Format: //devtools/kokoro/config/proto/build.proto
 
+env_vars: {
+    key: "STAGING_BUCKET"
+    value: "gcloud-python-test"
+}
+
 env_vars: {
     key: "V2_STAGING_BUCKET"
     value: "gcloud-python-test"
@@ -13,11 +18,11 @@ env_vars: {
 
 env_vars: {
     key: "TRAMPOLINE_BUILD_FILE"
-    value: ".kokoro/build.sh"
+    value: "github/python-bigquery-dataframes/.kokoro/build.sh"
 }
 
 # Only run this nox session.
 env_vars: {
     key: "NOX_SESSION"
-    value: "docfx"
+    value: "docs docfx"
 }
diff --git a/.kokoro/docs/docs.cfg b/.kokoro/docs/docs.cfg
index 18a4c35325..8f43917d92 100644
--- a/.kokoro/docs/docs.cfg
+++ b/.kokoro/docs/docs.cfg
@@ -1 +1 @@
-# Format: //devtools/kokoro/config/proto/build.proto
+# Format: //devtools/kokoro/config/proto/build.proto
\ No newline at end of file
diff --git a/.kokoro/populate-secrets.sh b/.kokoro/populate-secrets.sh
new file mode 100755
index 0000000000..6f3972140e
--- /dev/null
+++ b/.kokoro/populate-secrets.sh
@@ -0,0 +1,43 @@
+#!/bin/bash
+# Copyright 2023 Google LLC.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+set -eo pipefail
+
+function now { date +"%Y-%m-%d %H:%M:%S" | tr -d '\n' ;}
+function msg { println "$*" >&2 ;}
+function println { printf '%s\n' "$(now) $*" ;}
+
+
+# Populates requested secrets set in SECRET_MANAGER_KEYS from service account:
+# kokoro-trampoline@cloud-devrel-kokoro-resources.iam.gserviceaccount.com
+SECRET_LOCATION="${KOKORO_GFILE_DIR}/secret_manager"
+msg "Creating folder on disk for secrets: ${SECRET_LOCATION}"
+mkdir -p ${SECRET_LOCATION}
+for key in $(echo ${SECRET_MANAGER_KEYS} | sed "s/,/ /g")
+do
+  msg "Retrieving secret ${key}"
+  docker run --entrypoint=gcloud \
+    --volume=${KOKORO_GFILE_DIR}:${KOKORO_GFILE_DIR} \
+    gcr.io/google.com/cloudsdktool/cloud-sdk \
+    secrets versions access latest \
+    --project cloud-devrel-kokoro-resources \
+    --secret ${key} > \
+    "${SECRET_LOCATION}/${key}"
+  if [[ $? == 0 ]]; then
+    msg "Secret written to ${SECRET_LOCATION}/${key}"
+  else
+    msg "Error retrieving secret ${key}"
+  fi
+done
diff --git a/.kokoro/presubmit/common.cfg b/.kokoro/presubmit/common.cfg
index 5d40578ac7..97e0651aa9 100644
--- a/.kokoro/presubmit/common.cfg
+++ b/.kokoro/presubmit/common.cfg
@@ -7,4 +7,4 @@ action {
   }
 }
 
-build_file: "bigframes/.kokoro/build.sh"
+build_file: "python-bigquery-dataframes/.kokoro/build.sh"
diff --git a/.kokoro/presubmit/e2e-gerrit.cfg b/.kokoro/presubmit/e2e-gerrit.cfg
new file mode 100644
index 0000000000..d875f36060
--- /dev/null
+++ b/.kokoro/presubmit/e2e-gerrit.cfg
@@ -0,0 +1,7 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+# Only run this nox session.
+env_vars: {
+    key: "NOX_SESSION"
+    value: "system_noextras e2e notebook samples"
+}
diff --git a/.kokoro/presubmit/prerelease-deps.cfg b/.kokoro/presubmit/prerelease-deps.cfg
new file mode 100644
index 0000000000..3595fb43f5
--- /dev/null
+++ b/.kokoro/presubmit/prerelease-deps.cfg
@@ -0,0 +1,7 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+# Only run this nox session.
+env_vars: {
+    key: "NOX_SESSION"
+    value: "prerelease_deps"
+}
diff --git a/.kokoro/presubmit/presubmit-gerrit.cfg b/.kokoro/presubmit/presubmit-gerrit.cfg
new file mode 100644
index 0000000000..18a4c35325
--- /dev/null
+++ b/.kokoro/presubmit/presubmit-gerrit.cfg
@@ -0,0 +1 @@
+# Format: //devtools/kokoro/config/proto/build.proto
diff --git a/.kokoro/presubmit/presubmit.cfg b/.kokoro/presubmit/presubmit.cfg
index 18a4c35325..8f43917d92 100644
--- a/.kokoro/presubmit/presubmit.cfg
+++ b/.kokoro/presubmit/presubmit.cfg
@@ -1 +1 @@
-# Format: //devtools/kokoro/config/proto/build.proto
+# Format: //devtools/kokoro/config/proto/build.proto
\ No newline at end of file
diff --git a/.kokoro/publish-docs.sh b/.kokoro/publish-docs.sh
index 3fc3905ac1..7700c90ee9 100755
--- a/.kokoro/publish-docs.sh
+++ b/.kokoro/publish-docs.sh
@@ -24,6 +24,25 @@ export PATH="${HOME}/.local/bin:${PATH}"
 python3 -m pip install --require-hashes -r .kokoro/requirements.txt
 python3 -m nox --version
 
+# build docs
+nox -s docs
+
+# create metadata
+python3 -m docuploader create-metadata \
+  --name=$(jq --raw-output '.name // empty' .repo-metadata.json) \
+  --version=$(python3 setup.py --version) \
+  --language=$(jq --raw-output '.language // empty' .repo-metadata.json) \
+  --distribution-name=$(python3 setup.py --name) \
+  --product-page=$(jq --raw-output '.product_documentation // empty' .repo-metadata.json) \
+  --github-repository=$(jq --raw-output '.repo // empty' .repo-metadata.json) \
+  --issue-tracker=$(jq --raw-output '.issue_tracker // empty' .repo-metadata.json)
+
+cat docs.metadata
+
+# upload docs
+python3 -m docuploader upload docs/_build/html --metadata-file docs.metadata --staging-bucket "${STAGING_BUCKET}"
+
+
 # docfx yaml files
 nox -s docfx
 
diff --git a/.kokoro/release-nightly.sh b/.kokoro/release-nightly.sh
index 582808a15c..488dbb9e13 100755
--- a/.kokoro/release-nightly.sh
+++ b/.kokoro/release-nightly.sh
@@ -35,13 +35,13 @@ while [ $# -gt 0 ] ; do
 done
 
 if [[ -z "${KOKORO_GOB_COMMIT}" ]]; then
-    PROJECT_SCM="github"
+    PROJECT_SCM="github/python-bigquery-dataframes"
 else
-    PROJECT_SCM="git"
+    PROJECT_SCM="git/bigframes"
 fi
 
 if [ -z "${PROJECT_ROOT:-}" ]; then
-    PROJECT_ROOT="${KOKORO_ARTIFACTS_DIR}/${PROJECT_SCM}/bigframes"
+    PROJECT_ROOT="${KOKORO_ARTIFACTS_DIR}/${PROJECT_SCM}"
 fi
 
 # Move into the package, build the distribution and upload to shared bucket.
@@ -158,49 +158,6 @@ sed -i -e "s/$RELEASE_VERSION/$BIGFRAMES_VERSION/g" bigframes/version.py
 mv -f ${SETUP_CFG_BKP} setup.cfg
 rm -f ${THIRD_PARTY_NOTICES_FILE}
 
-# Keep this last so as not to block the release on PDF docs build.
-pdf_docs () {
-    sudo apt update
-    sudo apt install -y texlive texlive-latex-extra latexmk
-
-    pushd "${PROJECT_ROOT}/docs"
-    make latexpdf
-
-    cp "_build/latex/bigframes.pdf" "_build/latex/bigframes-${RELEASE_VERSION}.pdf"
-    cp "_build/latex/bigframes.pdf" "_build/latex/bigframes-latest.pdf"
-
-    if ! [ ${DRY_RUN} ]; then
-        for gcs_path in gs://vertex_sdk_private_releases/bigframe/ \
-                        gs://dl-platform-colab/bigframes/ \
-                        gs://bigframes-wheels/;
-        do
-          gsutil cp -v "_build/latex/bigframes-*.pdf" ${gcs_path}
-        done
-    fi
-
-    popd
-}
-
-pdf_docs
-
-# Copy html docs to GCS from where it can be deployed to anywhere else
-gcs_docs () {
-    docs_gcs_bucket=gs://bigframes-docs
-    docs_local_html_folder=docs/_build/html
-    if [ ! -d ${docs_local_html_folder} ]; then
-      python3.10 -m nox -s docs
-    fi
-
-    if ! [ ${DRY_RUN} ]; then
-        gsutil -m cp -v -r ${docs_local_html_folder} ${docs_gcs_bucket}/${GIT_HASH}
-
-        # Copy the script to refresh firebase docs website from GCS to GCS itself
-        gsutil -m cp -v scripts/update_firebase_docs_site.sh ${docs_gcs_bucket}
-    fi
-}
-
-gcs_docs
-
 if ! [ ${DRY_RUN} ]; then
     # Copy docs and wheels to Google Drive
     python3.10 scripts/upload_to_google_drive.py
diff --git a/.kokoro/release.sh b/.kokoro/release.sh
index 2bce143e1c..320ac51271 100755
--- a/.kokoro/release.sh
+++ b/.kokoro/release.sh
@@ -16,7 +16,7 @@
 set -eo pipefail
 
 # Start the releasetool reporter
-python3 -m pip install --require-hashes -r github/bigframes/.kokoro/requirements.txt
+python3 -m pip install --require-hashes -r github/python-bigquery-dataframes/.kokoro/requirements.txt
 python3 -m releasetool publish-reporter-script > /tmp/publisher-script; source /tmp/publisher-script
 
 # Disable buffering, so that the logs stream through.
@@ -24,6 +24,6 @@ export PYTHONUNBUFFERED=1
 
 # Move into the package, build the distribution and upload.
 TWINE_PASSWORD=$(cat "${KOKORO_KEYSTORE_DIR}/73713_google-cloud-pypi-token-keystore-1")
-cd github/bigframes
+cd github/python-bigquery-dataframes
 python3 setup.py sdist bdist_wheel
 twine upload --username __token__ --password "${TWINE_PASSWORD}" dist/*
diff --git a/.kokoro/release/common.cfg b/.kokoro/release/common.cfg
index 021198fb26..a0c39946cf 100644
--- a/.kokoro/release/common.cfg
+++ b/.kokoro/release/common.cfg
@@ -11,7 +11,7 @@ action {
 gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline"
 
 # Use the trampoline script to run in docker.
-build_file: "bigframes/.kokoro/trampoline_v2.sh"
+build_file: "python-bigquery-dataframes/.kokoro/trampoline.sh"
 
 # Configure the docker image for kokoro-trampoline.
 env_vars: {
@@ -20,7 +20,7 @@ env_vars: {
 }
 env_vars: {
     key: "TRAMPOLINE_BUILD_FILE"
-    value: "github/bigframes/.kokoro/release.sh"
+    value: "github/python-bigquery-dataframes/.kokoro/release.sh"
 }
 
 # Fetch PyPI password
@@ -43,7 +43,7 @@ env_vars: {
 # what we published, which we can use to generate SBOMs and attestations.
 action {
   define_artifacts {
-    regex: "github/bigframes/**/*.tar.gz"
-    strip_prefix: "github/bigframes"
+    regex: "github/python-bigquery-dataframes/**/*.tar.gz"
+    strip_prefix: "github/python-bigquery-dataframes"
   }
 }
diff --git a/.kokoro/requirements.in b/.kokoro/requirements.in
index 9fd9e127ba..ec867d9fd6 100644
--- a/.kokoro/requirements.in
+++ b/.kokoro/requirements.in
@@ -8,6 +8,3 @@ setuptools
 nox>=2022.11.21 # required to remove dependency on py
 charset-normalizer<3
 click<8.1.0
-sphinx==4.5.0 # docs PDF see noxfile for HTML docs build
-alabaster # docs PDF
-recommonmark # docs PDF
diff --git a/.kokoro/requirements.txt b/.kokoro/requirements.txt
index fd0e0b9d48..029bd342de 100644
--- a/.kokoro/requirements.txt
+++ b/.kokoro/requirements.txt
@@ -1,38 +1,28 @@
 #
-# This file is autogenerated by pip-compile with Python 3.10
+# This file is autogenerated by pip-compile with Python 3.9
 # by the following command:
 #
 #    pip-compile --allow-unsafe --generate-hashes requirements.in
 #
-alabaster==0.7.13 \
-    --hash=sha256:1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3 \
-    --hash=sha256:a27a4a084d5e690e16e01e03ad2b2e552c61a65469419b907243193de1a84ae2
-    # via
-    #   -r requirements.in
-    #   sphinx
-argcomplete==3.1.1 \
-    --hash=sha256:35fa893a88deea85ea7b20d241100e64516d6af6d7b0ae2bed1d263d26f70948 \
-    --hash=sha256:6c4c563f14f01440aaffa3eae13441c5db2357b5eec639abe7c0b15334627dff
+argcomplete==2.0.0 \
+    --hash=sha256:6372ad78c89d662035101418ae253668445b391755cfe94ea52f1b9d22425b20 \
+    --hash=sha256:cffa11ea77999bb0dd27bb25ff6dc142a6796142f68d45b1a26b11f58724561e
     # via nox
-attrs==23.1.0 \
-    --hash=sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04 \
-    --hash=sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015
+attrs==22.1.0 \
+    --hash=sha256:29adc2665447e5191d0e7c568fde78b21f9672d344281d0c6e1ab085429b22b6 \
+    --hash=sha256:86efa402f67bf2df34f51a335487cf46b1ec130d02b8d39fd248abfd30da551c
     # via gcp-releasetool
-babel==2.12.1 \
-    --hash=sha256:b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610 \
-    --hash=sha256:cc2d99999cd01d44420ae725a21c9e3711b3aadc7976d6147f622d8581963455
-    # via sphinx
-bleach==6.0.0 \
-    --hash=sha256:1a1a85c1595e07d8db14c5f09f09e6433502c51c595970edc090551f0db99414 \
-    --hash=sha256:33c16e3353dbd13028ab4799a0f89a83f113405c766e9c122df8a06f5b85b3f4
+bleach==5.0.1 \
+    --hash=sha256:085f7f33c15bd408dd9b17a4ad77c577db66d76203e5984b1bd59baeee948b2a \
+    --hash=sha256:0d03255c47eb9bd2f26aa9bb7f2107732e7e8fe195ca2f64709fcf3b0a4a085c
     # via readme-renderer
-cachetools==5.3.1 \
-    --hash=sha256:95ef631eeaea14ba2e36f06437f36463aac3a096799e876ee55e5cdccb102590 \
-    --hash=sha256:dce83f2d9b4e1f732a8cd44af8e8fab2dbe46201467fc98b3ef8f269092bf62b
+cachetools==5.2.0 \
+    --hash=sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757 \
+    --hash=sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db
     # via google-auth
-certifi==2023.5.7 \
-    --hash=sha256:0f0d56dc5a6ad56fd4ba36484d6cc34451e1c6548c61daad8c320169f91eddc7 \
-    --hash=sha256:c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716
+certifi==2023.7.22 \
+    --hash=sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082 \
+    --hash=sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9
     # via requests
 cffi==1.15.1 \
     --hash=sha256:00a9ed42e88df81ffae7a8ab6d9356b371399b91dbdf0c3cb1e84c03a13aceb5 \
@@ -122,7 +112,7 @@ colorlog==6.7.0 \
 commonmark==0.9.1 \
     --hash=sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60 \
     --hash=sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9
-    # via recommonmark
+    # via rich
 cryptography==41.0.3 \
     --hash=sha256:0d09fb5356f975974dbcb595ad2d178305e5050656affb7890a1583f5e02a306 \
     --hash=sha256:23c2d778cf829f7d0ae180600b17e9fceea3c2ef8b31a99e3c694cbbf3a24b84 \
@@ -154,34 +144,31 @@ distlib==0.3.6 \
     --hash=sha256:14bad2d9b04d3a36127ac97f30b12a19268f211063d8f8ee4f47108896e11b46 \
     --hash=sha256:f35c4b692542ca110de7ef0bea44d73981caeb34ca0b9b6b2e6d7790dda8f80e
     # via virtualenv
-docutils==0.17.1 \
-    --hash=sha256:686577d2e4c32380bb50cbb22f575ed742d58168cee37e99117a854bcd88f125 \
-    --hash=sha256:cf316c8370a737a022b72b56874f6602acf974a37a9fba42ec2876387549fc61
-    # via
-    #   readme-renderer
-    #   recommonmark
-    #   sphinx
-filelock==3.12.2 \
-    --hash=sha256:002740518d8aa59a26b0c76e10fb8c6e15eae825d34b6fdf670333fd7b938d81 \
-    --hash=sha256:cbb791cdea2a72f23da6ac5b5269ab0a0d161e9ef0100e653b69049a7706d1ec
+docutils==0.19 \
+    --hash=sha256:33995a6753c30b7f577febfc2c50411fec6aac7f7ffeb7c4cfe5991072dcf9e6 \
+    --hash=sha256:5e1de4d849fee02c63b040a4a3fd567f4ab104defd8a5511fbbc24a8a017efbc
+    # via readme-renderer
+filelock==3.8.0 \
+    --hash=sha256:55447caa666f2198c5b6b13a26d2084d26fa5b115c00d065664b2124680c4edc \
+    --hash=sha256:617eb4e5eedc82fc5f47b6d61e4d11cb837c56cb4544e39081099fa17ad109d4
     # via virtualenv
-gcp-docuploader==0.6.5 \
-    --hash=sha256:30221d4ac3e5a2b9c69aa52fdbef68cc3f27d0e6d0d90e220fc024584b8d2318 \
-    --hash=sha256:b7458ef93f605b9d46a4bf3a8dc1755dad1f31d030c8679edf304e343b347eea
+gcp-docuploader==0.6.4 \
+    --hash=sha256:01486419e24633af78fd0167db74a2763974765ee8078ca6eb6964d0ebd388af \
+    --hash=sha256:70861190c123d907b3b067da896265ead2eeb9263969d6955c9e0bb091b5ccbf
     # via -r requirements.in
-gcp-releasetool==1.14.0 \
-    --hash=sha256:a39708206838477bf42804a9dd7b549131ff3fc08c6abb0dd6939e274bd6dfd4 \
-    --hash=sha256:aa163ec8322045c9d46e03f8f7500974568bd5d8d322b6a2d492068a5bdf57bf
+gcp-releasetool==1.10.5 \
+    --hash=sha256:174b7b102d704b254f2a26a3eda2c684fd3543320ec239baf771542a2e58e109 \
+    --hash=sha256:e29d29927fe2ca493105a82958c6873bb2b90d503acac56be2c229e74de0eec9
     # via -r requirements.in
-google-api-core==2.11.1 \
-    --hash=sha256:25d29e05a0058ed5f19c61c0a78b1b53adea4d9364b464d014fbda941f6d1c9a \
-    --hash=sha256:d92a5a92dc36dd4f4b9ee4e55528a90e432b059f93aee6ad857f9de8cc7ae94a
+google-api-core==2.10.2 \
+    --hash=sha256:10c06f7739fe57781f87523375e8e1a3a4674bf6392cd6131a3222182b971320 \
+    --hash=sha256:34f24bd1d5f72a8c4519773d99ca6bf080a6c4e041b4e9f024fe230191dda62e
     # via
     #   google-cloud-core
     #   google-cloud-storage
-google-auth==2.20.0 \
-    --hash=sha256:030af34138909ccde0fbce611afc178f1d65d32fbff281f25738b1fe1c6f3eaa \
-    --hash=sha256:23b7b0950fcda519bfb6692bf0d5289d2ea49fc143717cc7188458ec620e63fa
+google-auth==2.14.1 \
+    --hash=sha256:ccaa901f31ad5cbb562615eb8b664b3dd0bf5404a67618e642307f00613eda4d \
+    --hash=sha256:f5d8701633bebc12e0deea4df8abd8aff31c28b355360597f7f2ee60f2e4d016
     # via
     #   gcp-releasetool
     #   google-api-core
@@ -191,9 +178,9 @@ google-cloud-core==2.3.2 \
     --hash=sha256:8417acf6466be2fa85123441696c4badda48db314c607cf1e5d543fa8bdc22fe \
     --hash=sha256:b9529ee7047fd8d4bf4a2182de619154240df17fbe60ead399078c1ae152af9a
     # via google-cloud-storage
-google-cloud-storage==2.9.0 \
-    --hash=sha256:83a90447f23d5edd045e0037982c270302e3aeb45fc1288d2c2ca713d27bad94 \
-    --hash=sha256:9b6ae7b509fc294bdacb84d0f3ea8e20e2c54a8b4bbe39c5707635fec214eff3
+google-cloud-storage==2.6.0 \
+    --hash=sha256:104ca28ae61243b637f2f01455cc8a05e8f15a2a18ced96cb587241cdd3820f5 \
+    --hash=sha256:4ad0415ff61abdd8bb2ae81c1f8f7ec7d91a1011613f2db87c614c550f97bfe9
     # via gcp-docuploader
 google-crc32c==1.5.0 \
     --hash=sha256:024894d9d3cfbc5943f8f230e23950cd4906b2fe004c72e29b209420a1e6b05a \
@@ -265,25 +252,21 @@ google-crc32c==1.5.0 \
     --hash=sha256:fd8536e902db7e365f49e7d9029283403974ccf29b13fc7028b97e2295b33556 \
     --hash=sha256:fe70e325aa68fa4b5edf7d1a4b6f691eb04bbccac0ace68e34820d283b5f80d4
     # via google-resumable-media
-google-resumable-media==2.5.0 \
-    --hash=sha256:218931e8e2b2a73a58eb354a288e03a0fd5fb1c4583261ac6e4c078666468c93 \
-    --hash=sha256:da1bd943e2e114a56d85d6848497ebf9be6a14d3db23e9fc57581e7c3e8170ec
+google-resumable-media==2.4.0 \
+    --hash=sha256:2aa004c16d295c8f6c33b2b4788ba59d366677c0a25ae7382436cb30f776deaa \
+    --hash=sha256:8d5518502f92b9ecc84ac46779bd4f09694ecb3ba38a3e7ca737a86d15cbca1f
     # via google-cloud-storage
-googleapis-common-protos==1.59.1 \
-    --hash=sha256:0cbedb6fb68f1c07e18eb4c48256320777707e7d0c55063ae56c15db3224a61e \
-    --hash=sha256:b35d530fe825fb4227857bc47ad84c33c809ac96f312e13182bdeaa2abe1178a
+googleapis-common-protos==1.57.0 \
+    --hash=sha256:27a849d6205838fb6cc3c1c21cb9800707a661bb21c6ce7fb13e99eb1f8a0c46 \
+    --hash=sha256:a9f4a1d7f6d9809657b7f1316a1aa527f6664891531bcfcc13b6696e685f443c
     # via google-api-core
 idna==3.4 \
     --hash=sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4 \
     --hash=sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2
     # via requests
-imagesize==1.4.1 \
-    --hash=sha256:0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b \
-    --hash=sha256:69150444affb9cb0d5cc5a92b3676f0b2fb7cd9ae39e947a5e11a36b4497cd4a
-    # via sphinx
-importlib-metadata==6.7.0 \
-    --hash=sha256:1aaf550d4f73e5d6783e7acb77aec43d49da8017410afae93822cc9cca98c4d4 \
-    --hash=sha256:cb52082e659e97afc5dac71e79de97d8681de3aa07ff18578330904a9d18e5b5
+importlib-metadata==5.0.0 \
+    --hash=sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab \
+    --hash=sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43
     # via
     #   -r requirements.in
     #   keyring
@@ -301,97 +284,76 @@ jeepney==0.8.0 \
 jinja2==3.1.2 \
     --hash=sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852 \
     --hash=sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61
-    # via
-    #   gcp-releasetool
-    #   sphinx
-keyring==24.0.0 \
-    --hash=sha256:4e87665a19c514c7edada8b15015cf89bd99b8d7edabc5c43cca77166fa8dfad \
-    --hash=sha256:770f609eed2a16c65a6349f3ba1545d00c73f9fed4254c13766c674fe6d0d22b
+    # via gcp-releasetool
+keyring==23.11.0 \
+    --hash=sha256:3dd30011d555f1345dec2c262f0153f2f0ca6bca041fb1dc4588349bb4c0ac1e \
+    --hash=sha256:ad192263e2cdd5f12875dedc2da13534359a7e760e77f8d04b50968a821c2361
     # via
     #   gcp-releasetool
     #   twine
-markdown-it-py==3.0.0 \
-    --hash=sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1 \
-    --hash=sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb
-    # via rich
-markupsafe==2.1.3 \
-    --hash=sha256:05fb21170423db021895e1ea1e1f3ab3adb85d1c2333cbc2310f2a26bc77272e \
-    --hash=sha256:0a4e4a1aff6c7ac4cd55792abf96c915634c2b97e3cc1c7129578aa68ebd754e \
-    --hash=sha256:10bbfe99883db80bdbaff2dcf681dfc6533a614f700da1287707e8a5d78a8431 \
-    --hash=sha256:134da1eca9ec0ae528110ccc9e48041e0828d79f24121a1a146161103c76e686 \
-    --hash=sha256:1577735524cdad32f9f694208aa75e422adba74f1baee7551620e43a3141f559 \
-    --hash=sha256:1b40069d487e7edb2676d3fbdb2b0829ffa2cd63a2ec26c4938b2d34391b4ecc \
-    --hash=sha256:282c2cb35b5b673bbcadb33a585408104df04f14b2d9b01d4c345a3b92861c2c \
-    --hash=sha256:2c1b19b3aaacc6e57b7e25710ff571c24d6c3613a45e905b1fde04d691b98ee0 \
-    --hash=sha256:2ef12179d3a291be237280175b542c07a36e7f60718296278d8593d21ca937d4 \
-    --hash=sha256:338ae27d6b8745585f87218a3f23f1512dbf52c26c28e322dbe54bcede54ccb9 \
-    --hash=sha256:3c0fae6c3be832a0a0473ac912810b2877c8cb9d76ca48de1ed31e1c68386575 \
-    --hash=sha256:3fd4abcb888d15a94f32b75d8fd18ee162ca0c064f35b11134be77050296d6ba \
-    --hash=sha256:42de32b22b6b804f42c5d98be4f7e5e977ecdd9ee9b660fda1a3edf03b11792d \
-    --hash=sha256:504b320cd4b7eff6f968eddf81127112db685e81f7e36e75f9f84f0df46041c3 \
-    --hash=sha256:525808b8019e36eb524b8c68acdd63a37e75714eac50e988180b169d64480a00 \
-    --hash=sha256:56d9f2ecac662ca1611d183feb03a3fa4406469dafe241673d521dd5ae92a155 \
-    --hash=sha256:5bbe06f8eeafd38e5d0a4894ffec89378b6c6a625ff57e3028921f8ff59318ac \
-    --hash=sha256:65c1a9bcdadc6c28eecee2c119465aebff8f7a584dd719facdd9e825ec61ab52 \
-    --hash=sha256:68e78619a61ecf91e76aa3e6e8e33fc4894a2bebe93410754bd28fce0a8a4f9f \
-    --hash=sha256:69c0f17e9f5a7afdf2cc9fb2d1ce6aabdb3bafb7f38017c0b77862bcec2bbad8 \
-    --hash=sha256:6b2b56950d93e41f33b4223ead100ea0fe11f8e6ee5f641eb753ce4b77a7042b \
-    --hash=sha256:787003c0ddb00500e49a10f2844fac87aa6ce977b90b0feaaf9de23c22508b24 \
-    --hash=sha256:7ef3cb2ebbf91e330e3bb937efada0edd9003683db6b57bb108c4001f37a02ea \
-    --hash=sha256:8023faf4e01efadfa183e863fefde0046de576c6f14659e8782065bcece22198 \
-    --hash=sha256:8758846a7e80910096950b67071243da3e5a20ed2546e6392603c096778d48e0 \
-    --hash=sha256:8afafd99945ead6e075b973fefa56379c5b5c53fd8937dad92c662da5d8fd5ee \
-    --hash=sha256:8c41976a29d078bb235fea9b2ecd3da465df42a562910f9022f1a03107bd02be \
-    --hash=sha256:8e254ae696c88d98da6555f5ace2279cf7cd5b3f52be2b5cf97feafe883b58d2 \
-    --hash=sha256:9402b03f1a1b4dc4c19845e5c749e3ab82d5078d16a2a4c2cd2df62d57bb0707 \
-    --hash=sha256:962f82a3086483f5e5f64dbad880d31038b698494799b097bc59c2edf392fce6 \
-    --hash=sha256:9dcdfd0eaf283af041973bff14a2e143b8bd64e069f4c383416ecd79a81aab58 \
-    --hash=sha256:aa7bd130efab1c280bed0f45501b7c8795f9fdbeb02e965371bbef3523627779 \
-    --hash=sha256:ab4a0df41e7c16a1392727727e7998a467472d0ad65f3ad5e6e765015df08636 \
-    --hash=sha256:ad9e82fb8f09ade1c3e1b996a6337afac2b8b9e365f926f5a61aacc71adc5b3c \
-    --hash=sha256:af598ed32d6ae86f1b747b82783958b1a4ab8f617b06fe68795c7f026abbdcad \
-    --hash=sha256:b076b6226fb84157e3f7c971a47ff3a679d837cf338547532ab866c57930dbee \
-    --hash=sha256:b7ff0f54cb4ff66dd38bebd335a38e2c22c41a8ee45aa608efc890ac3e3931bc \
-    --hash=sha256:bfce63a9e7834b12b87c64d6b155fdd9b3b96191b6bd334bf37db7ff1fe457f2 \
-    --hash=sha256:c011a4149cfbcf9f03994ec2edffcb8b1dc2d2aede7ca243746df97a5d41ce48 \
-    --hash=sha256:c9c804664ebe8f83a211cace637506669e7890fec1b4195b505c214e50dd4eb7 \
-    --hash=sha256:ca379055a47383d02a5400cb0d110cef0a776fc644cda797db0c5696cfd7e18e \
-    --hash=sha256:cb0932dc158471523c9637e807d9bfb93e06a95cbf010f1a38b98623b929ef2b \
-    --hash=sha256:cd0f502fe016460680cd20aaa5a76d241d6f35a1c3350c474bac1273803893fa \
-    --hash=sha256:ceb01949af7121f9fc39f7d27f91be8546f3fb112c608bc4029aef0bab86a2a5 \
-    --hash=sha256:d080e0a5eb2529460b30190fcfcc4199bd7f827663f858a226a81bc27beaa97e \
-    --hash=sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb \
-    --hash=sha256:df0be2b576a7abbf737b1575f048c23fb1d769f267ec4358296f31c2479db8f9 \
-    --hash=sha256:e09031c87a1e51556fdcb46e5bd4f59dfb743061cf93c4d6831bf894f125eb57 \
-    --hash=sha256:e4dd52d80b8c83fdce44e12478ad2e85c64ea965e75d66dbeafb0a3e77308fcc \
-    --hash=sha256:fec21693218efe39aa7f8599346e90c705afa52c5b31ae019b2e57e8f6542bb2
+markupsafe==2.1.1 \
+    --hash=sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003 \
+    --hash=sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88 \
+    --hash=sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5 \
+    --hash=sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7 \
+    --hash=sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a \
+    --hash=sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603 \
+    --hash=sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1 \
+    --hash=sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135 \
+    --hash=sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247 \
+    --hash=sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6 \
+    --hash=sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601 \
+    --hash=sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77 \
+    --hash=sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02 \
+    --hash=sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e \
+    --hash=sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63 \
+    --hash=sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f \
+    --hash=sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980 \
+    --hash=sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b \
+    --hash=sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812 \
+    --hash=sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff \
+    --hash=sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96 \
+    --hash=sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1 \
+    --hash=sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925 \
+    --hash=sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a \
+    --hash=sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6 \
+    --hash=sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e \
+    --hash=sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f \
+    --hash=sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4 \
+    --hash=sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f \
+    --hash=sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3 \
+    --hash=sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c \
+    --hash=sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a \
+    --hash=sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417 \
+    --hash=sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a \
+    --hash=sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a \
+    --hash=sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37 \
+    --hash=sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452 \
+    --hash=sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933 \
+    --hash=sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a \
+    --hash=sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7
     # via jinja2
-mdurl==0.1.2 \
-    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
-    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
-    # via markdown-it-py
-more-itertools==9.1.0 \
-    --hash=sha256:cabaa341ad0389ea83c17a94566a53ae4c9d07349861ecb14dc6d0345cf9ac5d \
-    --hash=sha256:d2bc7f02446e86a68911e58ded76d6561eea00cddfb2a91e7019bbb586c799f3
+more-itertools==9.0.0 \
+    --hash=sha256:250e83d7e81d0c87ca6bd942e6aeab8cc9daa6096d12c5308f3f92fa5e5c1f41 \
+    --hash=sha256:5a6257e40878ef0520b1803990e3e22303a41b5714006c32a3fd8304b26ea1ab
     # via jaraco-classes
-nox==2023.4.22 \
-    --hash=sha256:0b1adc619c58ab4fa57d6ab2e7823fe47a32e70202f287d78474adcc7bda1891 \
-    --hash=sha256:46c0560b0dc609d7d967dc99e22cb463d3c4caf54a5fda735d6c11b5177e3a9f
+nox==2022.11.21 \
+    --hash=sha256:0e41a990e290e274cb205a976c4c97ee3c5234441a8132c8c3fd9ea3c22149eb \
+    --hash=sha256:e21c31de0711d1274ca585a2c5fde36b1aa962005ba8e9322bf5eeed16dcd684
     # via -r requirements.in
-packaging==23.1 \
-    --hash=sha256:994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61 \
-    --hash=sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f
+packaging==21.3 \
+    --hash=sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb \
+    --hash=sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522
     # via
     #   gcp-releasetool
     #   nox
-    #   sphinx
-pkginfo==1.9.6 \
-    --hash=sha256:4b7a555a6d5a22169fcc9cf7bfd78d296b0361adad412a346c1226849af5e546 \
-    --hash=sha256:8fd5896e8718a4372f0ea9cc9d96f6417c9b986e23a4d116dda26b62cc29d046
+pkginfo==1.8.3 \
+    --hash=sha256:848865108ec99d4901b2f7e84058b6e7660aae8ae10164e015a6dcf5b242a594 \
+    --hash=sha256:a84da4318dd86f870a9447a8c98340aa06216bfc6f2b7bdc4b8766984ae1867c
     # via twine
-platformdirs==3.7.0 \
-    --hash=sha256:87fbf6473e87c078d536980ba970a472422e94f17b752cfad17024c18876d481 \
-    --hash=sha256:cfd065ba43133ff103ab3bd10aecb095c2a0035fcd1f07217c9376900d94ba07
+platformdirs==2.5.4 \
+    --hash=sha256:1006647646d80f16130f052404c6b901e80ee4ed6bef6792e1f238a8969106f7 \
+    --hash=sha256:af0276409f9a02373d540bf8480021a048711d572745aef4b7842dad245eba10
     # via virtualenv
 protobuf==3.20.3 \
     --hash=sha256:03038ac1cfbc41aa21f6afcbcd357281d7521b4157926f30ebecc8d4ea59dcb7 \
@@ -420,32 +382,34 @@ protobuf==3.20.3 \
     #   gcp-docuploader
     #   gcp-releasetool
     #   google-api-core
-    #   googleapis-common-protos
-pyasn1==0.5.0 \
-    --hash=sha256:87a2121042a1ac9358cabcaf1d07680ff97ee6404333bacca15f76aa8ad01a57 \
-    --hash=sha256:97b7290ca68e62a832558ec3976f15cbf911bf5d7c7039d8b861c2a0ece69fde
+pyasn1==0.4.8 \
+    --hash=sha256:39c7e2ec30515947ff4e87fb6f456dfc6e84857d34be479c9d4a4ba4bf46aa5d \
+    --hash=sha256:aef77c9fb94a3ac588e87841208bdec464471d9871bd5050a287cc9a475cd0ba
     # via
     #   pyasn1-modules
     #   rsa
-pyasn1-modules==0.3.0 \
-    --hash=sha256:5bd01446b736eb9d31512a30d46c1ac3395d676c6f3cafa4c03eb54b9925631c \
-    --hash=sha256:d3ccd6ed470d9ffbc716be08bd90efbd44d0734bc9303818f7336070984a162d
+pyasn1-modules==0.2.8 \
+    --hash=sha256:905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e \
+    --hash=sha256:a50b808ffeb97cb3601dd25981f6b016cbb3d31fbf57a8b8a87428e6158d0c74
     # via google-auth
 pycparser==2.21 \
     --hash=sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9 \
     --hash=sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206
     # via cffi
-pygments==2.15.1 \
-    --hash=sha256:8ace4d3c1dd481894b2005f560ead0f9f19ee64fe983366be1a21e171d12775c \
-    --hash=sha256:db2db3deb4b4179f399a09054b023b6a586b76499d36965813c71aa8ed7b5fd1
+pygments==2.15.0 \
+    --hash=sha256:77a3299119af881904cd5ecd1ac6a66214b6e9bed1f2db16993b54adede64094 \
+    --hash=sha256:f7e36cffc4c517fbc252861b9a6e4644ca0e5abadf9a113c72d1358ad09b9500
     # via
     #   readme-renderer
     #   rich
-    #   sphinx
-pyjwt==2.7.0 \
-    --hash=sha256:ba2b425b15ad5ef12f200dc67dd56af4e26de2331f965c5439994dad075876e1 \
-    --hash=sha256:bd6ca4a3c4285c1a2d4349e5a035fdf8fb94e04ccd0fcbe6ba289dae9cc3e074
+pyjwt==2.6.0 \
+    --hash=sha256:69285c7e31fc44f68a1feb309e948e0df53259d579295e6cfe2b1792329f05fd \
+    --hash=sha256:d83c3d892a77bbb74d3e1a2cfa90afaadb60945205d1095d9221f04466f64c14
     # via gcp-releasetool
+pyparsing==3.0.9 \
+    --hash=sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb \
+    --hash=sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc
+    # via packaging
 pyperclip==1.8.2 \
     --hash=sha256:105254a8b04934f0bc84e9c24eb360a591aaf6535c9def5f29d92af107a9bf57
     # via gcp-releasetool
@@ -453,14 +417,10 @@ python-dateutil==2.8.2 \
     --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \
     --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
     # via gcp-releasetool
-readme-renderer==40.0 \
-    --hash=sha256:9f77b519d96d03d7d7dce44977ba543090a14397c4f60de5b6eb5b8048110aa4 \
-    --hash=sha256:e18feb2a1e7706f2865b81ebb460056d93fb29d69daa10b223c00faa7bd9a00a
+readme-renderer==37.3 \
+    --hash=sha256:cd653186dfc73055656f090f227f5cb22a046d7f71a841dfa305f55c9a513273 \
+    --hash=sha256:f67a16caedfa71eef48a31b39708637a6f4664c4394801a7b0d6432d13907343
     # via twine
-recommonmark==0.7.1 \
-    --hash=sha256:1b1db69af0231efce3fa21b94ff627ea33dee7079a01dd0a7f8482c3da148b3f \
-    --hash=sha256:bdb4db649f2222dcd8d2d844f0006b958d627f732415d399791ee436a3686d67
-    # via -r requirements.in
 requests==2.31.0 \
     --hash=sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f \
     --hash=sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1
@@ -469,19 +429,18 @@ requests==2.31.0 \
     #   google-api-core
     #   google-cloud-storage
     #   requests-toolbelt
-    #   sphinx
     #   twine
-requests-toolbelt==1.0.0 \
-    --hash=sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6 \
-    --hash=sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06
+requests-toolbelt==0.10.1 \
+    --hash=sha256:18565aa58116d9951ac39baa288d3adb5b3ff975c4f25eee78555d89e8f247f7 \
+    --hash=sha256:62e09f7ff5ccbda92772a29f394a49c3ad6cb181d568b1337626b2abb628a63d
     # via twine
 rfc3986==2.0.0 \
     --hash=sha256:50b1502b60e289cb37883f3dfd34532b8873c7de9f49bb546641ce9cbd256ebd \
     --hash=sha256:97aacf9dbd4bfd829baad6e6309fa6573aaf1be3f6fa735c8ab05e46cecb261c
     # via twine
-rich==13.4.2 \
-    --hash=sha256:8f87bc7ee54675732fa66a05ebfe489e27264caeeff3728c945d25971b6485ec \
-    --hash=sha256:d653d6bccede5844304c605d5aac802c7cf9621efd700b46c7ec2b51ea914898
+rich==12.6.0 \
+    --hash=sha256:a4eb26484f2c82589bd9a17c73d32a010b1e29d89f1604cd9bf3a2097b81bb5e \
+    --hash=sha256:ba3a3775974105c221d31141f2c116f4fd65c5ceb0698657a11e9f295ec93fd0
     # via twine
 rsa==4.9 \
     --hash=sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7 \
@@ -499,74 +458,39 @@ six==1.16.0 \
     #   gcp-docuploader
     #   google-auth
     #   python-dateutil
-snowballstemmer==2.2.0 \
-    --hash=sha256:09b16deb8547d3412ad7b590689584cd0fe25ec8db3be37788be3810cbf19cb1 \
-    --hash=sha256:c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a
-    # via sphinx
-sphinx==4.5.0 \
-    --hash=sha256:7bf8ca9637a4ee15af412d1a1d9689fec70523a68ca9bb9127c2f3eeb344e2e6 \
-    --hash=sha256:ebf612653238bcc8f4359627a9b7ce44ede6fdd75d9d30f68255c7383d3a6226
-    # via
-    #   -r requirements.in
-    #   recommonmark
-sphinxcontrib-applehelp==1.0.4 \
-    --hash=sha256:29d341f67fb0f6f586b23ad80e072c8e6ad0b48417db2bde114a4c9746feb228 \
-    --hash=sha256:828f867945bbe39817c210a1abfd1bc4895c8b73fcaade56d45357a348a07d7e
-    # via sphinx
-sphinxcontrib-devhelp==1.0.2 \
-    --hash=sha256:8165223f9a335cc1af7ffe1ed31d2871f325254c0423bc0c4c7cd1c1e4734a2e \
-    --hash=sha256:ff7f1afa7b9642e7060379360a67e9c41e8f3121f2ce9164266f61b9f4b338e4
-    # via sphinx
-sphinxcontrib-htmlhelp==2.0.1 \
-    --hash=sha256:0cbdd302815330058422b98a113195c9249825d681e18f11e8b1f78a2f11efff \
-    --hash=sha256:c38cb46dccf316c79de6e5515e1770414b797162b23cd3d06e67020e1d2a6903
-    # via sphinx
-sphinxcontrib-jsmath==1.0.1 \
-    --hash=sha256:2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178 \
-    --hash=sha256:a9925e4a4587247ed2191a22df5f6970656cb8ca2bd6284309578f2153e0c4b8
-    # via sphinx
-sphinxcontrib-qthelp==1.0.3 \
-    --hash=sha256:4c33767ee058b70dba89a6fc5c1892c0d57a54be67ddd3e7875a18d14cba5a72 \
-    --hash=sha256:bd9fc24bcb748a8d51fd4ecaade681350aa63009a347a8c14e637895444dfab6
-    # via sphinx
-sphinxcontrib-serializinghtml==1.1.5 \
-    --hash=sha256:352a9a00ae864471d3a7ead8d7d79f5fc0b57e8b3f95e9867eb9eb28999b92fd \
-    --hash=sha256:aa5f6de5dfdf809ef505c4895e51ef5c9eac17d0f287933eb49ec495280b6952
-    # via sphinx
-twine==4.0.2 \
-    --hash=sha256:929bc3c280033347a00f847236564d1c52a3e61b1ac2516c97c48f3ceab756d8 \
-    --hash=sha256:9e102ef5fdd5a20661eb88fad46338806c3bd32cf1db729603fe3697b1bc83c8
+twine==4.0.1 \
+    --hash=sha256:42026c18e394eac3e06693ee52010baa5313e4811d5a11050e7d48436cf41b9e \
+    --hash=sha256:96b1cf12f7ae611a4a40b6ae8e9570215daff0611828f5fe1f37a16255ab24a0
     # via -r requirements.in
-typing-extensions==4.6.3 \
-    --hash=sha256:88a4153d8505aabbb4e13aacb7c486c2b4a33ca3b3f807914a9b4c844c471c26 \
-    --hash=sha256:d91d5919357fe7f681a9f2b5b4cb2a5f1ef0a1e9f59c4d8ff0d3491e05c0ffd5
+typing-extensions==4.4.0 \
+    --hash=sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa \
+    --hash=sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e
     # via -r requirements.in
-urllib3==1.26.16 \
-    --hash=sha256:8d36afa7616d8ab714608411b4a3b13e58f463aee519024578e062e141dce20f \
-    --hash=sha256:8f135f6502756bde6b2a9b28989df5fbe87c9970cecaa69041edcce7f0589b14
+urllib3==1.26.12 \
+    --hash=sha256:3fa96cf423e6987997fc326ae8df396db2a8b7c667747d47ddd8ecba91f4a74e \
+    --hash=sha256:b930dd878d5a8afb066a637fbb35144fe7901e3b209d1cd4f524bd0e9deee997
     # via
-    #   google-auth
     #   requests
     #   twine
-virtualenv==20.23.1 \
-    --hash=sha256:34da10f14fea9be20e0fd7f04aba9732f84e593dac291b757ce42e3368a39419 \
-    --hash=sha256:8ff19a38c1021c742148edc4f81cb43d7f8c6816d2ede2ab72af5b84c749ade1
+virtualenv==20.16.7 \
+    --hash=sha256:8691e3ff9387f743e00f6bb20f70121f5e4f596cae754531f2b3b3a1b1ac696e \
+    --hash=sha256:efd66b00386fdb7dbe4822d172303f40cd05e50e01740b19ea42425cbe653e29
     # via nox
 webencodings==0.5.1 \
     --hash=sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 \
     --hash=sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923
     # via bleach
-wheel==0.40.0 \
-    --hash=sha256:cd1196f3faee2b31968d626e1731c94f99cbdb67cf5a46e4f5656cbee7738873 \
-    --hash=sha256:d236b20e7cb522daf2390fa84c55eea81c5c30190f90f29ae2ca1ad8355bf247
+wheel==0.38.4 \
+    --hash=sha256:965f5259b566725405b05e7cf774052044b1ed30119b5d586b2703aafe8719ac \
+    --hash=sha256:b60533f3f5d530e971d6737ca6d58681ee434818fab630c83a734bb10c083ce8
     # via -r requirements.in
-zipp==3.15.0 \
-    --hash=sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b \
-    --hash=sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556
+zipp==3.10.0 \
+    --hash=sha256:4fcb6f278987a6605757302a6e40e896257570d11c51628968ccb2a47e80c6c1 \
+    --hash=sha256:7a7262fd930bd3e36c50b9a64897aec3fafff3dfdeec9623ae22b40e93f99bb8
     # via importlib-metadata
 
 # The following packages are considered to be unsafe in a requirements file:
-setuptools==68.0.0 \
-    --hash=sha256:11e52c67415a381d10d6b462ced9cfb97066179f0e871399e006c4ab101fc85f \
-    --hash=sha256:baf1fdb41c6da4cd2eae722e135500da913332ab3f2f5c7d33af9b492acb5235
+setuptools==65.5.1 \
+    --hash=sha256:d0b9a8433464d5800cbe05094acf5c6d52a91bfac9b52bcfc4d41382be5d5d31 \
+    --hash=sha256:e197a19aa8ec9722928f2206f8de752def0e4c9fc6953527360d1c36d94ddb2f
     # via -r requirements.in
diff --git a/.kokoro/samples/lint/common.cfg b/.kokoro/samples/lint/common.cfg
new file mode 100644
index 0000000000..b4d26c1f98
--- /dev/null
+++ b/.kokoro/samples/lint/common.cfg
@@ -0,0 +1,34 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+# Build logs will be here
+action {
+  define_artifacts {
+    regex: "**/*sponge_log.xml"
+  }
+}
+
+# Specify which tests to run
+env_vars: {
+    key: "RUN_TESTS_SESSION"
+    value: "lint"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh"
+}
+
+# Configure the docker image for kokoro-trampoline.
+env_vars: {
+    key: "TRAMPOLINE_IMAGE"
+    value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker"
+}
+
+# Download secrets for samples
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples"
+
+# Download trampoline resources.
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline"
+
+# Use the trampoline script to run in docker.
+build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh"
\ No newline at end of file
diff --git a/.kokoro/samples/lint/continuous.cfg b/.kokoro/samples/lint/continuous.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/lint/continuous.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/lint/periodic.cfg b/.kokoro/samples/lint/periodic.cfg
new file mode 100644
index 0000000000..50fec96497
--- /dev/null
+++ b/.kokoro/samples/lint/periodic.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "False"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/lint/presubmit.cfg b/.kokoro/samples/lint/presubmit.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/lint/presubmit.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/python3.10/common.cfg b/.kokoro/samples/python3.10/common.cfg
new file mode 100644
index 0000000000..8f9c66c571
--- /dev/null
+++ b/.kokoro/samples/python3.10/common.cfg
@@ -0,0 +1,40 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+# Build logs will be here
+action {
+  define_artifacts {
+    regex: "**/*sponge_log.xml"
+  }
+}
+
+# Specify which tests to run
+env_vars: {
+    key: "RUN_TESTS_SESSION"
+    value: "py-3.10"
+}
+
+# Declare build specific Cloud project.
+env_vars: {
+    key: "BUILD_SPECIFIC_GCLOUD_PROJECT"
+    value: "python-docs-samples-tests-310"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh"
+}
+
+# Configure the docker image for kokoro-trampoline.
+env_vars: {
+    key: "TRAMPOLINE_IMAGE"
+    value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker"
+}
+
+# Download secrets for samples
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples"
+
+# Download trampoline resources.
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline"
+
+# Use the trampoline script to run in docker.
+build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh"
\ No newline at end of file
diff --git a/.kokoro/samples/python3.10/continuous.cfg b/.kokoro/samples/python3.10/continuous.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/python3.10/continuous.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/python3.10/periodic-head.cfg b/.kokoro/samples/python3.10/periodic-head.cfg
new file mode 100644
index 0000000000..123a35fbd3
--- /dev/null
+++ b/.kokoro/samples/python3.10/periodic-head.cfg
@@ -0,0 +1,11 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples-against-head.sh"
+}
diff --git a/.kokoro/samples/python3.10/periodic.cfg b/.kokoro/samples/python3.10/periodic.cfg
new file mode 100644
index 0000000000..71cd1e597e
--- /dev/null
+++ b/.kokoro/samples/python3.10/periodic.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "False"
+}
diff --git a/.kokoro/samples/python3.10/presubmit.cfg b/.kokoro/samples/python3.10/presubmit.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/python3.10/presubmit.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/python3.11/common.cfg b/.kokoro/samples/python3.11/common.cfg
new file mode 100644
index 0000000000..1bba39114a
--- /dev/null
+++ b/.kokoro/samples/python3.11/common.cfg
@@ -0,0 +1,40 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+# Build logs will be here
+action {
+  define_artifacts {
+    regex: "**/*sponge_log.xml"
+  }
+}
+
+# Specify which tests to run
+env_vars: {
+    key: "RUN_TESTS_SESSION"
+    value: "py-3.11"
+}
+
+# Declare build specific Cloud project.
+env_vars: {
+    key: "BUILD_SPECIFIC_GCLOUD_PROJECT"
+    value: "python-docs-samples-tests-311"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh"
+}
+
+# Configure the docker image for kokoro-trampoline.
+env_vars: {
+    key: "TRAMPOLINE_IMAGE"
+    value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker"
+}
+
+# Download secrets for samples
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples"
+
+# Download trampoline resources.
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline"
+
+# Use the trampoline script to run in docker.
+build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh"
\ No newline at end of file
diff --git a/.kokoro/samples/python3.11/continuous.cfg b/.kokoro/samples/python3.11/continuous.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/python3.11/continuous.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/python3.11/periodic-head.cfg b/.kokoro/samples/python3.11/periodic-head.cfg
new file mode 100644
index 0000000000..123a35fbd3
--- /dev/null
+++ b/.kokoro/samples/python3.11/periodic-head.cfg
@@ -0,0 +1,11 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples-against-head.sh"
+}
diff --git a/.kokoro/samples/python3.11/periodic.cfg b/.kokoro/samples/python3.11/periodic.cfg
new file mode 100644
index 0000000000..71cd1e597e
--- /dev/null
+++ b/.kokoro/samples/python3.11/periodic.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "False"
+}
diff --git a/.kokoro/samples/python3.11/presubmit.cfg b/.kokoro/samples/python3.11/presubmit.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/python3.11/presubmit.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/python3.7/common.cfg b/.kokoro/samples/python3.7/common.cfg
new file mode 100644
index 0000000000..09d7af02ba
--- /dev/null
+++ b/.kokoro/samples/python3.7/common.cfg
@@ -0,0 +1,40 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+# Build logs will be here
+action {
+  define_artifacts {
+    regex: "**/*sponge_log.xml"
+  }
+}
+
+# Specify which tests to run
+env_vars: {
+    key: "RUN_TESTS_SESSION"
+    value: "py-3.7"
+}
+
+# Declare build specific Cloud project.
+env_vars: {
+    key: "BUILD_SPECIFIC_GCLOUD_PROJECT"
+    value: "python-docs-samples-tests-py37"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh"
+}
+
+# Configure the docker image for kokoro-trampoline.
+env_vars: {
+    key: "TRAMPOLINE_IMAGE"
+    value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker"
+}
+
+# Download secrets for samples
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples"
+
+# Download trampoline resources.
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline"
+
+# Use the trampoline script to run in docker.
+build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh"
\ No newline at end of file
diff --git a/.kokoro/samples/python3.7/continuous.cfg b/.kokoro/samples/python3.7/continuous.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/python3.7/continuous.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/python3.7/periodic-head.cfg b/.kokoro/samples/python3.7/periodic-head.cfg
new file mode 100644
index 0000000000..123a35fbd3
--- /dev/null
+++ b/.kokoro/samples/python3.7/periodic-head.cfg
@@ -0,0 +1,11 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples-against-head.sh"
+}
diff --git a/.kokoro/samples/python3.7/periodic.cfg b/.kokoro/samples/python3.7/periodic.cfg
new file mode 100644
index 0000000000..71cd1e597e
--- /dev/null
+++ b/.kokoro/samples/python3.7/periodic.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "False"
+}
diff --git a/.kokoro/samples/python3.7/presubmit.cfg b/.kokoro/samples/python3.7/presubmit.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/python3.7/presubmit.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/python3.8/common.cfg b/.kokoro/samples/python3.8/common.cfg
new file mode 100644
index 0000000000..976d9ce8c5
--- /dev/null
+++ b/.kokoro/samples/python3.8/common.cfg
@@ -0,0 +1,40 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+# Build logs will be here
+action {
+  define_artifacts {
+    regex: "**/*sponge_log.xml"
+  }
+}
+
+# Specify which tests to run
+env_vars: {
+    key: "RUN_TESTS_SESSION"
+    value: "py-3.8"
+}
+
+# Declare build specific Cloud project.
+env_vars: {
+    key: "BUILD_SPECIFIC_GCLOUD_PROJECT"
+    value: "python-docs-samples-tests-py38"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh"
+}
+
+# Configure the docker image for kokoro-trampoline.
+env_vars: {
+    key: "TRAMPOLINE_IMAGE"
+    value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker"
+}
+
+# Download secrets for samples
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples"
+
+# Download trampoline resources.
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline"
+
+# Use the trampoline script to run in docker.
+build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh"
\ No newline at end of file
diff --git a/.kokoro/samples/python3.8/continuous.cfg b/.kokoro/samples/python3.8/continuous.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/python3.8/continuous.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/python3.8/periodic-head.cfg b/.kokoro/samples/python3.8/periodic-head.cfg
new file mode 100644
index 0000000000..123a35fbd3
--- /dev/null
+++ b/.kokoro/samples/python3.8/periodic-head.cfg
@@ -0,0 +1,11 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples-against-head.sh"
+}
diff --git a/.kokoro/samples/python3.8/periodic.cfg b/.kokoro/samples/python3.8/periodic.cfg
new file mode 100644
index 0000000000..71cd1e597e
--- /dev/null
+++ b/.kokoro/samples/python3.8/periodic.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "False"
+}
diff --git a/.kokoro/samples/python3.8/presubmit.cfg b/.kokoro/samples/python3.8/presubmit.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/python3.8/presubmit.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/python3.9/common.cfg b/.kokoro/samples/python3.9/common.cfg
new file mode 100644
index 0000000000..603cfffa28
--- /dev/null
+++ b/.kokoro/samples/python3.9/common.cfg
@@ -0,0 +1,40 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+# Build logs will be here
+action {
+  define_artifacts {
+    regex: "**/*sponge_log.xml"
+  }
+}
+
+# Specify which tests to run
+env_vars: {
+    key: "RUN_TESTS_SESSION"
+    value: "py-3.9"
+}
+
+# Declare build specific Cloud project.
+env_vars: {
+    key: "BUILD_SPECIFIC_GCLOUD_PROJECT"
+    value: "python-docs-samples-tests-py39"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples.sh"
+}
+
+# Configure the docker image for kokoro-trampoline.
+env_vars: {
+    key: "TRAMPOLINE_IMAGE"
+    value: "gcr.io/cloud-devrel-kokoro-resources/python-samples-testing-docker"
+}
+
+# Download secrets for samples
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/python-docs-samples"
+
+# Download trampoline resources.
+gfile_resources: "/bigstore/cloud-devrel-kokoro-resources/trampoline"
+
+# Use the trampoline script to run in docker.
+build_file: "python-bigquery-dataframes/.kokoro/trampoline_v2.sh"
\ No newline at end of file
diff --git a/.kokoro/samples/python3.9/continuous.cfg b/.kokoro/samples/python3.9/continuous.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/python3.9/continuous.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/samples/python3.9/periodic-head.cfg b/.kokoro/samples/python3.9/periodic-head.cfg
new file mode 100644
index 0000000000..123a35fbd3
--- /dev/null
+++ b/.kokoro/samples/python3.9/periodic-head.cfg
@@ -0,0 +1,11 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
+
+env_vars: {
+    key: "TRAMPOLINE_BUILD_FILE"
+    value: "github/python-bigquery-dataframes/.kokoro/test-samples-against-head.sh"
+}
diff --git a/.kokoro/samples/python3.9/periodic.cfg b/.kokoro/samples/python3.9/periodic.cfg
new file mode 100644
index 0000000000..71cd1e597e
--- /dev/null
+++ b/.kokoro/samples/python3.9/periodic.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "False"
+}
diff --git a/.kokoro/samples/python3.9/presubmit.cfg b/.kokoro/samples/python3.9/presubmit.cfg
new file mode 100644
index 0000000000..a1c8d9759c
--- /dev/null
+++ b/.kokoro/samples/python3.9/presubmit.cfg
@@ -0,0 +1,6 @@
+# Format: //devtools/kokoro/config/proto/build.proto
+
+env_vars: {
+    key: "INSTALL_LIBRARY_FROM_SOURCE"
+    value: "True"
+}
\ No newline at end of file
diff --git a/.kokoro/test-samples-against-head.sh b/.kokoro/test-samples-against-head.sh
new file mode 100755
index 0000000000..63ac41dfae
--- /dev/null
+++ b/.kokoro/test-samples-against-head.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# A customized test runner for samples.
+#
+# For periodic builds, you can specify this file for testing against head.
+
+# `-e` enables the script to automatically fail when a command fails
+# `-o pipefail` sets the exit code to the rightmost comment to exit with a non-zero
+set -eo pipefail
+# Enables `**` to include files nested inside sub-folders
+shopt -s globstar
+
+exec .kokoro/test-samples-impl.sh
diff --git a/.kokoro/test-samples-impl.sh b/.kokoro/test-samples-impl.sh
new file mode 100755
index 0000000000..5a0f5fab6a
--- /dev/null
+++ b/.kokoro/test-samples-impl.sh
@@ -0,0 +1,102 @@
+#!/bin/bash
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+# `-e` enables the script to automatically fail when a command fails
+# `-o pipefail` sets the exit code to the rightmost comment to exit with a non-zero
+set -eo pipefail
+# Enables `**` to include files nested inside sub-folders
+shopt -s globstar
+
+# Exit early if samples don't exist
+if ! find samples -name 'requirements.txt' | grep -q .; then
+  echo "No tests run. './samples/**/requirements.txt' not found"
+  exit 0
+fi
+
+# Disable buffering, so that the logs stream through.
+export PYTHONUNBUFFERED=1
+
+# Debug: show build environment
+env | grep KOKORO
+
+# Install nox
+python3.9 -m pip install --upgrade --quiet nox
+
+# Use secrets acessor service account to get secrets
+if [[ -f "${KOKORO_GFILE_DIR}/secrets_viewer_service_account.json" ]]; then
+    gcloud auth activate-service-account \
+	   --key-file="${KOKORO_GFILE_DIR}/secrets_viewer_service_account.json" \
+	   --project="cloud-devrel-kokoro-resources"
+fi
+
+# This script will create 3 files:
+# - testing/test-env.sh
+# - testing/service-account.json
+# - testing/client-secrets.json
+./scripts/decrypt-secrets.sh
+
+source ./testing/test-env.sh
+export GOOGLE_APPLICATION_CREDENTIALS=$(pwd)/testing/service-account.json
+
+# For cloud-run session, we activate the service account for gcloud sdk.
+gcloud auth activate-service-account \
+       --key-file "${GOOGLE_APPLICATION_CREDENTIALS}"
+
+export GOOGLE_CLIENT_SECRETS=$(pwd)/testing/client-secrets.json
+
+echo -e "\n******************** TESTING PROJECTS ********************"
+
+# Switch to 'fail at end' to allow all tests to complete before exiting.
+set +e
+# Use RTN to return a non-zero value if the test fails.
+RTN=0
+ROOT=$(pwd)
+# Find all requirements.txt in the samples directory (may break on whitespace).
+for file in samples/**/requirements.txt; do
+    cd "$ROOT"
+    # Navigate to the project folder.
+    file=$(dirname "$file")
+    cd "$file"
+
+    echo "------------------------------------------------------------"
+    echo "- testing $file"
+    echo "------------------------------------------------------------"
+
+    # Use nox to execute the tests for the project.
+    python3.9 -m nox -s "$RUN_TESTS_SESSION"
+    EXIT=$?
+
+    # If this is a periodic build, send the test log to the FlakyBot.
+    # See https://github.com/googleapis/repo-automation-bots/tree/main/packages/flakybot.
+    if [[ $KOKORO_BUILD_ARTIFACTS_SUBDIR = *"periodic"* ]]; then
+      chmod +x $KOKORO_GFILE_DIR/linux_amd64/flakybot
+      $KOKORO_GFILE_DIR/linux_amd64/flakybot
+    fi
+
+    if [[ $EXIT -ne 0 ]]; then
+      RTN=1
+      echo -e "\n Testing failed: Nox returned a non-zero exit code. \n"
+    else
+      echo -e "\n Testing completed.\n"
+    fi
+
+done
+cd "$ROOT"
+
+# Workaround for Kokoro permissions issue: delete secrets
+rm testing/{test-env.sh,client-secrets.json,service-account.json}
+
+exit "$RTN"
diff --git a/.kokoro/test-samples.sh b/.kokoro/test-samples.sh
new file mode 100755
index 0000000000..50b35a48c1
--- /dev/null
+++ b/.kokoro/test-samples.sh
@@ -0,0 +1,44 @@
+#!/bin/bash
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# The default test runner for samples.
+#
+# For periodic builds, we rewinds the repo to the latest release, and
+# run test-samples-impl.sh.
+
+# `-e` enables the script to automatically fail when a command fails
+# `-o pipefail` sets the exit code to the rightmost comment to exit with a non-zero
+set -eo pipefail
+# Enables `**` to include files nested inside sub-folders
+shopt -s globstar
+
+# Run periodic samples tests at latest release
+if [[ $KOKORO_BUILD_ARTIFACTS_SUBDIR = *"periodic"* ]]; then
+    # preserving the test runner implementation.
+    cp .kokoro/test-samples-impl.sh "${TMPDIR}/test-samples-impl.sh"
+    echo "--- IMPORTANT IMPORTANT IMPORTANT ---"
+    echo "Now we rewind the repo back to the latest release..."
+    LATEST_RELEASE=$(git describe --abbrev=0 --tags)
+    git checkout $LATEST_RELEASE
+    echo "The current head is: "
+    echo $(git rev-parse --verify HEAD)
+    echo "--- IMPORTANT IMPORTANT IMPORTANT ---"
+    # move back the test runner implementation if there's no file.
+    if [ ! -f .kokoro/test-samples-impl.sh ]; then
+	cp "${TMPDIR}/test-samples-impl.sh" .kokoro/test-samples-impl.sh
+    fi
+fi
+
+exec .kokoro/test-samples-impl.sh
diff --git a/.kokoro/trampoline.sh b/.kokoro/trampoline.sh
new file mode 100755
index 0000000000..d85b1f2676
--- /dev/null
+++ b/.kokoro/trampoline.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+set -eo pipefail
+
+# Always run the cleanup script, regardless of the success of bouncing into
+# the container.
+function cleanup() {
+    chmod +x ${KOKORO_GFILE_DIR}/trampoline_cleanup.sh
+    ${KOKORO_GFILE_DIR}/trampoline_cleanup.sh
+    echo "cleanup";
+}
+trap cleanup EXIT
+
+$(dirname $0)/populate-secrets.sh # Secret Manager secrets.
+python3 "${KOKORO_GFILE_DIR}/trampoline_v1.py"
\ No newline at end of file
diff --git a/.kokoro/trampoline_v2.sh b/.kokoro/trampoline_v2.sh
old mode 100644
new mode 100755
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 07d7e6b744..6e0fd8b98f 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,4 +1,4 @@
-# Copyright 2021 Google LLC
+# Copyright 2023 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -29,11 +29,11 @@ repos:
 -   repo: https://github.com/psf/black
     rev: 22.3.0
     hooks:
-    -   id: black
+    - id: black
 -   repo: https://github.com/pycqa/flake8
-    rev: 3.9.2
+    rev: 6.1.0
     hooks:
-    -   id: flake8
+    - id: flake8
 -   repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.1.1
     hooks:
diff --git a/.trampolinerc b/.trampolinerc
index 4c16e33fee..a7dfeb42c6 100644
--- a/.trampolinerc
+++ b/.trampolinerc
@@ -20,6 +20,9 @@ required_envvars+=(
 pass_down_envvars+=(
     "NOX_SESSION"
     ###############
+    # Docs builds
+    ###############
+    "STAGING_BUCKET"
     "V2_STAGING_BUCKET"
     ##################
     # Samples builds
diff --git a/CHANGELOG.md b/CHANGELOG.md
index aa736877f4..de2edcf31e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,49 @@
 
 [1]: https://pypi.org/project/bigframes/#history
 
+## [0.4.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.3.2...v0.4.0) (2023-09-16)
+
+
+### Features
+
+* Add `axis` parameter to `droplevel` and `reorder_levels` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add `bfill` and `ffill` to `DataFrame` and `Series` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add `DataFrame.combine` and `DataFrame.combine_first` ([#27](https://github.com/googleapis/python-bigquery-dataframes/issues/27)) ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add `DataFrame.nlargest`, `nsmallest` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add `DataFrame.pct_change` and `Series.pct_change` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add `DataFrame.skew` and `GroupBy.skew` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add `DataFrame.to_dict`, `to_excel`, `to_latex`, `to_records`, `to_string`, `to_markdown`, `to_pickle`, `to_orc` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add `diff` method to `DataFrame` and `GroupBy` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add `filter` and `reindex` to `Series` and `DataFrame` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add `reindex_like` to `DataFrame` and `Series` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add `swaplevel` to `DataFrame` and `Series` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add partial support for `Sereies.replace` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Support `DataFrame.loc[bool_series, column] = scalar` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Support a persistent `name` in `remote_function` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+
+
+### Bug Fixes
+
+* `remote_function` uses same credentials as other APIs ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Add type hints to models ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Raise error when ARIMAPlus is used with Pipeline ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Remove `transforms` parameter in `model.fit` (**breaking change**) ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Support column joins with "None indexer" ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Use for literals `Int64Dtype` in `cut` ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Use lowercase strings for parameter literals in `bigframes.ml` (**breaking change**) ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+
+
+### Performance Improvements
+
+* `bigframes-api` label to I/O query jobs ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+
+
+### Documentation
+
+* Document possible parameter values for PaLM2TextGenerator ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Document region logic in README ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+* Fix OneHotEncoder sample ([7c6b0dd](https://github.com/googleapis/python-bigquery-dataframes/commit/7c6b0dd2f99139c8830e762201a45b28486532ff))
+
 ## [0.3.2](https://github.com/googleapis/python-bigquery-dataframes/compare/v0.3.1...v0.3.2) (2023-09-06)
 
 
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
index 51d6abc4d3..039f436812 100644
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -1,3 +1,4 @@
+<!-- # Generated by synthtool. DO NOT EDIT! !-->
 # Code of Conduct
 
 ## Our Pledge
@@ -91,4 +92,4 @@ harassment or threats to anyone's safety, we may take action without notice.
 
 This Code of Conduct is adapted from the Contributor Covenant, version 1.4,
 available at
-https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
\ No newline at end of file
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
new file mode 100644
index 0000000000..3933152cf7
--- /dev/null
+++ b/CONTRIBUTING.rst
@@ -0,0 +1,277 @@
+.. Generated by synthtool. DO NOT EDIT!
+############
+Contributing
+############
+
+#. **Please sign one of the contributor license agreements below.**
+#. Fork the repo, develop and test your code changes, add docs.
+#. Make sure that your commit messages clearly describe the changes.
+#. Send a pull request. (Please Read: `Faster Pull Request Reviews`_)
+
+.. _Faster Pull Request Reviews: https://github.com/kubernetes/community/blob/master/contributors/guide/pull-requests.md#best-practices-for-faster-reviews
+
+.. contents:: Here are some guidelines for hacking on the Google Cloud Client libraries.
+
+***************
+Adding Features
+***************
+
+In order to add a feature:
+
+- The feature must be documented in both the API and narrative
+  documentation.
+
+- The feature must work fully on the following CPython versions:
+  3.9, 3.10 and 3.11 on both UNIX and Windows.
+
+- The feature must not add unnecessary dependencies (where
+  "unnecessary" is of course subjective, but new dependencies should
+  be discussed).
+
+****************************
+Using a Development Checkout
+****************************
+
+You'll have to create a development environment using a Git checkout:
+
+- While logged into your GitHub account, navigate to the
+  ``python-bigquery-dataframes`` `repo`_ on GitHub.
+
+- Fork and clone the ``python-bigquery-dataframes`` repository to your GitHub account by
+  clicking the "Fork" button.
+
+- Clone your fork of ``python-bigquery-dataframes`` from your GitHub account to your local
+  computer, substituting your account username and specifying the destination
+  as ``hack-on-python-bigquery-dataframes``.  E.g.::
+
+   $ cd ${HOME}
+   $ git clone git@github.com:USERNAME/python-bigquery-dataframes.git hack-on-python-bigquery-dataframes
+   $ cd hack-on-python-bigquery-dataframes
+   # Configure remotes such that you can pull changes from the googleapis/python-bigquery-dataframes
+   # repository into your local repository.
+   $ git remote add upstream git@github.com:googleapis/python-bigquery-dataframes.git
+   # fetch and merge changes from upstream into main
+   $ git fetch upstream
+   $ git merge upstream/main
+
+Now your local repo is set up such that you will push changes to your GitHub
+repo, from which you can submit a pull request.
+
+To work on the codebase and run the tests, we recommend using ``nox``,
+but you can also use a ``virtualenv`` of your own creation.
+
+.. _repo: https://github.com/googleapis/python-bigquery-dataframes
+
+Using ``nox``
+=============
+
+We use `nox <https://nox.readthedocs.io/en/latest/>`__ to instrument our tests.
+
+- To test your changes, run unit tests with ``nox``::
+    $ nox -s unit
+
+- To run a single unit test::
+
+    $ nox -s unit-3.11 -- -k <name of test>
+
+
+  .. note::
+
+    The unit tests and system tests are described in the
+    ``noxfile.py`` files in each directory.
+
+.. nox: https://pypi.org/project/nox/
+
+*****************************************
+I'm getting weird errors... Can you help?
+*****************************************
+
+If the error mentions ``Python.h`` not being found,
+install ``python-dev`` and try again.
+On Debian/Ubuntu::
+
+  $ sudo apt-get install python-dev
+
+************
+Coding Style
+************
+- We use the automatic code formatter ``black``. You can run it using
+  the nox session ``blacken``. This will eliminate many lint errors. Run via::
+
+   $ nox -s blacken
+
+- PEP8 compliance is required, with exceptions defined in the linter configuration.
+  If you have ``nox`` installed, you can test that you have not introduced
+  any non-compliant code via::
+
+   $ nox -s lint
+
+- In order to make ``nox -s lint`` run faster, you can set some environment
+  variables::
+
+   export GOOGLE_CLOUD_TESTING_REMOTE="upstream"
+   export GOOGLE_CLOUD_TESTING_BRANCH="main"
+
+  By doing this, you are specifying the location of the most up-to-date
+  version of ``python-bigquery-dataframes``. The
+  remote name ``upstream`` should point to the official ``googleapis``
+  checkout and the branch should be the default branch on that remote (``main``).
+
+- This repository contains configuration for the
+  `pre-commit <https://pre-commit.com/>`__ tool, which automates checking
+  our linters during a commit.  If you have it installed on your ``$PATH``,
+  you can enable enforcing those checks via:
+
+.. code-block:: bash
+
+   $ pre-commit install
+   pre-commit installed at .git/hooks/pre-commit
+
+Exceptions to PEP8:
+
+- Many unit tests use a helper method, ``_call_fut`` ("FUT" is short for
+  "Function-Under-Test"), which is PEP8-incompliant, but more readable.
+  Some also use a local variable, ``MUT`` (short for "Module-Under-Test").
+
+********************
+Running System Tests
+********************
+
+- To run system tests, you can execute::
+
+   # Run all system tests
+   $ nox -s system
+
+   # Run a single system test
+   $ nox -s system-3.11 -- -k <name of test>
+
+
+  .. note::
+
+      System tests are only configured to run under Python 3.9 and 3.11.
+      For expediency, we do not run them in older versions of Python 3.
+
+  This alone will not run the tests. You'll need to change some local
+  auth settings and change some configuration in your project to
+  run all the tests.
+
+- System tests will be run against an actual project. You should use local credentials from gcloud when possible. See `Best practices for application authentication <https://cloud.google.com/docs/authentication/best-practices-applications#local_development_and_testing_with_the>`__. Some tests require a service account. For those tests see `Authenticating as a service account <https://cloud.google.com/docs/authentication/production>`__.
+
+*************
+Test Coverage
+*************
+
+- The codebase *must* have 100% test statement coverage after each commit.
+  You can test coverage via ``nox -s cover``.
+
+******************************************************
+Documentation Coverage and Building HTML Documentation
+******************************************************
+
+If you fix a bug, and the bug requires an API or behavior modification, all
+documentation in this package which references that API or behavior must be
+changed to reflect the bug fix, ideally in the same commit that fixes the bug
+or adds the feature.
+
+Build the docs via:
+
+   $ nox -s docs
+
+*************************
+Samples and code snippets
+*************************
+
+Code samples and snippets live in the `samples/` catalogue. Feel free to
+provide more examples, but make sure to write tests for those examples.
+Each folder containing example code requires its own `noxfile.py` script
+which automates testing. If you decide to create a new folder, you can
+base it on the `samples/snippets` folder (providing `noxfile.py` and
+the requirements files).
+
+The tests will run against a real Google Cloud Project, so you should
+configure them just like the System Tests.
+
+- To run sample tests, you can execute::
+
+   # Run all tests in a folder
+   $ cd samples/snippets
+   $ nox -s py-3.8
+
+   # Run a single sample test
+   $ cd samples/snippets
+   $ nox -s py-3.8 -- -k <name of test>
+
+********************************************
+Note About ``README`` as it pertains to PyPI
+********************************************
+
+The `description on PyPI`_ for the project comes directly from the
+``README``. Due to the reStructuredText (``rst``) parser used by
+PyPI, relative links which will work on GitHub (e.g. ``CONTRIBUTING.rst``
+instead of
+``https://github.com/googleapis/python-bigquery-dataframes/blob/main/CONTRIBUTING.rst``)
+may cause problems creating links or rendering the description.
+
+.. _description on PyPI: https://pypi.org/project/bigframes
+
+
+*************************
+Supported Python Versions
+*************************
+
+We support:
+
+-  `Python 3.9`_
+-  `Python 3.10`_
+-  `Python 3.11`_
+
+.. _Python 3.9: https://docs.python.org/3.9/
+.. _Python 3.10: https://docs.python.org/3.10/
+.. _Python 3.11: https://docs.python.org/3.11/
+
+
+Supported versions can be found in our ``noxfile.py`` `config`_.
+
+.. _config: https://github.com/googleapis/python-bigquery-dataframes/blob/main/noxfile.py
+
+
+We also explicitly decided to support Python 3 beginning with version 3.9.
+Reasons for this include:
+
+-  Encouraging use of newest versions of Python 3
+-  Taking the lead of `prominent`_ open-source `projects`_
+-  `Unicode literal support`_ which allows for a cleaner codebase that
+   works in both Python 2 and Python 3
+
+.. _prominent: https://docs.djangoproject.com/en/1.9/faq/install/#what-python-version-can-i-use-with-django
+.. _projects: http://flask.pocoo.org/docs/0.10/python3/
+.. _Unicode literal support: https://www.python.org/dev/peps/pep-0414/
+
+**********
+Versioning
+**********
+
+This library follows `Semantic Versioning`_.
+
+.. _Semantic Versioning: http://semver.org/
+
+Some packages are currently in major version zero (``0.y.z``), which means that
+anything may change at any time and the public API should not be considered
+stable.
+
+******************************
+Contributor License Agreements
+******************************
+
+Before we can accept your pull requests you'll need to sign a Contributor
+License Agreement (CLA):
+
+- **If you are an individual writing original source code** and **you own the
+  intellectual property**, then you'll need to sign an
+  `individual CLA <https://developers.google.com/open-source/cla/individual>`__.
+- **If you work for a company that wants to allow you to contribute your work**,
+  then you'll need to sign a
+  `corporate CLA <https://developers.google.com/open-source/cla/corporate>`__.
+
+You can sign these electronically (just scroll to the bottom). After that,
+we'll be able to accept your pull requests.
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000000..b422266a96
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Generated by synthtool. DO NOT EDIT!
+include README.rst LICENSE
+recursive-include third_party *
+recursive-include bigframes *.json *.proto py.typed
+recursive-include tests *
+global-exclude *.py[co]
+global-exclude __pycache__
+
+# Exclude scripts for samples readmegen
+prune scripts/readme-gen
diff --git a/OWNERS b/OWNERS
index 672da38afa..f86ad551ef 100644
--- a/OWNERS
+++ b/OWNERS
@@ -1,3 +1,4 @@
+ashleyxu@google.com
 bmil@google.com
 chelsealin@google.com
 garrettwu@google.com
diff --git a/README.rst b/README.rst
index 935c54cc8b..23aea446ff 100644
--- a/README.rst
+++ b/README.rst
@@ -57,9 +57,13 @@ internally to manage metadata on the service side. This session is tied to a
 BigQuery DataFrames uses the US multi-region as the default location, but you
 can use ``session_options.location`` to set a different location. Every query
 in a session is executed in the location where the session was created.
+BigQuery DataFrames
+auto-populates ``bf.options.bigquery.location`` if the user starts with
+``read_gbq/read_gbq_table/read_gbq_query()`` and specifies a table, either
+directly or in a SQL statement.
 
 If you want to reset the location of the created DataFrame or Series objects,
-can reset the session by executing ``bigframes.pandas.reset_session()``.
+you can reset the session by executing ``bigframes.pandas.reset_session()``.
 After that, you can reuse ``bigframes.pandas.options.bigquery.location`` to
 specify another location.
 
@@ -68,6 +72,11 @@ specify another location.
 querying is not in the US multi-region. If you try to read a table from another
 location, you get a NotFound exception.
 
+Project
+-------
+If ``bf.options.bigquery.project`` is not set, the ``$GOOGLE_CLOUD_PROJECT``
+environment variable is used, which is set in the notebook runtime serving the
+BigQuery Studio/Vertex Notebooks.
 
 ML Capabilities
 ---------------
diff --git a/bigframes/core/__init__.py b/bigframes/core/__init__.py
index 7086269af9..27fe4a4fe6 100644
--- a/bigframes/core/__init__.py
+++ b/bigframes/core/__init__.py
@@ -518,8 +518,8 @@ def aggregate(
         """
         Apply aggregations to the expression.
         Arguments:
-            by_column_id: column id of the aggregation key, this is preserved through the transform
             aggregations: input_column_id, operation, output_column_id tuples
+            by_column_id: column id of the aggregation key, this is preserved through the transform
             dropna: whether null keys should be dropped
         """
         table = self.to_ibis_expr(ordering_mode="unordered")
diff --git a/bigframes/core/block_transforms.py b/bigframes/core/block_transforms.py
index abf8b887d8..5dcd9fe753 100644
--- a/bigframes/core/block_transforms.py
+++ b/bigframes/core/block_transforms.py
@@ -117,6 +117,25 @@ def value_counts(
     return block.select_column(count_id).with_column_labels(["count"])
 
 
+def pct_change(block: blocks.Block, periods: int = 1) -> blocks.Block:
+    column_labels = block.column_labels
+    window_spec = core.WindowSpec(
+        preceding=periods if periods > 0 else None,
+        following=-periods if periods < 0 else None,
+    )
+
+    original_columns = block.value_columns
+    block, shift_columns = block.multi_apply_window_op(
+        original_columns, agg_ops.ShiftOp(periods), window_spec=window_spec
+    )
+    result_ids = []
+    for original_col, shifted_col in zip(original_columns, shift_columns):
+        block, change_id = block.apply_binary_op(original_col, shifted_col, ops.sub_op)
+        block, pct_change_id = block.apply_binary_op(change_id, shifted_col, ops.div_op)
+        result_ids.append(pct_change_id)
+    return block.select_columns(result_ids).with_column_labels(column_labels)
+
+
 def rank(
     block: blocks.Block,
     method: str = "average",
@@ -229,3 +248,160 @@ def dropna(block: blocks.Block, how: typing.Literal["all", "any"] = "any"):
             filtered_block = filtered_block.filter(predicate)
         filtered_block = filtered_block.select_columns(block.value_columns)
         return filtered_block
+
+
+def nsmallest(
+    block: blocks.Block,
+    n: int,
+    column_ids: typing.Sequence[str],
+    keep: str,
+) -> blocks.Block:
+    if keep not in ("first", "last", "all"):
+        raise ValueError("'keep must be one of 'first', 'last', or 'all'")
+    if keep == "last":
+        block = block.reversed()
+    order_refs = [
+        ordering.OrderingColumnReference(
+            col_id, direction=ordering.OrderingDirection.ASC
+        )
+        for col_id in column_ids
+    ]
+    block = block.order_by(order_refs, stable=True)
+    if keep in ("first", "last"):
+        return block.slice(0, n)
+    else:  # keep == "all":
+        block, counter = block.apply_window_op(
+            column_ids[0],
+            agg_ops.rank_op,
+            window_spec=core.WindowSpec(ordering=order_refs),
+        )
+        block, condition = block.apply_unary_op(
+            counter, ops.partial_right(ops.le_op, n)
+        )
+        block = block.filter(condition)
+        return block.drop_columns([counter, condition])
+
+
+def nlargest(
+    block: blocks.Block,
+    n: int,
+    column_ids: typing.Sequence[str],
+    keep: str,
+) -> blocks.Block:
+    if keep not in ("first", "last", "all"):
+        raise ValueError("'keep must be one of 'first', 'last', or 'all'")
+    if keep == "last":
+        block = block.reversed()
+    order_refs = [
+        ordering.OrderingColumnReference(
+            col_id, direction=ordering.OrderingDirection.DESC
+        )
+        for col_id in column_ids
+    ]
+    block = block.order_by(order_refs, stable=True)
+    if keep in ("first", "last"):
+        return block.slice(0, n)
+    else:  # keep == "all":
+        block, counter = block.apply_window_op(
+            column_ids[0],
+            agg_ops.rank_op,
+            window_spec=core.WindowSpec(ordering=order_refs),
+        )
+        block, condition = block.apply_unary_op(
+            counter, ops.partial_right(ops.le_op, n)
+        )
+        block = block.filter(condition)
+        return block.drop_columns([counter, condition])
+
+
+def skew(
+    block: blocks.Block,
+    skew_column_ids: typing.Sequence[str],
+    grouping_column_ids: typing.Sequence[str] = (),
+) -> blocks.Block:
+
+    original_columns = skew_column_ids
+    column_labels = block.select_columns(original_columns).column_labels
+
+    block, delta3_ids = _mean_delta_to_power(
+        block, 3, original_columns, grouping_column_ids
+    )
+    # counts, moment3 for each column
+    aggregations = []
+    for i, col in enumerate(original_columns):
+        count_agg = (col, agg_ops.count_op)
+        moment3_agg = (delta3_ids[i], agg_ops.mean_op)
+        variance_agg = (col, agg_ops.PopVarOp())
+        aggregations.extend([count_agg, moment3_agg, variance_agg])
+
+    block, agg_ids = block.aggregate(
+        by_column_ids=grouping_column_ids, aggregations=aggregations
+    )
+
+    skew_ids = []
+    for i, col in enumerate(original_columns):
+        # Corresponds to order of aggregations in preceding loop
+        count_id, moment3_id, var_id = agg_ids[i * 3 : (i * 3) + 3]
+        block, skew_id = _skew_from_moments_and_count(
+            block, count_id, moment3_id, var_id
+        )
+        skew_ids.append(skew_id)
+
+    block = block.select_columns(skew_ids).with_column_labels(column_labels)
+    if not grouping_column_ids:
+        # When ungrouped, stack everything into single column so can be returned as series
+        block = block.stack()
+        block = block.drop_levels([block.index_columns[0]])
+    return block
+
+
+def _mean_delta_to_power(
+    block: blocks.Block,
+    n_power,
+    column_ids: typing.Sequence[str],
+    grouping_column_ids: typing.Sequence[str],
+) -> typing.Tuple[blocks.Block, typing.Sequence[str]]:
+    """Calculate (x-mean(x))^n. Useful for calculating moment statistics such as skew and kurtosis."""
+    window = core.WindowSpec(grouping_keys=grouping_column_ids)
+    block, mean_ids = block.multi_apply_window_op(column_ids, agg_ops.mean_op, window)
+    delta_ids = []
+    cube_op = ops.partial_right(ops.pow_op, n_power)
+    for val_id, mean_val_id in zip(column_ids, mean_ids):
+        block, delta_id = block.apply_binary_op(val_id, mean_val_id, ops.sub_op)
+        block, delta_power_id = block.apply_unary_op(delta_id, cube_op)
+        block = block.drop_columns(delta_id)
+        delta_ids.append(delta_power_id)
+    return block, delta_ids
+
+
+def _skew_from_moments_and_count(
+    block: blocks.Block, count_id: str, moment3_id: str, var_id: str
+) -> typing.Tuple[blocks.Block, str]:
+    # Calculate skew using count, third moment and population variance
+    # See G1 estimator:
+    # https://en.wikipedia.org/wiki/Skewness#Sample_skewness
+    block, denominator_id = block.apply_unary_op(
+        var_id, ops.partial_right(ops.pow_op, 3 / 2)
+    )
+    block, base_id = block.apply_binary_op(moment3_id, denominator_id, ops.div_op)
+    block, countminus1_id = block.apply_unary_op(
+        count_id, ops.partial_right(ops.sub_op, 1)
+    )
+    block, countminus2_id = block.apply_unary_op(
+        count_id, ops.partial_right(ops.sub_op, 2)
+    )
+    block, adjustment_id = block.apply_binary_op(count_id, countminus1_id, ops.mul_op)
+    block, adjustment_id = block.apply_unary_op(
+        adjustment_id, ops.partial_right(ops.pow_op, 1 / 2)
+    )
+    block, adjustment_id = block.apply_binary_op(
+        adjustment_id, countminus2_id, ops.div_op
+    )
+    block, skew_id = block.apply_binary_op(base_id, adjustment_id, ops.mul_op)
+
+    # Need to produce NA if have less than 3 data points
+    block, na_cond_id = block.apply_unary_op(count_id, ops.partial_right(ops.ge_op, 3))
+    block, skew_id = block.apply_binary_op(
+        skew_id, na_cond_id, ops.partial_arg3(ops.where_op, None)
+    )
+    return block, skew_id
diff --git a/bigframes/core/blocks.py b/bigframes/core/blocks.py
index 482cfd0141..5b414252ee 100644
--- a/bigframes/core/blocks.py
+++ b/bigframes/core/blocks.py
@@ -709,8 +709,9 @@ def multi_apply_window_op(
         window_spec: core.WindowSpec,
         *,
         skip_null_groups: bool = False,
-    ) -> Block:
+    ) -> typing.Tuple[Block, typing.Sequence[str]]:
         block = self
+        result_ids = []
         for i, col_id in enumerate(columns):
             label = self.col_id_to_label[col_id]
             block, result_id = block.apply_window_op(
@@ -721,9 +722,8 @@ def multi_apply_window_op(
                 result_label=label,
                 skip_null_groups=skip_null_groups,
             )
-            block = block.copy_values(result_id, col_id)
-            block = block.drop_columns([result_id])
-        return block
+            result_ids.append(result_id)
+        return block, result_ids
 
     def multi_apply_unary_op(
         self,
@@ -1123,7 +1123,9 @@ def promote_offsets(self, label: Label = None) -> typing.Tuple[Block, str]:
         )
 
     def add_prefix(self, prefix: str, axis: str | int | None = None) -> Block:
-        axis_number = bigframes.core.utils.get_axis_number(axis)
+        axis_number = bigframes.core.utils.get_axis_number(
+            "rows" if (axis is None) else axis
+        )
         if axis_number == 0:
             expr = self._expr
             for index_col in self._index_columns:
@@ -1140,7 +1142,9 @@ def add_prefix(self, prefix: str, axis: str | int | None = None) -> Block:
             return self.rename(columns=lambda label: f"{prefix}{label}")
 
     def add_suffix(self, suffix: str, axis: str | int | None = None) -> Block:
-        axis_number = bigframes.core.utils.get_axis_number(axis)
+        axis_number = bigframes.core.utils.get_axis_number(
+            "rows" if (axis is None) else axis
+        )
         if axis_number == 0:
             expr = self._expr
             for index_col in self._index_columns:
diff --git a/bigframes/core/groupby/__init__.py b/bigframes/core/groupby/__init__.py
index 589c5c251c..810e145d33 100644
--- a/bigframes/core/groupby/__init__.py
+++ b/bigframes/core/groupby/__init__.py
@@ -20,6 +20,7 @@
 
 import bigframes.constants as constants
 import bigframes.core as core
+import bigframes.core.block_transforms as block_ops
 import bigframes.core.blocks as blocks
 import bigframes.core.ordering as order
 import bigframes.core.utils as utils
@@ -145,6 +146,16 @@ def var(
             self._raise_on_non_numeric("var")
         return self._aggregate_all(agg_ops.var_op, numeric_only=True)
 
+    def skew(
+        self,
+        *,
+        numeric_only: bool = False,
+    ) -> df.DataFrame:
+        if not numeric_only:
+            self._raise_on_non_numeric("skew")
+        block = block_ops.skew(self._block, self._selected_cols, self._by_col_ids)
+        return df.DataFrame(block)
+
     def all(self) -> df.DataFrame:
         return self._aggregate_all(agg_ops.all_op)
 
@@ -168,6 +179,22 @@ def cummax(self, *args, numeric_only: bool = False, **kwargs) -> df.DataFrame:
     def cumprod(self, *args, **kwargs) -> df.DataFrame:
         return self._apply_window_op(agg_ops.product_op, numeric_only=True)
 
+    def shift(self, periods=1) -> series.Series:
+        window = core.WindowSpec(
+            grouping_keys=self._by_col_ids,
+            preceding=periods if periods > 0 else None,
+            following=-periods if periods < 0 else None,
+        )
+        return self._apply_window_op(agg_ops.ShiftOp(periods), window=window)
+
+    def diff(self, periods=1) -> series.Series:
+        window = core.WindowSpec(
+            grouping_keys=self._by_col_ids,
+            preceding=periods if periods > 0 else None,
+            following=-periods if periods < 0 else None,
+        )
+        return self._apply_window_op(agg_ops.DiffOp(periods), window=window)
+
     def agg(self, func=None, **kwargs) -> df.DataFrame:
         if func:
             if isinstance(func, str):
@@ -323,10 +350,10 @@ def _apply_window_op(
             grouping_keys=self._by_col_ids, following=0
         )
         columns = self._aggregated_columns(numeric_only=numeric_only)
-        block = self._block.multi_apply_window_op(
+        block, result_ids = self._block.multi_apply_window_op(
             columns, op, window_spec=window_spec, skip_null_groups=self._dropna
         )
-        block = block.select_columns(columns)
+        block = block.select_columns(result_ids)
         return df.DataFrame(block)
 
     def _resolve_label(self, label: blocks.Label) -> str:
@@ -391,6 +418,10 @@ def std(self, *args, **kwargs) -> series.Series:
     def var(self, *args, **kwargs) -> series.Series:
         return self._aggregate(agg_ops.var_op)
 
+    def skew(self, *args, **kwargs) -> series.Series:
+        block = block_ops.skew(self._block, [self._value_column], self._by_col_ids)
+        return series.Series(block)
+
     def prod(self, *args) -> series.Series:
         return self._aggregate(agg_ops.product_op)
 
@@ -459,8 +490,13 @@ def shift(self, periods=1) -> series.Series:
         )
         return self._apply_window_op(agg_ops.ShiftOp(periods), window=window)
 
-    def diff(self) -> series.Series:
-        return self._ungroup() - self.shift(1)
+    def diff(self, periods=1) -> series.Series:
+        window = core.WindowSpec(
+            grouping_keys=self._by_col_ids,
+            preceding=periods if periods > 0 else None,
+            following=-periods if periods < 0 else None,
+        )
+        return self._apply_window_op(agg_ops.DiffOp(periods), window=window)
 
     def rolling(self, window: int, min_periods=None) -> windows.Window:
         # To get n size window, need current row and n-1 preceding rows.
diff --git a/bigframes/core/indexers.py b/bigframes/core/indexers.py
index 46091f211a..28bce05338 100644
--- a/bigframes/core/indexers.py
+++ b/bigframes/core/indexers.py
@@ -145,23 +145,41 @@ def __setitem__(
         value: bigframes.dataframe.SingleItemValue,
     ):
         if (
-            not isinstance(key, tuple)
-            or len(key) != 2
-            or not isinstance(key[0], slice)
-            or (key[0].start is not None and key[0].start != 0)
-            or (key[0].step is not None and key[0].step != 1)
-            or key[0].stop is not None
+            isinstance(key, tuple)
+            and len(key) == 2
+            and isinstance(key[0], slice)
+            and (key[0].start is None or key[0].start == 0)
+            and (key[0].step is None or key[0].step == 1)
+            and key[0].stop is None
         ):
+            # TODO(swast): Support setting multiple columns with key[1] as a list
+            # of labels and value as a DataFrame.
+            df = self._dataframe.assign(**{key[1]: value})
+            self._dataframe._set_block(df._get_block())
+        elif (
+            isinstance(key, tuple)
+            and len(key) == 2
+            and isinstance(key[0], bigframes.series.Series)
+            and key[0].dtype == "boolean"
+        ) and pd.api.types.is_scalar(value):
+            new_column = key[0].map({True: value, False: None})
+            try:
+                original_column = self._dataframe[key[1]]
+            except KeyError:
+                self._dataframe[key[1]] = new_column
+                return
+            try:
+                self._dataframe[key[1]] = new_column.fillna(original_column)
+            except ibis.common.exceptions.IbisTypeError:
+                raise TypeError(
+                    f"Cannot assign scalar of type {type(value)} to column of type {original_column.dtype}, or index type of series argument does not match dataframe."
+                )
+        else:
             raise NotImplementedError(
-                "Only setting a column by DataFrame.loc[:, 'column'] is supported."
+                "Only DataFrame.loc[:, 'column'] and DataFrame.loc[bool series, 'column'] = Scalar are supported."
                 f"{constants.FEEDBACK_LINK}"
             )
 
-        # TODO(swast): Support setting multiple columns with key[1] as a list
-        # of labels and value as a DataFrame.
-        df = self._dataframe.assign(**{key[1]: value})
-        self._dataframe._set_block(df._get_block())
-
 
 class ILocDataFrameIndexer:
     def __init__(self, dataframe: bigframes.dataframe.DataFrame):
diff --git a/bigframes/core/indexes/index.py b/bigframes/core/indexes/index.py
index 04b9a36b64..748a68c944 100644
--- a/bigframes/core/indexes/index.py
+++ b/bigframes/core/indexes/index.py
@@ -53,6 +53,10 @@ def names(self) -> typing.Sequence[blocks.Label]:
     def names(self, values: typing.Sequence[blocks.Label]):
         return self._data._set_block(self._data._get_block().with_index_labels(values))
 
+    @property
+    def nlevels(self) -> int:
+        return len(self._data._get_block().index_columns)
+
     @property
     def shape(self) -> typing.Tuple[int]:
         return (self._data._get_block().shape[0],)
@@ -97,6 +101,22 @@ def is_monotonic_decreasing(self) -> bool:
             ),
         )
 
+    @property
+    def is_unique(self) -> bool:
+        # TODO: Cache this at block level
+        # Avoid circular imports
+        import bigframes.core.block_transforms as block_ops
+        import bigframes.dataframe as df
+
+        duplicates_block, _ = block_ops.indicate_duplicates(
+            self._data._get_block(), self._data._get_block().index_columns
+        )
+        duplicates_block = duplicates_block.with_column_labels(
+            ["values", "is_duplicate"]
+        )
+        duplicates_df = df.DataFrame(duplicates_block)
+        return not duplicates_df["is_duplicate"].any()
+
     def __getitem__(self, key: int) -> typing.Any:
         if isinstance(key, int):
             result_pd_df, _ = self._data._get_block().slice(key, key + 1, 1).to_pandas()
diff --git a/bigframes/core/utils.py b/bigframes/core/utils.py
index 1c0a2a1a81..75175690ce 100644
--- a/bigframes/core/utils.py
+++ b/bigframes/core/utils.py
@@ -23,8 +23,8 @@
 UNNAMED_INDEX_ID = "bigframes_unnamed_index"
 
 
-def get_axis_number(axis: typing.Union[str, int, None]) -> typing.Literal[0, 1]:
-    if axis in {0, "index", "rows", None}:
+def get_axis_number(axis: typing.Union[str, int]) -> typing.Literal[0, 1]:
+    if axis in {0, "index", "rows"}:
         return 0
     elif axis in {1, "columns"}:
         return 1
diff --git a/bigframes/dataframe.py b/bigframes/dataframe.py
index 31777f3fac..d65d4ce344 100644
--- a/bigframes/dataframe.py
+++ b/bigframes/dataframe.py
@@ -455,7 +455,7 @@ def __getattr__(self, key: str):
             raise AttributeError(key)
 
     def __repr__(self) -> str:
-        """Converts a DataFrame to a string. Calls compute.
+        """Converts a DataFrame to a string. Calls to_pandas.
 
         Only represents the first `bigframes.options.display.max_rows`.
         """
@@ -532,13 +532,14 @@ def _apply_binop(
         other: float | int | bigframes.series.Series | DataFrame,
         op,
         axis: str | int = "columns",
+        how: str = "outer",
     ):
         if isinstance(other, (float, int)):
             return self._apply_scalar_binop(other, op)
         elif isinstance(other, bigframes.series.Series):
-            return self._apply_series_binop(other, op, axis=axis)
+            return self._apply_series_binop(other, op, axis=axis, how=how)
         elif isinstance(other, DataFrame):
-            return self._apply_dataframe_binop(other, op)
+            return self._apply_dataframe_binop(other, op, how=how)
         raise NotImplementedError(
             f"binary operation is not implemented on the second operand of type {type(other).__name__}."
             f"{constants.FEEDBACK_LINK}"
@@ -559,6 +560,7 @@ def _apply_series_binop(
         other: bigframes.series.Series,
         op: ops.BinaryOp,
         axis: str | int = "columns",
+        how: str = "outer",
     ) -> DataFrame:
         if axis not in ("columns", "index", 0, 1):
             raise ValueError(f"Invalid input: axis {axis}.")
@@ -569,7 +571,7 @@ def _apply_series_binop(
             )
 
         joined_index, (get_column_left, get_column_right) = self._block.index.join(
-            other._block.index, how="outer"
+            other._block.index, how=how
         )
 
         series_column_id = other._value.get_name()
@@ -591,22 +593,27 @@ def _apply_series_binop(
         return DataFrame(block)
 
     def _apply_dataframe_binop(
-        self,
-        other: DataFrame,
-        op: ops.BinaryOp,
+        self, other: DataFrame, op: ops.BinaryOp, how: str = "outer"
     ) -> DataFrame:
         # Join rows
         joined_index, (get_column_left, get_column_right) = self._block.index.join(
-            other._block.index, how="outer"
+            other._block.index, how=how
         )
         # join columns schema
+        # indexers will be none for exact match
         columns, lcol_indexer, rcol_indexer = self.columns.join(
-            other.columns, how="outer", return_indexers=True
+            other.columns, how=how, return_indexers=True
         )
 
         binop_result_ids = []
         block = joined_index._block
-        for left_index, right_index in zip(lcol_indexer, rcol_indexer):
+
+        column_indices = zip(
+            lcol_indexer if (lcol_indexer is not None) else range(len(columns)),
+            rcol_indexer if (lcol_indexer is not None) else range(len(columns)),
+        )
+
+        for left_index, right_index in column_indices:
             if left_index >= 0 and right_index >= 0:  # -1 indices indicate missing
                 left_col_id = self._block.value_columns[left_index]
                 right_col_id = other._block.value_columns[right_index]
@@ -617,13 +624,19 @@ def _apply_dataframe_binop(
                 )
                 binop_result_ids.append(result_col_id)
             elif left_index >= 0:
-                dtype = self.dtypes[left_index]
-                block, null_col_id = block.create_constant(None, dtype=dtype)
-                binop_result_ids.append(null_col_id)
+                left_col_id = self._block.value_columns[left_index]
+                block, result_col_id = block.apply_unary_op(
+                    get_column_left(left_col_id),
+                    ops.partial_right(op, None),
+                )
+                binop_result_ids.append(result_col_id)
             elif right_index >= 0:
-                dtype = other.dtypes[right_index]
-                block, null_col_id = block.create_constant(None, dtype=dtype)
-                binop_result_ids.append(null_col_id)
+                right_col_id = other._block.value_columns[right_index]
+                block, result_col_id = block.apply_unary_op(
+                    get_column_right(right_col_id),
+                    ops.partial_left(op, None),
+                )
+                binop_result_ids.append(result_col_id)
             else:
                 # Should not be possible
                 raise ValueError("No right or left index.")
@@ -759,6 +772,75 @@ def rpow(
 
     __rpow__ = rpow
 
+    def combine(
+        self,
+        other: DataFrame,
+        func: typing.Callable[
+            [bigframes.series.Series, bigframes.series.Series], bigframes.series.Series
+        ],
+        fill_value=None,
+        overwrite: bool = True,
+    ) -> DataFrame:
+        # Join rows
+        joined_index, (get_column_left, get_column_right) = self._block.index.join(
+            other._block.index, how="outer"
+        )
+        columns, lcol_indexer, rcol_indexer = self.columns.join(
+            other.columns, how="outer", return_indexers=True
+        )
+
+        column_indices = zip(
+            lcol_indexer if (lcol_indexer is not None) else range(len(columns)),
+            rcol_indexer if (lcol_indexer is not None) else range(len(columns)),
+        )
+
+        block = joined_index._block
+        results = []
+        for left_index, right_index in column_indices:
+            if left_index >= 0 and right_index >= 0:  # -1 indices indicate missing
+                left_col_id = get_column_left(self._block.value_columns[left_index])
+                right_col_id = get_column_right(other._block.value_columns[right_index])
+                left_series = bigframes.series.Series(block.select_column(left_col_id))
+                right_series = bigframes.series.Series(
+                    block.select_column(right_col_id)
+                )
+                if fill_value is not None:
+                    left_series = left_series.fillna(fill_value)
+                    right_series = right_series.fillna(fill_value)
+                results.append(func(left_series, right_series))
+            elif left_index >= 0:
+                # Does not exist in other
+                if overwrite:
+                    dtype = self.dtypes[left_index]
+                    block, null_col_id = block.create_constant(None, dtype=dtype)
+                    result = bigframes.series.Series(block.select_column(null_col_id))
+                    results.append(result)
+                else:
+                    left_col_id = get_column_left(self._block.value_columns[left_index])
+                    result = bigframes.series.Series(block.select_column(left_col_id))
+                    if fill_value is not None:
+                        result = result.fillna(fill_value)
+                    results.append(result)
+            elif right_index >= 0:
+                right_col_id = get_column_right(other._block.value_columns[right_index])
+                result = bigframes.series.Series(block.select_column(right_col_id))
+                if fill_value is not None:
+                    result = result.fillna(fill_value)
+                results.append(result)
+            else:
+                # Should not be possible
+                raise ValueError("No right or left index.")
+
+        if all([isinstance(val, bigframes.series.Series) for val in results]):
+            import bigframes.core.reshape as rs
+
+            return rs.concat(results, axis=1)
+        else:
+            raise ValueError("'func' must return Series")
+
+    def combine_first(self, other: DataFrame):
+        return self._apply_dataframe_binop(other, ops.fillna_op)
+
     def to_pandas(
         self,
         max_download_size: Optional[int] = None,
@@ -810,6 +892,28 @@ def head(self, n: int = 5) -> DataFrame:
     def tail(self, n: int = 5) -> DataFrame:
         return typing.cast(DataFrame, self.iloc[-n:])
 
+    def nlargest(
+        self,
+        n: int,
+        columns: typing.Union[blocks.Label, typing.Sequence[blocks.Label]],
+        keep: str = "first",
+    ) -> DataFrame:
+        if keep not in ("first", "last", "all"):
+            raise ValueError("'keep must be one of 'first', 'last', or 'all'")
+        column_ids = self._sql_names(columns)
+        return DataFrame(block_ops.nlargest(self._block, n, column_ids, keep=keep))
+
+    def nsmallest(
+        self,
+        n: int,
+        columns: typing.Union[blocks.Label, typing.Sequence[blocks.Label]],
+        keep: str = "first",
+    ) -> DataFrame:
+        if keep not in ("first", "last", "all"):
+            raise ValueError("'keep must be one of 'first', 'last', or 'all'")
+        column_ids = self._sql_names(columns)
+        return DataFrame(block_ops.nsmallest(self._block, n, column_ids, keep=keep))
+
     def drop(
         self,
         labels: typing.Any = None,
@@ -852,13 +956,50 @@ def drop(
             raise ValueError("Must specify 'labels' or 'index'/'columns")
         return DataFrame(block)
 
-    def droplevel(self, level: LevelsType):
-        resolved_level_ids = self._resolve_levels(level)
-        return DataFrame(self._block.drop_levels(resolved_level_ids))
+    def droplevel(self, level: LevelsType, axis: int | str = 0):
+        axis_n = utils.get_axis_number(axis)
+        if axis_n == 0:
+            resolved_level_ids = self._resolve_levels(level)
+            return DataFrame(self._block.drop_levels(resolved_level_ids))
+        else:
+            if isinstance(self.columns, pandas.MultiIndex):
+                new_df = self.copy()
+                new_df.columns = self.columns.droplevel(level)
+                return new_df
+            else:
+                raise ValueError("Columns must be a multiindex to drop levels.")
+
+    def swaplevel(self, i: int = -2, j: int = -1, axis: int | str = 0):
+        axis_n = utils.get_axis_number(axis)
+        if axis_n == 0:
+            level_i = self._block.index_columns[i]
+            level_j = self._block.index_columns[j]
+            mapping = {level_i: level_j, level_j: level_i}
+            reordering = [
+                mapping.get(index_id, index_id)
+                for index_id in self._block.index_columns
+            ]
+            return DataFrame(self._block.reorder_levels(reordering))
+        else:
+            if isinstance(self.columns, pandas.MultiIndex):
+                new_df = self.copy()
+                new_df.columns = self.columns.swaplevel(i, j)
+                return new_df
+            else:
+                raise ValueError("Columns must be a multiindex to reorder levels.")
 
-    def reorder_levels(self, order: LevelsType):
-        resolved_level_ids = self._resolve_levels(order)
-        return DataFrame(self._block.reorder_levels(resolved_level_ids))
+    def reorder_levels(self, order: LevelsType, axis: int | str = 0):
+        axis_n = utils.get_axis_number(axis)
+        if axis_n == 0:
+            resolved_level_ids = self._resolve_levels(order)
+            return DataFrame(self._block.reorder_levels(resolved_level_ids))
+        else:
+            if isinstance(self.columns, pandas.MultiIndex):
+                new_df = self.copy()
+                new_df.columns = self.columns.reorder_levels(order)
+                return new_df
+            else:
+                raise ValueError("Columns must be a multiindex to reorder levels.")
 
     def _resolve_levels(self, level: LevelsType) -> typing.Sequence[str]:
         if utils.is_list_like(level):
@@ -1096,8 +1237,177 @@ def add_suffix(self, suffix: str, axis: int | str | None = None) -> DataFrame:
         axis = 1 if axis is None else axis
         return DataFrame(self._get_block().add_suffix(suffix, axis))
 
+    def filter(
+        self,
+        items: typing.Optional[typing.Iterable] = None,
+        like: typing.Optional[str] = None,
+        regex: typing.Optional[str] = None,
+        axis: int | str | None = None,
+    ) -> DataFrame:
+        if sum([(items is not None), (like is not None), (regex is not None)]) != 1:
+            raise ValueError(
+                "Need to provide exactly one of 'items', 'like', or 'regex'"
+            )
+        axis_n = utils.get_axis_number(axis) if (axis is not None) else 1
+        if axis_n == 0:  # row labels
+            return self._filter_rows(items, like, regex)
+        else:  # column labels
+            return self._filter_columns(items, like, regex)
+
+    def _filter_rows(
+        self,
+        items: typing.Optional[typing.Iterable] = None,
+        like: typing.Optional[str] = None,
+        regex: typing.Optional[str] = None,
+    ) -> DataFrame:
+        if len(self._block.index_columns) > 1:
+            raise NotImplementedError(
+                "Method filter does not support rows multiindex. {constants.FEEDBACK_LINK}"
+            )
+        if (like is not None) or (regex is not None):
+            block = self._block
+            block, label_string_id = block.apply_unary_op(
+                self._block.index_columns[0],
+                ops.AsTypeOp(pandas.StringDtype(storage="pyarrow")),
+            )
+            if like is not None:
+                block, mask_id = block.apply_unary_op(
+                    label_string_id, ops.ContainsStringOp(pat=like)
+                )
+            else:  # regex
+                assert regex is not None
+                block, mask_id = block.apply_unary_op(
+                    label_string_id, ops.ContainsRegexOp(pat=regex)
+                )
+
+            block = block.filter(mask_id)
+            block = block.select_columns(self._block.value_columns)
+            return DataFrame(block)
+        elif items is not None:
+            # Behavior matches pandas 2.1+, older pandas versions would reindex
+            block = self._block
+            block, mask_id = block.apply_unary_op(
+                self._block.index_columns[0], ops.IsInOp(values=list(items))
+            )
+            block = block.filter(mask_id)
+            block = block.select_columns(self._block.value_columns)
+            return DataFrame(block)
+        else:
+            raise ValueError("Need to provide 'items', 'like', or 'regex'")
+
+    def _filter_columns(
+        self,
+        items: typing.Optional[typing.Iterable] = None,
+        like: typing.Optional[str] = None,
+        regex: typing.Optional[str] = None,
+    ) -> DataFrame:
+        if (like is not None) or (regex is not None):
+
+            def label_filter(label):
+                label_str = label if isinstance(label, str) else str(label)
+                if like:
+                    return like in label_str
+                else:  # regex
+                    return re.match(regex, label_str) is not None
+
+            cols = [
+                col_id
+                for col_id, label in zip(self._block.value_columns, self.columns)
+                if label_filter(label)
+            ]
+            return DataFrame(self._block.select_columns(cols))
+        if items is not None:
+            # Behavior matches pandas 2.1+, older pandas versions would reorder using order of items
+            new_columns = self.columns.intersection(pandas.Index(items))
+            return self.reindex(columns=new_columns)
+        else:
+            raise ValueError("Need to provide 'items', 'like', or 'regex'")
+
+    def reindex(
+        self,
+        labels=None,
+        *,
+        index=None,
+        columns=None,
+        axis: typing.Optional[typing.Union[str, int]] = None,
+        validate: typing.Optional[bool] = None,
+    ):
+        if labels:
+            if index or columns:
+                raise ValueError("Cannot specify both 'labels' and 'index'/'columns")
+            axis_n = utils.get_axis_number(axis) if (axis is not None) else 0
+            if axis_n == 0:
+                index = labels
+            else:
+                columns = labels
+        if (index is not None) and (columns is not None):
+            return self._reindex_columns(columns)._reindex_rows(
+                index, validate=validate or False
+            )
+        if index is not None:
+            return self._reindex_rows(index, validate=validate or False)
+        if columns is not None:
+            return self._reindex_columns(columns)
+
+    def _reindex_rows(
+        self,
+        index,
+        *,
+        validate: typing.Optional[bool] = None,
+    ):
+        if validate and not self.index.is_unique:
+            raise ValueError("Original index must be unique to reindex")
+        keep_original_names = False
+        if isinstance(index, indexes.Index):
+            new_indexer = DataFrame(data=index._data._get_block())[[]]
+        else:
+            if not isinstance(index, pandas.Index):
+                keep_original_names = True
+                index = pandas.Index(index)
+            if index.nlevels != self.index.nlevels:
+                raise NotImplementedError(
+                    "Cannot reindex with index with different nlevels"
+                )
+            new_indexer = DataFrame(index=index)[[]]
+        # multiindex join is senstive to index names, so we will set all these
+        result = new_indexer.rename_axis(range(new_indexer.index.nlevels)).join(
+            self.rename_axis(range(self.index.nlevels)),
+            how="left",
+        )
+        # and then reset the names after the join
+        return result.rename_axis(
+            self.index.names if keep_original_names else index.names
+        )
+
+    def _reindex_columns(self, columns):
+        block = self._block
+        new_column_index, indexer = self.columns.reindex(columns)
+        result_cols = []
+        for label, index in zip(columns, indexer):
+            if index >= 0:
+                result_cols.append(self._block.value_columns[index])
+            else:
+                block, null_col = block.create_constant(
+                    pandas.NA, label, dtype=pandas.Float64Dtype()
+                )
+                result_cols.append(null_col)
+        result_df = DataFrame(block.select_columns(result_cols))
+        result_df.columns = new_column_index
+        return result_df
+
+    def reindex_like(self, other: DataFrame, *, validate: typing.Optional[bool] = None):
+        return self.reindex(index=other.index, columns=other.columns, validate=validate)
+
     def fillna(self, value=None) -> DataFrame:
-        return self._apply_binop(value, ops.fillna_op)
+        return self._apply_binop(value, ops.fillna_op, how="left")
+
+    def ffill(self, *, limit: typing.Optional[int] = None) -> DataFrame:
+        window = bigframes.core.WindowSpec(preceding=limit, following=0)
+        return self._apply_window_op(agg_ops.LastNonNullOp(), window)
+
+    def bfill(self, *, limit: typing.Optional[int] = None) -> DataFrame:
+        window = bigframes.core.WindowSpec(preceding=0, following=limit)
+        return self._apply_window_op(agg_ops.FirstNonNullOp(), window)
 
     def isin(self, values) -> DataFrame:
         if utils.is_dict_like(values):
@@ -1309,6 +1619,14 @@ def describe(self) -> DataFrame:
         )
         return typing.cast(DataFrame, result)
 
+    def skew(self, *, numeric_only: bool = False):
+        if not numeric_only:
+            frame = self._raise_on_non_numeric("skew")
+        else:
+            frame = self._drop_non_numeric()
+        result_block = block_ops.skew(frame._block, frame._block.value_columns)
+        return bigframes.series.Series(result_block)
+
     def pivot(
         self,
         *,
@@ -1702,17 +2020,29 @@ def shift(self, periods: int = 1) -> DataFrame:
         )
         return self._apply_window_op(agg_ops.ShiftOp(periods), window)
 
+    def diff(self, periods: int = 1) -> DataFrame:
+        window = bigframes.core.WindowSpec(
+            preceding=periods if periods > 0 else None,
+            following=-periods if periods < 0 else None,
+        )
+        return self._apply_window_op(agg_ops.DiffOp(periods), window)
+
+    def pct_change(self, periods: int = 1) -> DataFrame:
+        # Future versions of pandas will not perfrom ffill automatically
+        df = self.ffill()
+        return DataFrame(block_ops.pct_change(df._block, periods=periods))
+
     def _apply_window_op(
         self,
         op: agg_ops.WindowOp,
         window_spec: bigframes.core.WindowSpec,
     ):
-        block = self._block.multi_apply_window_op(
+        block, result_ids = self._block.multi_apply_window_op(
             self._block.value_columns,
             op,
             window_spec=window_spec,
         )
-        return DataFrame(block)
+        return DataFrame(block.select_columns(result_ids))
 
     def sample(
         self,
@@ -1875,6 +2205,98 @@ def to_parquet(self, path: str, *, index: bool = True) -> None:
         _, query_job = self._block.expr._session._start_query(export_data_statement)
         self._set_internal_query_job(query_job)
 
+    def to_dict(
+        self,
+        orient: Literal[
+            "dict", "list", "series", "split", "tight", "records", "index"
+        ] = "dict",
+        into: type[dict] = dict,
+        **kwargs,
+    ) -> dict | list[dict]:
+        return self.to_pandas().to_dict(orient, into, **kwargs)  # type: ignore
+
+    def to_excel(self, excel_writer, sheet_name: str = "Sheet1", **kwargs) -> None:
+        return self.to_pandas().to_excel(excel_writer, sheet_name, **kwargs)
+
+    def to_latex(
+        self,
+        buf=None,
+        columns: Sequence | None = None,
+        header: bool | Sequence[str] = True,
+        index: bool = True,
+        **kwargs,
+    ) -> str | None:
+        return self.to_pandas().to_latex(
+            buf, columns=columns, header=header, index=index, **kwargs  # type: ignore
+        )
+
+    def to_records(
+        self, index: bool = True, column_dtypes=None, index_dtypes=None
+    ) -> numpy.recarray:
+        return self.to_pandas().to_records(index, column_dtypes, index_dtypes)
+
+    def to_string(
+        self,
+        buf=None,
+        columns: Sequence[str] | None = None,
+        col_space=None,
+        header: bool | Sequence[str] = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        formatters=None,
+        float_format=None,
+        sparsify: bool | None = None,
+        index_names: bool = True,
+        justify: str | None = None,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        show_dimensions: bool = False,
+        decimal: str = ".",
+        line_width: int | None = None,
+        min_rows: int | None = None,
+        max_colwidth: int | None = None,
+        encoding: str | None = None,
+    ) -> str | None:
+        return self.to_pandas().to_string(
+            buf,
+            columns,  # type: ignore
+            col_space,
+            header,  # type: ignore
+            index,
+            na_rep,
+            formatters,
+            float_format,
+            sparsify,
+            index_names,
+            justify,
+            max_rows,
+            max_cols,
+            show_dimensions,
+            decimal,
+            line_width,
+            min_rows,
+            max_colwidth,
+            encoding,
+        )
+
+    def to_markdown(
+        self,
+        buf=None,
+        mode: str = "wt",
+        index: bool = True,
+        **kwargs,
+    ) -> str | None:
+        return self.to_pandas().to_markdown(buf, mode, index, **kwargs)  # type: ignore
+
+    def to_pickle(self, path, **kwargs) -> None:
+        return self.to_pandas().to_pickle(path, **kwargs)
+
+    def to_orc(self, path=None, **kwargs) -> bytes | None:
+        as_pandas = self.to_pandas()
+        # to_orc only works with default index
+        as_pandas_default_index = as_pandas.reset_index()
+        return as_pandas_default_index.to_orc(path, **kwargs)
+
     def _apply_unary_op(self, operation: ops.UnaryOp) -> DataFrame:
         block = self._block.multi_apply_unary_op(self._block.value_columns, operation)
         return DataFrame(block)
diff --git a/bigframes/dtypes.py b/bigframes/dtypes.py
index 95cf737b2e..af3209b0e1 100644
--- a/bigframes/dtypes.py
+++ b/bigframes/dtypes.py
@@ -157,7 +157,7 @@ def ibis_dtype_to_bigframes_dtype(
         return IBIS_TO_BIGFRAMES[ibis_dtype]
     else:
         raise ValueError(
-            f"Unexpected Ibis data type {type(ibis_dtype)}. {constants.FEEDBACK_LINK}"
+            f"Unexpected Ibis data type {ibis_dtype}. {constants.FEEDBACK_LINK}"
         )
 
 
diff --git a/bigframes/ml/base.py b/bigframes/ml/base.py
index 9f9d9f85d0..f07274f8fc 100644
--- a/bigframes/ml/base.py
+++ b/bigframes/ml/base.py
@@ -133,7 +133,7 @@ class TrainablePredictor(Predictor):
     Also the predictor can be attached to a pipeline with transformers."""
 
     @abc.abstractmethod
-    def fit(self, X, y, transforms):
+    def _fit(self, X, y, transforms=None):
         pass
 
     @abc.abstractmethod
@@ -146,6 +146,36 @@ def to_gbq(self, model_name, replace):
         pass
 
 
+class SupervisedTrainablePredictor(TrainablePredictor):
+    """A BigQuery DataFrames ML Supervised Model base class that can be used to fit and predict outputs.
+
+    Need to provide both X and y in supervised tasks."""
+
+    _T = TypeVar("_T", bound="SupervisedTrainablePredictor")
+
+    def fit(
+        self: _T,
+        X: Union[bpd.DataFrame, bpd.Series],
+        y: Union[bpd.DataFrame, bpd.Series],
+    ) -> _T:
+        return self._fit(X, y)
+
+
+class UnsupervisedTrainablePredictor(TrainablePredictor):
+    """A BigQuery DataFrames ML Unsupervised Model base class that can be used to fit and predict outputs.
+
+    Only need to provide both X (y is optional and ignored) in unsupervised tasks."""
+
+    _T = TypeVar("_T", bound="UnsupervisedTrainablePredictor")
+
+    def fit(
+        self: _T,
+        X: Union[bpd.DataFrame, bpd.Series],
+        y: Optional[Union[bpd.DataFrame, bpd.Series]] = None,
+    ) -> _T:
+        return self._fit(X, y)
+
+
 class Transformer(BaseEstimator):
     """A BigQuery DataFrames Transformer base class that transforms data.
 
diff --git a/bigframes/ml/cluster.py b/bigframes/ml/cluster.py
index 2501d2b21f..14cce2879e 100644
--- a/bigframes/ml/cluster.py
+++ b/bigframes/ml/cluster.py
@@ -28,13 +28,13 @@
 
 
 class KMeans(
+    base.UnsupervisedTrainablePredictor,
     third_party.bigframes_vendored.sklearn.cluster._kmeans.KMeans,
-    base.TrainablePredictor,
 ):
 
     __doc__ = third_party.bigframes_vendored.sklearn.cluster._kmeans.KMeans.__doc__
 
-    def __init__(self, n_clusters=8):
+    def __init__(self, n_clusters: int = 8):
         self.n_clusters = n_clusters
         self._bqml_model: Optional[core.BqmlModel] = None
 
@@ -58,7 +58,7 @@ def _bqml_options(self) -> Dict[str, str | int | float | List[str]]:
         """The model options as they will be set for BQML"""
         return {"model_type": "KMEANS", "num_clusters": self.n_clusters}
 
-    def fit(
+    def _fit(
         self,
         X: Union[bpd.DataFrame, bpd.Series],
         y=None,  # ignored
diff --git a/bigframes/ml/compose.py b/bigframes/ml/compose.py
index df01303ffa..a3d3503ad0 100644
--- a/bigframes/ml/compose.py
+++ b/bigframes/ml/compose.py
@@ -33,8 +33,8 @@
 
 
 class ColumnTransformer(
-    third_party.bigframes_vendored.sklearn.compose._column_transformer.ColumnTransformer,
     base.Transformer,
+    third_party.bigframes_vendored.sklearn.compose._column_transformer.ColumnTransformer,
 ):
     __doc__ = (
         third_party.bigframes_vendored.sklearn.compose._column_transformer.ColumnTransformer.__doc__
diff --git a/bigframes/ml/decomposition.py b/bigframes/ml/decomposition.py
index 75b57f2e54..0cfe3b3ddf 100644
--- a/bigframes/ml/decomposition.py
+++ b/bigframes/ml/decomposition.py
@@ -28,12 +28,12 @@
 
 
 class PCA(
+    base.UnsupervisedTrainablePredictor,
     third_party.bigframes_vendored.sklearn.decomposition._pca.PCA,
-    base.TrainablePredictor,
 ):
     __doc__ = third_party.bigframes_vendored.sklearn.decomposition._pca.PCA.__doc__
 
-    def __init__(self, n_components=3):
+    def __init__(self, n_components: int = 3):
         self.n_components = n_components
         self._bqml_model: Optional[core.BqmlModel] = None
 
@@ -52,7 +52,7 @@ def _from_bq(cls, session: bigframes.Session, model: bigquery.Model) -> PCA:
         new_pca._bqml_model = core.BqmlModel(session, model)
         return new_pca
 
-    def fit(
+    def _fit(
         self,
         X: Union[bpd.DataFrame, bpd.Series],
         y=None,
diff --git a/bigframes/ml/ensemble.py b/bigframes/ml/ensemble.py
index 56a0cc3d94..142edaa00f 100644
--- a/bigframes/ml/ensemble.py
+++ b/bigframes/ml/ensemble.py
@@ -48,8 +48,8 @@
 
 
 class XGBRegressor(
+    base.SupervisedTrainablePredictor,
     third_party.bigframes_vendored.xgboost.sklearn.XGBRegressor,
-    base.TrainablePredictor,
 ):
     __doc__ = third_party.bigframes_vendored.xgboost.sklearn.XGBRegressor.__doc__
 
@@ -57,22 +57,22 @@ def __init__(
         self,
         num_parallel_tree: int = 1,
         booster: Literal["gbtree", "dart"] = "gbtree",
-        dart_normalized_type: Literal["TREE", "FOREST"] = "TREE",
+        dart_normalized_type: Literal["tree", "forest"] = "tree",
         tree_method: Literal["auto", "exact", "approx", "hist"] = "auto",
         min_tree_child_weight: int = 1,
-        colsample_bytree=1.0,
-        colsample_bylevel=1.0,
-        colsample_bynode=1.0,
-        gamma=0.0,
+        colsample_bytree: float = 1.0,
+        colsample_bylevel: float = 1.0,
+        colsample_bynode: float = 1.0,
+        gamma: float = 0.0,
         max_depth: int = 6,
-        subsample=1.0,
-        reg_alpha=0.0,
-        reg_lambda=1.0,
-        early_stop=True,
-        learning_rate=0.3,
+        subsample: float = 1.0,
+        reg_alpha: float = 0.0,
+        reg_lambda: float = 1.0,
+        early_stop: float = True,
+        learning_rate: float = 0.3,
         max_iterations: int = 20,
-        min_rel_progress=0.01,
-        enable_global_explain=False,
+        min_rel_progress: float = 0.01,
+        enable_global_explain: bool = False,
         xgboost_version: Literal["0.9", "1.1"] = "0.9",
     ):
         self.num_parallel_tree = num_parallel_tree
@@ -143,7 +143,7 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
             "xgboost_version": self.xgboost_version,
         }
 
-    def fit(
+    def _fit(
         self,
         X: Union[bpd.DataFrame, bpd.Series],
         y: Union[bpd.DataFrame, bpd.Series],
@@ -211,8 +211,8 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBRegressor:
 
 
 class XGBClassifier(
+    base.SupervisedTrainablePredictor,
     third_party.bigframes_vendored.xgboost.sklearn.XGBClassifier,
-    base.TrainablePredictor,
 ):
 
     __doc__ = third_party.bigframes_vendored.xgboost.sklearn.XGBClassifier.__doc__
@@ -221,22 +221,22 @@ def __init__(
         self,
         num_parallel_tree: int = 1,
         booster: Literal["gbtree", "dart"] = "gbtree",
-        dart_normalized_type: Literal["TREE", "FOREST"] = "TREE",
+        dart_normalized_type: Literal["tree", "forest"] = "tree",
         tree_method: Literal["auto", "exact", "approx", "hist"] = "auto",
         min_tree_child_weight: int = 1,
-        colsample_bytree=1.0,
-        colsample_bylevel=1.0,
-        colsample_bynode=1.0,
-        gamma=0.0,
+        colsample_bytree: float = 1.0,
+        colsample_bylevel: float = 1.0,
+        colsample_bynode: float = 1.0,
+        gamma: float = 0.0,
         max_depth: int = 6,
-        subsample=1.0,
-        reg_alpha=0.0,
-        reg_lambda=1.0,
-        early_stop=True,
-        learning_rate=0.3,
+        subsample: float = 1.0,
+        reg_alpha: float = 0.0,
+        reg_lambda: float = 1.0,
+        early_stop: bool = True,
+        learning_rate: float = 0.3,
         max_iterations: int = 20,
-        min_rel_progress=0.01,
-        enable_global_explain=False,
+        min_rel_progress: float = 0.01,
+        enable_global_explain: bool = False,
         xgboost_version: Literal["0.9", "1.1"] = "0.9",
     ):
         self.num_parallel_tree = num_parallel_tree
@@ -307,7 +307,7 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
             "xgboost_version": self.xgboost_version,
         }
 
-    def fit(
+    def _fit(
         self,
         X: Union[bpd.DataFrame, bpd.Series],
         y: Union[bpd.DataFrame, bpd.Series],
@@ -374,8 +374,8 @@ def to_gbq(self, model_name: str, replace: bool = False) -> XGBClassifier:
 
 
 class RandomForestRegressor(
+    base.SupervisedTrainablePredictor,
     third_party.bigframes_vendored.sklearn.ensemble._forest.RandomForestRegressor,
-    base.TrainablePredictor,
 ):
 
     __doc__ = (
@@ -461,7 +461,7 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
             "xgboost_version": self.xgboost_version,
         }
 
-    def fit(
+    def _fit(
         self,
         X: Union[bpd.DataFrame, bpd.Series],
         y: Union[bpd.DataFrame, bpd.Series],
@@ -542,8 +542,8 @@ def to_gbq(self, model_name: str, replace: bool = False) -> RandomForestRegresso
 
 
 class RandomForestClassifier(
+    base.SupervisedTrainablePredictor,
     third_party.bigframes_vendored.sklearn.ensemble._forest.RandomForestClassifier,
-    base.TrainablePredictor,
 ):
 
     __doc__ = (
@@ -629,7 +629,7 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
             "xgboost_version": self.xgboost_version,
         }
 
-    def fit(
+    def _fit(
         self,
         X: Union[bpd.DataFrame, bpd.Series],
         y: Union[bpd.DataFrame, bpd.Series],
diff --git a/bigframes/ml/forecasting.py b/bigframes/ml/forecasting.py
index b7e0553ecb..22d81294fc 100644
--- a/bigframes/ml/forecasting.py
+++ b/bigframes/ml/forecasting.py
@@ -27,7 +27,7 @@
 _PREDICT_OUTPUT_COLUMNS = ["forecast_timestamp", "forecast_value"]
 
 
-class ARIMAPlus(base.TrainablePredictor):
+class ARIMAPlus(base.SupervisedTrainablePredictor):
     """Time Series ARIMA Plus model."""
 
     def __init__(self):
@@ -48,7 +48,7 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
         """The model options as they will be set for BQML."""
         return {"model_type": "ARIMA_PLUS"}
 
-    def fit(
+    def _fit(
         self,
         X: Union[bpd.DataFrame, bpd.Series],
         y: Union[bpd.DataFrame, bpd.Series],
diff --git a/bigframes/ml/linear_model.py b/bigframes/ml/linear_model.py
index 0b18db9315..1606a15d73 100644
--- a/bigframes/ml/linear_model.py
+++ b/bigframes/ml/linear_model.py
@@ -30,8 +30,8 @@
 
 
 class LinearRegression(
+    base.SupervisedTrainablePredictor,
     third_party.bigframes_vendored.sklearn.linear_model._base.LinearRegression,
-    base.TrainablePredictor,
 ):
     __doc__ = (
         third_party.bigframes_vendored.sklearn.linear_model._base.LinearRegression.__doc__
@@ -39,7 +39,7 @@ class LinearRegression(
 
     def __init__(
         self,
-        fit_intercept=True,
+        fit_intercept: bool = True,
     ):
         self.fit_intercept = fit_intercept
         self._bqml_model: Optional[core.BqmlModel] = None
@@ -71,7 +71,7 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
             "fit_intercept": self.fit_intercept,
         }
 
-    def fit(
+    def _fit(
         self,
         X: Union[bpd.DataFrame, bpd.Series],
         y: Union[bpd.DataFrame, bpd.Series],
@@ -136,8 +136,8 @@ def to_gbq(self, model_name: str, replace: bool = False) -> LinearRegression:
 
 
 class LogisticRegression(
+    base.SupervisedTrainablePredictor,
     third_party.bigframes_vendored.sklearn.linear_model._logistic.LogisticRegression,
-    base.TrainablePredictor,
 ):
     __doc__ = (
         third_party.bigframes_vendored.sklearn.linear_model._logistic.LogisticRegression.__doc__
@@ -189,12 +189,13 @@ def _bqml_options(self) -> Dict[str, str | int | float | List[str]]:
             # "class_weights": self.class_weights,
         }
 
-    def fit(
+    def _fit(
         self,
         X: Union[bpd.DataFrame, bpd.Series],
         y: Union[bpd.DataFrame, bpd.Series],
         transforms: Optional[List[str]] = None,
     ) -> LogisticRegression:
+        """Fit model with transforms."""
         X, y = utils.convert_to_dataframe(X, y)
 
         self._bqml_model = core.create_bqml_model(
diff --git a/bigframes/ml/llm.py b/bigframes/ml/llm.py
index 345e3deb72..973fbf2ad9 100644
--- a/bigframes/ml/llm.py
+++ b/bigframes/ml/llm.py
@@ -100,26 +100,26 @@ def predict(
                 Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that expect a true or correct response,
                 while higher temperatures can lead to more diverse or unexpected results. A temperature of 0 is deterministic:
                 the highest probability token is always selected. For most use cases, try starting with a temperature of 0.2.
-                Default 0.
+                Default 0. Possible values [0.0, 1.0].
 
             max_output_tokens (int, default 128):
                 Maximum number of tokens that can be generated in the response. Specify a lower value for shorter responses and a higher value for longer responses.
                 A token may be smaller than a word. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words.
-                Default 128.
+                Default 128. Possible values [1, 1024].
 
             top_k (int, default 40):
                 Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens
                 in the model’s vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature).
                 For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling.
                 Specify a lower value for less random responses and a higher value for more random responses.
-                Default 40.
+                Default 40. Possible values [1, 40].
 
             top_p (float, default 0.95)::
                 Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value.
                 For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature)
                 and not consider C at all.
                 Specify a lower value for less random responses and a higher value for more random responses.
-                Default 0.95.
+                Default 0.95. Possible values [0.0, 1.0].
 
 
         Returns:
diff --git a/bigframes/ml/pipeline.py b/bigframes/ml/pipeline.py
index bfd0392526..bff0bf36ad 100644
--- a/bigframes/ml/pipeline.py
+++ b/bigframes/ml/pipeline.py
@@ -24,14 +24,14 @@
 
 import bigframes
 import bigframes.constants as constants
-from bigframes.ml import base, compose, loader, preprocessing, utils
+from bigframes.ml import base, compose, forecasting, loader, preprocessing, utils
 import bigframes.pandas as bpd
 import third_party.bigframes_vendored.sklearn.pipeline
 
 
 class Pipeline(
-    third_party.bigframes_vendored.sklearn.pipeline.Pipeline,
     base.BaseEstimator,
+    third_party.bigframes_vendored.sklearn.pipeline.Pipeline,
 ):
     __doc__ = third_party.bigframes_vendored.sklearn.pipeline.Pipeline.__doc__
 
@@ -55,7 +55,7 @@ def __init__(self, steps: List[Tuple[str, base.BaseEstimator]]):
             self._transform = transform
         else:
             raise NotImplementedError(
-                f"Transform {transform} is not yet supported by Pipeline. {constants.FEEDBACK_LINK}"
+                f"Transformer type {type(transform)} is not yet supported by Pipeline. {constants.FEEDBACK_LINK}"
             )
 
         if not isinstance(
@@ -63,7 +63,13 @@ def __init__(self, steps: List[Tuple[str, base.BaseEstimator]]):
             base.TrainablePredictor,
         ):
             raise NotImplementedError(
-                f"Estimator {estimator} is not supported by Pipeline. {constants.FEEDBACK_LINK}"
+                f"Estimator type {type(estimator)} is not supported by Pipeline. {constants.FEEDBACK_LINK}"
+            )
+
+        # BQML doesn't support ARIMA_PLUS with transformers. b/298676367
+        if isinstance(estimator, forecasting.ARIMAPlus):
+            raise NotImplementedError(
+                f"Estimator type {type(estimator)} is not supported by Pipeline. {constants.FEEDBACK_LINK}"
             )
 
         self._transform = transform
@@ -92,7 +98,7 @@ def fit(
             (y,) = utils.convert_to_dataframe(y)
             transform_sqls.extend(y.columns.tolist())
 
-        self._estimator.fit(X=X, y=y, transforms=transform_sqls)
+        self._estimator._fit(X=X, y=y, transforms=transform_sqls)
         return self
 
     def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame:
diff --git a/bigframes/ml/preprocessing.py b/bigframes/ml/preprocessing.py
index ee46a37052..8add7bdd76 100644
--- a/bigframes/ml/preprocessing.py
+++ b/bigframes/ml/preprocessing.py
@@ -29,8 +29,8 @@
 
 
 class StandardScaler(
-    third_party.bigframes_vendored.sklearn.preprocessing._data.StandardScaler,
     base.Transformer,
+    third_party.bigframes_vendored.sklearn.preprocessing._data.StandardScaler,
 ):
     __doc__ = (
         third_party.bigframes_vendored.sklearn.preprocessing._data.StandardScaler.__doc__
@@ -105,8 +105,8 @@ def transform(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame:
 
 
 class OneHotEncoder(
-    third_party.bigframes_vendored.sklearn.preprocessing._encoder.OneHotEncoder,
     base.Transformer,
+    third_party.bigframes_vendored.sklearn.preprocessing._encoder.OneHotEncoder,
 ):
     # BQML max value https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-one-hot-encoder#syntax
     TOP_K_DEFAULT = 1000000
diff --git a/bigframes/operations/aggregations.py b/bigframes/operations/aggregations.py
index 874c264194..23271e8220 100644
--- a/bigframes/operations/aggregations.py
+++ b/bigframes/operations/aggregations.py
@@ -19,8 +19,10 @@
 import ibis
 import ibis.expr.datatypes as ibis_dtypes
 import ibis.expr.types as ibis_types
+from pandas import Int64Dtype
 
 import bigframes.constants as constants
+import bigframes.dtypes as dtypes
 import third_party.bigframes_vendored.ibis.expr.operations as vendored_ibis_ops
 
 
@@ -202,6 +204,16 @@ def _as_ibis(self, x: ibis_types.Column, window=None) -> ibis_types.Value:
         )
 
 
+class PopVarOp(AggregateOp):
+    name = "popvar"
+
+    @numeric_op
+    def _as_ibis(self, x: ibis_types.Column, window=None) -> ibis_types.Value:
+        return _apply_window_if_present(
+            typing.cast(ibis_types.NumericColumn, x).var(how="pop"), window
+        )
+
+
 class CountOp(AggregateOp):
     name = "count"
 
@@ -217,16 +229,20 @@ def skips_nulls(self):
 
 class CutOp(WindowOp):
     def __init__(self, bins: int):
-        self._bins = bins
+        self._bins_ibis = dtypes.literal_to_ibis_scalar(bins, force_dtype=Int64Dtype())
+        self._bins_int = bins
 
     def _as_ibis(self, x: ibis_types.Column, window=None):
         col_min = _apply_window_if_present(x.min(), window)
         col_max = _apply_window_if_present(x.max(), window)
-        bin_width = (col_max - col_min) / self._bins
+        bin_width = (col_max - col_min) / self._bins_ibis
         out = ibis.case()
-        for bin in range(self._bins - 1):
-            out = out.when(x <= (col_min + (bin + 1) * bin_width), bin)
-        out = out.when(x.notnull(), self._bins - 1)
+        for this_bin in range(self._bins_int - 1):
+            out = out.when(
+                x <= (col_min + (this_bin + 1) * bin_width),
+                dtypes.literal_to_ibis_scalar(this_bin, force_dtype=Int64Dtype()),
+            )
+        out = out.when(x.notnull(), self._bins_ibis - 1)
         return out.end()
 
     @property
@@ -305,6 +321,28 @@ def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value:
         return _apply_window_if_present(column.first(), window)
 
 
+class FirstNonNullOp(WindowOp):
+    @property
+    def skips_nulls(self):
+        return False
+
+    def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value:
+        return _apply_window_if_present(
+            vendored_ibis_ops.FirstNonNullValue(column).to_expr(), window  # type: ignore
+        )
+
+
+class LastNonNullOp(WindowOp):
+    @property
+    def skips_nulls(self):
+        return False
+
+    def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value:
+        return _apply_window_if_present(
+            vendored_ibis_ops.LastNonNullValue(column).to_expr(), window  # type: ignore
+        )
+
+
 class ShiftOp(WindowOp):
     def __init__(self, periods: int):
         self._periods = periods
@@ -321,6 +359,28 @@ def skips_nulls(self):
         return False
 
 
+class DiffOp(WindowOp):
+    def __init__(self, periods: int):
+        self._periods = periods
+
+    def _as_ibis(self, column: ibis_types.Column, window=None) -> ibis_types.Value:
+        shifted = ShiftOp(self._periods)._as_ibis(column, window)
+        if column.type().is_boolean():
+            return typing.cast(ibis_types.BooleanColumn, column) != typing.cast(
+                ibis_types.BooleanColumn, shifted
+            )
+        elif column.type().is_numeric():
+            return typing.cast(ibis_types.NumericColumn, column) - typing.cast(
+                ibis_types.NumericColumn, shifted
+            )
+        else:
+            raise TypeError(f"Cannot perform diff on type{column.type()}")
+
+    @property
+    def skips_nulls(self):
+        return False
+
+
 class AllOp(AggregateOp):
     def _as_ibis(
         self, column: ibis_types.Column, window=None
diff --git a/bigframes/pandas/__init__.py b/bigframes/pandas/__init__.py
index e1a23e67a1..cb27834590 100644
--- a/bigframes/pandas/__init__.py
+++ b/bigframes/pandas/__init__.py
@@ -399,6 +399,7 @@ def remote_function(
     dataset: Optional[str] = None,
     bigquery_connection: Optional[str] = None,
     reuse: bool = True,
+    name: Optional[str] = None,
 ):
     return global_session.with_default_session(
         bigframes.session.Session.remote_function,
@@ -407,6 +408,7 @@ def remote_function(
         dataset=dataset,
         bigquery_connection=bigquery_connection,
         reuse=reuse,
+        name=name,
     )
 
 
diff --git a/bigframes/remote_function.py b/bigframes/remote_function.py
index 6932e5b580..6fc2f8e59f 100644
--- a/bigframes/remote_function.py
+++ b/bigframes/remote_function.py
@@ -28,6 +28,8 @@
 import textwrap
 from typing import List, NamedTuple, Optional, Sequence, TYPE_CHECKING
 
+import requests
+
 if TYPE_CHECKING:
     from bigframes.session import Session
 
@@ -99,7 +101,7 @@ def get_remote_function_locations(bq_location):
 
 
 def _get_hash(def_):
-    "Get hash of a function."
+    "Get hash (32 digits alphanumeric) of a function."
     def_repr = cloudpickle.dumps(def_, protocol=_pickle_protocol_version)
     return hashlib.md5(def_repr).hexdigest()
 
@@ -128,7 +130,7 @@ class IbisSignature(NamedTuple):
 
 
 def get_cloud_function_name(def_, uniq_suffix=None):
-    """Get the name of the cloud function."""
+    "Get a name for the cloud function for the given user defined function."
     cf_name = _get_hash(def_)
     cf_name = f"bigframes-{cf_name}"  # for identification
     if uniq_suffix:
@@ -137,7 +139,7 @@ def get_cloud_function_name(def_, uniq_suffix=None):
 
 
 def get_remote_function_name(def_, uniq_suffix=None):
-    """Get the name for the BQ remote function."""
+    "Get a name for the BQ remote function for the given user defined function."
     bq_rf_name = _get_hash(def_)
     bq_rf_name = f"bigframes_{bq_rf_name}"  # for identification
     if uniq_suffix:
@@ -206,9 +208,15 @@ def create_bq_remote_function(
         query_job.result()  # Wait for the job to complete.
         logger.info(f"Created remote function {query_job.ddl_target_routine}")
 
+    def get_cloud_function_fully_qualified_parent(self):
+        "Get the fully qualilfied parent for a cloud function."
+        return self._cloud_functions_client.common_location_path(
+            self._gcp_project_id, self._cloud_function_region
+        )
+
     def get_cloud_function_fully_qualified_name(self, name):
         "Get the fully qualilfied name for a cloud function."
-        return "projects/{}/locations/{}/functions/{}".format(
+        return self._cloud_functions_client.function_path(
             self._gcp_project_id, self._cloud_function_region, name
         )
 
@@ -319,6 +327,7 @@ def create_cloud_function(self, def_, cf_name):
         # Build and deploy folder structure containing cloud function
         with tempfile.TemporaryDirectory() as dir:
             entry_point = self.generate_cloud_function_code(def_, dir)
+            archive_path = shutil.make_archive(dir, "zip", dir)
 
             # We are creating cloud function source code from the currently running
             # python version. Use the same version to deploy. This is necessary
@@ -331,50 +340,56 @@ def create_cloud_function(self, def_, cf_name):
                 sys.version_info.major, sys.version_info.minor
             )
 
-            # deploy/redeploy the cloud function
-            # TODO(shobs): Figure out a way to skip this step if a cloud function
-            # already exists with the same name and source code
-            command = (
-                "gcloud functions deploy"
-                + f" {cf_name} --gen2"
-                + f" --runtime={python_version}"
-                + f" --project={self._gcp_project_id}"
-                + f" --region={self._cloud_function_region}"
-                + f" --source={dir}"
-                + f" --entry-point={entry_point}"
-                + " --trigger-http"
+            # Determine an upload URL for user code
+            upload_url_request = functions_v2.GenerateUploadUrlRequest()
+            upload_url_request.parent = self.get_cloud_function_fully_qualified_parent()
+            upload_url_response = self._cloud_functions_client.generate_upload_url(
+                request=upload_url_request
             )
 
-            # If the cloud function is being created for the first time, then let's
-            # make it not allow unauthenticated calls. If it was previously created
-            # then this invocation will update it, in which case do not touch that
-            # aspect and let the previous policy hold. The reason we do this is to
-            # avoid an IAM permission needed to update the invocation policy.
-            # For example, when a cloud function is being created for the first
-            # time, i.e.
-            # $ gcloud functions deploy python-foo-http --gen2 --runtime=python310
-            #       --region=us-central1
-            #       --source=/source/code/dir
-            #       --entry-point=foo_http
-            #       --trigger-http
-            #       --no-allow-unauthenticated
-            # It works. When an invocation of the same command is done for the
-            # second time, it may run into an error like:
-            # ERROR: (gcloud.functions.deploy) PERMISSION_DENIED: Permission
-            # 'run.services.setIamPolicy' denied on resource
-            # 'projects/my_project/locations/us-central1/services/python-foo-http' (or resource may not exist)
-            # But when --no-allow-unauthenticated is omitted then it goes through.
-            # It suggests that in the second invocation the command is trying to set
-            # the IAM policy of the service, and the user running BigQuery
-            # DataFrame may not have privilege to do so, so better avoid this
-            # if we can.
-            if self.get_cloud_function_endpoint(cf_name):
-                logger.info(f"Updating existing cloud function: {command}")
-            else:
-                command = f"{command} --no-allow-unauthenticated"
-                logger.info(f"Creating new cloud function: {command}")
-
-            _run_system_command(command)
+            # Upload the code to GCS
+            with open(archive_path, "rb") as f:
+                response = requests.put(
+                    upload_url_response.upload_url,
+                    data=f,
+                    headers={"content-type": "application/zip"},
+                )
+                if response.status_code != 200:
+                    raise RuntimeError(
+                        "Failed to upload user code. code={}, reason={}, text={}".format(
+                            response.status_code, response.reason, response.text
+                        )
+                    )
+
+            # Deploy Cloud Function
+            create_function_request = functions_v2.CreateFunctionRequest()
+            create_function_request.parent = (
+                self.get_cloud_function_fully_qualified_parent()
+            )
+            create_function_request.function_id = cf_name
+            function = functions_v2.Function()
+            function.name = self.get_cloud_function_fully_qualified_name(cf_name)
+            function.build_config = functions_v2.BuildConfig()
+            function.build_config.runtime = python_version
+            function.build_config.entry_point = entry_point
+            function.build_config.source = functions_v2.Source()
+            function.build_config.source.storage_source = functions_v2.StorageSource()
+            function.build_config.source.storage_source.bucket = (
+                upload_url_response.storage_source.bucket
+            )
+            function.build_config.source.storage_source.object_ = (
+                upload_url_response.storage_source.object_
+            )
+            create_function_request.function = function
+
+            # Create the cloud function and wait for it to be ready to use
+            operation = self._cloud_functions_client.create_function(
+                request=create_function_request
+            )
+            operation.result()
+
+            # Cleanup
+            os.remove(archive_path)
 
         # Fetch the endpoint of the just created function
         endpoint = self.get_cloud_function_endpoint(cf_name)
@@ -389,23 +404,47 @@ def create_cloud_function(self, def_, cf_name):
         return endpoint
 
     def provision_bq_remote_function(
-        self, def_, input_types, output_type, uniq_suffix=None
+        self,
+        def_,
+        input_types,
+        output_type,
+        reuse,
+        name,
     ):
         """Provision a BigQuery remote function."""
-        # Derive the name of the underlying cloud function and first create
-        # it if it does not exist
+        # If reuse of any existing function with the same name (indicated by the
+        # same hash of its source code) is not intended, then attach a unique
+        # suffix to the intended function name to make it unique.
+        uniq_suffix = None
+        if not reuse:
+            uniq_suffix = "".join(
+                random.choices(string.ascii_lowercase + string.digits, k=8)
+            )
+
+        # Derive the name of the cloud function underlying the intended BQ
+        # remote function
         cloud_function_name = get_cloud_function_name(def_, uniq_suffix)
         cf_endpoint = self.get_cloud_function_endpoint(cloud_function_name)
+
+        # Create the cloud function if it does not exist
         if not cf_endpoint:
-            self.check_cloud_function_tools_and_permissions()
             cf_endpoint = self.create_cloud_function(def_, cloud_function_name)
         else:
             logger.info(f"Cloud function {cloud_function_name} already exists.")
 
-        # Derive the name of the remote function and create/replace it if needed
-        remote_function_name = get_remote_function_name(def_, uniq_suffix)
+        # Derive the name of the remote function
+        remote_function_name = name
+        if not remote_function_name:
+            remote_function_name = get_remote_function_name(def_, uniq_suffix)
         rf_endpoint, rf_conn = self.get_remote_function_specs(remote_function_name)
-        if rf_endpoint != cf_endpoint or rf_conn != self._bq_connection_id:
+
+        # Create the BQ remote function in following circumstances:
+        # 1. It does not exist
+        # 2. It exists but the existing remote function has different
+        #    configuration than intended
+        if not rf_endpoint or (
+            rf_endpoint != cf_endpoint or rf_conn != self._bq_connection_id
+        ):
             input_args = inspect.getargs(def_.__code__).args
             if len(input_args) != len(input_types):
                 raise ValueError(
@@ -439,27 +478,6 @@ def get_remote_function_specs(self, remote_function_name):
                 break
         return (http_endpoint, bq_connection)
 
-    def check_cloud_function_tools_and_permissions(self):
-        """Check if the necessary tools and permissions are in place for creating remote function"""
-        # gcloud CLI comes with bq CLI and they are required for creating google
-        # cloud function and BigQuery remote function respectively
-        if not shutil.which("gcloud"):
-            raise ValueError(
-                "gcloud tool not installed, install it from https://cloud.google.com/sdk/docs/install. "
-                f"{constants.FEEDBACK_LINK}"
-            )
-
-        # TODO(shobs): Check for permissions too
-        # I (shobs) tried the following method
-        # $ gcloud asset search-all-iam-policies \
-        #   --format=json \
-        #   --scope=projects/{gcp_project_id} \
-        #   --query='policy.role.permissions:cloudfunctions.functions.create'
-        # as a proxy to all the privilges necessary to create cloud function
-        # https://cloud.google.com/functions/docs/reference/iam/roles#cloudfunctions.developer
-        # but that itself required the runner to have the permission to enable
-        # `cloudasset.googleapis.com`
-
 
 def remote_function_node(
     routine_ref: bigquery.RoutineReference, ibis_signature: IbisSignature
@@ -583,6 +601,7 @@ def remote_function(
     dataset: Optional[str] = None,
     bigquery_connection: Optional[str] = None,
     reuse: bool = True,
+    name: Optional[str] = None,
 ):
     """Decorator to turn a user defined function into a BigQuery remote function.
 
@@ -613,7 +632,7 @@ def remote_function(
             * BigQuery Data Editor (roles/bigquery.dataEditor)
             * BigQuery Connection Admin (roles/bigquery.connectionAdmin)
             * Cloud Functions Developer (roles/cloudfunctions.developer)
-            * Service Account User (roles/iam.serviceAccountUser)
+            * Service Account User (roles/iam.serviceAccountUser) on the service account `PROJECT_NUMBER-compute@developer.gserviceaccount.com`
             * Storage Object Viewer (roles/storage.objectViewer)
             * Project IAM Admin (roles/resourcemanager.projectIamAdmin) (Only required if the bigquery connection being used is not pre-created and is created dynamically with user credentials.)
 
@@ -664,10 +683,16 @@ def remote_function(
         reuse (bool, Optional):
             Reuse the remote function if is already exists.
             `True` by default, which results in reusing an existing remote
-            function (if any) that was previously created for the same udf.
-            Setting it to false forces the creation of creating a unique remote function.
+            function and corresponding cloud function (if any) that was
+            previously created for the same udf.
+            Setting it to `False` forces the creation of a unique remote function.
             If the required remote function does not exist then it would be
             created irrespective of this param.
+        name (str, Optional):
+            Explicit name of the persisted BigQuery remote function. Use it with
+            caution, because two users working in the same project and dataset
+            could overwrite each other's remote functions if they use the same
+            persistent name.
 
     """
 
@@ -739,12 +764,6 @@ def remote_function(
             f"{constants.FEEDBACK_LINK}"
         )
 
-    uniq_suffix = None
-    if not reuse:
-        uniq_suffix = "".join(
-            random.choices(string.ascii_lowercase + string.digits, k=8)
-        )
-
     # Check connection_id with `LOCATION.CONNECTION_ID` or `PROJECT_ID.LOCATION.CONNECTION_ID` format.
     if bigquery_connection.count(".") == 1:
         bq_connection_location, bq_connection_id = bigquery_connection.split(".")
@@ -792,8 +811,13 @@ def wrapper(f):
             bigquery_connection,
             resource_manager_client,
         )
+
         rf_name, cf_name = remote_function_client.provision_bq_remote_function(
-            f, ibis_signature.input_types, ibis_signature.output_type, uniq_suffix
+            f,
+            ibis_signature.input_types,
+            ibis_signature.output_type,
+            reuse,
+            name,
         )
 
         node = remote_function_node(dataset_ref.routine(rf_name), ibis_signature)
diff --git a/bigframes/series.py b/bigframes/series.py
index 8eadee37ed..12e72c58b6 100644
--- a/bigframes/series.py
+++ b/bigframes/series.py
@@ -42,6 +42,7 @@
     STABLE_SORTS,
 )
 import bigframes.core.scalar as scalars
+import bigframes.core.utils as utils
 import bigframes.core.window
 import bigframes.dataframe
 import bigframes.dtypes
@@ -310,11 +311,20 @@ def drop(
         block = block.drop_columns([condition_id])
         return Series(block.select_column(self._value_column))
 
-    def droplevel(self, level: LevelsType):
+    def droplevel(self, level: LevelsType, axis: int | str = 0):
         resolved_level_ids = self._resolve_levels(level)
         return Series(self._block.drop_levels(resolved_level_ids))
 
-    def reorder_levels(self, order: LevelsType):
+    def swaplevel(self, i: int = -2, j: int = -1):
+        level_i = self._block.index_columns[i]
+        level_j = self._block.index_columns[j]
+        mapping = {level_i: level_j, level_j: level_i}
+        reordering = [
+            mapping.get(index_id, index_id) for index_id in self._block.index_columns
+        ]
+        return Series(self._block.reorder_levels(reordering))
+
+    def reorder_levels(self, order: LevelsType, axis: int | str = 0):
         resolved_level_ids = self._resolve_levels(order)
         return Series(self._block.reorder_levels(resolved_level_ids))
 
@@ -352,6 +362,14 @@ def cumsum(self) -> Series:
             agg_ops.sum_op, bigframes.core.WindowSpec(following=0)
         )
 
+    def ffill(self, *, limit: typing.Optional[int] = None) -> Series:
+        window = bigframes.core.WindowSpec(preceding=limit, following=0)
+        return self._apply_window_op(agg_ops.LastNonNullOp(), window)
+
+    def bfill(self, *, limit: typing.Optional[int] = None) -> Series:
+        window = bigframes.core.WindowSpec(preceding=0, following=limit)
+        return self._apply_window_op(agg_ops.FirstNonNullOp(), window)
+
     def cummax(self) -> Series:
         return self._apply_window_op(
             agg_ops.max_op, bigframes.core.WindowSpec(following=0)
@@ -375,7 +393,16 @@ def shift(self, periods: int = 1) -> Series:
         return self._apply_window_op(agg_ops.ShiftOp(periods), window)
 
     def diff(self, periods: int = 1) -> Series:
-        return self - self.shift(periods=periods)
+        window = bigframes.core.WindowSpec(
+            preceding=periods if periods > 0 else None,
+            following=-periods if periods < 0 else None,
+        )
+        return self._apply_window_op(agg_ops.DiffOp(periods), window)
+
+    def pct_change(self, periods: int = 1) -> Series:
+        # Future versions of pandas will not perfrom ffill automatically
+        series = self.ffill()
+        return Series(block_ops.pct_change(series._block, periods=periods))
 
     def rank(
         self,
@@ -390,6 +417,47 @@ def rank(
     def fillna(self, value=None) -> Series:
         return self._apply_binary_op(value, ops.fillna_op)
 
+    def replace(
+        self, to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False
+    ):
+        if regex:
+            if not (isinstance(to_replace, str) and isinstance(value, str)):
+                raise NotImplementedError(
+                    f"replace regex mode only supports strings for 'to_replace' and 'value'. {constants.FEEDBACK_LINK}"
+                )
+            block, result_col = self._block.apply_unary_op(
+                self._value_column,
+                ops.ReplaceRegexOp(to_replace, value),
+                result_label=self.name,
+            )
+            return Series(block.select_column(result_col))
+        elif utils.is_dict_like(to_replace):
+            raise NotImplementedError(
+                f"Dict 'to_replace' not supported. {constants.FEEDBACK_LINK}"
+            )
+        elif utils.is_list_like(to_replace):
+            block, cond = self._block.apply_unary_op(
+                self._value_column, ops.IsInOp(to_replace)
+            )
+            block, result_col = block.apply_binary_op(
+                cond,
+                self._value_column,
+                ops.partial_arg1(ops.where_op, value),
+                result_label=self.name,
+            )
+            return Series(block.select_column(result_col))
+        else:  # Scalar
+            block, cond = self._block.apply_unary_op(
+                self._value_column, ops.BinopPartialLeft(ops.eq_op, to_replace)
+            )
+            block, result_col = block.apply_binary_op(
+                cond,
+                self._value_column,
+                ops.partial_arg1(ops.where_op, value),
+                result_label=self.name,
+            )
+            return Series(block.select_column(result_col))
+
     def dropna(
         self,
         *,
@@ -414,52 +482,16 @@ def tail(self, n: int = 5) -> Series:
     def nlargest(self, n: int = 5, keep: str = "first") -> Series:
         if keep not in ("first", "last", "all"):
             raise ValueError("'keep must be one of 'first', 'last', or 'all'")
-        block = self._block
-        if keep == "last":
-            block = block.reversed()
-        ordering = (
-            OrderingColumnReference(
-                self._value_column, direction=OrderingDirection.DESC
-            ),
+        return Series(
+            block_ops.nlargest(self._block, n, [self._value_column], keep=keep)
         )
-        block = block.order_by(ordering, stable=True)
-        if keep in ("first", "last"):
-            return Series(block.slice(0, n))
-        else:  # keep == "all":
-            block, counter = block.apply_window_op(
-                self._value_column,
-                agg_ops.rank_op,
-                window_spec=WindowSpec(ordering=ordering),
-            )
-            block, condition = block.apply_unary_op(
-                counter, ops.partial_right(ops.le_op, n)
-            )
-            block = block.filter(condition)
-            block = block.select_column(self._value_column)
-            return Series(block)
 
     def nsmallest(self, n: int = 5, keep: str = "first") -> Series:
         if keep not in ("first", "last", "all"):
             raise ValueError("'keep must be one of 'first', 'last', or 'all'")
-        block = self._block
-        if keep == "last":
-            block = block.reversed()
-        ordering = (OrderingColumnReference(self._value_column),)
-        block = block.order_by(ordering, stable=True)
-        if keep in ("first", "last"):
-            return Series(block.slice(0, n))
-        else:  # keep == "all":
-            block, counter = block.apply_window_op(
-                self._value_column,
-                agg_ops.rank_op,
-                window_spec=WindowSpec(ordering=ordering),
-            )
-            block, condition = block.apply_unary_op(
-                counter, ops.partial_right(ops.le_op, n)
-            )
-            block = block.filter(condition)
-            block = block.select_column(self._value_column)
-            return Series(block)
+        return Series(
+            block_ops.nsmallest(self._block, n, [self._value_column], keep=keep)
+        )
 
     def isin(self, values) -> "Series" | None:
         if not _is_list_like(values):
@@ -697,13 +729,9 @@ def _central_moment(self, n: int) -> float:
         """Useful helper for calculating central moment statistics"""
         # Nth central moment is mean((x-mean(x))^n)
         # See: https://en.wikipedia.org/wiki/Moment_(mathematics)
-        mean = self.mean()
-        mean_deltas = self - mean
-        delta_power = mean_deltas
-        # TODO(tbergeron): Replace with pow once implemented
-        for i in range(1, n):
-            delta_power = delta_power * mean_deltas
-        return delta_power.mean()
+        mean_deltas = self - self.mean()
+        delta_powers = mean_deltas**n
+        return delta_powers.mean()
 
     def agg(self, func: str | typing.Sequence[str]) -> scalars.Scalar | Series:
         if _is_list_like(func):
@@ -1096,6 +1124,85 @@ def add_prefix(self, prefix: str, axis: int | str | None = None) -> Series:
     def add_suffix(self, suffix: str, axis: int | str | None = None) -> Series:
         return Series(self._get_block().add_suffix(suffix))
 
+    def filter(
+        self,
+        items: typing.Optional[typing.Iterable] = None,
+        like: typing.Optional[str] = None,
+        regex: typing.Optional[str] = None,
+        axis: typing.Optional[typing.Union[str, int]] = None,
+    ) -> Series:
+        if (axis is not None) and utils.get_axis_number(axis) != 0:
+            raise ValueError(f"Invalid axis for series: {axis}")
+        if sum([(items is not None), (like is not None), (regex is not None)]) != 1:
+            raise ValueError(
+                "Need to provide exactly one of 'items', 'like', or 'regex'"
+            )
+        if len(self._block.index_columns) > 1:
+            raise NotImplementedError(
+                "Method filter does not support rows multiindex. {constants.FEEDBACK_LINK}"
+            )
+        if (like is not None) or (regex is not None):
+            block = self._block
+            block, label_string_id = block.apply_unary_op(
+                self._block.index_columns[0],
+                ops.AsTypeOp(pandas.StringDtype(storage="pyarrow")),
+            )
+            if like is not None:
+                block, mask_id = block.apply_unary_op(
+                    label_string_id, ops.ContainsStringOp(pat=like)
+                )
+            else:  # regex
+                assert regex is not None
+                block, mask_id = block.apply_unary_op(
+                    label_string_id, ops.ContainsRegexOp(pat=regex)
+                )
+
+            block = block.filter(mask_id)
+            block = block.select_columns([self._value_column])
+            return Series(block)
+        elif items is not None:
+            # Behavior matches pandas 2.1+, older pandas versions would reindex
+            block = self._block
+            block, mask_id = block.apply_unary_op(
+                self._block.index_columns[0], ops.IsInOp(values=list(items))
+            )
+            block = block.filter(mask_id)
+            block = block.select_columns([self._value_column])
+            return Series(block)
+        else:
+            raise ValueError("Need to provide 'items', 'like', or 'regex'")
+
+    def reindex(self, index=None, *, validate: typing.Optional[bool] = None):
+        if validate and not self.index.is_unique:
+            raise ValueError("Original index must be unique to reindex")
+        keep_original_names = False
+        if isinstance(index, indexes.Index):
+            new_indexer = bigframes.dataframe.DataFrame(data=index._data._get_block())[
+                []
+            ]
+        else:
+            if not isinstance(index, pandas.Index):
+                keep_original_names = True
+                index = pandas.Index(index)
+            if index.nlevels != self.index.nlevels:
+                raise NotImplementedError(
+                    "Cannot reindex with index with different nlevels"
+                )
+            new_indexer = bigframes.dataframe.DataFrame(index=index)[[]]
+        # multiindex join is senstive to index names, so we will set all these
+        result = new_indexer.rename_axis(range(new_indexer.index.nlevels)).join(
+            self.to_frame().rename_axis(range(self.index.nlevels)),
+            how="left",
+        )
+        # and then reset the names after the join
+        result_block = result.rename_axis(
+            self.index.names if keep_original_names else index.names
+        )._block
+        return Series(result_block)
+
+    def reindex_like(self, other: Series, *, validate: typing.Optional[bool] = None):
+        return self.reindex(other.index, validate=validate)
+
     def drop_duplicates(self, *, keep: str = "first") -> Series:
         block = block_ops.drop_duplicates(self._block, (self._value_column,), keep)
         return Series(block)
@@ -1216,14 +1323,7 @@ def to_xarray(self):
     def _throw_if_index_contains_duplicates(
         self, error_message: typing.Optional[str] = None
     ) -> None:
-        duplicates_block, _ = block_ops.indicate_duplicates(
-            self._get_block(), self._get_block().index_columns
-        )
-        duplicates_block = duplicates_block.with_column_labels(
-            ["values", "is_duplicate"]
-        )
-        duplicates_df = bigframes.dataframe.DataFrame(duplicates_block)
-        if duplicates_df["is_duplicate"].any():
+        if not self.index.is_unique:
             error_message = (
                 error_message
                 if error_message
diff --git a/bigframes/session.py b/bigframes/session.py
index 1744407772..3ca79a7b53 100644
--- a/bigframes/session.py
+++ b/bigframes/session.py
@@ -424,25 +424,30 @@ def read_gbq(
     ) -> dataframe.DataFrame:
         # TODO(b/281571214): Generate prompt to show the progress of read_gbq.
         if _is_query(query):
-            return self.read_gbq_query(
+            return self._read_gbq_query(
                 query,
                 index_col=index_col,
                 col_order=col_order,
                 max_results=max_results,
+                api_name="read_gbq",
             )
         else:
             # TODO(swast): Query the snapshot table but mark it as a
             # deterministic query so we can avoid serializing if we have a
             # unique index.
-            return self.read_gbq_table(
+            return self._read_gbq_table(
                 query,
                 index_col=index_col,
                 col_order=col_order,
                 max_results=max_results,
+                api_name="read_gbq",
             )
 
     def _query_to_destination(
-        self, query: str, index_cols: List[str]
+        self,
+        query: str,
+        index_cols: List[str],
+        api_name: str,
     ) -> Tuple[Optional[bigquery.TableReference], Optional[bigquery.QueryJob]]:
         # If there are no index columns, then there's no reason to cache to a
         # (clustered) session table, as we'll just have to query it again to
@@ -464,7 +469,7 @@ def _query_to_destination(
         # operations are as speedy as they can be.
         try:
             ibis_expr = self.ibis_client.sql(query)
-            return self._ibis_to_session_table(ibis_expr, index_cols), None
+            return self._ibis_to_session_table(ibis_expr, index_cols, api_name), None
         except google.api_core.exceptions.BadRequest:
             # Some SELECT statements still aren't compatible with CREATE TEMP
             # TABLE ... AS SELECT ... statements. For example, if the query has
@@ -490,15 +495,33 @@ def read_gbq_query(
 
         See also: :meth:`Session.read_gbq`.
         """
+        return self._read_gbq_query(
+            query=query,
+            index_col=index_col,
+            col_order=col_order,
+            max_results=max_results,
+            api_name="read_gbq_query",
+        )
+
+    def _read_gbq_query(
+        self,
+        query: str,
+        *,
+        index_col: Iterable[str] | str = (),
+        col_order: Iterable[str] = (),
+        max_results: Optional[int] = None,
+        api_name: str,
+    ) -> dataframe.DataFrame:
         # NOTE: This method doesn't (yet) exist in pandas or pandas-gbq, so
         # these docstrings are inline.
-
         if isinstance(index_col, str):
             index_cols = [index_col]
         else:
             index_cols = list(index_col)
 
-        destination, query_job = self._query_to_destination(query, index_cols)
+        destination, query_job = self._query_to_destination(
+            query, index_cols, api_name="read_gbq_query"
+        )
 
         # If there was no destination table, that means the query must have
         # been DDL or DML. Return some job metadata, instead.
@@ -535,6 +558,23 @@ def read_gbq_table(
 
         See also: :meth:`Session.read_gbq`.
         """
+        return self._read_gbq_table(
+            query=query,
+            index_col=index_col,
+            col_order=col_order,
+            max_results=max_results,
+            api_name="read_gbq_table",
+        )
+
+    def _read_gbq_table(
+        self,
+        query: str,
+        *,
+        index_col: Iterable[str] | str = (),
+        col_order: Iterable[str] = (),
+        max_results: Optional[int] = None,
+        api_name: str,
+    ) -> dataframe.DataFrame:
         if max_results and max_results <= 0:
             raise ValueError("`max_results` should be a positive number.")
 
@@ -646,7 +686,8 @@ def read_gbq_table(
                 # rows for which row numbers must be generated
                 table_expression = table_expression.limit(max_results)
             table_expression, ordering = self._create_sequential_ordering(
-                table_expression
+                table=table_expression,
+                api_name=api_name,
             )
             hidden_cols = (
                 (ordering.total_order_col.column_id,)
@@ -667,6 +708,7 @@ def read_gbq_table(
             hidden_cols=hidden_cols,
             ordering=ordering,
             is_total_ordering=is_total_ordering,
+            api_name=api_name,
         )
 
     def _read_gbq_with_ordering(
@@ -680,6 +722,7 @@ def _read_gbq_with_ordering(
         hidden_cols: Iterable[str] = (),
         ordering: core.ExpressionOrdering,
         is_total_ordering: bool = False,
+        api_name: str,
     ) -> dataframe.DataFrame:
         """Internal helper method that loads DataFrame from Google BigQuery given an ordering column.
 
@@ -698,6 +741,8 @@ def _read_gbq_with_ordering(
                 Columns that should be hidden. Ordering columns may (not always) be hidden
             ordering:
                 Column name to be used for ordering. If not supplied, a default ordering is generated.
+            api_name:
+                The name of the API method.
 
         Returns:
             A DataFrame representing results of the query or table.
@@ -723,7 +768,9 @@ def _read_gbq_with_ordering(
         if not is_total_ordering:
             # Rows are not ordered, we need to generate a default ordering and materialize it
             table_expression, ordering = self._create_sequential_ordering(
-                table_expression, index_cols
+                table=table_expression,
+                index_cols=index_cols,
+                api_name=api_name,
             )
         index_col_values = [table_expression[index_id] for index_id in index_cols]
         if not col_labels:
@@ -846,6 +893,11 @@ def read_pandas(self, pandas_dataframe: pandas.DataFrame) -> dataframe.DataFrame
         Returns:
             bigframes.dataframe.DataFrame: The BigQuery DataFrame.
         """
+        return self._read_pandas(pandas_dataframe, "read_pandas")
+
+    def _read_pandas(
+        self, pandas_dataframe: pandas.DataFrame, api_name: str
+    ) -> dataframe.DataFrame:
         col_labels, idx_labels = (
             pandas_dataframe.columns.to_list(),
             pandas_dataframe.index.names,
@@ -878,6 +930,7 @@ def read_pandas(self, pandas_dataframe: pandas.DataFrame) -> dataframe.DataFrame
 
         job_config = bigquery.LoadJobConfig(schema=schema)
         job_config.clustering_fields = cluster_cols
+        job_config.labels = {"bigframes-api": api_name}
 
         load_table_destination = self._create_session_table()
         load_job = self.bqclient.load_table_from_dataframe(
@@ -910,6 +963,7 @@ def read_pandas(self, pandas_dataframe: pandas.DataFrame) -> dataframe.DataFrame
             hidden_cols=(ordering_col,),
             ordering=ordering,
             is_total_ordering=True,
+            api_name=api_name,
         )
         return df
 
@@ -991,6 +1045,7 @@ def read_csv(
             job_config.autodetect = True
             job_config.field_delimiter = sep
             job_config.encoding = encoding
+            job_config.labels = {"bigframes-api": "read_csv"}
 
             # We want to match pandas behavior. If header is 0, no rows should be skipped, so we
             # do not need to set `skip_leading_rows`. If header is None, then there is no header.
@@ -1048,7 +1103,7 @@ def read_pickle(
                 pandas_obj.name = "0"
             bigframes_df = self.read_pandas(pandas_obj.to_frame())
             return bigframes_df[bigframes_df.columns[0]]
-        return self.read_pandas(pandas_obj)
+        return self._read_pandas(pandas_obj, "read_pickle")
 
     def read_parquet(
         self,
@@ -1063,6 +1118,7 @@ def read_parquet(
         job_config.create_disposition = bigquery.CreateDisposition.CREATE_IF_NEEDED
         job_config.source_format = bigquery.SourceFormat.PARQUET
         job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY
+        job_config.labels = {"bigframes-api": "read_parquet"}
 
         return self._read_bigquery_load_job(path, table, job_config=job_config)
 
@@ -1109,6 +1165,7 @@ def read_json(
             job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY
             job_config.autodetect = True
             job_config.encoding = encoding
+            job_config.labels = {"bigframes-api": "read_json"}
 
             return self._read_bigquery_load_job(
                 path_or_buf,
@@ -1176,7 +1233,10 @@ def _create_session_table(self) -> bigquery.TableReference:
         return dataset.table(table_name)
 
     def _create_sequential_ordering(
-        self, table: ibis_types.Table, index_cols: Iterable[str] = ()
+        self,
+        table: ibis_types.Table,
+        index_cols: Iterable[str] = (),
+        api_name: str = "",
     ) -> Tuple[ibis_types.Table, core.ExpressionOrdering]:
         # Since this might also be used as the index, don't use the default
         # "ordering ID" name.
@@ -1188,6 +1248,7 @@ def _create_sequential_ordering(
         table_ref = self._ibis_to_session_table(
             table,
             cluster_cols=list(index_cols) + [default_ordering_name],
+            api_name=api_name,
         )
         table = self.ibis_client.sql(f"SELECT * FROM `{table_ref.table_id}`")
         ordering_reference = core.OrderingColumnReference(default_ordering_name)
@@ -1199,7 +1260,10 @@ def _create_sequential_ordering(
         return table, ordering
 
     def _ibis_to_session_table(
-        self, table: ibis_types.Table, cluster_cols: Iterable[str]
+        self,
+        table: ibis_types.Table,
+        cluster_cols: Iterable[str],
+        api_name: str,
     ) -> bigquery.TableReference:
         clusterable_cols = [
             col for col in cluster_cols if _can_cluster(table[col].type())
@@ -1207,10 +1271,14 @@ def _ibis_to_session_table(
         return self._query_to_session_table(
             self.ibis_client.compile(table),
             cluster_cols=clusterable_cols,
+            api_name=api_name,
         )
 
     def _query_to_session_table(
-        self, query_text: str, cluster_cols: Iterable[str]
+        self,
+        query_text: str,
+        cluster_cols: Iterable[str],
+        api_name: str,
     ) -> bigquery.TableReference:
         if len(list(cluster_cols)) > _MAX_CLUSTER_COLUMNS:
             raise ValueError(
@@ -1236,6 +1304,7 @@ def _query_to_session_table(
         # otherwise we get `BadRequest: 400 OPTIONS on temporary tables are not
         # supported`.
         job_config.labels = {"source": "bigquery-dataframes-temp"}
+        job_config.labels["bigframes-api"] = api_name
 
         try:
             self._start_query(
@@ -1253,6 +1322,7 @@ def remote_function(
         dataset: Optional[str] = None,
         bigquery_connection: Optional[str] = None,
         reuse: bool = True,
+        name: Optional[str] = None,
     ):
         """Decorator to turn a user defined function into a BigQuery remote function.
 
@@ -1280,7 +1350,7 @@ def remote_function(
             * BigQuery Data Editor (roles/bigquery.dataEditor)
             * BigQuery Connection Admin (roles/bigquery.connectionAdmin)
             * Cloud Functions Developer (roles/cloudfunctions.developer)
-            * Service Account User (roles/iam.serviceAccountUser)
+            * Service Account User (roles/iam.serviceAccountUser) on the service account `PROJECT_NUMBER-compute@developer.gserviceaccount.com`
             * Storage Object Viewer (roles/storage.objectViewer)
             * Project IAM Admin (roles/resourcemanager.projectIamAdmin) (Only required if the bigquery connection being used is not pre-created and is created dynamically with user credentials.)
 
@@ -1311,10 +1381,16 @@ def remote_function(
             reuse (bool, Optional):
                 Reuse the remote function if already exists.
                 `True` by default, which will result in reusing an existing remote
-                function (if any) that was previously created for the same udf.
-                Setting it to false would force creating a unique remote function.
+                function and corresponding cloud function (if any) that was
+                previously created for the same udf.
+                Setting it to `False` would force creating a unique remote function.
                 If the required remote function does not exist then it would be
                 created irrespective of this param.
+            name (str, Optional):
+                Explicit name of the persisted BigQuery remote function. Use it with
+                caution, because two users working in the same project and dataset
+                could overwrite each other's remote functions if they use the same
+                persistent name.
         Returns:
             callable: A remote function object pointing to the cloud assets created
             in the background to support the remote execution. The cloud assets can be
@@ -1331,6 +1407,7 @@ def remote_function(
             dataset=dataset,
             bigquery_connection=bigquery_connection,
             reuse=reuse,
+            name=name,
         )
 
     def read_gbq_function(
diff --git a/bigframes/version.py b/bigframes/version.py
index ef4f01f629..65b984a0d7 100644
--- a/bigframes/version.py
+++ b/bigframes/version.py
@@ -12,4 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "0.3.2"
+__version__ = "0.4.0"
diff --git a/docs/_static/custom.css b/docs/_static/custom.css
index c663a6ef33..b0a295464b 100644
--- a/docs/_static/custom.css
+++ b/docs/_static/custom.css
@@ -1,3 +1,8 @@
+div#python2-eol {
+	border-color: red;
+	border-width: medium;
+}
+
 /* Ensure minimum width for 'Parameters' / 'Returns' column */
 dl.field-list > dt {
     min-width: 100px
diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
index bfdcc4759c..6316a537f7 100644
--- a/docs/_templates/layout.html
+++ b/docs/_templates/layout.html
@@ -20,6 +20,11 @@
           {% endblock %}
 
           <div class="body" role="main">
+          	<div class="admonition" id="python2-eol"> 
+          	 As of January 1, 2020 this library no longer supports Python 2 on the latest released version. 
+          	 Library versions released prior to that date will continue to be available. For more information please
+          	 visit <a href="https://cloud.google.com/python/docs/python2-sunset/">Python 2 support on Google Cloud</a>.
+          	</div>
             {% block body %} {% endblock %}
           </div>
 
diff --git a/docs/conf.py b/docs/conf.py
index 3ab5be5103..af8c5efda8 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# Copyright 2021 Google LLC
+# Copyright 2023 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -25,8 +25,8 @@
 # serve to show the default.
 
 import os
+import shlex
 import sys
-from typing import Any, Dict
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
@@ -80,8 +80,8 @@
 root_doc = "index"
 
 # General information about the project.
-project = "BigQuery DataFrames"
-copyright = "2022-2023 Google LLC"
+project = "bigframes"
+copyright = "2019, Google"
 author = "Google APIs"
 
 # The version info for the project you're documenting, acts as replacement for
@@ -98,7 +98,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = "en"
+language = None
 
 # There are two options for replacing |today|: either, you set today to some
 # non-false value, then it is used:
@@ -154,10 +154,10 @@
 # further.  For a list of options available for each theme, see the
 # documentation.
 html_theme_options = {
-    "description": "BigQuery DataFrames provides DataFrame APIs on the BigQuery engine.",
-    # "github_user": "googleapis",
-    # "github_repo": "python-bigquery-storage",
-    # "github_banner": True,
+    "description": "BigQuery DataFrames provides DataFrame APIs on the BigQuery engine",
+    "github_user": "googleapis",
+    "github_repo": "python-bigquery-dataframes",
+    "github_banner": True,
     "font_family": "'Roboto', Georgia, sans",
     "head_font_family": "'Roboto', Georgia, serif",
     "code_font_family": "'Roboto Mono', 'Consolas', monospace",
@@ -185,7 +185,7 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-# html_static_path = ["_static"]
+html_static_path = ["_static"]
 
 # Add any extra paths that contain custom files (such as robots.txt or
 # .htaccess) here, relative to this directory. These files are copied
@@ -264,13 +264,15 @@
 
 # -- Options for LaTeX output ---------------------------------------------
 
-latex_elements: Dict[str, Any] = {
-    # Avoid "too deeply nested" error by using enumitem package.
-    # See: https://stackoverflow.com/a/28454426/101923
-    "preamble": r"""
-\usepackage{enumitem}
-\setlistdepth{99}
-"""
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #'papersize': 'letterpaper',
+    # The font size ('10pt', '11pt' or '12pt').
+    #'pointsize': '10pt',
+    # Additional stuff for the LaTeX preamble.
+    #'preamble': '',
+    # Latex figure (float) alignment
+    #'figure_align': 'htbp',
 }
 
 # Grouping the document tree into LaTeX files. List of tuples
@@ -366,6 +368,11 @@
     "grpc": ("https://grpc.github.io/grpc/python/", None),
     "proto-plus": ("https://proto-plus-python.readthedocs.io/en/latest/", None),
     "protobuf": ("https://googleapis.dev/python/protobuf/latest/", None),
+    "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
+    "pydata-google-auth": (
+        "https://pydata-google-auth.readthedocs.io/en/latest/",
+        None,
+    ),
 }
 
 
diff --git a/noxfile.py b/noxfile.py
index 1ceca6831b..033bbfefe4 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -31,7 +31,7 @@
 BLACK_VERSION = "black==22.3.0"
 ISORT_VERSION = "isort==5.12.0"
 SPHINX_VERSION = "sphinx==4.5.0"
-LINT_PATHS = ["docs", "bigframes", "tests", "noxfile.py", "setup.py"]
+LINT_PATHS = ["docs", "bigframes", "tests", "third_party", "noxfile.py", "setup.py"]
 
 DEFAULT_PYTHON_VERSION = "3.10"
 
@@ -42,6 +42,7 @@
     "pytest",
     "pytest-cov",
     "pytest-asyncio",
+    "pytest-mock",
 ]
 UNIT_TEST_EXTERNAL_DEPENDENCIES: List[str] = []
 UNIT_TEST_LOCAL_DEPENDENCIES: List[str] = []
@@ -108,6 +109,7 @@ def lint(session):
         "--check",
         *LINT_PATHS,
     )
+    # TODO(tswast): lint all LINT_PATHS
     session.run("flake8", "bigframes", "tests")
 
 
diff --git a/owlbot.py b/owlbot.py
index 4ba7d14eb5..be30eea5c2 100644
--- a/owlbot.py
+++ b/owlbot.py
@@ -15,6 +15,7 @@
 """This script is used to synthesize generated parts of this library."""
 
 import pathlib
+import re
 
 from synthtool import gcp
 import synthtool as s
@@ -27,11 +28,10 @@
 # ----------------------------------------------------------------------------
 # Add templated files
 # ----------------------------------------------------------------------------
-
 templated_files = common.py_library(
     unit_test_python_versions=["3.9", "3.10", "3.11"],
     system_test_python_versions=["3.9", "3.11"],
-    cov_level=40,
+    cov_level=35,
     intersphinx_dependencies={
         "pandas": "https://pandas.pydata.org/pandas-docs/stable/",
         "pydata-google-auth": "https://pydata-google-auth.readthedocs.io/en/latest/",
@@ -40,11 +40,17 @@
 s.move(
     templated_files,
     excludes=[
-        # Multi-processing note isn't relevant, as pandas_gbq is responsible for
+        # Multi-processing note isn't relevant, as bigframes is responsible for
         # creating clients, not the end user.
         "docs/multiprocessing.rst",
         "noxfile.py",
+        ".pre-commit-config.yaml",
         "README.rst",
+        ".github/release-trigger.yml",
+        # BigQuery DataFrames manages its own Kokoro cluster for presubmit & continuous tests.
+        ".kokoro/build.sh",
+        ".kokoro/continuous/common.cfg",
+        ".kokoro/presubmit/common.cfg",
     ],
 )
 
@@ -52,6 +58,46 @@
 # Fixup files
 # ----------------------------------------------------------------------------
 
+# Make sure build includes all necessary files.
+s.replace(
+    ["MANIFEST.in"],
+    re.escape("recursive-include google"),
+    "recursive-include third_party *\nrecursive-include bigframes",
+)
+
+# Even though BigQuery DataFrames isn't technically a client library, we are
+# opting into Cloud RAD for docs hosting.
+s.replace(
+    [".kokoro/docs/common.cfg"],
+    re.escape('value: "docs-staging-v2-staging"'),
+    'value: "docs-staging-v2"',
+)
+
+# Use a custom table of contents since the default one isn't organized well
+# enough for the number of classes we have.
+s.replace(
+    [".kokoro/publish-docs.sh"],
+    (
+        re.escape("# upload docs")
+        + "\n"
+        + re.escape(
+            'python3 -m docuploader upload docs/_build/html/docfx_yaml --metadata-file docs.metadata --destination-prefix docfx --staging-bucket "${V2_STAGING_BUCKET}"'
+        )
+    ),
+    (
+        "# Replace toc.yml template file\n"
+        + "mv docs/templates/toc.yml docs/_build/html/docfx_yaml/toc.yml\n\n"
+        + "# upload docs\n"
+        + 'python3 -m docuploader upload docs/_build/html/docfx_yaml --metadata-file docs.metadata --destination-prefix docfx --staging-bucket "${V2_STAGING_BUCKET}"'
+    ),
+)
+
+# Fixup the documentation.
+s.replace(
+    ["docs/conf.py"],
+    re.escape("Google Cloud Client Libraries for bigframes"),
+    "BigQuery DataFrames provides DataFrame APIs on the BigQuery engine",
+)
 
 # ----------------------------------------------------------------------------
 # Samples templates
@@ -63,6 +109,6 @@
 # Final cleanup
 # ----------------------------------------------------------------------------
 
-s.shell.run(["nox", "-s", "blacken"], hide_output=False)
+s.shell.run(["nox", "-s", "format"], hide_output=False)
 for noxfile in REPO_ROOT.glob("samples/**/noxfile.py"):
     s.shell.run(["nox", "-s", "blacken"], cwd=noxfile.parent, hide_output=False)
diff --git a/renovate.json b/renovate.json
new file mode 100644
index 0000000000..39b2a0ec92
--- /dev/null
+++ b/renovate.json
@@ -0,0 +1,12 @@
+{
+  "extends": [
+    "config:base",
+    "group:all",
+    ":preserveSemverRanges",
+    ":disableDependencyDashboard"
+  ],
+  "ignorePaths": [".pre-commit-config.yaml", ".kokoro/requirements.txt", "setup.py"],
+  "pip_requirements": {
+    "fileMatch": ["requirements-test.txt", "samples/[\\S/]*constraints.txt", "samples/[\\S/]*constraints-test.txt"]
+  }
+}
diff --git a/samples/snippets/remote_function.py b/samples/snippets/remote_function.py
index 37972672c3..9998a23eb2 100644
--- a/samples/snippets/remote_function.py
+++ b/samples/snippets/remote_function.py
@@ -39,11 +39,19 @@ def run_remote_function_and_read_gbq_function(project_id: str):
     # already created, BigQuery DataFrames will attempt to create one assuming
     # the necessary APIs and IAM permissions are setup in the project. In our
     # examples we would be using a pre-created connection named
-    # `bigframes-rf-conn`. Let's try a `pandas`-like use case in which we want
-    # to apply a user defined scalar function to every value in a `Series`, more
-    # specifically bucketize the `body_mass_g` value of the penguins, which is a
-    # real number, into a category, which is a string.
-    @bpd.remote_function([float], str, bigquery_connection="bigframes-rf-conn")
+    # `bigframes-rf-conn`. We will also set `reuse=False` to make sure we don't
+    # step over someone else creating remote function in the same project from
+    # the exact same source code at the same time. Let's try a `pandas`-like use
+    # case in which we want to apply a user defined scalar function to every
+    # value in a `Series`, more specifically bucketize the `body_mass_g` value
+    # of the penguins, which is a real number, into a category, which is a
+    # string.
+    @bpd.remote_function(
+        [float],
+        str,
+        bigquery_connection="bigframes-rf-conn",
+        reuse=False,
+    )
     def get_bucket(num):
         if not num:
             return "NA"
@@ -80,9 +88,11 @@ def get_bucket(num):
     # Let's continue trying other potential use cases of remote functions. Let's
     # say we consider the `species`, `island` and `sex` of the penguins
     # sensitive information and want to redact that by replacing with their hash
-    # code instead. Let's define another scalar custom function and decorated it
+    # code instead. Let's define another scalar custom function and decorate it
     # as a remote function
-    @bpd.remote_function([str], str, bigquery_connection="bigframes-rf-conn")
+    @bpd.remote_function(
+        [str], str, bigquery_connection="bigframes-rf-conn", reuse=False
+    )
     def get_hash(input):
         import hashlib
 
diff --git a/scripts/decrypt-secrets.sh b/scripts/decrypt-secrets.sh
new file mode 100755
index 0000000000..0018b421dd
--- /dev/null
+++ b/scripts/decrypt-secrets.sh
@@ -0,0 +1,46 @@
+#!/bin/bash
+
+# Copyright 2023 Google LLC All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+ROOT=$( dirname "$DIR" )
+
+# Work from the project root.
+cd $ROOT
+
+# Prevent it from overriding files.
+# We recommend that sample authors use their own service account files and cloud project.
+# In that case, they are supposed to prepare these files by themselves.
+if [[ -f "testing/test-env.sh" ]] || \
+       [[ -f "testing/service-account.json" ]] || \
+       [[ -f "testing/client-secrets.json" ]]; then
+    echo "One or more target files exist, aborting."
+    exit 1
+fi
+
+# Use SECRET_MANAGER_PROJECT if set, fallback to cloud-devrel-kokoro-resources.
+PROJECT_ID="${SECRET_MANAGER_PROJECT:-cloud-devrel-kokoro-resources}"
+
+gcloud secrets versions access latest --secret="python-docs-samples-test-env" \
+       --project="${PROJECT_ID}" \
+       > testing/test-env.sh
+gcloud secrets versions access latest \
+       --secret="python-docs-samples-service-account" \
+       --project="${PROJECT_ID}" \
+       > testing/service-account.json
+gcloud secrets versions access latest \
+       --secret="python-docs-samples-client-secrets" \
+       --project="${PROJECT_ID}" \
+       > testing/client-secrets.json
diff --git a/scripts/readme-gen/readme_gen.py b/scripts/readme-gen/readme_gen.py
new file mode 100644
index 0000000000..1acc119835
--- /dev/null
+++ b/scripts/readme-gen/readme_gen.py
@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Generates READMEs using configuration defined in yaml."""
+
+import argparse
+import io
+import os
+import subprocess
+
+import jinja2
+import yaml
+
+
+jinja_env = jinja2.Environment(
+    trim_blocks=True,
+    loader=jinja2.FileSystemLoader(
+        os.path.abspath(os.path.join(os.path.dirname(__file__), "templates"))
+    ),
+    autoescape=True,
+)
+
+README_TMPL = jinja_env.get_template("README.tmpl.rst")
+
+
+def get_help(file):
+    return subprocess.check_output(["python", file, "--help"]).decode()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("source")
+    parser.add_argument("--destination", default="README.rst")
+
+    args = parser.parse_args()
+
+    source = os.path.abspath(args.source)
+    root = os.path.dirname(source)
+    destination = os.path.join(root, args.destination)
+
+    jinja_env.globals["get_help"] = get_help
+
+    with io.open(source, "r") as f:
+        config = yaml.load(f)
+
+    # This allows get_help to execute in the right directory.
+    os.chdir(root)
+
+    output = README_TMPL.render(config)
+
+    with io.open(destination, "w") as f:
+        f.write(output)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/readme-gen/templates/README.tmpl.rst b/scripts/readme-gen/templates/README.tmpl.rst
new file mode 100644
index 0000000000..4fd239765b
--- /dev/null
+++ b/scripts/readme-gen/templates/README.tmpl.rst
@@ -0,0 +1,87 @@
+{# The following line is a lie. BUT! Once jinja2 is done with it, it will
+    become truth! #}
+.. This file is automatically generated. Do not edit this file directly.
+
+{{product.name}} Python Samples
+===============================================================================
+
+.. image:: https://gstatic.com/cloudssh/images/open-btn.png
+   :target: https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/GoogleCloudPlatform/python-docs-samples&page=editor&open_in_editor={{folder}}/README.rst
+
+
+This directory contains samples for {{product.name}}. {{product.description}}
+
+{{description}}
+
+.. _{{product.name}}: {{product.url}}
+
+{% if required_api_url %}
+To run the sample, you need to enable the API at: {{required_api_url}}
+{% endif %}
+
+{% if required_role %}
+To run the sample, you need to have `{{required_role}}` role.
+{% endif %}
+
+{{other_required_steps}}
+
+{% if setup %}
+Setup
+-------------------------------------------------------------------------------
+
+{% for section in setup %}
+
+{% include section + '.tmpl.rst' %}
+
+{% endfor %}
+{% endif %}
+
+{% if samples %}
+Samples
+-------------------------------------------------------------------------------
+
+{% for sample in samples %}
+{{sample.name}}
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+{% if not sample.hide_cloudshell_button %}
+.. image:: https://gstatic.com/cloudssh/images/open-btn.png
+   :target: https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/GoogleCloudPlatform/python-docs-samples&page=editor&open_in_editor={{folder}}/{{sample.file}},{{folder}}/README.rst
+{% endif %}
+
+
+{{sample.description}}
+
+To run this sample:
+
+.. code-block:: bash
+
+    $ python {{sample.file}}
+{% if sample.show_help %}
+
+    {{get_help(sample.file)|indent}}
+{% endif %}
+
+
+{% endfor %}
+{% endif %}
+
+{% if cloud_client_library %}
+
+The client library
+-------------------------------------------------------------------------------
+
+This sample uses the `Google Cloud Client Library for Python`_.
+You can read the documentation for more details on API usage and use GitHub
+to `browse the source`_ and  `report issues`_.
+
+.. _Google Cloud Client Library for Python:
+    https://googlecloudplatform.github.io/google-cloud-python/
+.. _browse the source:
+    https://github.com/GoogleCloudPlatform/google-cloud-python
+.. _report issues:
+    https://github.com/GoogleCloudPlatform/google-cloud-python/issues
+
+{% endif %}
+
+.. _Google Cloud SDK: https://cloud.google.com/sdk/
\ No newline at end of file
diff --git a/scripts/readme-gen/templates/auth.tmpl.rst b/scripts/readme-gen/templates/auth.tmpl.rst
new file mode 100644
index 0000000000..1446b94a5e
--- /dev/null
+++ b/scripts/readme-gen/templates/auth.tmpl.rst
@@ -0,0 +1,9 @@
+Authentication
+++++++++++++++
+
+This sample requires you to have authentication setup. Refer to the
+`Authentication Getting Started Guide`_ for instructions on setting up
+credentials for applications.
+
+.. _Authentication Getting Started Guide:
+    https://cloud.google.com/docs/authentication/getting-started
diff --git a/scripts/readme-gen/templates/auth_api_key.tmpl.rst b/scripts/readme-gen/templates/auth_api_key.tmpl.rst
new file mode 100644
index 0000000000..11957ce271
--- /dev/null
+++ b/scripts/readme-gen/templates/auth_api_key.tmpl.rst
@@ -0,0 +1,14 @@
+Authentication
+++++++++++++++
+
+Authentication for this service is done via an `API Key`_. To obtain an API
+Key:
+
+1. Open the `Cloud Platform Console`_
+2. Make sure that billing is enabled for your project.
+3. From the **Credentials** page, create a new **API Key** or use an existing
+   one for your project.
+
+.. _API Key:
+    https://developers.google.com/api-client-library/python/guide/aaa_apikeys
+.. _Cloud Console: https://console.cloud.google.com/project?_
diff --git a/scripts/readme-gen/templates/install_deps.tmpl.rst b/scripts/readme-gen/templates/install_deps.tmpl.rst
new file mode 100644
index 0000000000..6f069c6c87
--- /dev/null
+++ b/scripts/readme-gen/templates/install_deps.tmpl.rst
@@ -0,0 +1,29 @@
+Install Dependencies
+++++++++++++++++++++
+
+#. Clone python-docs-samples and change directory to the sample directory you want to use.
+
+    .. code-block:: bash
+
+        $ git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
+
+#. Install `pip`_ and `virtualenv`_ if you do not already have them. You may want to refer to the `Python Development Environment Setup Guide`_ for Google Cloud Platform for instructions.
+
+   .. _Python Development Environment Setup Guide:
+       https://cloud.google.com/python/setup
+
+#. Create a virtualenv. Samples are compatible with Python 3.7+.
+
+    .. code-block:: bash
+
+        $ virtualenv env
+        $ source env/bin/activate
+
+#. Install the dependencies needed to run the samples.
+
+    .. code-block:: bash
+
+        $ pip install -r requirements.txt
+
+.. _pip: https://pip.pypa.io/
+.. _virtualenv: https://virtualenv.pypa.io/
diff --git a/scripts/readme-gen/templates/install_portaudio.tmpl.rst b/scripts/readme-gen/templates/install_portaudio.tmpl.rst
new file mode 100644
index 0000000000..5ea33d18c0
--- /dev/null
+++ b/scripts/readme-gen/templates/install_portaudio.tmpl.rst
@@ -0,0 +1,35 @@
+Install PortAudio
++++++++++++++++++
+
+Install `PortAudio`_. This is required by the `PyAudio`_ library to stream
+audio from your computer's microphone. PyAudio depends on PortAudio for cross-platform compatibility, and is installed differently depending on the
+platform.
+
+* For Mac OS X, you can use `Homebrew`_::
+
+      brew install portaudio
+
+  **Note**: if you encounter an error when running `pip install` that indicates
+  it can't find `portaudio.h`, try running `pip install` with the following
+  flags::
+
+      pip install --global-option='build_ext' \
+          --global-option='-I/usr/local/include' \
+          --global-option='-L/usr/local/lib' \
+          pyaudio
+
+* For Debian / Ubuntu Linux::
+
+      apt-get install portaudio19-dev python-all-dev
+
+* Windows may work without having to install PortAudio explicitly (it will get
+  installed with PyAudio).
+
+For more details, see the `PyAudio installation`_ page.
+
+
+.. _PyAudio: https://people.csail.mit.edu/hubert/pyaudio/
+.. _PortAudio: http://www.portaudio.com/
+.. _PyAudio installation:
+  https://people.csail.mit.edu/hubert/pyaudio/#downloads
+.. _Homebrew: http://brew.sh
diff --git a/scripts/upload_to_google_drive.py b/scripts/upload_to_google_drive.py
index e579151359..dcdc9168ba 100644
--- a/scripts/upload_to_google_drive.py
+++ b/scripts/upload_to_google_drive.py
@@ -41,12 +41,9 @@
 wheel_id = "15fZ1DkrFDk4ibMNTzms4akpxmf2pzeAR"
 wheel_path = next(iter((repo_root / "dist").glob("bigframes-*.whl")))
 
-pdf_id = "1agYjxmPLrxelsaHI-lc41QHcgnQYemcX"
-pdf_path = repo_root / "docs" / "_build" / "latex" / "bigframes-latest.pdf"
-
 uploads = (
     (wheel_id, wheel_path, "application/octet-stream"),
-    (pdf_id, pdf_path, "application/pdf"),
+    # (pdf_id, pdf_path, "application/pdf"),
 )
 
 upload_template = (
diff --git a/setup.cfg b/setup.cfg
index 8bd749387e..0523500895 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright 2020 Google LLC
+# Copyright 2023 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,17 +17,3 @@
 # Generated by synthtool. DO NOT EDIT!
 [bdist_wheel]
 universal = 1
-
-[pytype]
-python_version = 3.9
-inputs =
-    google/cloud/
-exclude =
-    tests/
-    google/cloud/bigquery_v2/  # Legacy proto-based types.
-output = .pytype/
-disable =
-    # There's some issue with finding some pyi files, thus disabling.
-    # The issue https://github.com/google/pytype/issues/150 is closed, but the
-    # error still occurs for some reason.
-    pyi-error
diff --git a/setup.py b/setup.py
index 139873e6fc..69b71c88f1 100644
--- a/setup.py
+++ b/setup.py
@@ -47,6 +47,7 @@
     "ibis-framework[bigquery] >=6.0.0,<=6.1.0",
     "pandas >=1.5.0",
     "pydata-google-auth >=1.8.2",
+    "requests >=2.27.1",
     "scikit-learn >=1.2.2",
     "sqlalchemy >=1.4,<3.0",
     "ipywidgets >=7.7.1",
@@ -58,7 +59,7 @@
         "pandas-gbq >=0.19.0",
     ],
     # Packages required for basic development flow.
-    "dev": ["pytest", "pre-commit", "nox", "google-cloud-testutils"],
+    "dev": ["pytest", "pytest-mock", "pre-commit", "nox", "google-cloud-testutils"],
 }
 extras["all"] = list(sorted(frozenset(itertools.chain.from_iterable(extras.values()))))
 
diff --git a/testing/.gitignore b/testing/.gitignore
new file mode 100644
index 0000000000..b05fbd6308
--- /dev/null
+++ b/testing/.gitignore
@@ -0,0 +1,3 @@
+test-env.sh
+service-account.json
+client-secrets.json
\ No newline at end of file
diff --git a/testing/constraints-3.9.txt b/testing/constraints-3.9.txt
index 523256ee83..cd69d45dc9 100644
--- a/testing/constraints-3.9.txt
+++ b/testing/constraints-3.9.txt
@@ -9,7 +9,7 @@ cachetools==5.3.0
 certifi==2022.12.7
 cffi==1.15.1
 cfgv==3.3.1
-charset-normalizer==3.1.0
+charset-normalizer==2.0.0
 click==8.1.3
 cloudpickle==2.0.0
 colorlog==6.7.0
@@ -90,13 +90,14 @@ pyperclip==1.8.2
 pytest==7.2.2
 pytest-asyncio==0.21.0
 pytest-cov==4.0.0
+pytest-mock==3.11.1
 pytest-retry==1.1.0
 pytest-xdist==3.2.1
 python-dateutil==2.8.2
 pytz==2023.3
 PyYAML==6.0
 readme-renderer==37.3
-requests==2.28.2
+requests==2.27.1
 requests-oauthlib==1.3.1
 requests-toolbelt==0.10.1
 rfc3986==2.0.0
diff --git a/tests/system/large/ml/test_ensemble.py b/tests/system/large/ml/test_ensemble.py
index 9b2872d673..a8613dfeb9 100644
--- a/tests/system/large/ml/test_ensemble.py
+++ b/tests/system/large/ml/test_ensemble.py
@@ -70,7 +70,7 @@ def test_xgbregressor_dart_booster_multiple_params(
 ):
     model = bigframes.ml.ensemble.XGBRegressor(
         booster="dart",
-        tree_method="AUTO",
+        tree_method="auto",
         min_tree_child_weight=2,
         colsample_bytree=0.95,
         colsample_bylevel=0.95,
@@ -121,7 +121,7 @@ def test_xgbregressor_dart_booster_multiple_params(
         in reloaded_model._bqml_model.model_name
     )
     assert reloaded_model.booster == "DART"
-    assert reloaded_model.dart_normalized_type == "TREE"
+    assert reloaded_model.dart_normalized_type == "tree"
     assert reloaded_model.tree_method == "AUTO"
     assert reloaded_model.colsample_bytree == 0.95
     assert reloaded_model.colsample_bylevel == 0.95
@@ -185,7 +185,7 @@ def test_xgbclassifier_dart_booster_multiple_params(
 ):
     model = bigframes.ml.ensemble.XGBClassifier(
         booster="dart",
-        tree_method="AUTO",
+        tree_method="auto",
         min_tree_child_weight=2,
         colsample_bytree=0.95,
         colsample_bylevel=0.95,
@@ -235,7 +235,7 @@ def test_xgbclassifier_dart_booster_multiple_params(
         in reloaded_model._bqml_model.model_name
     )
     assert reloaded_model.booster == "DART"
-    assert reloaded_model.dart_normalized_type == "TREE"
+    assert reloaded_model.dart_normalized_type == "tree"
     assert reloaded_model.tree_method == "AUTO"
     assert reloaded_model.colsample_bytree == 0.95
     assert reloaded_model.colsample_bylevel == 0.95
@@ -297,7 +297,7 @@ def test_randomforestregressor_default_params(penguins_df_default_index, dataset
 @pytest.mark.flaky(retries=2, delay=120)
 def test_randomforestregressor_multiple_params(penguins_df_default_index, dataset_id):
     model = bigframes.ml.ensemble.RandomForestRegressor(
-        tree_method="AUTO",
+        tree_method="auto",
         min_tree_child_weight=2,
         colsample_bytree=0.95,
         colsample_bylevel=0.95,
diff --git a/tests/system/large/test_remote_function.py b/tests/system/large/test_remote_function.py
index 8033f79c47..2f231f40c9 100644
--- a/tests/system/large/test_remote_function.py
+++ b/tests/system/large/test_remote_function.py
@@ -61,16 +61,32 @@ def get_remote_function_endpoints(bigquery_client, dataset_id):
     return endpoints
 
 
-def get_cloud_functions(functions_client, project, location, name_prefix="bigframes-"):
+def get_cloud_functions(
+    functions_client, project, location, name=None, name_prefix=None
+):
     """Get the cloud functions in the given project and location."""
+
+    assert (
+        not name or not name_prefix
+    ), f"At most one of the {name.__name__} or {name_prefix.__name__} can be passed."
+
     _, location = get_remote_function_locations(location)
     parent = f"projects/{project}/locations/{location}"
     request = functions_v2.ListFunctionsRequest(parent=parent)
     page_result = functions_client.list_functions(request=request)
-    full_name_prefix = parent + f"/functions/{name_prefix}"
     for response in page_result:
-        if not name_prefix or response.name.startswith(full_name_prefix):
-            yield response
+        # If name is provided and it does not match then skip
+        if bool(name):
+            full_name = parent + f"/functions/{name}"
+            if response.name != full_name:
+                continue
+        # If name prefix is provided and it does not match then skip
+        elif bool(name_prefix):
+            full_name_prefix = parent + f"/functions/{name_prefix}"
+            if not response.name.startswith(full_name_prefix):
+                continue
+
+        yield response
 
 
 def delete_cloud_function(functions_client, full_name):
@@ -84,8 +100,17 @@ def cleanup_remote_function_assets(
     bigquery_client, functions_client, remote_udf, ignore_failures=True
 ):
     """Clean up the GCP assets behind a bigframes remote function."""
+
+    # Clean up BQ remote function
     try:
         bigquery_client.delete_routine(remote_udf.bigframes_remote_function)
+    except Exception:
+        # By default don't raise exception in cleanup
+        if not ignore_failures:
+            raise
+
+    # Clean up cloud function
+    try:
         delete_cloud_function(functions_client, remote_udf.bigframes_cloud_function)
     except Exception:
         # By default don't raise exception in cleanup
@@ -94,7 +119,15 @@ def cleanup_remote_function_assets(
 
 
 def make_uniq_udf(udf):
-    """Transform a udf to another with same behavior but a unique name."""
+    """Transform a udf to another with same behavior but a unique name.
+    Use this to test remote functions with reuse=True, in which case parallel
+    instances of the same tests may evaluate same named cloud functions and BQ
+    remote functions, therefore interacting with each other and causing unwanted
+    failures. With this method one can transform a udf into another with the
+    same behavior but a different name which will remain unique for the
+    lifetime of one test instance.
+    """
+
     prefixer = test_utils.prefixer.Prefixer(udf.__name__, "")
     udf_uniq_name = prefixer.create_prefix()
     udf_file_name = f"{udf_uniq_name}.py"
@@ -111,7 +144,18 @@ def make_uniq_udf(udf):
         target_code = source_code.replace(source_key, target_key, 1)
         f.write(target_code)
     spec = importlib.util.spec_from_file_location(udf_file_name, udf_file_path)
-    return getattr(spec.loader.load_module(), udf_uniq_name), tmpdir
+    udf_uniq = getattr(spec.loader.load_module(), udf_uniq_name)
+
+    # This is a bit of a hack but we need to remove the reference to a foreign
+    # module, otherwise the serialization would keep the foreign module
+    # reference and deserialization would fail with error like following:
+    #     ModuleNotFoundError: No module named 'add_one_2nxcmd9j'
+    # TODO(shobs): Figure out if there is a better way of generating the unique
+    # function object, but for now let's just set it to same module as the
+    # original udf.
+    udf_uniq.__module__ = udf.__module__
+
+    return udf_uniq, tmpdir
 
 
 @pytest.fixture(scope="module")
@@ -136,7 +180,10 @@ def cleanup_cloud_functions(session, functions_client, dataset_id_permanent):
     )
     delete_count = 0
     for cloud_function in get_cloud_functions(
-        functions_client, session.bqclient.project, session.bqclient.location
+        functions_client,
+        session.bqclient.project,
+        session.bqclient.location,
+        name_prefix="bigframes-",
     ):
         # Ignore bigframes cloud functions referred by the remote functions in
         # the permanent dataset
@@ -524,15 +571,6 @@ def add_one(x):
         # Make a unique udf
         add_one_uniq, add_one_uniq_dir = make_uniq_udf(add_one)
 
-        # This is a bit of a hack but we need to remove the reference to a foreign
-        # module, otherwise the serialization would keep the foreign module
-        # reference and deserialization would fail with error like following:
-        #     ModuleNotFoundError: No module named 'add_one_2nxcmd9j'
-        # TODO(shobs): Figure out if there is a better way of generating the unique
-        # function object, but for now let's just set it to same module as the
-        # original udf.
-        add_one_uniq.__module__ = add_one.__module__
-
         # Expected cloud function name for the unique udf
         add_one_uniq_cf_name = get_cloud_function_name(add_one_uniq)
 
@@ -542,7 +580,7 @@ def add_one(x):
                 functions_client,
                 session.bqclient.project,
                 session.bqclient.location,
-                name_prefix=add_one_uniq_cf_name,
+                name=add_one_uniq_cf_name,
             )
         )
         assert len(cloud_functions) == 0
@@ -563,7 +601,7 @@ def add_one(x):
                 functions_client,
                 session.bqclient.project,
                 session.bqclient.location,
-                name_prefix=add_one_uniq_cf_name,
+                name=add_one_uniq_cf_name,
             )
         )
         assert len(cloud_functions) == 1
@@ -611,7 +649,7 @@ def inner_test():
                 functions_client,
                 session.bqclient.project,
                 session.bqclient.location,
-                name_prefix=add_one_uniq_cf_name,
+                name=add_one_uniq_cf_name,
             )
         )
         assert len(cloud_functions) == 0
@@ -633,7 +671,7 @@ def inner_test():
                 functions_client,
                 session.bqclient.project,
                 session.bqclient.location,
-                name_prefix=add_one_uniq_cf_name,
+                name=add_one_uniq_cf_name,
             )
         )
         assert len(cloud_functions) == 1
@@ -776,3 +814,221 @@ def test_remote_udf_lambda(
         cleanup_remote_function_assets(
             session.bqclient, functions_client, add_one_lambda_remote
         )
+
+
+@pytest.mark.flaky(retries=2, delay=120)
+def test_remote_function_with_explicit_name(
+    session, scalars_dfs, dataset_id, bq_cf_connection, functions_client
+):
+    try:
+
+        def square(x):
+            return x * x
+
+        prefixer = test_utils.prefixer.Prefixer(square.__name__, "")
+        rf_name = prefixer.create_prefix()
+        expected_remote_function = f"{dataset_id}.{rf_name}"
+
+        # Initially the expected BQ remote function should not exist
+        with pytest.raises(NotFound):
+            session.bqclient.get_routine(expected_remote_function)
+
+        # Create the remote function with the name provided explicitly
+        square_remote = session.remote_function(
+            [int],
+            int,
+            dataset_id,
+            bq_cf_connection,
+            reuse=False,
+            name=rf_name,
+        )(square)
+
+        # The remote function should reflect the explicitly provided name
+        assert square_remote.bigframes_remote_function == expected_remote_function
+
+        # Now the expected BQ remote function should exist
+        session.bqclient.get_routine(expected_remote_function)
+
+        # The behavior of the created remote function should be as expected
+        scalars_df, scalars_pandas_df = scalars_dfs
+
+        bf_int64_col = scalars_df["int64_too"]
+        bf_result_col = bf_int64_col.apply(square_remote)
+        bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas()
+
+        pd_int64_col = scalars_pandas_df["int64_too"]
+        pd_result_col = pd_int64_col.apply(square)
+        # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e.
+        # pd_int64_col.dtype is Int64Dtype()
+        # pd_int64_col.apply(square).dtype is int64.
+        # For this test let's force the pandas dtype to be same as bigframes' dtype.
+        pd_result_col = pd_result_col.astype(pandas.Int64Dtype())
+        pd_result = pd_int64_col.to_frame().assign(result=pd_result_col)
+
+        assert_pandas_df_equal_ignore_ordering(bf_result, pd_result)
+    finally:
+        # clean up the gcp assets created for the remote function
+        cleanup_remote_function_assets(
+            session.bqclient, functions_client, square_remote
+        )
+
+
+@pytest.mark.flaky(retries=2, delay=120)
+def test_remote_function_with_explicit_name_reuse(
+    session, scalars_dfs, dataset_id, bq_cf_connection, functions_client
+):
+    try:
+
+        dirs_to_cleanup = []
+
+        # Define a user code
+        def square(x):
+            return x * x
+
+        # Make it a unique udf
+        square_uniq, square_uniq_dir = make_uniq_udf(square)
+        dirs_to_cleanup.append(square_uniq_dir)
+
+        # Define a common routine which accepts a remote function and the
+        # corresponding user defined function and tests that bigframes bahavior
+        # on the former is in parity with the pandas behaviour on the latter
+        def test_internal(rf, udf):
+            # The behavior of the created remote function should be as expected
+            scalars_df, scalars_pandas_df = scalars_dfs
+
+            bf_int64_col = scalars_df["int64_too"]
+            bf_result_col = bf_int64_col.apply(rf)
+            bf_result = bf_int64_col.to_frame().assign(result=bf_result_col).to_pandas()
+
+            pd_int64_col = scalars_pandas_df["int64_too"]
+            pd_result_col = pd_int64_col.apply(udf)
+            # TODO(shobs): Figure why pandas .apply() changes the dtype, i.e.
+            # pd_int64_col.dtype is Int64Dtype()
+            # pd_int64_col.apply(square).dtype is int64.
+            # For this test let's force the pandas dtype to be same as bigframes' dtype.
+            pd_result_col = pd_result_col.astype(pandas.Int64Dtype())
+            pd_result = pd_int64_col.to_frame().assign(result=pd_result_col)
+
+            assert_pandas_df_equal_ignore_ordering(bf_result, pd_result)
+
+        # Create an explicit name for the remote function
+        prefixer = test_utils.prefixer.Prefixer("foo", "")
+        rf_name = prefixer.create_prefix()
+        expected_remote_function = f"{dataset_id}.{rf_name}"
+
+        # Initially the expected BQ remote function should not exist
+        with pytest.raises(NotFound):
+            session.bqclient.get_routine(expected_remote_function)
+
+        # Create a new remote function with the name provided explicitly
+        square_remote1 = session.remote_function(
+            [int],
+            int,
+            dataset_id,
+            bq_cf_connection,
+            name=rf_name,
+        )(square_uniq)
+
+        # The remote function should reflect the explicitly provided name
+        assert square_remote1.bigframes_remote_function == expected_remote_function
+
+        # Now the expected BQ remote function should exist
+        routine = session.bqclient.get_routine(expected_remote_function)
+        square_remote1_created = routine.created
+        square_remote1_cf_updated = session.cloudfunctionsclient.get_function(
+            name=square_remote1.bigframes_cloud_function
+        ).update_time
+
+        # Test pandas parity with square udf
+        test_internal(square_remote1, square)
+
+        # Now Create another remote function with the same name provided
+        # explicitly. Since reuse is True by default, the previously created
+        # remote function with the same name will be reused.
+        square_remote2 = session.remote_function(
+            [int],
+            int,
+            dataset_id,
+            bq_cf_connection,
+            name=rf_name,
+        )(square_uniq)
+
+        # The new remote function should still reflect the explicitly provided name
+        assert square_remote2.bigframes_remote_function == expected_remote_function
+
+        # The expected BQ remote function should still exist
+        routine = session.bqclient.get_routine(expected_remote_function)
+        square_remote2_created = routine.created
+        square_remote2_cf_updated = session.cloudfunctionsclient.get_function(
+            name=square_remote2.bigframes_cloud_function
+        ).update_time
+
+        # The new remote function should reflect that the previous BQ remote
+        # function and the cloud function were reused instead of creating anew
+        assert square_remote2_created == square_remote1_created
+        assert (
+            square_remote2.bigframes_cloud_function
+            == square_remote1.bigframes_cloud_function
+        )
+        assert square_remote2_cf_updated == square_remote1_cf_updated
+
+        # Test again that the new remote function is actually same as the
+        # previous remote function
+        test_internal(square_remote2, square)
+
+        # Now define a different user code
+        def plusone(x):
+            return x + 1
+
+        # Make it a unique udf
+        plusone_uniq, plusone_uniq_dir = make_uniq_udf(plusone)
+        dirs_to_cleanup.append(plusone_uniq_dir)
+
+        # Now Create a third remote function with the same name provided
+        # explicitly. Even though reuse is True by default, the previously
+        # created remote function with the same name should not be reused since
+        # this time it is a different user code.
+        plusone_remote = session.remote_function(
+            [int],
+            int,
+            dataset_id,
+            bq_cf_connection,
+            name=rf_name,
+        )(plusone_uniq)
+
+        # The new remote function should still reflect the explicitly provided name
+        assert plusone_remote.bigframes_remote_function == expected_remote_function
+
+        # The expected BQ remote function should still exist
+        routine = session.bqclient.get_routine(expected_remote_function)
+        plusone_remote_created = routine.created
+        plusone_remote_cf_updated = session.cloudfunctionsclient.get_function(
+            name=plusone_remote.bigframes_cloud_function
+        ).update_time
+
+        # The new remote function should reflect that the previous BQ remote
+        # function and the cloud function were NOT reused, instead were created
+        # anew
+        assert plusone_remote_created > square_remote2_created
+        assert (
+            plusone_remote.bigframes_cloud_function
+            != square_remote2.bigframes_cloud_function
+        )
+        assert plusone_remote_cf_updated > square_remote2_cf_updated
+
+        # Test again that the new remote function is equivalent to the new user
+        # defined function
+        test_internal(plusone_remote, plusone)
+    finally:
+        # clean up the gcp assets created for the remote function
+        cleanup_remote_function_assets(
+            session.bqclient, functions_client, square_remote1
+        )
+        cleanup_remote_function_assets(
+            session.bqclient, functions_client, square_remote2
+        )
+        cleanup_remote_function_assets(
+            session.bqclient, functions_client, plusone_remote
+        )
+        for dir_ in dirs_to_cleanup:
+            shutil.rmtree(dir_)
diff --git a/tests/system/small/ml/test_core.py b/tests/system/small/ml/test_core.py
index 6c3e8e06f5..ace943956f 100644
--- a/tests/system/small/ml/test_core.py
+++ b/tests/system/small/ml/test_core.py
@@ -18,6 +18,7 @@
 
 import pandas as pd
 import pyarrow as pa
+import pytest
 import pytz
 
 import bigframes
@@ -278,6 +279,7 @@ def test_model_predict_with_unnamed_index(
     )
 
 
+@pytest.mark.flaky(retries=2, delay=120)
 def test_model_generate_text(
     bqml_palm2_text_generator_model: core.BqmlModel, llm_text_df
 ):
diff --git a/tests/system/small/ml/test_decomposition.py b/tests/system/small/ml/test_decomposition.py
index 8df4145fcf..c71bbbe3b0 100644
--- a/tests/system/small/ml/test_decomposition.py
+++ b/tests/system/small/ml/test_decomposition.py
@@ -16,33 +16,14 @@
 
 from bigframes.ml import decomposition
 
-_PD_NEW_PENGUINS = pd.DataFrame(
-    {
-        "tag_number": [1633, 1672, 1690],
-        "species": [
-            "Adelie Penguin (Pygoscelis adeliae)",
-            "Gentoo penguin (Pygoscelis papua)",
-            "Adelie Penguin (Pygoscelis adeliae)",
-        ],
-        "island": ["Dream", "Biscoe", "Torgersen"],
-        "culmen_length_mm": [37.8, 46.5, 41.1],
-        "culmen_depth_mm": [18.1, 14.8, 18.6],
-        "flipper_length_mm": [193.0, 217.0, 189.0],
-        "body_mass_g": [3750.0, 5200.0, 3325.0],
-        "sex": ["MALE", "FEMALE", "MALE"],
-    }
-).set_index("tag_number")
 
-
-def test_pca_predict(session, penguins_pca_model: decomposition.PCA):
-    new_penguins = session.read_pandas(_PD_NEW_PENGUINS)
-
-    predictions = penguins_pca_model.predict(new_penguins).to_pandas()
+def test_pca_predict(penguins_pca_model, new_penguins_df):
+    predictions = penguins_pca_model.predict(new_penguins_df).to_pandas()
     expected = pd.DataFrame(
         {
-            "principal_component_1": [-1.459, 2.258, -1.685],
-            "principal_component_2": [-1.120, -1.351, -0.874],
-            "principal_component_3": [-0.646, 0.443, -0.704],
+            "principal_component_1": [-1.314041, -0.855813, -1.848786],
+            "principal_component_2": [-0.889106, -1.259753, -0.983304],
+            "principal_component_3": [-0.704345, 0.322555, -0.095759],
         },
         dtype="Float64",
         index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
diff --git a/tests/system/small/test_dataframe.py b/tests/system/small/test_dataframe.py
index 85c3cce1d7..a85777c59d 100644
--- a/tests/system/small/test_dataframe.py
+++ b/tests/system/small/test_dataframe.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import operator
+import tempfile
 import typing
 from typing import Tuple
 
@@ -137,6 +138,46 @@ def test_tail_with_custom_column_labels(scalars_df_index, scalars_pandas_df_inde
     pandas.testing.assert_frame_equal(bf_result, pd_result)
 
 
+@pytest.mark.parametrize(
+    ("keep",),
+    [
+        ("first",),
+        ("last",),
+        ("all",),
+    ],
+)
+def test_df_nlargest(scalars_df_index, scalars_pandas_df_index, keep):
+    bf_result = scalars_df_index.nlargest(
+        3, ["bool_col", "int64_too"], keep=keep
+    ).to_pandas()
+    pd_result = scalars_pandas_df_index.nlargest(
+        3, ["bool_col", "int64_too"], keep=keep
+    )
+
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+@pytest.mark.parametrize(
+    ("keep",),
+    [
+        ("first",),
+        ("last",),
+        ("all",),
+    ],
+)
+def test_df_nsmallest(scalars_df_index, scalars_pandas_df_index, keep):
+    bf_result = scalars_df_index.nsmallest(6, ["bool_col"], keep=keep).to_pandas()
+    pd_result = scalars_pandas_df_index.nsmallest(6, ["bool_col"], keep=keep)
+
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
 def test_get_column_by_attr(scalars_dfs):
     scalars_df, scalars_pandas_df = scalars_dfs
     series = scalars_df.int64_col
@@ -582,6 +623,22 @@ def test_df_fillna(scalars_dfs):
     pandas.testing.assert_frame_equal(bf_result, pd_result)
 
 
+def test_df_ffill(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_result = scalars_df[["int64_col", "float64_col"]].ffill(limit=1).to_pandas()
+    pd_result = scalars_pandas_df[["int64_col", "float64_col"]].ffill(limit=1)
+
+    pandas.testing.assert_frame_equal(bf_result, pd_result)
+
+
+def test_df_bfill(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_result = scalars_df[["int64_col", "float64_col"]].bfill().to_pandas()
+    pd_result = scalars_pandas_df[["int64_col", "float64_col"]].bfill()
+
+    pandas.testing.assert_frame_equal(bf_result, pd_result)
+
+
 def test_df_isin_list(scalars_dfs):
     scalars_df, scalars_pandas_df = scalars_dfs
     values = ["Hello, World!", 55555, 2.51, pd.NA, True]
@@ -1027,6 +1084,88 @@ def test_df_notnull(scalars_dfs):
     assert_pandas_df_equal_ignore_ordering(bf_result, pd_result)
 
 
+@pytest.mark.parametrize(
+    ("left_labels", "right_labels", "overwrite", "fill_value"),
+    [
+        (["a", "b", "c"], ["c", "a", "b"], True, None),
+        (["a", "b", "c"], ["c", "a", "b"], False, None),
+        (["a", "b", "c"], ["a", "b", "c"], False, 2),
+    ],
+    ids=[
+        "one_one_match_overwrite",
+        "one_one_match_no_overwrite",
+        "exact_match",
+    ],
+)
+def test_combine(
+    scalars_df_index,
+    scalars_df_2_index,
+    scalars_pandas_df_index,
+    left_labels,
+    right_labels,
+    overwrite,
+    fill_value,
+):
+    if pd.__version__.startswith("1."):
+        pytest.skip("pd.NA vs NaN not handled well in pandas 1.x.")
+    columns = ["int64_too", "int64_col", "float64_col"]
+
+    bf_df_a = scalars_df_index[columns]
+    bf_df_a.columns = left_labels
+    bf_df_b = scalars_df_2_index[columns]
+    bf_df_b.columns = right_labels
+    bf_result = bf_df_a.combine(
+        bf_df_b,
+        lambda x, y: x**2 + 2 * x * y + y**2,
+        overwrite=overwrite,
+        fill_value=fill_value,
+    ).to_pandas()
+
+    pd_df_a = scalars_pandas_df_index[columns]
+    pd_df_a.columns = left_labels
+    pd_df_b = scalars_pandas_df_index[columns]
+    pd_df_b.columns = right_labels
+    pd_result = pd_df_a.combine(
+        pd_df_b,
+        lambda x, y: x**2 + 2 * x * y + y**2,
+        overwrite=overwrite,
+        fill_value=fill_value,
+    )
+
+    # Some dtype inconsistency for all-NULL columns
+    pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
+
+
+def test_combine_first(
+    scalars_df_index,
+    scalars_df_2_index,
+    scalars_pandas_df_index,
+):
+    if pd.__version__.startswith("1."):
+        pytest.skip("pd.NA vs NaN not handled well in pandas 1.x.")
+    columns = ["int64_too", "int64_col", "float64_col"]
+
+    bf_df_a = scalars_df_index[columns].iloc[0:6]
+    bf_df_a.columns = ["a", "b", "c"]
+    bf_df_b = scalars_df_2_index[columns].iloc[2:8]
+    bf_df_b.columns = ["b", "a", "d"]
+    bf_result = bf_df_a.combine_first(bf_df_b).to_pandas()
+
+    pd_df_a = scalars_pandas_df_index[columns].iloc[0:6]
+    pd_df_a.columns = ["a", "b", "c"]
+    pd_df_b = scalars_pandas_df_index[columns].iloc[2:8]
+    pd_df_b.columns = ["b", "a", "d"]
+    pd_result = pd_df_a.combine_first(pd_df_b)
+
+    print("pandas")
+    print(pd_result.to_string())
+    print("bigframes")
+    print(bf_result.to_string())
+
+    # Some dtype inconsistency for all-NULL columns
+    pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)
+
+
 @pytest.mark.parametrize(
     ("op"),
     [
@@ -1145,11 +1284,13 @@ def test_series_binop_axis_index(
         (["a", "a", "b"], ["c", "c", "d"]),
         (["a", "b", "c"], ["c", "a", "b"]),
         (["a", "c", "c"], ["c", "a", "c"]),
+        (["a", "b", "c"], ["a", "b", "c"]),
     ],
     ids=[
         "no_overlap",
         "one_one_match",
         "multi_match",
+        "exact_match",
     ],
 )
 def test_binop_df_df_binary_op(
@@ -1361,6 +1502,42 @@ def test_dataframe_general_analytic_op(
     )
 
 
+@pytest.mark.parametrize(
+    ("periods",),
+    [
+        (1,),
+        (2,),
+        (-1,),
+    ],
+)
+def test_dataframe_diff(scalars_df_index, scalars_pandas_df_index, periods):
+    col_names = ["int64_too", "float64_col", "int64_col"]
+    bf_result = scalars_df_index[col_names].diff(periods=periods).to_pandas()
+    pd_result = scalars_pandas_df_index[col_names].diff(periods=periods)
+    pd.testing.assert_frame_equal(
+        pd_result,
+        bf_result,
+    )
+
+
+@pytest.mark.parametrize(
+    ("periods",),
+    [
+        (1,),
+        (2,),
+        (-1,),
+    ],
+)
+def test_dataframe_pct_change(scalars_df_index, scalars_pandas_df_index, periods):
+    col_names = ["int64_too", "float64_col", "int64_col"]
+    bf_result = scalars_df_index[col_names].pct_change(periods=periods).to_pandas()
+    pd_result = scalars_pandas_df_index[col_names].pct_change(periods=periods)
+    pd.testing.assert_frame_equal(
+        pd_result,
+        bf_result,
+    )
+
+
 def test_dataframe_agg_single_string(scalars_dfs):
     numeric_cols = ["int64_col", "int64_too", "float64_col"]
     scalars_df, scalars_pandas_df = scalars_dfs
@@ -1675,6 +1852,52 @@ def test_loc_single_index_no_duplicate(scalars_df_index, scalars_pandas_df_index
     )
 
 
+def test_loc_setitem_bool_series_scalar_new_col(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_df = scalars_df.copy()
+    pd_df = scalars_pandas_df.copy()
+    bf_df.loc[bf_df["int64_too"] == 0, "new_col"] = 99
+    pd_df.loc[pd_df["int64_too"] == 0, "new_col"] = 99
+
+    # pandas type difference
+    pd_df["new_col"] = pd_df["new_col"].astype("Float64")
+
+    pd.testing.assert_frame_equal(
+        bf_df.to_pandas(),
+        pd_df,
+    )
+
+
+def test_loc_setitem_bool_series_scalar_existing_col(scalars_dfs):
+    if pd.__version__.startswith("1."):
+        pytest.skip("this loc overload not supported in pandas 1.x.")
+
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_df = scalars_df.copy()
+    pd_df = scalars_pandas_df.copy()
+    bf_df.loc[bf_df["int64_too"] == 1, "string_col"] = "hello"
+    pd_df.loc[pd_df["int64_too"] == 1, "string_col"] = "hello"
+
+    pd.testing.assert_frame_equal(
+        bf_df.to_pandas(),
+        pd_df,
+    )
+
+
+def test_loc_setitem_bool_series_scalar_type_error(scalars_dfs):
+    if pd.__version__.startswith("1."):
+        pytest.skip("this loc overload not supported in pandas 1.x.")
+
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_df = scalars_df.copy()
+    pd_df = scalars_pandas_df.copy()
+
+    with pytest.raises(TypeError):
+        bf_df.loc[bf_df["int64_too"] == 1, "string_col"] = 99
+    with pytest.raises(TypeError):
+        pd_df.loc[pd_df["int64_too"] == 1, "string_col"] = 99
+
+
 @pytest.mark.parametrize(
     ("op"),
     [
@@ -1749,6 +1972,30 @@ def test_dataframe_prod(scalars_df_index, scalars_pandas_df_index):
     pd.testing.assert_series_equal(pd_series, bf_result, check_index_type=False)
 
 
+def test_df_skew_too_few_values(scalars_dfs):
+    columns = ["float64_col", "int64_col"]
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_result = scalars_df[columns].head(2).skew().to_pandas()
+    pd_result = scalars_pandas_df[columns].head(2).skew()
+
+    # Pandas may produce narrower numeric types, but bigframes always produces Float64
+    pd_result = pd_result.astype("Float64")
+
+    pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False)
+
+
+def test_df_skew(scalars_dfs):
+    columns = ["float64_col", "int64_col"]
+    scalars_df, scalars_pandas_df = scalars_dfs
+    bf_result = scalars_df[columns].skew().to_pandas()
+    pd_result = scalars_pandas_df[columns].skew()
+
+    # Pandas may produce narrower numeric types, but bigframes always produces Float64
+    pd_result = pd_result.astype("Float64")
+
+    pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False)
+
+
 @pytest.mark.parametrize(
     ("frac", "n", "random_state"),
     [
@@ -1828,6 +2075,161 @@ def test_df_add_suffix(scalars_df_index, scalars_pandas_df_index, axis):
     )
 
 
+def test_df_columns_filter_items(scalars_df_index, scalars_pandas_df_index):
+    if pd.__version__.startswith("2.0") or pd.__version__.startswith("1."):
+        pytest.skip("pandas filter items behavior different pre-2.1")
+    bf_result = scalars_df_index.filter(items=["string_col", "int64_col"]).to_pandas()
+
+    pd_result = scalars_pandas_df_index.filter(items=["string_col", "int64_col"])
+
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_df_columns_filter_like(scalars_df_index, scalars_pandas_df_index):
+    bf_result = scalars_df_index.filter(like="64_col").to_pandas()
+
+    pd_result = scalars_pandas_df_index.filter(like="64_col")
+
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_df_columns_filter_regex(scalars_df_index, scalars_pandas_df_index):
+    bf_result = scalars_df_index.filter(regex="^[^_]+$").to_pandas()
+
+    pd_result = scalars_pandas_df_index.filter(regex="^[^_]+$")
+
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_df_rows_filter_items(scalars_df_index, scalars_pandas_df_index):
+    if pd.__version__.startswith("2.0") or pd.__version__.startswith("1."):
+        pytest.skip("pandas filter items behavior different pre-2.1")
+    bf_result = scalars_df_index.filter(items=[5, 1, 3], axis=0).to_pandas()
+
+    pd_result = scalars_pandas_df_index.filter(items=[5, 1, 3], axis=0)
+
+    # Pandas uses int64 instead of Int64 (nullable) dtype.
+    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_df_rows_filter_like(scalars_df_index, scalars_pandas_df_index):
+    scalars_df_index = scalars_df_index.copy().set_index("string_col")
+    scalars_pandas_df_index = scalars_pandas_df_index.copy().set_index("string_col")
+
+    bf_result = scalars_df_index.filter(like="ello", axis=0).to_pandas()
+
+    pd_result = scalars_pandas_df_index.filter(like="ello", axis=0)
+
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_df_rows_filter_regex(scalars_df_index, scalars_pandas_df_index):
+    scalars_df_index = scalars_df_index.copy().set_index("string_col")
+    scalars_pandas_df_index = scalars_pandas_df_index.copy().set_index("string_col")
+
+    bf_result = scalars_df_index.filter(regex="^[GH].*", axis=0).to_pandas()
+
+    pd_result = scalars_pandas_df_index.filter(regex="^[GH].*", axis=0)
+
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_df_reindex_rows_list(scalars_df_index, scalars_pandas_df_index):
+    bf_result = scalars_df_index.reindex(index=[5, 1, 3, 99, 1]).to_pandas()
+
+    pd_result = scalars_pandas_df_index.reindex(index=[5, 1, 3, 99, 1])
+
+    # Pandas uses int64 instead of Int64 (nullable) dtype.
+    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_df_reindex_rows_index(scalars_df_index, scalars_pandas_df_index):
+    bf_result = scalars_df_index.reindex(
+        index=pd.Index([5, 1, 3, 99, 1], name="newname")
+    ).to_pandas()
+
+    pd_result = scalars_pandas_df_index.reindex(
+        index=pd.Index([5, 1, 3, 99, 1], name="newname")
+    )
+
+    # Pandas uses int64 instead of Int64 (nullable) dtype.
+    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_df_reindex_nonunique(scalars_df_index):
+    with pytest.raises(ValueError):
+        # int64_too is non-unique
+        scalars_df_index.set_index("int64_too").reindex(
+            index=[5, 1, 3, 99, 1], validate=True
+        )
+
+
+def test_df_reindex_columns(scalars_df_index, scalars_pandas_df_index):
+    bf_result = scalars_df_index.reindex(
+        columns=["not_a_col", "int64_col", "int64_too"]
+    ).to_pandas()
+
+    pd_result = scalars_pandas_df_index.reindex(
+        columns=["not_a_col", "int64_col", "int64_too"]
+    )
+
+    # Pandas uses float64 as default for newly created empty column, bf uses Float64
+    pd_result.not_a_col = pd_result.not_a_col.astype(pandas.Float64Dtype())
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_df_reindex_like(scalars_df_index, scalars_pandas_df_index):
+    reindex_target_bf = scalars_df_index.reindex(
+        columns=["not_a_col", "int64_col", "int64_too"], index=[5, 1, 3, 99, 1]
+    )
+    bf_result = scalars_df_index.reindex_like(reindex_target_bf).to_pandas()
+
+    reindex_target_pd = scalars_pandas_df_index.reindex(
+        columns=["not_a_col", "int64_col", "int64_too"], index=[5, 1, 3, 99, 1]
+    )
+    pd_result = scalars_pandas_df_index.reindex_like(reindex_target_pd)
+
+    # Pandas uses float64 as default for newly created empty column, bf uses Float64
+    # Pandas uses int64 instead of Int64 (nullable) dtype.
+    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
+    # Pandas uses float64 as default for newly created empty column, bf uses Float64
+    pd_result.not_a_col = pd_result.not_a_col.astype(pandas.Float64Dtype())
+    pd.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
 def test_df_values(scalars_df_index, scalars_pandas_df_index):
     bf_result = scalars_df_index.values
 
@@ -2035,6 +2437,93 @@ def test_df_duplicated(scalars_df_index, scalars_pandas_df_index, keep, subset):
     pd.testing.assert_series_equal(pd_series, bf_series, check_dtype=False)
 
 
+def test_df_to_dict(scalars_df_index, scalars_pandas_df_index):
+    unsupported = ["numeric_col"]  # formatted differently
+    bf_result = scalars_df_index.drop(columns=unsupported).to_dict()
+    pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_dict()
+
+    assert bf_result == pd_result
+
+
+def test_df_to_excel(scalars_df_index, scalars_pandas_df_index):
+    unsupported = ["timestamp_col"]
+    with tempfile.TemporaryFile() as bf_result_file, tempfile.TemporaryFile() as pd_result_file:
+        scalars_df_index.drop(columns=unsupported).to_excel(bf_result_file)
+        scalars_pandas_df_index.drop(columns=unsupported).to_excel(pd_result_file)
+        bf_result = bf_result_file.read()
+        pd_result = bf_result_file.read()
+
+    assert bf_result == pd_result
+
+
+def test_df_to_latex(scalars_df_index, scalars_pandas_df_index):
+    unsupported = ["numeric_col"]  # formatted differently
+    bf_result = scalars_df_index.drop(columns=unsupported).to_latex()
+    pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_latex()
+
+    assert bf_result == pd_result
+
+
+def test_df_to_records(scalars_df_index, scalars_pandas_df_index):
+    unsupported = ["numeric_col"]
+    bf_result = scalars_df_index.drop(columns=unsupported).to_records()
+    pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_records()
+
+    for bfi, pdi in zip(bf_result, pd_result):
+        for bfj, pdj in zip(bfi, pdi):
+            assert pd.isna(bfj) and pd.isna(pdj) or bfj == pdj
+
+
+def test_df_to_string(scalars_df_index, scalars_pandas_df_index):
+    unsupported = ["numeric_col"]  # formatted differently
+
+    bf_result = scalars_df_index.drop(columns=unsupported).to_string()
+    pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_string()
+
+    assert bf_result == pd_result
+
+
+def test_df_to_markdown(scalars_df_index, scalars_pandas_df_index):
+    # Nulls have bug from tabulate https://github.com/astanin/python-tabulate/issues/231
+    bf_result = scalars_df_index.dropna().to_markdown()
+    pd_result = scalars_pandas_df_index.dropna().to_markdown()
+
+    assert bf_result == pd_result
+
+
+def test_df_to_pickle(scalars_df_index, scalars_pandas_df_index):
+    with tempfile.TemporaryFile() as bf_result_file, tempfile.TemporaryFile() as pd_result_file:
+        scalars_df_index.to_pickle(bf_result_file)
+        scalars_pandas_df_index.to_pickle(pd_result_file)
+        bf_result = bf_result_file.read()
+        pd_result = bf_result_file.read()
+
+    assert bf_result == pd_result
+
+
+def test_df_to_orc(scalars_df_index, scalars_pandas_df_index):
+    unsupported = [
+        "numeric_col",
+        "bytes_col",
+        "date_col",
+        "datetime_col",
+        "time_col",
+        "timestamp_col",
+        "geography_col",
+    ]
+
+    bf_result_file = tempfile.TemporaryFile()
+    pd_result_file = tempfile.TemporaryFile()
+    scalars_df_index.drop(columns=unsupported).to_orc(bf_result_file)
+    scalars_pandas_df_index.drop(columns=unsupported).reset_index().to_orc(
+        pd_result_file
+    )
+    bf_result = bf_result_file.read()
+    pd_result = bf_result_file.read()
+
+    assert bf_result == pd_result
+
+
 @pytest.mark.parametrize(
     ("subset", "normalize", "ascending", "dropna"),
     [
diff --git a/tests/system/small/test_groupby.py b/tests/system/small/test_groupby.py
index 987368ce77..18741468c5 100644
--- a/tests/system/small/test_groupby.py
+++ b/tests/system/small/test_groupby.py
@@ -210,12 +210,14 @@ def test_dataframe_groupby_multi_sum(
         (lambda x: x.cummax(numeric_only=True)),
         (lambda x: x.cummin(numeric_only=True)),
         (lambda x: x.cumprod()),
+        (lambda x: x.shift(periods=2)),
     ],
     ids=[
         "cumsum",
         "cummax",
         "cummin",
         "cumprod",
+        "shift",
     ],
 )
 def test_dataframe_groupby_analytic(
@@ -229,6 +231,30 @@ def test_dataframe_groupby_analytic(
     pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False)
 
 
+def test_series_groupby_skew(scalars_df_index, scalars_pandas_df_index):
+    bf_result = scalars_df_index.groupby("bool_col")["int64_too"].skew().to_pandas()
+    pd_result = scalars_pandas_df_index.groupby("bool_col")["int64_too"].skew()
+
+    pd.testing.assert_series_equal(pd_result, bf_result, check_dtype=False)
+
+
+def test_dataframe_groupby_skew(scalars_df_index, scalars_pandas_df_index):
+    col_names = ["float64_col", "int64_col", "bool_col"]
+    bf_result = scalars_df_index[col_names].groupby("bool_col").skew().to_pandas()
+    pd_result = scalars_pandas_df_index[col_names].groupby("bool_col").skew()
+
+    pd.testing.assert_frame_equal(pd_result, bf_result, check_dtype=False)
+
+
+def test_dataframe_groupby_diff(scalars_df_index, scalars_pandas_df_index):
+    col_names = ["float64_col", "int64_col", "string_col"]
+    bf_result = scalars_df_index[col_names].groupby("string_col").diff(-1)
+    pd_result = scalars_pandas_df_index[col_names].groupby("string_col").diff(-1)
+    bf_result_computed = bf_result.to_pandas()
+
+    pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False)
+
+
 def test_dataframe_groupby_getitem(
     scalars_df_index,
     scalars_pandas_df_index,
diff --git a/tests/system/small/test_ipython.py b/tests/system/small/test_ipython.py
index 6725805d9a..be98ce0067 100644
--- a/tests/system/small/test_ipython.py
+++ b/tests/system/small/test_ipython.py
@@ -22,7 +22,8 @@ def test_repr_cache(scalars_df_index):
     # Make sure the df has a new block that the method return value
     # is not already cached.
     test_df = scalars_df_index.head()
+    test_df._block.retrieve_repr_request_results.cache_clear()
     results = display_formatter.format(test_df)
     assert results[0].keys() == {"text/plain", "text/html"}
-    assert test_df._block.retrieve_repr_request_results.cache_info().misses == 1
-    assert test_df._block.retrieve_repr_request_results.cache_info().hits == 1
+    assert test_df._block.retrieve_repr_request_results.cache_info().misses >= 1
+    assert test_df._block.retrieve_repr_request_results.cache_info().hits >= 1
diff --git a/tests/system/small/test_multiindex.py b/tests/system/small/test_multiindex.py
index 25d1e2ad49..1e38b47b4c 100644
--- a/tests/system/small/test_multiindex.py
+++ b/tests/system/small/test_multiindex.py
@@ -157,7 +157,7 @@ def test_multi_index_getitem_bool(scalars_df_index, scalars_pandas_df_index):
     ],
     ids=["level_num", "level_name", "list", "mixed_list"],
 )
-def test_multi_index_droplevel(scalars_df_index, scalars_pandas_df_index, level):
+def test_df_multi_index_droplevel(scalars_df_index, scalars_pandas_df_index, level):
     bf_frame = scalars_df_index.set_index(["int64_too", "bool_col", "int64_col"])
     pd_frame = scalars_pandas_df_index.set_index(["int64_too", "bool_col", "int64_col"])
 
@@ -167,6 +167,26 @@ def test_multi_index_droplevel(scalars_df_index, scalars_pandas_df_index, level)
     pandas.testing.assert_frame_equal(bf_result, pd_result)
 
 
+@pytest.mark.parametrize(
+    ("level"),
+    [
+        (1),
+        ("int64_too"),
+        ([0, 2]),
+        ([2, "bool_col"]),
+    ],
+    ids=["level_num", "level_name", "list", "mixed_list"],
+)
+def test_series_multi_index_droplevel(scalars_df_index, scalars_pandas_df_index, level):
+    bf_frame = scalars_df_index.set_index(["int64_too", "bool_col", "int64_col"])
+    pd_frame = scalars_pandas_df_index.set_index(["int64_too", "bool_col", "int64_col"])
+
+    bf_result = bf_frame["string_col"].droplevel(level).to_pandas()
+    pd_result = pd_frame["string_col"].droplevel(level)
+
+    pandas.testing.assert_series_equal(bf_result, pd_result)
+
+
 @pytest.mark.parametrize(
     ("labels", "level"),
     [
@@ -198,7 +218,9 @@ def test_multi_index_drop(scalars_df_index, scalars_pandas_df_index, labels, lev
         "num_names_mixed",
     ],
 )
-def test_multi_index_reorder_levels(scalars_df_index, scalars_pandas_df_index, order):
+def test_df_multi_index_reorder_levels(
+    scalars_df_index, scalars_pandas_df_index, order
+):
     bf_frame = scalars_df_index.set_index(["int64_too", "bool_col", "int64_col"])
     pd_frame = scalars_pandas_df_index.set_index(["int64_too", "bool_col", "int64_col"])
 
@@ -208,6 +230,51 @@ def test_multi_index_reorder_levels(scalars_df_index, scalars_pandas_df_index, o
     pandas.testing.assert_frame_equal(bf_result, pd_result)
 
 
+@pytest.mark.parametrize(
+    ("order"),
+    [
+        (1, 0, 2),
+        (["int64_col", "bool_col", "int64_too"]),
+        (["int64_col", "bool_col", 0]),
+    ],
+    ids=[
+        "level_nums",
+        "level_names",
+        "num_names_mixed",
+    ],
+)
+def test_series_multi_index_reorder_levels(
+    scalars_df_index, scalars_pandas_df_index, order
+):
+    bf_frame = scalars_df_index.set_index(["int64_too", "bool_col", "int64_col"])
+    pd_frame = scalars_pandas_df_index.set_index(["int64_too", "bool_col", "int64_col"])
+
+    bf_result = bf_frame["string_col"].reorder_levels(order).to_pandas()
+    pd_result = pd_frame["string_col"].reorder_levels(order)
+
+    pandas.testing.assert_series_equal(bf_result, pd_result)
+
+
+def test_df_multi_index_swaplevel(scalars_df_index, scalars_pandas_df_index):
+    bf_frame = scalars_df_index.set_index(["int64_too", "bool_col", "int64_col"])
+    pd_frame = scalars_pandas_df_index.set_index(["int64_too", "bool_col", "int64_col"])
+
+    bf_result = bf_frame.swaplevel().to_pandas()
+    pd_result = pd_frame.swaplevel()
+
+    pandas.testing.assert_frame_equal(bf_result, pd_result)
+
+
+def test_series_multi_index_swaplevel(scalars_df_index, scalars_pandas_df_index):
+    bf_frame = scalars_df_index.set_index(["int64_too", "bool_col", "int64_col"])
+    pd_frame = scalars_pandas_df_index.set_index(["int64_too", "bool_col", "int64_col"])
+
+    bf_result = bf_frame["string_col"].swaplevel(0, 2).to_pandas()
+    pd_result = pd_frame["string_col"].swaplevel(0, 2)
+
+    pandas.testing.assert_series_equal(bf_result, pd_result)
+
+
 def test_multi_index_series_groupby(scalars_df_index, scalars_pandas_df_index):
     bf_frame = scalars_df_index.set_index(["int64_too", "bool_col"])
     bf_result = (
@@ -446,6 +513,24 @@ def test_multi_index_series_rename_dict_same_type(
     )
 
 
+def test_multi_index_df_reindex(scalars_df_index, scalars_pandas_df_index):
+    new_index = pandas.MultiIndex.from_tuples(
+        [(4, "Hello, World!"), (99, "some_new_string")],
+        names=["new_index1", "new_index2"],
+    )
+    bf_result = (
+        scalars_df_index.set_index(["rowindex_2", "string_col"])
+        .reindex(index=new_index)
+        .to_pandas()
+    )
+    pd_result = scalars_pandas_df_index.set_index(["rowindex_2", "string_col"]).reindex(
+        index=new_index
+    )
+    pandas.testing.assert_frame_equal(
+        bf_result, pd_result, check_dtype=False, check_index_type=False
+    )
+
+
 # Column Multi-index tests
 
 
@@ -722,3 +807,76 @@ def test_is_monotonic_decreasing_extra(indexes):
         bf_result.index.is_monotonic_decreasing
         == pd_result.index.is_monotonic_decreasing
     )
+
+
+def test_column_multi_index_droplevel(scalars_df_index, scalars_pandas_df_index):
+    columns = ["int64_too", "string_col", "bool_col"]
+    multi_columns = pandas.MultiIndex.from_tuples(
+        zip(["a", "b", "a"], ["c", "d", "e"], ["f", "g", "f"])
+    )
+    bf_df = scalars_df_index[columns].copy()
+    bf_df.columns = multi_columns
+    pd_df = scalars_pandas_df_index[columns].copy()
+    pd_df.columns = multi_columns
+
+    bf_result = bf_df.droplevel(1, axis=1).to_pandas()
+    pd_result = pd_df.droplevel(1, axis=1)
+
+    pandas.testing.assert_frame_equal(bf_result, pd_result)
+
+
+def test_df_column_multi_index_reindex(scalars_df_index, scalars_pandas_df_index):
+    columns = ["int64_too", "int64_col", "rowindex_2"]
+    multi_columns = pandas.MultiIndex.from_tuples(zip(["a", "b", "a"], ["a", "b", "b"]))
+    bf_df = scalars_df_index[columns].copy()
+    bf_df.columns = multi_columns
+    pd_df = scalars_pandas_df_index[columns].copy()
+    pd_df.columns = multi_columns
+
+    new_index = pandas.MultiIndex.from_tuples(
+        [("z", "a"), ("a", "a")], names=["newname1", "newname2"]
+    )
+
+    bf_result = bf_df.reindex(columns=new_index).to_pandas()
+
+    pd_result = pd_df.reindex(columns=new_index)
+
+    # Pandas uses float64 as default for newly created empty column, bf uses Float64
+    pd_result[("z", "a")] = pd_result[("z", "a")].astype(pandas.Float64Dtype())
+
+    pandas.testing.assert_frame_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_column_multi_index_reorder_levels(scalars_df_index, scalars_pandas_df_index):
+    columns = ["int64_too", "string_col", "bool_col"]
+    multi_columns = pandas.MultiIndex.from_tuples(
+        zip(["a", "b", "a"], ["c", "d", "e"], ["f", "g", "f"])
+    )
+    bf_df = scalars_df_index[columns].copy()
+    bf_df.columns = multi_columns
+    pd_df = scalars_pandas_df_index[columns].copy()
+    pd_df.columns = multi_columns
+
+    bf_result = bf_df.reorder_levels([-2, -1, 0], axis=1).to_pandas()
+    pd_result = pd_df.reorder_levels([-2, -1, 0], axis=1)
+
+    pandas.testing.assert_frame_equal(bf_result, pd_result)
+
+
+def test_column_multi_index_swaplevel(scalars_df_index, scalars_pandas_df_index):
+    columns = ["int64_too", "string_col", "bool_col"]
+    multi_columns = pandas.MultiIndex.from_tuples(
+        zip(["a", "b", "a"], ["c", "d", "e"], ["f", "g", "f"])
+    )
+    bf_df = scalars_df_index[columns].copy()
+    bf_df.columns = multi_columns
+    pd_df = scalars_pandas_df_index[columns].copy()
+    pd_df.columns = multi_columns
+
+    bf_result = bf_df.swaplevel(-3, -1, axis=1).to_pandas()
+    pd_result = pd_df.swaplevel(-3, -1, axis=1)
+
+    pandas.testing.assert_frame_equal(bf_result, pd_result)
diff --git a/tests/system/small/test_pandas.py b/tests/system/small/test_pandas.py
index e451d5c3a2..a429c6551d 100644
--- a/tests/system/small/test_pandas.py
+++ b/tests/system/small/test_pandas.py
@@ -209,3 +209,17 @@ def test_merge_series(scalars_dfs, merge_how):
     )
 
     assert_pandas_df_equal_ignore_ordering(bf_result, pd_result)
+
+
+def test_cut(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+
+    pd_result = pd.cut(scalars_pandas_df["float64_col"], 5, labels=False)
+    bf_result = bpd.cut(scalars_df["float64_col"], 5, labels=False)
+
+    # make sure the result is a supported dtype
+    assert bf_result.dtype == bpd.Int64Dtype()
+
+    bf_result = bf_result.to_pandas()
+    pd_result = pd_result.astype("Int64")
+    pd.testing.assert_series_equal(bf_result, pd_result)
diff --git a/tests/system/small/test_series.py b/tests/system/small/test_series.py
index 07dc892ddc..d3560540cc 100644
--- a/tests/system/small/test_series.py
+++ b/tests/system/small/test_series.py
@@ -186,6 +186,54 @@ def test_fillna(scalars_dfs):
     )
 
 
+def test_series_replace_scalar_scalar(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    col_name = "string_col"
+    bf_result = (
+        scalars_df[col_name].replace("Hello, World!", "Howdy, Planet!").to_pandas()
+    )
+    pd_result = scalars_pandas_df[col_name].replace("Hello, World!", "Howdy, Planet!")
+
+    pd.testing.assert_series_equal(
+        pd_result,
+        bf_result,
+    )
+
+
+def test_series_replace_regex_scalar(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    col_name = "string_col"
+    bf_result = (
+        scalars_df[col_name].replace("^H.l", "Howdy, Planet!", regex=True).to_pandas()
+    )
+    pd_result = scalars_pandas_df[col_name].replace(
+        "^H.l", "Howdy, Planet!", regex=True
+    )
+
+    pd.testing.assert_series_equal(
+        pd_result,
+        bf_result,
+    )
+
+
+def test_series_replace_list_scalar(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    col_name = "string_col"
+    bf_result = (
+        scalars_df[col_name]
+        .replace(["Hello, World!", "T"], "Howdy, Planet!")
+        .to_pandas()
+    )
+    pd_result = scalars_pandas_df[col_name].replace(
+        ["Hello, World!", "T"], "Howdy, Planet!"
+    )
+
+    pd.testing.assert_series_equal(
+        pd_result,
+        bf_result,
+    )
+
+
 @pytest.mark.parametrize(
     ("ignore_index",),
     (
@@ -759,7 +807,6 @@ def test_isin_raise_error(scalars_df_index, scalars_pandas_df_index):
 )
 def test_isin(scalars_dfs, col_name, test_set):
     scalars_df, scalars_pandas_df = scalars_dfs
-    print(type(scalars_pandas_df["datetime_col"].iloc[0]))
     bf_result = scalars_df[col_name].isin(test_set).to_pandas()
     pd_result = scalars_pandas_df[col_name].isin(test_set).astype("boolean")
     pd.testing.assert_series_equal(
@@ -1506,6 +1553,28 @@ def test_shift(scalars_df_index, scalars_pandas_df_index):
     )
 
 
+def test_series_ffill(scalars_df_index, scalars_pandas_df_index):
+    col_name = "numeric_col"
+    bf_result = scalars_df_index[col_name].ffill(limit=1).to_pandas()
+    pd_result = scalars_pandas_df_index[col_name].ffill(limit=1)
+
+    pd.testing.assert_series_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_series_bfill(scalars_df_index, scalars_pandas_df_index):
+    col_name = "numeric_col"
+    bf_result = scalars_df_index[col_name].bfill(limit=2).to_pandas()
+    pd_result = scalars_pandas_df_index[col_name].bfill(limit=2)
+
+    pd.testing.assert_series_equal(
+        bf_result,
+        pd_result,
+    )
+
+
 def test_cumsum_int(scalars_df_index, scalars_pandas_df_index):
     if pd.__version__.startswith("1."):
         pytest.skip("Series.cumsum NA mask are different in pandas 1.x.")
@@ -1588,7 +1657,7 @@ def test_rank_with_nulls(scalars_df_index, scalars_pandas_df_index, na_option, m
         ("all",),
     ],
 )
-def test_nlargest(scalars_df_index, scalars_pandas_df_index, keep):
+def test_series_nlargest(scalars_df_index, scalars_pandas_df_index, keep):
     col_name = "bool_col"
     bf_result = scalars_df_index[col_name].nlargest(4, keep=keep).to_pandas()
     pd_result = scalars_pandas_df_index[col_name].nlargest(4, keep=keep)
@@ -1622,6 +1691,25 @@ def test_diff(scalars_df_index, scalars_pandas_df_index, periods):
     )
 
 
+@pytest.mark.parametrize(
+    ("periods",),
+    [
+        (1,),
+        (2,),
+        (-1,),
+    ],
+)
+def test_series_pct_change(scalars_df_index, scalars_pandas_df_index, periods):
+    bf_result = scalars_df_index["int64_col"].pct_change(periods=periods).to_pandas()
+    # cumsum does not behave well on nullable ints in pandas, produces object type and never ignores NA
+    pd_result = scalars_pandas_df_index["int64_col"].pct_change(periods=periods)
+
+    pd.testing.assert_series_equal(
+        bf_result,
+        pd_result,
+    )
+
+
 @pytest.mark.parametrize(
     ("keep",),
     [
@@ -1630,7 +1718,7 @@ def test_diff(scalars_df_index, scalars_pandas_df_index, periods):
         ("all",),
     ],
 )
-def test_nsmallest(scalars_df_index, scalars_pandas_df_index, keep):
+def test_series_nsmallest(scalars_df_index, scalars_pandas_df_index, keep):
     col_name = "bool_col"
     bf_result = scalars_df_index[col_name].nsmallest(2, keep=keep).to_pandas()
     pd_result = scalars_pandas_df_index[col_name].nsmallest(2, keep=keep)
@@ -1853,6 +1941,91 @@ def test_series_add_suffix(scalars_df_index, scalars_pandas_df_index):
     )
 
 
+def test_series_filter_items(scalars_df_index, scalars_pandas_df_index):
+    if pd.__version__.startswith("2.0") or pd.__version__.startswith("1."):
+        pytest.skip("pandas filter items behavior different pre-2.1")
+    bf_result = scalars_df_index["float64_col"].filter(items=[5, 1, 3]).to_pandas()
+
+    pd_result = scalars_pandas_df_index["float64_col"].filter(items=[5, 1, 3])
+
+    # Pandas uses int64 instead of Int64 (nullable) dtype.
+    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
+    pd.testing.assert_series_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_series_filter_like(scalars_df_index, scalars_pandas_df_index):
+    scalars_df_index = scalars_df_index.copy().set_index("string_col")
+    scalars_pandas_df_index = scalars_pandas_df_index.copy().set_index("string_col")
+
+    bf_result = scalars_df_index["float64_col"].filter(like="ello").to_pandas()
+
+    pd_result = scalars_pandas_df_index["float64_col"].filter(like="ello")
+
+    pd.testing.assert_series_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_series_filter_regex(scalars_df_index, scalars_pandas_df_index):
+    scalars_df_index = scalars_df_index.copy().set_index("string_col")
+    scalars_pandas_df_index = scalars_pandas_df_index.copy().set_index("string_col")
+
+    bf_result = scalars_df_index["float64_col"].filter(regex="^[GH].*").to_pandas()
+
+    pd_result = scalars_pandas_df_index["float64_col"].filter(regex="^[GH].*")
+
+    pd.testing.assert_series_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_series_reindex(scalars_df_index, scalars_pandas_df_index):
+    bf_result = (
+        scalars_df_index["float64_col"].reindex(index=[5, 1, 3, 99, 1]).to_pandas()
+    )
+
+    pd_result = scalars_pandas_df_index["float64_col"].reindex(index=[5, 1, 3, 99, 1])
+
+    # Pandas uses int64 instead of Int64 (nullable) dtype.
+    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
+    pd.testing.assert_series_equal(
+        bf_result,
+        pd_result,
+    )
+
+
+def test_series_reindex_nonunique(scalars_df_index):
+    with pytest.raises(ValueError):
+        # int64_too is non-unique
+        scalars_df_index.set_index("int64_too")["float64_col"].reindex(
+            index=[5, 1, 3, 99, 1], validate=True
+        )
+
+
+def test_series_reindex_like(scalars_df_index, scalars_pandas_df_index):
+    bf_reindex_target = scalars_df_index["float64_col"].reindex(index=[5, 1, 3, 99, 1])
+    bf_result = (
+        scalars_df_index["int64_too"].reindex_like(bf_reindex_target).to_pandas()
+    )
+
+    pd_reindex_target = scalars_pandas_df_index["float64_col"].reindex(
+        index=[5, 1, 3, 99, 1]
+    )
+    pd_result = scalars_pandas_df_index["int64_too"].reindex_like(pd_reindex_target)
+
+    # Pandas uses int64 instead of Int64 (nullable) dtype.
+    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
+    pd.testing.assert_series_equal(
+        bf_result,
+        pd_result,
+    )
+
+
 def test_where_with_series(scalars_df_index, scalars_pandas_df_index):
     bf_result = (
         scalars_df_index["int64_col"]
diff --git a/tests/unit/ml/test_golden_sql.py b/tests/unit/ml/test_golden_sql.py
new file mode 100644
index 0000000000..8d4932a3c3
--- /dev/null
+++ b/tests/unit/ml/test_golden_sql.py
@@ -0,0 +1,47 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from unittest import mock
+
+import pytest_mock
+
+import bigframes
+from bigframes.ml import linear_model
+import bigframes.pandas as bpd
+
+
+def test_linear_regression_default_fit(mocker: pytest_mock.MockerFixture):
+    mock_session = mock.create_autospec(spec=bigframes.Session)
+
+    mock_X = mock.create_autospec(spec=bpd.DataFrame)
+    mock_X._get_block().expr._session = mock_session
+
+    mock_y = mock.create_autospec(spec=bpd.DataFrame)
+    mock_y.columns.tolist.return_value = ["input_label_column"]
+
+    mock_X.join(mock_y).sql = "input_dataframe_sql"
+
+    # return values we don't care about, but need to provide to continue the program
+    mock_session._start_query.return_value = (None, mock.MagicMock())
+
+    mocker.patch(
+        "bigframes.ml.core._create_temp_model_name", return_value="temp_model_name"
+    )
+
+    model = linear_model.LinearRegression()
+    model.fit(mock_X, mock_y)
+
+    mock_session._start_query.assert_called_once_with(
+        'CREATE TEMP MODEL `temp_model_name`\nOPTIONS(\n  model_type="LINEAR_REG",\n  data_split_method="NO_SPLIT",\n  fit_intercept=True,\n  INPUT_LABEL_COLS=["input_label_column"])\nAS input_dataframe_sql'
+    )
diff --git a/tests/unit/ml/test_pipeline.py b/tests/unit/ml/test_pipeline.py
index 27706a1a07..ed5c621b1d 100644
--- a/tests/unit/ml/test_pipeline.py
+++ b/tests/unit/ml/test_pipeline.py
@@ -18,38 +18,35 @@
 import sklearn.pipeline as sklearn_pipeline  # type: ignore
 import sklearn.preprocessing as sklearn_preprocessing  # type: ignore
 
-import bigframes.ml.compose
-import bigframes.ml.linear_model
-import bigframes.ml.pipeline
-import bigframes.ml.preprocessing
+from bigframes.ml import compose, forecasting, linear_model, pipeline, preprocessing
 
 
 def test_pipeline_repr():
-    pipeline = bigframes.ml.pipeline.Pipeline(
+    pl = pipeline.Pipeline(
         [
             (
                 "preproc",
-                bigframes.ml.compose.ColumnTransformer(
+                compose.ColumnTransformer(
                     [
                         (
                             "onehot",
-                            bigframes.ml.preprocessing.OneHotEncoder(),
+                            preprocessing.OneHotEncoder(),
                             "species",
                         ),
                         (
                             "scale",
-                            bigframes.ml.preprocessing.StandardScaler(),
+                            preprocessing.StandardScaler(),
                             ["culmen_length_mm", "flipper_length_mm"],
                         ),
                     ]
                 ),
             ),
-            ("linreg", bigframes.ml.linear_model.LinearRegression()),
+            ("linreg", linear_model.LinearRegression()),
         ]
     )
 
     assert (
-        pipeline.__repr__()
+        pl.__repr__()
         == """Pipeline(steps=[('preproc',
                  ColumnTransformer(transformers=[('onehot', OneHotEncoder(),
                                                   'species'),
@@ -62,29 +59,29 @@ def test_pipeline_repr():
 
 @pytest.mark.skipif(sklearn_pipeline is None, reason="requires sklearn")
 def test_pipeline_repr_matches_sklearn():
-    bf_pipeline = bigframes.ml.pipeline.Pipeline(
+    bf_pl = pipeline.Pipeline(
         [
             (
                 "preproc",
-                bigframes.ml.compose.ColumnTransformer(
+                compose.ColumnTransformer(
                     [
                         (
                             "onehot",
-                            bigframes.ml.preprocessing.OneHotEncoder(),
+                            preprocessing.OneHotEncoder(),
                             "species",
                         ),
                         (
                             "scale",
-                            bigframes.ml.preprocessing.StandardScaler(),
+                            preprocessing.StandardScaler(),
                             ["culmen_length_mm", "flipper_length_mm"],
                         ),
                     ]
                 ),
             ),
-            ("linreg", bigframes.ml.linear_model.LinearRegression()),
+            ("linreg", linear_model.LinearRegression()),
         ]
     )
-    sk_pipeline = sklearn_pipeline.Pipeline(
+    sk_pl = sklearn_pipeline.Pipeline(
         [
             (
                 "preproc",
@@ -107,4 +104,17 @@ def test_pipeline_repr_matches_sklearn():
         ]
     )
 
-    assert bf_pipeline.__repr__() == sk_pipeline.__repr__()
+    assert bf_pl.__repr__() == sk_pl.__repr__()
+
+
+def test_pipeline_arima_plus_not_implemented():
+    with pytest.raises(NotImplementedError):
+        pipeline.Pipeline(
+            [
+                (
+                    "transform",
+                    preprocessing.StandardScaler(),
+                ),
+                ("estimator", forecasting.ARIMAPlus()),
+            ]
+        )
diff --git a/tests/unit/test_core.py b/tests/unit/test_core.py
index e01638e22e..8f3e0beb0e 100644
--- a/tests/unit/test_core.py
+++ b/tests/unit/test_core.py
@@ -13,9 +13,12 @@
 # limitations under the License.
 
 import ibis
+import ibis.expr.types as ibis_types
 import pandas
 
 import bigframes.core as core
+import bigframes.operations as ops
+import bigframes.operations.aggregations as agg_ops
 
 from . import resources
 
@@ -46,6 +49,42 @@ def test_arrayvalue_constructor_from_ibis_table_adds_all_columns():
     assert len(actual.columns) == 3
 
 
+def test_arrayvalue_with_get_column_type():
+    value = resources.create_arrayvalue(
+        pandas.DataFrame(
+            {
+                "col1": [1, 2, 3],
+                "col2": ["a", "b", "c"],
+                "col3": [0.1, 0.2, 0.3],
+            }
+        ),
+        total_ordering_columns=["col1"],
+    )
+    col1_type = value.get_column_type("col1")
+    col2_type = value.get_column_type("col2")
+    col3_type = value.get_column_type("col3")
+    assert isinstance(col1_type, pandas.Int64Dtype)
+    assert isinstance(col2_type, pandas.StringDtype)
+    assert isinstance(col3_type, pandas.Float64Dtype)
+
+
+def test_arrayvalue_with_get_column():
+    value = resources.create_arrayvalue(
+        pandas.DataFrame(
+            {
+                "col1": [1, 2, 3],
+                "col2": ["a", "b", "c"],
+                "col3": [0.1, 0.2, 0.3],
+            }
+        ),
+        total_ordering_columns=["col1"],
+    )
+    col1 = value.get_column("col1")
+    assert isinstance(col1, ibis_types.Value)
+    assert col1.get_name() == "col1"
+    assert col1.type().is_int64()
+
+
 def test_arrayvalue_to_ibis_expr_with_projection():
     value = resources.create_arrayvalue(
         pandas.DataFrame(
@@ -69,3 +108,133 @@ def test_arrayvalue_to_ibis_expr_with_projection():
     assert actual.columns[0] == "int64_col"
     assert actual.columns[1] == "literals"
     assert actual.columns[2] == "string_col"
+
+
+def test_arrayvalues_to_ibis_expr_with_get_column():
+    value = resources.create_arrayvalue(
+        pandas.DataFrame(
+            {
+                "col1": [1, 2, 3],
+                "col2": ["a", "b", "c"],
+                "col3": [0.1, 0.2, 0.3],
+            }
+        ),
+        total_ordering_columns=["col1"],
+    )
+    expr = value.get_column("col1")
+    assert expr.get_name() == "col1"
+    assert expr.type().is_int64()
+
+
+def test_arrayvalues_to_ibis_expr_with_concat():
+    value = resources.create_arrayvalue(
+        pandas.DataFrame(
+            {
+                "col1": [1, 2, 3],
+                "col2": ["a", "b", "c"],
+                "col3": [0.1, 0.2, 0.3],
+            }
+        ),
+        total_ordering_columns=["col1"],
+    )
+    expr = value.concat([value])
+    actual = expr.to_ibis_expr()
+    assert len(actual.columns) == 3
+    # TODO(ashleyxu, b/299631930): test out the union expression
+    assert actual.columns[0] == "column_0"
+    assert actual.columns[1] == "column_1"
+    assert actual.columns[2] == "column_2"
+
+
+def test_arrayvalues_to_ibis_expr_with_project_unary_op():
+    value = resources.create_arrayvalue(
+        pandas.DataFrame(
+            {
+                "col1": [1, 2, 3],
+                "col2": ["a", "b", "c"],
+                "col3": [0.1, 0.2, 0.3],
+            }
+        ),
+        total_ordering_columns=["col1"],
+    )
+    expr = value.project_unary_op("col1", ops.AsTypeOp("string"))
+    assert value.columns[0].type().is_int64()
+    assert expr.columns[0].type().is_string()
+
+
+def test_arrayvalues_to_ibis_expr_with_project_binary_op():
+    value = resources.create_arrayvalue(
+        pandas.DataFrame(
+            {
+                "col1": [1, 2, 3],
+                "col2": [0.2, 0.3, 0.4],
+                "col3": [0.1, 0.2, 0.3],
+            }
+        ),
+        total_ordering_columns=["col1"],
+    )
+    expr = value.project_binary_op("col2", "col3", ops.add_op, "col4")
+    assert expr.columns[3].type().is_float64()
+    actual = expr.to_ibis_expr()
+    assert len(expr.columns) == 4
+    assert actual.columns[3] == "col4"
+
+
+def test_arrayvalues_to_ibis_expr_with_project_ternary_op():
+    value = resources.create_arrayvalue(
+        pandas.DataFrame(
+            {
+                "col1": [1, 2, 3],
+                "col2": [0.2, 0.3, 0.4],
+                "col3": [True, False, False],
+                "col4": [0.1, 0.2, 0.3],
+            }
+        ),
+        total_ordering_columns=["col1"],
+    )
+    expr = value.project_ternary_op("col2", "col3", "col4", ops.where_op, "col5")
+    assert expr.columns[4].type().is_float64()
+    actual = expr.to_ibis_expr()
+    assert len(expr.columns) == 5
+    assert actual.columns[4] == "col5"
+
+
+def test_arrayvalue_to_ibis_expr_with_aggregate():
+    value = resources.create_arrayvalue(
+        pandas.DataFrame(
+            {
+                "col1": [1, 2, 3],
+                "col2": ["a", "b", "c"],
+                "col3": [0.1, 0.2, 0.3],
+            }
+        ),
+        total_ordering_columns=["col1"],
+    )
+    expr = value.aggregate(
+        aggregations=(("col1", agg_ops.sum_op, "col4"),),
+        by_column_ids=["col1"],
+        dropna=False,
+    )
+    actual = expr.to_ibis_expr()
+    assert len(expr.columns) == 2
+    assert actual.columns[0] == "col1"
+    assert actual.columns[1] == "col4"
+    assert expr.columns[1].type().is_int64()
+
+
+def test_arrayvalue_to_ibis_expr_with_corr_aggregate():
+    value = resources.create_arrayvalue(
+        pandas.DataFrame(
+            {
+                "col1": [1, 2, 3],
+                "col2": ["a", "b", "c"],
+                "col3": [0.1, 0.2, 0.3],
+            }
+        ),
+        total_ordering_columns=["col1"],
+    )
+    expr = value.corr_aggregate(corr_aggregations=[("col1", "col3", "col4")])
+    actual = expr.to_ibis_expr()
+    assert len(expr.columns) == 1
+    assert actual.columns[0] == "col4"
+    assert expr.columns[0].type().is_float64()
diff --git a/third_party/bigframes_vendored/ibis/backends/bigquery/registry.py b/third_party/bigframes_vendored/ibis/backends/bigquery/registry.py
index d209284ab7..a4e61ca0f9 100644
--- a/third_party/bigframes_vendored/ibis/backends/bigquery/registry.py
+++ b/third_party/bigframes_vendored/ibis/backends/bigquery/registry.py
@@ -12,8 +12,20 @@ def _approx_quantiles(translator, op: vendored_ibis_ops.ApproximateMultiQuantile
     return f"APPROX_QUANTILES({arg}, {num_bins})"
 
 
+def _first_non_null_value(translator, op: vendored_ibis_ops.FirstNonNullValue):
+    arg = translator.translate(op.arg)
+    return f"FIRST_VALUE({arg} IGNORE NULLS)"
+
+
+def _last_non_null_value(translator, op: vendored_ibis_ops.LastNonNullValue):
+    arg = translator.translate(op.arg)
+    return f"LAST_VALUE({arg} IGNORE NULLS)"
+
+
 patched_ops = {
     vendored_ibis_ops.ApproximateMultiQuantile: _approx_quantiles,
+    vendored_ibis_ops.FirstNonNullValue: _first_non_null_value,
+    vendored_ibis_ops.LastNonNullValue: _last_non_null_value,
 }
 
 OPERATION_REGISTRY.update(patched_ops)
diff --git a/third_party/bigframes_vendored/ibis/expr/operations/__init__.py b/third_party/bigframes_vendored/ibis/expr/operations/__init__.py
index f3ab753a3b..1612d9c12e 100644
--- a/third_party/bigframes_vendored/ibis/expr/operations/__init__.py
+++ b/third_party/bigframes_vendored/ibis/expr/operations/__init__.py
@@ -1,4 +1,5 @@
 # Contains code from https://github.com/ibis-project/ibis/blob/master/ibis/expr/operations/__init__.py
 from __future__ import annotations
 
+from third_party.bigframes_vendored.ibis.expr.operations.analytic import *  # noqa: F403
 from third_party.bigframes_vendored.ibis.expr.operations.reductions import *  # noqa: F403
diff --git a/third_party/bigframes_vendored/ibis/expr/operations/analytic.py b/third_party/bigframes_vendored/ibis/expr/operations/analytic.py
new file mode 100644
index 0000000000..038987cac9
--- /dev/null
+++ b/third_party/bigframes_vendored/ibis/expr/operations/analytic.py
@@ -0,0 +1,26 @@
+# Contains code from https://github.com/ibis-project/ibis/blob/master/ibis/expr/operations/analytic.py
+
+from __future__ import annotations
+
+from ibis.expr.operations.analytic import Analytic
+import ibis.expr.rules as rlz
+
+
+class FirstNonNullValue(Analytic):
+    """Retrieve the first element."""
+
+    arg = rlz.column(rlz.any)
+    output_dtype = rlz.dtype_like("arg")
+
+
+class LastNonNullValue(Analytic):
+    """Retrieve the last element."""
+
+    arg = rlz.column(rlz.any)
+    output_dtype = rlz.dtype_like("arg")
+
+
+__all__ = [
+    "FirstNonNullValue",
+    "LastNonNullValue",
+]
diff --git a/third_party/bigframes_vendored/pandas/core/frame.py b/third_party/bigframes_vendored/pandas/core/frame.py
index 8c81b23b6c..113c6547a0 100644
--- a/third_party/bigframes_vendored/pandas/core/frame.py
+++ b/third_party/bigframes_vendored/pandas/core/frame.py
@@ -13,7 +13,7 @@
 
 from typing import Iterable, Literal, Mapping, Optional, Sequence, Union
 
-import numpy
+import numpy as np
 
 from bigframes import constants
 from third_party.bigframes_vendored.pandas.core.generic import NDFrame
@@ -56,7 +56,7 @@ def axes(self) -> list:
         return [self.index, self.columns]
 
     @property
-    def values(self) -> numpy.ndarray:
+    def values(self) -> np.ndarray:
         """Return the values of DataFrame in the form of a NumPy array.
 
         Args:
@@ -72,9 +72,7 @@ def values(self) -> numpy.ndarray:
 
     # ----------------------------------------------------------------------
     # IO methods (to / from other formats)
-    def to_numpy(
-        self, dtype=None, copy=False, na_value=None, **kwargs
-    ) -> numpy.ndarray:
+    def to_numpy(self, dtype=None, copy=False, na_value=None, **kwargs) -> np.ndarray:
         """
         Convert the DataFrame to a NumPy array.
 
@@ -154,6 +152,250 @@ def to_parquet(
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def to_dict(
+        self,
+        orient: Literal[
+            "dict", "list", "series", "split", "tight", "records", "index"
+        ] = "dict",
+        into: type[dict] = dict,
+        **kwargs,
+    ) -> dict | list[dict]:
+        """
+        Convert the DataFrame to a dictionary.
+
+        The type of the key-value pairs can be customized with the parameters
+        (see below).
+
+        Args:
+            orient (str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}):
+                Determines the type of the values of the dictionary.
+                'dict' (default) : dict like {column -> {index -> value}}.
+                'list' : dict like {column -> [values]}.
+                'series' : dict like {column -> Series(values)}.
+                split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}.
+                'tight' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values],
+                'index_names' -> [index.names], 'column_names' -> [column.names]}.
+                'records' : list like [{column -> value}, ... , {column -> value}].
+                'index' : dict like {index -> {column -> value}}.
+            into (class, default dict):
+                The collections.abc.Mapping subclass used for all Mappings
+                in the return value.  Can be the actual class or an empty
+                instance of the mapping type you want.  If you want a
+                collections.defaultdict, you must pass it initialized.
+
+            index (bool, default True):
+                Whether to include the index item (and index_names item if `orient`
+                is 'tight') in the returned dictionary. Can only be ``False``
+                when `orient` is 'split' or 'tight'.
+
+        Returns:
+            dict or list of dict: Return a collections.abc.Mapping object representing the DataFrame.
+            The resulting transformation depends on the `orient` parameter.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def to_excel(self, excel_writer, sheet_name: str = "Sheet1", **kwargs) -> None:
+        """
+        Write DataFrame to an Excel sheet.
+
+        To write a single DataFrame to an Excel .xlsx file it is only necessary to
+        specify a target file name. To write to multiple sheets it is necessary to
+        create an `ExcelWriter` object with a target file name, and specify a sheet
+        in the file to write to.
+
+        Multiple sheets may be written to by specifying unique `sheet_name`.
+        With all data written to the file it is necessary to save the changes.
+        Note that creating an `ExcelWriter` object with a file name that already
+        exists will result in the contents of the existing file being erased.
+
+        Args:
+            excel_writer (path-like, file-like, or ExcelWriter object):
+                File path or existing ExcelWriter.
+            sheet_name (str, default 'Sheet1'):
+                Name of sheet which will contain DataFrame.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def to_latex(
+        self, buf=None, columns=None, header=True, index=True, **kwargs
+    ) -> str | None:
+        r"""
+        Render object to a LaTeX tabular, longtable, or nested table.
+
+        Requires ``\usepackage{{booktabs}}``.  The output can be copy/pasted
+        into a main LaTeX document or read from an external file
+        with ``\input{{table.tex}}``.
+
+        Args:
+            buf (str, Path or StringIO-like, optional, default None):
+                Buffer to write to. If None, the output is returned as a string.
+            columns (list of label, optional):
+                The subset of columns to write. Writes all columns by default.
+            header (bool or list of str, default True):
+                Write out the column names. If a list of strings is given,
+                it is assumed to be aliases for the column names.
+            index (bool, default True):
+                Write row names (index).
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def to_records(
+        self, index: bool = True, column_dtypes=None, index_dtypes=None
+    ) -> np.recarray:
+        """
+        Convert DataFrame to a NumPy record array.
+
+        Index will be included as the first field of the record array if
+        requested.
+
+        Args:
+            index (bool, default True):
+                Include index in resulting record array, stored in 'index'
+                field or using the index label, if set.
+            column_dtypes (str, type, dict, default None):
+                If a string or type, the data type to store all columns. If
+                a dictionary, a mapping of column names and indices (zero-indexed)
+                to specific data types.
+            index_dtypes (str, type, dict, default None):
+                If a string or type, the data type to store all index levels. If
+                a dictionary, a mapping of index level names and indices
+                (zero-indexed) to specific data types.
+
+                This mapping is applied only if `index=True`.
+
+        Returns:
+            np.recarray: NumPy ndarray with the DataFrame labels as fields and each row
+            of the DataFrame as entries.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def to_string(
+        self,
+        buf=None,
+        columns: Sequence[str] | None = None,
+        col_space=None,
+        header: bool | Sequence[str] = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        formatters=None,
+        float_format=None,
+        sparsify: bool | None = None,
+        index_names: bool = True,
+        justify: str | None = None,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        show_dimensions: bool = False,
+        decimal: str = ".",
+        line_width: int | None = None,
+        min_rows: int | None = None,
+        max_colwidth: int | None = None,
+        encoding: str | None = None,
+    ):
+        """Render a DataFrame to a console-friendly tabular output.
+
+        Args:
+            buf (str, Path or StringIO-like, optional, default None):
+                Buffer to write to. If None, the output is returned as a string.
+            columns (sequence, optional, default None):
+                The subset of columns to write. Writes all columns by default.
+            col_space (int, list or dict of int, optional):
+                The minimum width of each column.
+            header (bool or sequence, optional):
+                Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
+            index (bool, optional, default True):
+                Whether to print index (row) labels.
+            na_rep (str, optional, default 'NaN'):
+                String representation of NAN to use.
+            formatters (list, tuple or dict of one-param. functions, optional):
+                Formatter functions to apply to columns' elements by position or
+                name.
+                The result of each function must be a unicode string.
+                List/tuple must be of length equal to the number of columns.
+            float_format (one-parameter function, optional, default None):
+                Formatter function to apply to columns' elements if they are
+                floats. The result of this function must be a unicode string.
+            sparsify (bool, optional, default True):
+                Set to False for a DataFrame with a hierarchical index to print
+                every multiindex key at each row.
+            index_names (bool, optional, default True):
+                Prints the names of the indexes.
+            justify (str, default None):
+                How to justify the column labels. If None uses the option from
+                the print configuration (controlled by set_option), 'right' out
+                of the box. Valid values are, 'left', 'right', 'center', 'justify',
+                'justify-all', 'start', 'end', 'inherit', 'match-parent', 'initial',
+                'unset'.
+            max_rows (int, optional):
+                Maximum number of rows to display in the console.
+            min_rows (int, optional):
+                The number of rows to display in the console in a truncated repr
+                (when number of rows is above `max_rows`).
+            max_cols (int, optional):
+                Maximum number of columns to display in the console.
+            show_dimensions (bool, default False):
+                Display DataFrame dimensions (number of rows by number of columns).
+            decimal (str, default '.'):
+                Character recognized as decimal separator, e.g. ',' in Europe.
+            line_width (int, optional):
+                Width to wrap a line in characters.
+            max_colwidth (int, optional):
+                Max width to truncate each column in characters. By default, no limit.
+            encoding (str, default "utf-8"):
+                Set character encoding.
+
+        Returns:
+            str or None: If buf is None, returns the result as a string. Otherwise returns
+            None.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def to_markdown(
+        self,
+        buf=None,
+        mode: str = "wt",
+        index: bool = True,
+        **kwargs,
+    ):
+        """Print DataFrame in Markdown-friendly format.
+
+        Args:
+            buf (str, Path or StringIO-like, optional, default None):
+                Buffer to write to. If None, the output is returned as a string.
+            mode (str, optional):
+                Mode in which file is opened.
+            index (bool, optional, default True):
+                Add index (row) labels.
+            **kwargs
+                These parameters will be passed to `tabulate                 <https://pypi.org/project/tabulate>`_.
+
+        Returns:
+            DataFrame in Markdown-friendly format.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def to_pickle(self, path, **kwargs) -> None:
+        """Pickle (serialize) object to file.
+
+        Args:
+            path (str):
+                File path where the pickled object will be stored.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def to_orc(self, path=None, **kwargs) -> bytes | None:
+        """
+        Write a DataFrame to the ORC format.
+
+        Args:
+            path (str, file-like object or None, default None):
+                If a string, it will be used as Root Directory path
+                when writing a partitioned dataset. By file-like object,
+                we refer to objects with a write() method, such as a file handle
+                (e.g. via builtin open function). If path is None,
+                a bytes object is returned.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     # ----------------------------------------------------------------------
     # Unsorted
 
@@ -185,6 +427,53 @@ def assign(self, **kwargs) -> DataFrame:
     # ----------------------------------------------------------------------
     # Reindexing and alignment
 
+    def reindex(
+        self,
+        labels=None,
+        *,
+        index=None,
+        columns=None,
+        axis=None,
+    ):
+        """Conform DataFrame to new index with optional filling logic.
+
+        Places NA in locations having no value in the previous index. A new object
+        is produced.
+
+        Args:
+            labels (array-like, optional):
+                New labels / index to conform the axis specified by 'axis' to.
+            index (array-like, optional):
+                New labels for the index. Preferably an Index object to avoid
+                duplicating data.
+            columns (array-like, optional):
+                New labels for the columns. Preferably an Index object to avoid
+                duplicating data.
+            axis (int or str, optional):
+                Axis to target. Can be either the axis name ('index', 'columns')
+                or number (0, 1).
+        Returns:
+            DataFrame: DataFrame with changed index.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def reindex_like(self, other):
+        """Return an object with matching indices as other object.
+
+        Conform the object to the same index on all axes. Optional
+        filling logic, placing Null in locations having no value
+        in the previous index.
+
+        Args:
+            other (Object of the same data type):
+                Its row and column indices are used to define the new indices
+                of this object.
+
+        Returns:
+            Series or DataFrame: Same type as caller, but with changed indices on each axis.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def drop(
         self, labels=None, *, axis=0, index=None, columns=None, level=None
     ) -> DataFrame | None:
@@ -276,7 +565,9 @@ def set_index(
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
-    def reorder_levels(self, order: Sequence[int | str]) -> DataFrame:
+    def reorder_levels(
+        self, order: Sequence[int | str], axis: str | int = 0
+    ) -> DataFrame:
         """
         Rearrange index levels using input order. May not drop or duplicate levels.
 
@@ -284,13 +575,33 @@ def reorder_levels(self, order: Sequence[int | str]) -> DataFrame:
             order (list of int or list of str):
                 List representing new level order. Reference level by number
                 (position) or by key (label).
+            axis ({0 or 'index', 1 or 'columns'}, default 0):
+                Where to reorder levels.
 
         Returns:
             DataFrame: DataFrame of rearranged index.
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
-    def droplevel(self, level):
+    def swaplevel(self, i, j, axis: str | int = 0) -> DataFrame:
+        """
+        Swap levels i and j in a :class:`MultiIndex`.
+
+        Default is to swap the two innermost levels of the index.
+
+        Args:
+            i, j (int or str):
+                Levels of the indices to be swapped. Can pass level name as string.
+            axis ({0 or 'index', 1 or 'columns'}, default 0):
+                The axis to swap levels on. 0 or 'index' for row-wise, 1 or
+                'columns' for column-wise.
+
+        Returns:
+            DataFrame: DataFrame with levels swapped in MultiIndex.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def droplevel(self, level, axis: str | int = 0):
         """
         Return DataFrame with requested index / column level(s) removed.
 
@@ -299,6 +610,11 @@ def droplevel(self, level):
                 If a string is given, must be the name of a level
                 If list-like, elements must be names or positional indexes
                 of levels.
+            axis ({0 or 'index', 1 or 'columns'}, default 0):
+                Axis along which the level(s) is removed:
+
+                * 0 or 'index': remove level(s) in column.
+                * 1 or 'columns': remove level(s) in row.
         Returns:
             DataFrame: DataFrame with requested index / column level(s) removed.
         """
@@ -889,6 +1205,54 @@ def rpow(self, other, axis: str | int = "columns") -> DataFrame:
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def combine(
+        self, other, func, fill_value=None, overwrite: bool = True
+    ) -> DataFrame:
+        """Perform column-wise combine with another DataFrame.
+
+        Combines a DataFrame with `other` DataFrame using `func`
+        to element-wise combine columns. The row and column indexes of the
+        resulting DataFrame will be the union of the two.
+
+        Args:
+            other (DataFrame):
+                The DataFrame to merge column-wise.
+            func (function):
+                Function that takes two series as inputs and return a Series or a
+                scalar. Used to merge the two dataframes column by columns.
+            fill_value (scalar value, default None):
+                The value to fill NaNs with prior to passing any column to the
+                merge func.
+            overwrite (bool, default True):
+                If True, columns in `self` that do not exist in `other` will be
+                overwritten with NaNs.
+
+        Returns:
+            DataFrame: Combination of the provided DataFrames.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def combine_first(self, other) -> DataFrame:
+        """
+        Update null elements with value in the same location in `other`.
+
+        Combine two DataFrame objects by filling null values in one DataFrame
+        with non-null values from other DataFrame. The row and column indexes
+        of the resulting DataFrame will be the union of the two. The resulting
+        dataframe contains the 'first' dataframe values and overrides the
+        second one values where both first.loc[index, col] and
+        second.loc[index, col] are not missing values, upon calling
+        first.combine_first(second).
+
+        Args:
+            other (DataFrame):
+                Provided DataFrame to use to fill null values.
+
+        Returns:
+            DataFrame: The result of combining the provided DataFrame with the other object.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     # ----------------------------------------------------------------------
     # Data reshaping
 
@@ -1191,6 +1555,20 @@ def var(self, *, numeric_only: bool = False):
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def skew(self, *, numeric_only: bool = False):
+        """Return unbiased skew over requested axis.
+
+        Normalized by N-1.
+
+        Args:
+            numeric_only (bool, default False):
+                Include only float, int, boolean columns.
+
+        Returns:
+            Series
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def std(self, *, numeric_only: bool = False):
         """Return sample standard deviation over requested axis.
 
@@ -1222,6 +1600,76 @@ def count(self, *, numeric_only: bool = False):
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def nlargest(self, n: int, columns, keep: str = "first"):
+        """
+        Return the first `n` rows ordered by `columns` in descending order.
+
+        Return the first `n` rows with the largest values in `columns`, in
+        descending order. The columns that are not specified are returned as
+        well, but not used for ordering.
+
+        This method is equivalent to
+        ``df.sort_values(columns, ascending=False).head(n)``, but more
+        performant.
+
+        Args:
+            n (int):
+                Number of rows to return.
+            columns (label or list of labels):
+                Column label(s) to order by.
+            keep ({'first', 'last', 'all'}, default 'first'):
+                Where there are duplicate values:
+
+                - ``first`` : prioritize the first occurrence(s)
+                - ``last`` : prioritize the last occurrence(s)
+                - ``all`` : do not drop any duplicates, even it means
+                  selecting more than `n` items.
+
+        Returns:
+            DataFrame: The first `n` rows ordered by the given columns in descending order.
+
+        .. note::
+            This function cannot be used with all column types. For example, when
+            specifying columns with `object` or `category` dtypes, ``TypeError`` is
+            raised.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def nsmallest(self, n: int, columns, keep: str = "first"):
+        """
+        Return the first `n` rows ordered by `columns` in ascending order.
+
+        Return the first `n` rows with the smallest values in `columns`, in
+        ascending order. The columns that are not specified are returned as
+        well, but not used for ordering.
+
+        This method is equivalent to
+        ``df.sort_values(columns, ascending=True).head(n)``, but more
+        performant.
+
+        Args:
+            n (int):
+                Number of rows to return.
+            columns (label or list of labels):
+                Column label(s) to order by.
+            keep ({'first', 'last', 'all'}, default 'first'):
+                Where there are duplicate values:
+
+                - ``first`` : prioritize the first occurrence(s)
+                - ``last`` : prioritize the last occurrence(s)
+                - ``all`` : do not drop any duplicates, even it means
+                  selecting more than `n` items.
+
+        Returns:
+            DataFrame: The first `n` rows ordered by the given columns in ascending order.
+
+        .. note::
+            This function cannot be used with all column types. For example, when
+            specifying columns with `object` or `category` dtypes, ``TypeError`` is
+            raised.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def nunique(self):
         """
         Count number of distinct elements in specified axis.
@@ -1271,6 +1719,25 @@ def cumprod(self) -> DataFrame:
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def diff(
+        self,
+        periods: int = 1,
+    ) -> NDFrame:
+        """First discrete difference of element.
+
+        Calculates the difference of a DataFrame element compared with another
+        element in the DataFrame (default is element in previous row).
+
+        Args:
+            periods (int, default 1):
+                Periods to shift for calculating difference, accepts negative
+                values.
+
+        Returns:
+            bigframes.dataframe.DataFrame: First differences of the Series.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def agg(self, func):
         """
         Aggregate using one or more operations over the specified axis.
diff --git a/third_party/bigframes_vendored/pandas/core/generic.py b/third_party/bigframes_vendored/pandas/core/generic.py
index 56d3b2434f..7d496891b0 100644
--- a/third_party/bigframes_vendored/pandas/core/generic.py
+++ b/third_party/bigframes_vendored/pandas/core/generic.py
@@ -335,6 +335,41 @@ def copy(self):
     # ----------------------------------------------------------------------
     # Action Methods
 
+    def ffill(self, *, limit: Optional[int] = None):
+        """Fill NA/NaN values by propagating the last valid observation to next valid.
+
+        Args:
+            limit : int, default None
+                If method is specified, this is the maximum number of consecutive
+                NaN values to forward/backward fill. In other words, if there is
+                a gap with more than this number of consecutive NaNs, it will only
+                be partially filled. If method is not specified, this is the
+                maximum number of entries along the entire axis where NaNs will be
+                filled. Must be greater than 0 if not None.
+
+
+        Returns:
+            Series/DataFrame or None: Object with missing values filled.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def bfill(self, *, limit: Optional[int] = None):
+        """Fill NA/NaN values by using the next valid observation to fill the gap.
+
+        Args:
+            limit : int, default None
+                If method is specified, this is the maximum number of consecutive
+                NaN values to forward/backward fill. In other words, if there is
+                a gap with more than this number of consecutive NaNs, it will only
+                be partially filled. If method is not specified, this is the
+                maximum number of entries along the entire axis where NaNs will be
+                filled. Must be greater than 0 if not None.
+
+        Returns:
+            Series/DataFrame or None: Object with missing values filled.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def isna(self) -> NDFrame:
         """Detect missing values.
 
@@ -367,6 +402,36 @@ def notna(self) -> NDFrame:
 
     notnull = notna
 
+    def filter(
+        self,
+        items=None,
+        like: str | None = None,
+        regex: str | None = None,
+        axis=None,
+    ) -> NDFrame:
+        """
+        Subset the dataframe rows or columns according to the specified index labels.
+
+        Note that this routine does not filter a dataframe on its
+        contents. The filter is applied to the labels of the index.
+
+        Args:
+            items (list-like):
+                Keep labels from axis which are in items.
+            like (str):
+                Keep labels from axis for which "like in label == True".
+            regex (str (regular expression)):
+                Keep labels from axis for which re.search(regex, label) == True.
+            axis ({0 or 'index', 1 or 'columns', None}, default None):
+                The axis to filter on, expressed either as an index (int)
+                or axis name (str). By default this is the info axis, 'columns' for
+                DataFrame. For `Series` this parameter is unused and defaults to `None`.
+
+        Returns:
+            same type as input object
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def shift(
         self,
         periods: int = 1,
@@ -384,6 +449,30 @@ def shift(
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def pct_change(self, periods: int = 1):
+        """
+        Fractional change between the current and a prior element.
+
+        Computes the fractional change from the immediately previous row by
+        default. This is useful in comparing the fraction of change in a time
+        series of elements.
+
+        .. note::
+
+            Despite the name of this method, it calculates fractional change
+            (also known as per unit change or relative change) and not
+            percentage change. If you need the percentage change, multiply
+            these values by 100.
+
+        Args:
+            periods (int, default 1):
+                Periods to shift for forming percent change.
+
+        Returns:
+            Series or DataFrame: The same type as the calling object.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def rank(
         self,
         axis=0,
diff --git a/third_party/bigframes_vendored/pandas/core/groupby/__init__.py b/third_party/bigframes_vendored/pandas/core/groupby/__init__.py
index 9271da8a5e..7849a3afd5 100644
--- a/third_party/bigframes_vendored/pandas/core/groupby/__init__.py
+++ b/third_party/bigframes_vendored/pandas/core/groupby/__init__.py
@@ -124,6 +124,26 @@ def var(
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def skew(
+        self,
+        *,
+        numeric_only: bool = False,
+    ):
+        """
+        Return unbiased skew within groups.
+
+        Normalized by N-1.
+
+        Args:
+            numeric_only (bool, default False):
+                Include only `float`, `int` or `boolean` data.
+
+        Returns:
+            Series or DataFrame
+                Variance of values within each group.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def sum(
         self,
         numeric_only: bool = False,
diff --git a/third_party/bigframes_vendored/pandas/core/indexes/base.py b/third_party/bigframes_vendored/pandas/core/indexes/base.py
index 404a99809c..864007b774 100644
--- a/third_party/bigframes_vendored/pandas/core/indexes/base.py
+++ b/third_party/bigframes_vendored/pandas/core/indexes/base.py
@@ -21,6 +21,16 @@ def shape(self):
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    @property
+    def nlevels(self) -> int:
+        """Number of levels."""
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    @property
+    def is_unique(self) -> bool:
+        """Return if the index has unique values."""
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def to_numpy(self, dtype):
         """
         A NumPy ndarray representing the values in this Series or Index.
diff --git a/third_party/bigframes_vendored/pandas/core/series.py b/third_party/bigframes_vendored/pandas/core/series.py
index 76fb46a700..c6dd973372 100644
--- a/third_party/bigframes_vendored/pandas/core/series.py
+++ b/third_party/bigframes_vendored/pandas/core/series.py
@@ -758,6 +758,41 @@ def groupby(
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def reindex(self, index=None):
+        """
+        Conform Series to new index with optional filling logic.
+
+        Places NA/NaN in locations having no value in the previous index. A new object
+        is produced unless the new index is equivalent to the current one and
+        ``copy=False``.
+
+        Args:
+            index (array-like, optional):
+                New labels for the index. Preferably an Index object to avoid
+                duplicating data.
+
+        Returns:
+            Series: Series with changed index.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def reindex_like(self, other):
+        """Return an object with matching indices as other object.
+
+        Conform the object to the same index on all axes. Optional
+        filling logic, placing Null in locations having no value
+        in the previous index.
+
+        Args:
+            other (Object of the same data type):
+                Its row and column indices are used to define the new indices
+                of this object.
+
+        Returns:
+            Series or DataFrame: Same type as caller, but with changed indices on each axis.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def drop(
         self, labels=None, *, axis=0, index=None, columns=None, level=None
     ) -> Series | None:
@@ -790,7 +825,7 @@ def drop(
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
-    def reorder_levels(self, order: Sequence) -> Series:
+    def reorder_levels(self, order: Sequence, axis) -> Series:
         """
         Rearrange index levels using input order.
 
@@ -800,12 +835,31 @@ def reorder_levels(self, order: Sequence) -> Series:
             order (list of int representing new level order):
                 Reference level by number or key.
 
+            axis ({0 or 'index', 1 or 'columns'}, default 0):
+                For `Series` this parameter is unused and defaults to 0.
+
+
         Returns:
             type of caller (new object)
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
-    def droplevel(self, level):
+    def swaplevel(self, i, j):
+        """
+        Swap levels i and j in a `MultiIndex`.
+
+        Default is to swap the two innermost levels of the index.
+
+        Args:
+            i, j (int or str):
+                Levels of the indices to be swapped. Can pass level name as string.
+
+        Returns:
+            Series: Series with levels swapped in MultiIndex
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
+    def droplevel(self, level, axis):
         """
         Return Series with requested index / column level(s) removed.
 
@@ -815,6 +869,9 @@ def droplevel(self, level):
                 If list-like, elements must be names or positional indexes
                 of levels.
 
+            axis ({0 or 'index', 1 or 'columns'}, default 0):
+                For `Series` this parameter is unused and defaults to 0.
+
         Returns:
             Series with requested index / column level(s) removed.
         """
@@ -836,6 +893,69 @@ def fillna(
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def replace(
+        self,
+        to_replace,
+        value=None,
+    ) -> Series | None:
+        """
+        Replace values given in `to_replace` with `value`.
+
+        Values of the Series/DataFrame are replaced with other values dynamically.
+        This differs from updating with ``.loc`` or ``.iloc``, which require
+        you to specify a location to update with some value.
+
+        Args:
+            to_replace (str, regex, list, int, float or None):
+                How to find the values that will be replaced.
+
+                * numeric, str or regex:
+
+                    - numeric: numeric values equal to `to_replace` will be
+                      replaced with `value`
+                    - str: string exactly matching `to_replace` will be replaced
+                      with `value`
+                    - regex: regexs matching `to_replace` will be replaced with
+                      `value`
+
+                * list of str, regex, or numeric:
+
+                    - First, if `to_replace` and `value` are both lists, they
+                      **must** be the same length.
+                    - Second, if ``regex=True`` then all of the strings in **both**
+                      lists will be interpreted as regexs otherwise they will match
+                      directly. This doesn't matter much for `value` since there
+                      are only a few possible substitution regexes you can use.
+                    - str, regex and numeric rules apply as above.
+
+            value (scalar, default None):
+                Value to replace any values matching `to_replace` with.
+                For a DataFrame a dict of values can be used to specify which
+                value to use for each column (columns not in the dict will not be
+                filled). Regular expressions, strings and lists or dicts of such
+                objects are also allowed.
+            regex (bool, default False):
+                Whether to interpret `to_replace` and/or `value` as regular
+                expressions. If this is ``True`` then `to_replace` *must* be a
+                string.
+
+        Returns:
+            Series/DataFrame: Object after replacement.
+
+        Raises:
+            TypeError:
+                * If `to_replace` is not a scalar, array-like, ``dict``, or ``None``
+                * If `to_replace` is a ``dict`` and `value` is not a ``list``,
+                  ``dict``, ``ndarray``, or ``Series``
+                * If `to_replace` is ``None`` and `regex` is not compilable
+                  into a regular expression or is a list, dict, ndarray, or
+                  Series.
+                * When replacing multiple ``bool`` or ``datetime64`` objects and
+                  the arguments to `to_replace` does not match the type of the
+                  value being replaced
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def dropna(self, *, axis=0, inplace: bool = False, how=None) -> Series:
         """
         Return a new Series with missing values removed.
diff --git a/third_party/bigframes_vendored/sklearn/base.py b/third_party/bigframes_vendored/sklearn/base.py
index 847ad06f75..42868ce51f 100644
--- a/third_party/bigframes_vendored/sklearn/base.py
+++ b/third_party/bigframes_vendored/sklearn/base.py
@@ -144,6 +144,7 @@ def fit_transform(self, X, y=None):
             bigframes.dataframe.DataFrame: DataFrame of shape (n_samples, n_features_new)
                 Transformed DataFrame.
         """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
 
 class MetaEstimatorMixin:
diff --git a/third_party/bigframes_vendored/sklearn/cluster/_kmeans.py b/third_party/bigframes_vendored/sklearn/cluster/_kmeans.py
index ff1c04edbe..ece62dc147 100644
--- a/third_party/bigframes_vendored/sklearn/cluster/_kmeans.py
+++ b/third_party/bigframes_vendored/sklearn/cluster/_kmeans.py
@@ -49,7 +49,6 @@ def fit(
         self,
         X,
         y=None,
-        transforms: Optional[List[str]] = None,
     ):
         """Compute k-means clustering.
 
@@ -58,10 +57,6 @@ def fit(
                 DataFrame of shape (n_samples, n_features). Training data.
             y (default None):
                 Not used, present here for API consistency by convention.
-            transforms (Optional[List[str]], default None):
-                Do not use. Internal param to be deprecated.
-                Use bigframes.ml.pipeline instead.
-
 
         Returns:
             KMeans: Fitted Estimator.
diff --git a/third_party/bigframes_vendored/sklearn/decomposition/_pca.py b/third_party/bigframes_vendored/sklearn/decomposition/_pca.py
index 85feab0024..97fee5a501 100644
--- a/third_party/bigframes_vendored/sklearn/decomposition/_pca.py
+++ b/third_party/bigframes_vendored/sklearn/decomposition/_pca.py
@@ -49,10 +49,6 @@ def fit(self, X, y=None):
             y (default None):
                 Ignored.
 
-            transforms (Optional[List[str]], default None):
-                Do not use. Internal param to be deprecated.
-                Use bigframes.ml.pipeline instead.
-
         Returns:
             PCA: Fitted estimator.
         """
diff --git a/third_party/bigframes_vendored/sklearn/ensemble/_forest.py b/third_party/bigframes_vendored/sklearn/ensemble/_forest.py
index 79224a772d..6be41bf9aa 100644
--- a/third_party/bigframes_vendored/sklearn/ensemble/_forest.py
+++ b/third_party/bigframes_vendored/sklearn/ensemble/_forest.py
@@ -54,10 +54,6 @@ def fit(self, X, y):
                 Series or DataFrame of shape (n_samples,) or (n_samples, n_targets).
                 Target values. Will be cast to X's dtype if necessary.
 
-            transforms (Optional[List[str]], default None):
-                Do not use. Internal param to be deprecated.
-                Use bigframes.ml.pipeline instead.
-
 
         Returns:
             Fitted Estimator.
diff --git a/third_party/bigframes_vendored/sklearn/linear_model/_base.py b/third_party/bigframes_vendored/sklearn/linear_model/_base.py
index 8141da4e3b..81b4fca157 100644
--- a/third_party/bigframes_vendored/sklearn/linear_model/_base.py
+++ b/third_party/bigframes_vendored/sklearn/linear_model/_base.py
@@ -74,7 +74,6 @@ def fit(
         self,
         X,
         y,
-        transforms: Optional[List[str]] = None,
     ):
         """Fit linear model.
 
@@ -86,10 +85,6 @@ def fit(
                 Series or DataFrame of shape (n_samples,) or (n_samples, n_targets).
                 Target values. Will be cast to X's dtype if necessary.
 
-            transforms (Optional[List[str]], default None):
-                Do not use. Internal param to be deprecated.
-                Use bigframes.ml.pipeline instead.
-
         Returns:
             LinearRegression: Fitted Estimator.
         """
diff --git a/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py b/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py
index a06035eef6..133dc4498e 100644
--- a/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py
+++ b/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py
@@ -37,7 +37,6 @@ def fit(
         self,
         X,
         y,
-        transforms: Optional[List[str]] = None,
     ):
         """Fit the model according to the given training data.
 
@@ -50,10 +49,6 @@ def fit(
             y (bigframes.dataframe.DataFrame or bigframes.series.Series):
                 DataFrame of shape (n_samples,). Target vector relative to X.
 
-            transforms (Optional[List[str]], default None):
-                Do not use. Internal param to be deprecated.
-                Use bigframes.ml.pipeline instead.
-
 
         Returns:
             LogisticRegression: Fitted Estimator.
diff --git a/third_party/bigframes_vendored/sklearn/preprocessing/_data.py b/third_party/bigframes_vendored/sklearn/preprocessing/_data.py
index d013043467..89981e34c0 100644
--- a/third_party/bigframes_vendored/sklearn/preprocessing/_data.py
+++ b/third_party/bigframes_vendored/sklearn/preprocessing/_data.py
@@ -8,10 +8,10 @@
 # License: BSD 3 clause
 
 from bigframes import constants
-from third_party.bigframes_vendored.sklearn.base import BaseEstimator
+from third_party.bigframes_vendored.sklearn.base import BaseEstimator, TransformerMixin
 
 
-class StandardScaler(BaseEstimator):
+class StandardScaler(BaseEstimator, TransformerMixin):
     """Standardize features by removing the mean and scaling to unit variance.
 
     The standard score of a sample `x` is calculated as:z = (x - u) / s
@@ -28,30 +28,23 @@ class StandardScaler(BaseEstimator):
     machine learning estimators: they might behave badly if the
     individual features do not more or less look like standard normally
     distributed data (e.g. Gaussian with 0 mean and unit variance).
-    """
-
-    def fit(self, X):
-        """Compute the mean and std to be used for later scaling.
 
-        Examples:
+    Examples:
 
         .. code-block::
 
             from bigframes.ml.preprocessing import StandardScaler
+            import bigframes.pandas as bpd
 
-            enc = StandardScaler()
-            X = [['Male', 1], ['Female', 3], ['Female', 2]]
-            enc.fit(X)
-
-        Examples:
-
-        .. code-block::
-
-            from bigframes.ml import StandardScaler
+            scaler = StandardScaler()
+            data = bpd.DataFrame({"a": [0, 0, 1, 1], "b":[0, 0, 1, 1]})
+            scaler.fit(data)
+            print(scaler.transform(data))
+            print(scaler.transform(bpd.DataFrame({"a": [2], "b":[2]})))
+    """
 
-            enc = StandardScaler()
-            X = [['Male', 1], ['Female', 3], ['Female', 2]]
-            enc.fit(X)
+    def fit(self, X):
+        """Compute the mean and std to be used for later scaling.
 
         Args:
             X (bigframes.dataframe.DataFrame or bigframes.series.Series):
diff --git a/third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py b/third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py
index b1cf17e539..b0f0df8e15 100644
--- a/third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py
+++ b/third_party/bigframes_vendored/sklearn/preprocessing/_encoder.py
@@ -37,12 +37,8 @@ class OneHotEncoder(BaseEstimator):
             when considering infrequent categories. If there are infrequent categories,
             max_categories includes the category representing the infrequent categories along with the frequent categories.
             Default None, set limit to 1,000,000.
-    """
-
-    def fit(self, X):
-        """Fit OneHotEncoder to X.
 
-        Examples:
+    Examples:
 
         Given a dataset with two features, we let the encoder find the unique
         values per feature and transform the data to a binary one-hot encoding.
@@ -50,10 +46,16 @@ def fit(self, X):
         .. code-block::
 
             from bigframes.ml.preprocessing import OneHotEncoder
+            import bigframes.pandas as bpd
 
             enc = OneHotEncoder()
-            X = [['Male', 1], ['Female', 3], ['Female', 2]]
+            X = bpd.DataFrame({"a": ["Male", "Female", "Female"], "b": ["1", "3", "2"]})
             enc.fit(X)
+            print(enc.transform(bpd.DataFrame({"a": ["Female", "Male"], "b": ["1", "4"]})))
+    """
+
+    def fit(self, X):
+        """Fit OneHotEncoder to X.
 
         Args:
             X (bigframes.dataframe.DataFrame or bigframes.series.Series):
diff --git a/third_party/bigframes_vendored/xgboost/sklearn.py b/third_party/bigframes_vendored/xgboost/sklearn.py
index 620c87fa3d..b7b43b85a3 100644
--- a/third_party/bigframes_vendored/xgboost/sklearn.py
+++ b/third_party/bigframes_vendored/xgboost/sklearn.py
@@ -37,10 +37,6 @@ def fit(self, X, y):
                 DataFrame of shape (n_samples,) or (n_samples, n_targets).
                 Target values. Will be cast to X's dtype if necessary.
 
-            transforms (Optional[List[str]], default None):
-                Do not use. Internal param to be deprecated.
-                Use bigframes.ml.pipeline instead.
-
         Returns:
             XGBModel: Fitted Estimator.
         """