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updates to remove deprecation warnings
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‎docs/Winpython_checker.ipynb

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@@ -10,7 +10,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import warnings\n",
@@ -23,10 +25,12 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"%matplotlib widget\n",
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"# use %matplotlib widget for the adventurous"
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]
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},
@@ -56,7 +60,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# checking Numba JIT toolchain\n",
@@ -69,7 +75,7 @@
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"\n",
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"from numba import jit\n",
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"\n",
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"@jit\n",
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"@jit(nopython=True)\n",
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"def create_fractal(min_x, max_x, min_y, max_y, image, iters , mandelx):\n",
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" height = image.shape[0]\n",
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" width = image.shape[1]\n",
@@ -83,7 +89,7 @@
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" color = mandelx(real, imag, iters)\n",
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" image[y, x] = color\n",
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"\n",
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"@jit\n",
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"@jit(nopython=True)\n",
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"def mandel(x, y, max_iters):\n",
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" c = complex(x, y)\n",
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" z = 0.0j\n",
@@ -97,7 +103,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Numba speed\n",
@@ -122,7 +130,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Cython + Mingwpy compiler toolchain test\n",
@@ -132,7 +142,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%%cython -a\n",
@@ -166,7 +178,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#Cython speed\n",
@@ -189,7 +203,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Matplotlib 3.4.1\n",
@@ -225,7 +241,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Seaborn\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# altair-example \n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# temporary warning removal\n",
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"import warnings\n",
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"import matplotlib as mpl\n",
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"warnings.filterwarnings(\"ignore\", category=mpl.cbook.MatplotlibDeprecationWarning)\n",
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"#warnings.filterwarnings(\"ignore\", category=mpl.cbook.MatplotlibDeprecationWarning)\n",
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"warnings.filterwarnings(\"ignore\", category=mpl.MatplotlibDeprecationWarning)\n",
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"# Holoviews\n",
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"# for more example, see http://holoviews.org/Tutorials/index.html\n",
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"import numpy as np\n",
@@ -281,7 +304,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Bokeh 0.12.5 \n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Datashader (holoviews+Bokeh)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"np.random.seed(1)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"ropts = dict(colorbar=True, tools=[\"hover\"], width=350)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#bqplot\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# ipyleaflet (javascript library usage)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"dc.on_draw(handle_draw)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%matplotlib widget\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# plotnine: giving a taste of ggplot of R langage (formerly we were using ggpy)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Audio Example : https://github.com/ipython/ipywidgets/blob/master/examples/Beat%20Frequencies.ipynb\n",
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"%matplotlib inline\n",
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"%matplotlib widget\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from ipywidgets import interactive\n",
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"outputs": [],
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"source": [
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"# Networks graph Example : https://github.com/ipython/ipywidgets/blob/master/examples/Exploring%20Graphs.ipynb\n",
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"%matplotlib inline\n",
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"%matplotlib widget\n",
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"from ipywidgets import interact\n",
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"import matplotlib.pyplot as plt\n",
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"import networkx as nx\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#Pandas \n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"idx = pd.date_range('2000', '2005', freq='d', closed='left')\n",
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"idx = pd.date_range('2000', '2005', freq='d', inclusive='left')\n",
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"datas = pd.DataFrame({'Color': [ 'green' if x> 1 else 'red' for x in np.random.randn(len(idx))], \n",
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" 'Measure': np.random.randn(len(idx)), 'Year': idx.year},\n",
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" index=idx.date)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"datas.query('Measure > 0').groupby(['Color','Year']).size().unstack()"
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# checking sympy \n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# checking Ipython-sql, sqlparse, SQLalchemy\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%%sql sqlite:///.baresql.db\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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},
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"outputs": [],
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"source": [
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"# checking baresql\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"outputs": [],
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"source": [
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"# Transfering Datas to sqlite, doing transformation in sql, going back to Pandas and Matplotlib\n",
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"# checking db.py\n",
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{
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"outputs": [],
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"db.tables"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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"version": "3.10.11"
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},
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"widgets": {
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"state": {

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