diff --git a/Deployment/Linear_regression/requirements.txt b/Deployment/Linear_regression/requirements.txt
index 042b208..8cfc955 100644
--- a/Deployment/Linear_regression/requirements.txt
+++ b/Deployment/Linear_regression/requirements.txt
@@ -1,4 +1,4 @@
-numpy==1.16.3
+numpy==1.22.0
 pandas==0.24.2
 requests==2.22.0
 scikit-learn==0.21.2
diff --git a/Deployment/rnn_app/requirements.txt b/Deployment/rnn_app/requirements.txt
index 4fb6913..cee79e4 100644
--- a/Deployment/rnn_app/requirements.txt
+++ b/Deployment/rnn_app/requirements.txt
@@ -1,4 +1,4 @@
-numpy==1.16.3
+numpy==1.22.0
 pandas==0.24.2
 
 requests==2.22.0
diff --git a/Images/Height_Weight_file_picture.PNG b/Images/Height_Weight_file_picture.PNG
new file mode 100644
index 0000000..ca1623a
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diff --git a/Images/ML-DS-cycle-1.png b/Images/ML-DS-cycle-1.png
new file mode 100644
index 0000000..40b6f21
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diff --git a/Images/pandas-site.PNG b/Images/pandas-site.PNG
new file mode 100644
index 0000000..09eecfe
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diff --git a/Memory-profiling/Readme.md b/Memory-profiling/Readme.md
new file mode 100644
index 0000000..0c7f946
--- /dev/null
+++ b/Memory-profiling/Readme.md
@@ -0,0 +1 @@
+## Memory-profiling machine learning code
diff --git a/Memory-profiling/Scalene/Readme.md b/Memory-profiling/Scalene/Readme.md
new file mode 100644
index 0000000..e80142f
--- /dev/null
+++ b/Memory-profiling/Scalene/Readme.md
@@ -0,0 +1,29 @@
+## [Scalene](https://github.com/plasma-umass/scalene)
+
+### Install
+`pip install scalene`
+
+### Run (on CLI)
+`$ scalene <yourapp.py>`
+
+### Example output
+
+If you run the `mlp.py`
+
+`$ scalene mlp.py`
+
+You may see something like,
+
+![scalene-1](https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Memory-profiling/Scalene/scalene-1.PNG)
+
+### Features
+Here are some of the cool features of Scalene. Most of them are self-explanatory and can be gauged from the screenshot above,
+
+- **Lines or functions**: Reports information both for entire functions and for every independent code line
+- **Threads**: It supports Python threads.
+- **Multiprocessing**: supports use of the multiprocessing library
+- **Python vs. C time**: Scalene breaks out time spent in Python vs. native code (e.g., libraries)
+- **System time**: It distinguishes system time (e.g., sleeping or performing I/O operations)
+- **GPU**: It also can report the time spent on an NVIDIA GPU (if present)
+- **Copy volume**: It reports MBs of data being copied per second 
+- **Detects leaks**: Scalene can automatically pinpoint lines responsible for likely memory leaks!
diff --git a/Memory-profiling/Scalene/linearmodel.py b/Memory-profiling/Scalene/linearmodel.py
new file mode 100644
index 0000000..b4b3e23
--- /dev/null
+++ b/Memory-profiling/Scalene/linearmodel.py
@@ -0,0 +1,52 @@
+import pandas as pd 
+import pickle
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.datasets import make_regression
+
+NUM_FEATURES = 10
+NUM_SAMPLES = 1000
+
+# Make data
+def make_data():
+    X,y = make_regression(n_samples=NUM_SAMPLES,n_features=NUM_FEATURES,
+                      n_informative=NUM_FEATURES,noise=0.5)
+    data = pd.DataFrame(X,columns=['X'+str(i) for i in range(1,NUM_FEATURES+1)],dtype=np.float16)
+    data['y']=np.array(y,dtype=np.float16)
+    return data
+
+# Test/Train
+def test_train(data):
+    X_train,y_train = data.iloc[:int(NUM_SAMPLES/2)].drop(['y'],axis=1),data.iloc[:int(NUM_SAMPLES/2)]['y']
+    X_test,y_test = data.iloc[int(NUM_SAMPLES/2):].drop(['y'],axis=1),data.iloc[int(NUM_SAMPLES/2):]['y']
+    return (X_train,y_train,X_test,y_test)
+
+# Fitting
+def fitting(X_train,y_train):
+    lm = LinearRegression(n_jobs=1)
+    lm.fit(X_train,y_train)
+    del X_train
+    del y_train
+    return lm
+
+# Saving model
+def save(lm):
+    with open('LinearModel.sav',mode='wb') as f:
+        pickle.dump(lm,f)
+        
+def model_run(model,testfile):
+    """
+    Loads and runs a sklearn linear model
+    """
+    lm = pickle.load(open(model, 'rb'))
+    X_test = pd.read_csv(testfile)
+    _= lm.predict(X_test)
+    return None
+
+if __name__ == '__main__':
+    data = make_data()
+    X_train,y_train,X_test,y_test = test_train(data)
+    #X_test.to_csv("Test.csv",index=False)
+    lm = fitting(X_train,y_train)
+    save(lm)
+    model_run('LinearModel.sav','Test.csv')
\ No newline at end of file
diff --git a/Memory-profiling/Scalene/mlp.py b/Memory-profiling/Scalene/mlp.py
new file mode 100644
index 0000000..9b2d534
--- /dev/null
+++ b/Memory-profiling/Scalene/mlp.py
@@ -0,0 +1,52 @@
+import pandas as pd 
+import pickle
+import numpy as np
+from sklearn.neural_network import MLPRegressor
+from sklearn.datasets import make_regression
+
+NUM_FEATURES = 10
+NUM_SAMPLES = 1000
+
+# Make data
+def make_data():
+    X,y = make_regression(n_samples=NUM_SAMPLES,n_features=NUM_FEATURES,
+                      n_informative=NUM_FEATURES,noise=0.5)
+    data = pd.DataFrame(X,columns=['X'+str(i) for i in range(1,NUM_FEATURES+1)],dtype=np.float16)
+    data['y']=np.array(y,dtype=np.float16)
+    return data
+
+# Test/Train
+def test_train(data):
+    X_train,y_train = data.iloc[:int(NUM_SAMPLES/2)].drop(['y'],axis=1),data.iloc[:int(NUM_SAMPLES/2)]['y']
+    X_test,y_test = data.iloc[int(NUM_SAMPLES/2):].drop(['y'],axis=1),data.iloc[int(NUM_SAMPLES/2):]['y']
+    return (X_train,y_train,X_test,y_test)
+
+# Fitting
+def fitting(X_train,y_train):
+    mlp = MLPRegressor(max_iter=50)
+    mlp.fit(X_train,y_train)
+    del X_train
+    del y_train
+    return mlp
+
+# Saving model
+def save(mlp):
+    with open('MultiLayerPerceptron.sav',mode='wb') as f:
+        pickle.dump(mlp,f)
+        
+def model_run(model,testfile):
+    """
+    Loads and runs a sklearn linear model
+    """
+    mlp = pickle.load(open(model, 'rb'))
+    X_test = pd.read_csv(testfile)
+    _= mlp.predict(X_test)
+    return None
+
+if __name__ == '__main__':
+    data = make_data()
+    X_train,y_train,X_test,y_test = test_train(data)
+    X_test.to_csv("Test.csv",index=False)
+    mlp = fitting(X_train,y_train)
+    save(mlp)
+    model_run('MultiLayerPerceptron.sav','Test.csv')
\ No newline at end of file
diff --git a/Memory-profiling/Scalene/scalene-1.PNG b/Memory-profiling/Scalene/scalene-1.PNG
new file mode 100644
index 0000000..6324e41
Binary files /dev/null and b/Memory-profiling/Scalene/scalene-1.PNG differ
diff --git a/Pandas and Numpy/Pandas_iteration.ipynb b/Pandas and Numpy/Pandas_iteration.ipynb
new file mode 100644
index 0000000..675712b
--- /dev/null
+++ b/Pandas and Numpy/Pandas_iteration.ipynb	
@@ -0,0 +1,1074 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d860a88f-a209-4502-a326-df204702cdcd",
+   "metadata": {},
+   "source": [
+    "# Iteration with Pandas\n",
+    "\n",
+    "## [Dr. Tirthajyoti Sarkar](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7/), Fremont, CA\n",
+    "\n",
+    "As data scientists, all of us have been there.\n",
+    "\n",
+    "We are given a large Pandas DataFrame and asked to check some relationships between various fields in the columns - in a row-by-row fashion. It could be some logical operation or some sophisticated mathematical transformation on the raw data.\n",
+    "\n",
+    "Essentially, it is a **simple case of iterating over the rows of the DataFrame** and doing some processing at each iteration.\n",
+    "\n",
+    "However, it **may not be that simple in terms of choosing the most efficient method** of executing this type of task. You might already have a hunch that a simple for-loop is going to be quite inefficient for this iteration task. \n",
+    "\n",
+    "And, you will be absolutely right. So, what can we do to avoid such speed pitfall?\n",
+    "\n",
+    "This type of knowledge is critical to practice what we call [\"Productive Data Science\"](https://medium.com/productive-data-science/why-and-how-should-you-learn-productive-data-science-53377b473f37)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1c6f700f-9cf9-4793-872a-7e60c6c5d67b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np, pandas as pd, matplotlib.pyplot as plt\n",
+    "from tqdm import tqdm\n",
+    "from time import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "03e83313-dddd-4335-965e-e1c742795156",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>95</td>\n",
+       "      <td>11</td>\n",
+       "      <td>81</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>63</td>\n",
+       "      <td>87</td>\n",
+       "      <td>75</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>77</td>\n",
+       "      <td>40</td>\n",
+       "      <td>4</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40</td>\n",
+       "      <td>60</td>\n",
+       "      <td>92</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>93</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    A   B   C   D\n",
+       "0  95  11  81  70\n",
+       "1  63  87  75   9\n",
+       "2  77  40   4  63\n",
+       "3  40  60  92  64\n",
+       "4   5  12  93  40"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.seed(101)\n",
+    "df = pd.DataFrame(np.random.randint(0,100,size=(100000, 4)), \n",
+    "                  columns=list('ABCD'),dtype=np.int16)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cb40c948-489c-41ec-b454-b41dfb2689ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 100000 entries, 0 to 99999\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column  Non-Null Count   Dtype\n",
+      "---  ------  --------------   -----\n",
+      " 0   A       100000 non-null  int16\n",
+      " 1   B       100000 non-null  int16\n",
+      " 2   C       100000 non-null  int16\n",
+      " 3   D       100000 non-null  int16\n",
+      "dtypes: int16(4)\n",
+      "memory usage: 781.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info(memory_usage=\"deep\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2b863004-8a6b-423a-81a8-e8dd77b4d2dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>49.562640</td>\n",
+       "      <td>49.533820</td>\n",
+       "      <td>49.477140</td>\n",
+       "      <td>49.495560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>28.885105</td>\n",
+       "      <td>28.907816</td>\n",
+       "      <td>28.789594</td>\n",
+       "      <td>28.849037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>74.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   A              B              C              D\n",
+       "count  100000.000000  100000.000000  100000.000000  100000.000000\n",
+       "mean       49.562640      49.533820      49.477140      49.495560\n",
+       "std        28.885105      28.907816      28.789594      28.849037\n",
+       "min         0.000000       0.000000       0.000000       0.000000\n",
+       "25%        25.000000      24.000000      25.000000      24.000000\n",
+       "50%        50.000000      50.000000      50.000000      50.000000\n",
+       "75%        75.000000      75.000000      74.000000      74.000000\n",
+       "max        99.000000      99.000000      99.000000      99.000000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fcaa5a5-e4a7-4417-a00a-f71e49de79fe",
+   "metadata": {},
+   "source": [
+    "## Most inefficient _for_ loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "35667490-c0ce-49be-8005-5181d71d656f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:32.22 seconds\n",
+      "Count: 49769\n"
+     ]
+    }
+   ],
+   "source": [
+    "count=0\n",
+    "t1=time()\n",
+    "for i in range(len(df)):\n",
+    "    if df.iloc[i]['A']+df.iloc[i]['B'] > df.iloc[i]['C']+df.iloc[i]['D']:\n",
+    "        count+=1\n",
+    "t2=time()\n",
+    "delt1 = round((t2-t1),2)\n",
+    "print(f\"Time:{delt1} seconds\")\n",
+    "print(\"Count:\",count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bf463cd-e887-4257-ad58-8f3cda024101",
+   "metadata": {},
+   "source": [
+    "## Comparing `iterrows()` and `df.values` - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "37ecedc8-fd98-4e6c-a0c3-14a5a2a7324e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:6.91 seconds\n",
+      "Count: 49769\n"
+     ]
+    }
+   ],
+   "source": [
+    "count=0\n",
+    "t1=time()\n",
+    "for idx, row in df.iterrows():\n",
+    "    if row['A']+row['B'] > (row['C']+row['D']):\n",
+    "        count+=1\n",
+    "t2=time()\n",
+    "delt1 = round((t2-t1),2)\n",
+    "print(f\"Time:{delt1} seconds\")\n",
+    "print(\"Count:\",count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cc1c3e1d-6d2b-4f1b-908e-6af8a0a2ff8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:0.112 seconds\n",
+      "Count: 49769\n"
+     ]
+    }
+   ],
+   "source": [
+    "count=0\n",
+    "t1=time()\n",
+    "for row in df.values:\n",
+    "    if row[0]+row[1] > (row[2]+row[3]):\n",
+    "        count+=1\n",
+    "t2=time()\n",
+    "delt2 = round((t2-t1),3)\n",
+    "print(f\"Time:{delt2} seconds\")\n",
+    "print(\"Count:\",count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a09a19c8-df6c-4681-a40e-ba74006609c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.values is 61.7 times faster\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"df.values is {round(delt1/delt2,2)} times faster\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41979a9d-ee10-4fc4-a614-d75bd907bd27",
+   "metadata": {},
+   "source": [
+    "## Comparing `iterrows()` and `df.values` - 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6b5ccfa4-1a3d-462a-ac1a-14778ace0194",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:8.05 seconds\n",
+      "Count: 35886\n"
+     ]
+    }
+   ],
+   "source": [
+    "count=0\n",
+    "t1=time()\n",
+    "for idx, row in df.iterrows():\n",
+    "    if row['A']+row['B'] > 1.25*(row['C']+row['D']):\n",
+    "        count+=1\n",
+    "t2=time()\n",
+    "delt1 = round((t2-t1),2)\n",
+    "print(f\"Time:{delt1} seconds\")\n",
+    "print(\"Count:\",count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "34e52057-5ef4-4b14-997d-3ef3337d8ce7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:0.546 seconds\n",
+      "Count: 35886\n"
+     ]
+    }
+   ],
+   "source": [
+    "count=0\n",
+    "t1=time()\n",
+    "for row in df.values:\n",
+    "    if row[0]+row[1] > 1.25*(row[2]+row[3]):\n",
+    "        count+=1\n",
+    "t2=time()\n",
+    "delt2 = round((t2-t1),3)\n",
+    "print(f\"Time:{delt2} seconds\")\n",
+    "print(\"Count:\",count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d76203cf-a0a1-40db-97cc-591cabd210a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.values is 14.74 times faster\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"df.values is {round(delt1/delt2,2)} times faster\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f00b66c-e91c-4390-bb48-7edc9f785144",
+   "metadata": {},
+   "source": [
+    "## Comparing `iterrows()` and `df.values` - 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e619a3c6-712a-49a8-b47b-4eece6205e97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:8.76 seconds\n",
+      "Count: 9202\n"
+     ]
+    }
+   ],
+   "source": [
+    "count=0\n",
+    "t1=time()\n",
+    "for idx, row in df.iterrows():\n",
+    "    if np.log(1+row['A']+row['B']) > np.sqrt(0.5*(row['C']+row['D'])):\n",
+    "        count+=1\n",
+    "t2=time()\n",
+    "delt1 = round((t2-t1),2)\n",
+    "print(f\"Time:{delt1} seconds\")\n",
+    "print(\"Count:\",count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6163d0dc-4be7-4533-90a2-06d6a997fdb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:0.962 seconds\n",
+      "Count: 9202\n"
+     ]
+    }
+   ],
+   "source": [
+    "count=0\n",
+    "t1=time()\n",
+    "for row in df.values:\n",
+    "    if np.log(1+row[0]+row[1]) > np.sqrt(0.5*(row[2]+row[3])):\n",
+    "        count+=1\n",
+    "t2=time()\n",
+    "delt2 = round((t2-t1),3)\n",
+    "print(f\"Time:{delt2} seconds\")\n",
+    "print(\"Count:\",count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8821b345-847d-4d28-ba1a-0d05b887d22f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.values is 9.11 times faster\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"df.values is {round(delt1/delt2,2)} times faster\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c11a370-bd6b-4862-a628-536dfab3c09b",
+   "metadata": {},
+   "source": [
+    "## Simple vectorized operation is fastest in this counting example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "cfe80cb8-e4d1-4f51-aaad-e71b02c479e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:0.01 seconds\n",
+      "Count: 9202\n"
+     ]
+    }
+   ],
+   "source": [
+    "t1 = time()\n",
+    "df['result'] = np.log(1+df['A']+df['B']) > np.sqrt(0.5*(df['C']+df['D']))\n",
+    "t2=time()\n",
+    "delt3 = round((t2-t1),3)\n",
+    "print(f\"Time:{delt3} seconds\")\n",
+    "print(\"Count:\",df['result'].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0709632-41cc-4e9f-8482-7b9cff0dc480",
+   "metadata": {},
+   "source": [
+    "## String identifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "26d4fd9f-eaf6-4730-b6ba-ab353e677594",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def identifier():\n",
+    "    \"\"\"\n",
+    "    Generates random identifier string of 5 characters\n",
+    "    \"\"\"\n",
+    "    letters = list('CFJQZ')\n",
+    "    numbers = list('123456789')\n",
+    "    \n",
+    "    random_id = ''\n",
+    "    random_id+=np.random.choice(letters)\n",
+    "    random_id+=np.random.choice(letters)\n",
+    "    random_id+=np.random.choice(numbers)\n",
+    "    random_id+=np.random.choice(numbers)\n",
+    "    #random_id+=np.random.choice(numbers)\n",
+    "    #random_id+=np.random.choice(numbers)\n",
+    "    #random_id+=np.random.choice(letters)\n",
+    "    random_id+=np.random.choice(letters)\n",
+    "    \n",
+    "    return random_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "02d1f2f0-87d2-4125-8a0b-83382463bcb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "JQ98C\n",
+      "CC24Z\n",
+      "ZQ25Q\n",
+      "QC54C\n",
+      "ZZ24Z\n",
+      "JF91Z\n",
+      "FQ89C\n",
+      "FQ15F\n",
+      "CZ59F\n",
+      "ZC59F\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(10):\n",
+    "    print(identifier())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2c5e1160-91cc-49ed-a541-2bb424d79cad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>95</td>\n",
+       "      <td>11</td>\n",
+       "      <td>81</td>\n",
+       "      <td>70</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>63</td>\n",
+       "      <td>87</td>\n",
+       "      <td>75</td>\n",
+       "      <td>9</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>77</td>\n",
+       "      <td>40</td>\n",
+       "      <td>4</td>\n",
+       "      <td>63</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40</td>\n",
+       "      <td>60</td>\n",
+       "      <td>92</td>\n",
+       "      <td>64</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>93</td>\n",
+       "      <td>40</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    A   B   C   D  result\n",
+       "0  95  11  81  70   False\n",
+       "1  63  87  75   9   False\n",
+       "2  77  40   4  63   False\n",
+       "3  40  60  92  64   False\n",
+       "4   5  12  93  40   False"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e6b372dd-9f5e-4028-9770-b171025a97a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "id_lst=[]\n",
+    "for i in range(100000):\n",
+    "    id_lst.append(identifier())\n",
+    "id_lst=np.array(id_lst)\n",
+    "\n",
+    "df.insert(0,'ID',id_lst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0bf94057-e44c-4fba-ae12-7daff8c587a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>78891</th>\n",
+       "      <td>ZF36F</td>\n",
+       "      <td>19</td>\n",
+       "      <td>64</td>\n",
+       "      <td>85</td>\n",
+       "      <td>67</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6152</th>\n",
+       "      <td>FC69F</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>22</td>\n",
+       "      <td>12</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99247</th>\n",
+       "      <td>FC76F</td>\n",
+       "      <td>3</td>\n",
+       "      <td>77</td>\n",
+       "      <td>78</td>\n",
+       "      <td>32</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63451</th>\n",
+       "      <td>ZF94Q</td>\n",
+       "      <td>30</td>\n",
+       "      <td>95</td>\n",
+       "      <td>10</td>\n",
+       "      <td>12</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40941</th>\n",
+       "      <td>QJ46F</td>\n",
+       "      <td>14</td>\n",
+       "      <td>4</td>\n",
+       "      <td>95</td>\n",
+       "      <td>40</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          ID   A   B   C   D  result\n",
+       "78891  ZF36F  19  64  85  67   False\n",
+       "6152   FC69F  21   2  22  12   False\n",
+       "99247  FC76F   3  77  78  32   False\n",
+       "63451  ZF94Q  30  95  10  12    True\n",
+       "40941  QJ46F  14   4  95  40   False"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bbd50364-66b3-4703-99bf-a2e802772c04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10125"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['ID'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "23834f67-2c42-42a0-b533-3fdae891da5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:6.597 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "ratio_dict={'ID':[],'Ratio':[]}\n",
+    "t1=time()\n",
+    "for _,row in df.iterrows():\n",
+    "    if row['ID'][0:2] == 'ZZ' and row['ID'][-1]=='F':\n",
+    "        ratio = (row['A']+row['B'])/(0.01+row['C']+row['D'])\n",
+    "        ratio_dict['ID'].append(row[0])\n",
+    "        ratio_dict['Ratio'].append(ratio)\n",
+    "t2=time()\n",
+    "delt4 = round((t2-t1),3)\n",
+    "print(f\"Time:{delt4} seconds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4901d6f8-646c-4bcb-aedc-7502e69c02f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:0.056 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "ratio_dict={'ID':[],'Ratio':[]}\n",
+    "t1=time()\n",
+    "for row in df.values:\n",
+    "    if row[0][0:2] == 'ZZ' and row[0][-1]=='F':\n",
+    "        ratio = (row[1]+row[2])/(0.01+row[3]+row[4])\n",
+    "        ratio_dict['ID'].append(row[0])\n",
+    "        ratio_dict['Ratio'].append(ratio)\n",
+    "t2=time()\n",
+    "delt4 = round((t2-t1),3)\n",
+    "print(f\"Time:{delt4} seconds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a40af042-c101-43b6-a3f2-c8648c072672",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ZZ43F</td>\n",
+       "      <td>0.349377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ZZ79F</td>\n",
+       "      <td>1.285600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ZZ57F</td>\n",
+       "      <td>1.090744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ZZ17F</td>\n",
+       "      <td>0.862388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ZZ56F</td>\n",
+       "      <td>1.134152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>822</th>\n",
+       "      <td>ZZ34F</td>\n",
+       "      <td>0.888724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>823</th>\n",
+       "      <td>ZZ22F</td>\n",
+       "      <td>0.767811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>824</th>\n",
+       "      <td>ZZ59F</td>\n",
+       "      <td>2.063838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>825</th>\n",
+       "      <td>ZZ47F</td>\n",
+       "      <td>0.608283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>826</th>\n",
+       "      <td>ZZ71F</td>\n",
+       "      <td>1.550388</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>827 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        ID     Ratio\n",
+       "0    ZZ43F  0.349377\n",
+       "1    ZZ79F  1.285600\n",
+       "2    ZZ57F  1.090744\n",
+       "3    ZZ17F  0.862388\n",
+       "4    ZZ56F  1.134152\n",
+       "..     ...       ...\n",
+       "822  ZZ34F  0.888724\n",
+       "823  ZZ22F  0.767811\n",
+       "824  ZZ59F  2.063838\n",
+       "825  ZZ47F  0.608283\n",
+       "826  ZZ71F  1.550388\n",
+       "\n",
+       "[827 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(ratio_dict)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Pandas and Numpy/Read_data_various_sources/Pandas CSV vs. PyArrow parquet reading speed.ipynb b/Pandas and Numpy/Read_data_various_sources/Pandas CSV vs. PyArrow parquet reading speed.ipynb
index cdfdac1..f1e75eb 100644
--- a/Pandas and Numpy/Read_data_various_sources/Pandas CSV vs. PyArrow parquet reading speed.ipynb	
+++ b/Pandas and Numpy/Read_data_various_sources/Pandas CSV vs. PyArrow parquet reading speed.ipynb	
@@ -6,15 +6,14 @@
    "metadata": {},
    "source": [
     "# Pandas vs. PyArrow file reading speed comparison\n",
-    "\n",
-    "## Dr. Tirthajyoti Sarkar, Fremont, CA, April 2021\n",
+    "## April, 2021\n",
     "\n",
     "---"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 66,
    "id": "812531a4-bb0e-49a2-9604-ec566c8359d9",
    "metadata": {},
    "outputs": [],
@@ -24,7 +23,10 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pyarrow.parquet as pq\n",
-    "import os"
+    "import os\n",
+    "import sys\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['figure.dpi']=125"
    ]
   },
   {
@@ -129,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 76,
    "id": "f90897c2-c99f-4974-b52f-fa8f34b6f5d0",
    "metadata": {},
    "outputs": [
@@ -171,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 77,
    "id": "9e0f4eff-c3f9-4088-8785-4db160116975",
    "metadata": {},
    "outputs": [],
@@ -182,15 +184,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 78,
    "id": "86dfb137-3143-4ac4-970f-2d50cafc0bfe",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 792x360 with 1 Axes>"
+       "<Figure size 1375x625 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -227,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 79,
    "id": "hybrid-immigration",
    "metadata": {},
    "outputs": [
@@ -235,7 +237,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken to read 2 columns of a 100 MB (53250 rows) CSV file with Pandas: 1.107 seconds\n"
+      "Time taken to read 2 columns of a 100 MB (53250 rows) CSV file with Pandas: 1.114 seconds\n"
      ]
     }
    ],
@@ -256,12 +258,12 @@
    "id": "1fe506be-0aa7-4ae8-9363-a56ff5ae0cb6",
    "metadata": {},
    "source": [
-    "#### The reading speed of the 100 MB CSV file with `pd.read_csv()` is about 0.6 seconds."
+    "#### The reading speed of the 100 MB CSV file with `pd.read_csv()` is about 1.114 seconds."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 80,
    "id": "suffering-stake",
    "metadata": {},
    "outputs": [
@@ -329,7 +331,7 @@
        "4  1.021031  0.295909"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -340,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 81,
    "id": "assumed-harvard",
    "metadata": {},
    "outputs": [
@@ -348,7 +350,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken to read 2 columns of the identical 53250 rows zipped parquet file with PyArrow: 0.019 seconds\n"
+      "Time taken to read 2 columns of the identical 53250 rows zipped parquet file with PyArrow: 0.026 seconds\n"
      ]
     }
    ],
@@ -369,12 +371,12 @@
    "id": "0d8c9230-179e-45fe-be85-120d8286f5d8",
    "metadata": {},
    "source": [
-    "#### The reading speed of the same file (in the parquet gzipped version) with `pq.read_table()` is about 0.012 seconds!"
+    "#### The reading speed of the same file (in the parquet gzipped version) with `pq.read_table()` is about 0.026 seconds!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 82,
    "id": "joint-safety",
    "metadata": {},
    "outputs": [
@@ -442,7 +444,7 @@
        "4  1.021031  0.295909"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -473,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 83,
    "id": "deluxe-absence",
    "metadata": {},
    "outputs": [
@@ -596,6 +598,153 @@
     "plt.ylabel(\"Ratio of Pandas/Arrow read time\", fontsize=14)\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6b4ab83-6b96-4d36-aa4d-476d43130883",
+   "metadata": {},
+   "source": [
+    "## PyArrow (Parquet) reading time varies with sparsity in the file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "4370323a-7d89-4fe5-8f49-179f1e2aa686",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NaN-filled file written with 2.28%\n",
+      "NaN-filled file written with 5.49%\n",
+      "NaN-filled file written with 11.49%\n",
+      "NaN-filled file written with 21.21%\n",
+      "NaN-filled file written with 34.45%\n",
+      "NaN-filled file written with 50.04%\n",
+      "NaN-filled file written with 65.54%\n",
+      "NaN-filled file written with 78.81%\n",
+      "NaN-filled file written with 88.5%\n",
+      "NaN-filled file written with 94.53%\n"
+     ]
+    }
+   ],
+   "source": [
+    "pct_nan = []\n",
+    "for i in range(11,21):\n",
+    "    a = np.random.normal(size=(int(5325*5), int(1e2)))\n",
+    "    cutoff = -2+0.4*(i-11)\n",
+    "    a = np.where(a < cutoff, np.nan, a)\n",
+    "    p_nan = round(100*np.count_nonzero(np.isnan(a))/a.size,2)\n",
+    "    pct_nan.append(p_nan)\n",
+    "    df = pd.DataFrame(a, columns=[\"C\" + str(i) for i in range(100)])\n",
+    "    fname = \"test\"+str(i)+\".csv\"\n",
+    "    df.to_csv(fname)\n",
+    "    print(f\"NaN-filled file written with {p_nan}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "132f13c7-583c-46cd-b1db-3a3fc38006b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Created test11_parquet.zip\n",
+      "Created test12_parquet.zip\n",
+      "Created test13_parquet.zip\n",
+      "Created test14_parquet.zip\n",
+      "Created test15_parquet.zip\n",
+      "Created test16_parquet.zip\n",
+      "Created test17_parquet.zip\n",
+      "Created test18_parquet.zip\n",
+      "Created test19_parquet.zip\n",
+      "Created test20_parquet.zip\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(11,21):\n",
+    "    fname = \"test\"+str(i)+\".csv\"\n",
+    "    parquet_name = \"test\"+str(i)+\"_parquet.zip\"\n",
+    "    df = pd.read_csv(fname)\n",
+    "    df.to_parquet(parquet_name,compression=\"gzip\")\n",
+    "    print(f\"Created {parquet_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "b0f53bc3-5a73-496a-b574-b5535227c72c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done for file # 11\n",
+      "Done for file # 12\n",
+      "Done for file # 13\n",
+      "Done for file # 14\n",
+      "Done for file # 15\n",
+      "Done for file # 16\n",
+      "Done for file # 17\n",
+      "Done for file # 18\n",
+      "Done for file # 19\n",
+      "Done for file # 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_read_nan = []\n",
+    "m_read_nan = []\n",
+    "\n",
+    "for i in range(11,21):\n",
+    "    parquet_name = \"test\"+str(i)+\"_parquet.zip\"\n",
+    "    t1 = time.time()\n",
+    "    df2 = pq.read_table(parquet_name)\n",
+    "    t2 = time.time()\n",
+    "    delta_t = round(1000*(t2 - t1), 3)\n",
+    "    t_read_nan.append(delta_t)\n",
+    "    m_read_nan.append()\n",
+    "    print(f\"Done for file # {i}\")\n",
+    "t_read_nan=np.array(t_read_nan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "531f5393-d837-48b0-bccb-dfbf3562301c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1375x625 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(11, 5))\n",
+    "plt.plot(pct_nan,t_read_nan, \"bo--\", linewidth=2, markersize=8)\n",
+    "plt.grid(True)\n",
+    "plt.title(\"PyArrow (Parquet) reading time varies with sparsity in the file\",fontsize=16,)\n",
+    "#plt.xticks([10*i for i in range(1, 11)],fontsize=14)\n",
+    "plt.xlabel(\"Sparsity (% of NaN values)\", fontsize=14)\n",
+    "plt.ylabel(\"Read time (milliseconds)\", fontsize=14)\n",
+    "plt.ylim(int(t_read_nan.min()*0.9),int(t_read_nan.max()*1.1))\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
diff --git a/README.md b/README.md
index c9c35cc..077c9d7 100644
--- a/README.md
+++ b/README.md
@@ -7,13 +7,15 @@
 
 ### Dr. Tirthajyoti Sarkar, Fremont, California ([Please feel free to connect on LinkedIn here](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7))
 
+![ml-ds](https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Images/ML-DS-cycle-1.png)
+
 ---
 
 ## Also check out these super-useful Repos that I curated
 
-[Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning](https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI)
+- ### [Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning](https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI)
 
-[Carefully curated resource links for data science in one place](https://github.com/tirthajyoti/Data-science-best-resources)
+- ### [Carefully curated resource links for data science in one place](https://github.com/tirthajyoti/Data-science-best-resources)
 
 ## Requirements
 * **Python 3.6+**
@@ -146,4 +148,11 @@ See my articles on Medium on this topic.
 ### Unit testing ML code with Pytest
 Check the files and detailed instructions in the [Pytest](https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Pytest) directory to understand how one should write unit testing code/module for machine learning models
 
+---
+
+### Memory and timing profiling
+
+Profiling data science code and ML models for memory footprint and computing time is a critical but often overlooed area. Here are a couple of Notebooks showing the ideas,
 
+* [Memory profling using Scalene](https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Memory-profiling/Scalene)
+* [Time-profiling data science code](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Time-profiling/cProfile.ipynb)
diff --git a/Time-profiling/8.14 - profile-DS-workflow.png b/Time-profiling/8.14 - profile-DS-workflow.png
new file mode 100644
index 0000000..e17e393
Binary files /dev/null and b/Time-profiling/8.14 - profile-DS-workflow.png differ
diff --git a/Time-profiling/Readme.md b/Time-profiling/Readme.md
new file mode 100644
index 0000000..6d6d34d
--- /dev/null
+++ b/Time-profiling/Readme.md
@@ -0,0 +1 @@
+## Time-profiling ML code
diff --git a/Time-profiling/cProfile.ipynb b/Time-profiling/cProfile.ipynb
new file mode 100644
index 0000000..a65359a
--- /dev/null
+++ b/Time-profiling/cProfile.ipynb
@@ -0,0 +1,513 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fc118b68-6776-4504-ace0-0e076616fde6",
+   "metadata": {},
+   "source": [
+    "# Time-profiling Data Science code using `cProfile`\n",
+    "\n",
+    "## Dr. Tirthajyoti Sarkar\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "98192c2c-f90d-4a7b-b3b2-ae23870d6f4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         3 function calls in 0.064 seconds\n",
+      "\n",
+      "   Ordered by: standard name\n",
+      "\n",
+      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
+      "        1    0.064    0.064    0.064    0.064 <string>:1(<module>)\n",
+      "        1    0.000    0.000    0.064    0.064 {built-in method builtins.exec}\n",
+      "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import cProfile\n",
+    "\n",
+    "SIZE = 10_000_000\n",
+    "a = np.arange(SIZE)\n",
+    "b = np.random.normal(size=SIZE)\n",
+    "\n",
+    "cProfile.run('a+b')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9500a64c-9d19-4e05-97cf-231ab5f1842e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SIZE = 10_000_000\n",
+      "a = np.arange(SIZE)\n",
+      "b = np.random.normal(size=SIZE)\n",
+      "a+b\n"
+     ]
+    }
+   ],
+   "source": [
+    "code = \"\"\"SIZE = 10_000_000\n",
+    "a = np.arange(SIZE)\n",
+    "b = np.random.normal(size=SIZE)\n",
+    "a+b\"\"\"\n",
+    "\n",
+    "print(code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6d810ec6-245d-4082-8f64-d5c16b162936",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         5 function calls in 0.488 seconds\n",
+      "\n",
+      "   Ordered by: standard name\n",
+      "\n",
+      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
+      "        1    0.078    0.078    0.488    0.488 <string>:1(<module>)\n",
+      "        1    0.000    0.000    0.488    0.488 {built-in method builtins.exec}\n",
+      "        1    0.028    0.028    0.028    0.028 {built-in method numpy.arange}\n",
+      "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}\n",
+      "        1    0.381    0.381    0.381    0.381 {method 'normal' of 'numpy.random.mtrand.RandomState' objects}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "cProfile.run(code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8a5bf74f-fa5b-4997-a864-82a6d9503216",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         6 function calls in 0.531 seconds\n",
+      "\n",
+      "   Ordered by: standard name\n",
+      "\n",
+      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
+      "        1    0.055    0.055    0.510    0.510 1735574101.py:1(add)\n",
+      "        1    0.021    0.021    0.531    0.531 <string>:1(<module>)\n",
+      "        1    0.000    0.000    0.531    0.531 {built-in method builtins.exec}\n",
+      "        1    0.057    0.057    0.057    0.057 {built-in method numpy.arange}\n",
+      "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}\n",
+      "        1    0.397    0.397    0.397    0.397 {method 'normal' of 'numpy.random.mtrand.RandomState' objects}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add():\n",
+    "    SIZE = 10_000_000\n",
+    "    a = np.arange(SIZE)\n",
+    "    b = np.random.normal(size=SIZE)\n",
+    "    c=a+b\n",
+    "\n",
+    "cProfile.run('add()')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f899c334-2d1f-41ef-aceb-71348e5a6818",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add(size):\n",
+    "    a = np.arange(size)\n",
+    "    b = np.random.normal(size=size)\n",
+    "    c=a+b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cdf8d43a-f240-4de6-9a92-6dee6c1cd98b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         6 function calls in 0.500 seconds\n",
+      "\n",
+      "   Ordered by: standard name\n",
+      "\n",
+      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
+      "        1    0.054    0.054    0.478    0.478 1565836920.py:1(add)\n",
+      "        1    0.021    0.021    0.500    0.500 <string>:1(<module>)\n",
+      "        1    0.000    0.000    0.500    0.500 {built-in method builtins.exec}\n",
+      "        1    0.030    0.030    0.030    0.030 {built-in method numpy.arange}\n",
+      "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}\n",
+      "        1    0.394    0.394    0.394    0.394 {method 'normal' of 'numpy.random.mtrand.RandomState' objects}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "SIZE = 10_000_000\n",
+    "cProfile.run('add(SIZE)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8d038a76-4394-472b-9949-be9ef936dca5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         6 function calls in 1.034 seconds\n",
+      "\n",
+      "   Ordered by: standard name\n",
+      "\n",
+      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
+      "        1    0.114    0.114    0.987    0.987 1565836920.py:1(add)\n",
+      "        1    0.047    0.047    1.034    1.034 <string>:1(<module>)\n",
+      "        1    0.000    0.000    1.034    1.034 {built-in method builtins.exec}\n",
+      "        1    0.082    0.082    0.082    0.082 {built-in method numpy.arange}\n",
+      "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}\n",
+      "        1    0.791    0.791    0.791    0.791 {method 'normal' of 'numpy.random.mtrand.RandomState' objects}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "SIZE = 20_000_000\n",
+    "cProfile.run('add(SIZE)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f14c476d-cc19-4ce5-b74e-abf211eabc7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ops(a,b):\n",
+    "    x1 = a+b\n",
+    "    x2 = a-b\n",
+    "    x3 = a*b\n",
+    "    x4 = a/b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "771a1452-2d4a-476e-8775-3479beac3700",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         4 function calls in 0.287 seconds\n",
+      "\n",
+      "   Ordered by: standard name\n",
+      "\n",
+      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
+      "        1    0.251    0.251    0.251    0.251 3200973052.py:1(ops)\n",
+      "        1    0.036    0.036    0.286    0.286 <string>:1(<module>)\n",
+      "        1    0.000    0.000    0.287    0.287 {built-in method builtins.exec}\n",
+      "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "cProfile.run('ops(a,b)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5af6602e-a66f-4bf0-9d22-bc5b71e8c4d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total function calls: 48\n",
+      "Total time (seconds): 1.1839559\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cProfile, pstats\n",
+    "\n",
+    "profiler = cProfile.Profile()\n",
+    "# Enable profiler\n",
+    "profiler.enable()\n",
+    "# Function execution\n",
+    "add(SIZE)\n",
+    "# Disable profiler\n",
+    "profiler.disable()\n",
+    "# pstats\n",
+    "stats = pstats.Stats(profiler)\n",
+    "# Print the total time and function calls\n",
+    "print(\"Total function calls:\", stats.total_calls)\n",
+    "print(\"Total time (seconds):\", stats.total_tt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "91f2d625-37bf-41f6-9c92-ae52bf608d02",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         48 function calls in 1.184 seconds\n",
+      "\n",
+      "   Random listing order was used\n",
+      "\n",
+      "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\contextlib.py:86(__init__)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\contextlib.py:112(__enter__)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\contextlib.py:121(__exit__)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\contextlib.py:242(helper)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\traitlets\\traitlets.py:535(get)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\traitlets\\traitlets.py:566(__get__)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\IPython\\utils\\ipstruct.py:125(__getattr__)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\codeop.py:142(__call__)\n",
+      "        4    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\IPython\\core\\compilerop.py:166(extra_flags)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\IPython\\core\\interactiveshell.py:1286(user_global_ns)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3354(compare)\n",
+      "        2    0.000    0.000    1.184    0.592 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3416(run_code)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\IPython\\core\\hooks.py:103(__call__)\n",
+      "        2    0.000    0.000    0.000    0.000 c:\\users\\tirtha\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\IPython\\core\\hooks.py:168(pre_run_code_hook)\n",
+      "        1    0.000    0.000    0.000    0.000 C:\\Users\\Tirtha\\AppData\\Local\\Temp/ipykernel_12356/3775033682.py:9(<module>)\n",
+      "        1    0.044    0.044    1.184    1.184 C:\\Users\\Tirtha\\AppData\\Local\\Temp/ipykernel_12356/3775033682.py:7(<module>)\n",
+      "        1    0.114    0.114    1.140    1.140 C:\\Users\\Tirtha\\AppData\\Local\\Temp/ipykernel_12356/1565836920.py:1(add)\n",
+      "        1    0.081    0.081    0.081    0.081 {built-in method numpy.arange}\n",
+      "        1    0.945    0.945    0.945    0.945 {method 'normal' of 'numpy.random.mtrand.RandomState' objects}\n",
+      "        2    0.000    0.000    0.000    0.000 {built-in method builtins.compile}\n",
+      "        2    0.000    0.000    1.184    0.592 {built-in method builtins.exec}\n",
+      "        4    0.000    0.000    0.000    0.000 {built-in method builtins.getattr}\n",
+      "        4    0.000    0.000    0.000    0.000 {built-in method builtins.next}\n",
+      "        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pstats.Stats at 0x219d3a001f0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats = pstats.Stats(profiler)\n",
+    "stats.print_stats()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "06fa3a62-5fef-4d9e-8c6a-d9e309d80e16",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pstats.Stats"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(stats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cefecaa0-9933-49ae-b0dc-3f869e83fa89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.1839559"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.total_tt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "fe15e37e-d329-4168-a479-60c219126e16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stats.fcn_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "15c966eb-e094-4e5f-8e54-1a2c3923f6c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "560a8a4d-84fc-43c2-a1c8-79113dcc73ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = [int(i*1e6) for i in range(5,26,5)]\n",
+    "total_tt = []\n",
+    "for s in size:\n",
+    "    profiler = cProfile.Profile()\n",
+    "    profiler.enable()\n",
+    "    add(s)\n",
+    "    profiler.disable()\n",
+    "    stats = pstats.Stats(profiler)\n",
+    "    total_tt.append(round(stats.total_tt,3))       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "264f4589-e40b-4c2d-bb08-9116dc4eb2e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.274, 0.464, 0.706, 0.94, 1.187]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_tt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "695c9306-bcc6-45ea-8939-28e8e127eec3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6,3),dpi=120)\n",
+    "plt.bar(x=[str(i)+'-million' for i in range(5,26,5)],\n",
+    "        height=total_tt, \n",
+    "        edgecolor='k',\n",
+    "        color=\"#2c75b0\")\n",
+    "plt.xlabel(\"Array size\", fontsize=16)\n",
+    "plt.ylabel(\"Time taken (seconds)\",fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b1e4af3-385a-420a-be85-449119210f93",
+   "metadata": {},
+   "source": [
+    "## Data science workflow profiling\n",
+    "\n",
+    "While measuring the execution time of these small standalone functions serve as basic demonstration of the usage of these profilers, the real utility is realized when they are used in a large-scale data science workflow. Such a workflow has a variety of modules and functions and we can set up profiling for all of them if necessary. The output may be logged into a database or even be fed into a monitoring system that will track the performance of the modules over time and take action if needed (e.g., a function has poorly performed and took too much time in a certain run or for a certain input data).\n",
+    "\n",
+    "![ds-workflow](https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Time-profiling/8.14%20-%20profile-DS-workflow.png)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Timing-decorator/Readme.md b/Timing-decorator/Readme.md
new file mode 100644
index 0000000..8936534
--- /dev/null
+++ b/Timing-decorator/Readme.md
@@ -0,0 +1 @@
+## Timing decorator with ML estimators
diff --git a/Timing-decorator/Timing-decorator-ML-optimization.ipynb b/Timing-decorator/Timing-decorator-ML-optimization.ipynb
new file mode 100644
index 0000000..2d4bbc9
--- /dev/null
+++ b/Timing-decorator/Timing-decorator-ML-optimization.ipynb
@@ -0,0 +1,489 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "5a0b11ef-4d23-4678-98de-2e2c5d6ac365",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from functools import wraps\n",
+    "from time import time,sleep\n",
+    "import numpy as np, matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.datasets import make_classification,make_regression\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "179c44d3-b908-4544-a506-c24170c9f642",
+   "metadata": {},
+   "source": [
+    "## Timing decorator with `functools.wraps`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "id": "ed5eaae6-1f2b-490d-9a77-16dfca402aa0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def timing(func):\n",
+    "    @wraps(func)\n",
+    "    def wrap(*args, **kw):\n",
+    "        ts = time()\n",
+    "        result = func(*args, **kw)\n",
+    "        te = time()\n",
+    "        tdelta = round(1000*(te-ts),3)\n",
+    "        print (f\"Function '{func.__name__}' took {tdelta} milliseconds to run\")\n",
+    "        return result\n",
+    "    return wrap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "39761a68-afed-428f-9f2f-58d933bc61ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@timing\n",
+    "def list_length(a):\n",
+    "    if isinstance(a,list):\n",
+    "        sleep(0.1)\n",
+    "        s = len(a)\n",
+    "        return s\n",
+    "    else:\n",
+    "        print(\"Argument is not a list\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "573c97d3-b24f-4590-a492-d34588777c23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Function 'list_length' took 111.291 milliseconds to run\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_length([1,2,3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4e4096ba-226d-4f73-a801-eb85bfad14d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Argument is not a list\n",
+      "Function 'list_length' took 0.0 milliseconds to run\n"
+     ]
+    }
+   ],
+   "source": [
+    "list_length(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "bb695ae1-b0bf-4389-aff0-526929dd216a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def time_return(func):\n",
+    "    @wraps(func)\n",
+    "    def wrap(*args, **kw):\n",
+    "        ts = time()\n",
+    "        result = func(*args, **kw)\n",
+    "        te = time()\n",
+    "        tdelta = round(1000*(te-ts),3)\n",
+    "        return tdelta\n",
+    "    return wrap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "7fbea76a-a77d-408a-a1e4-92ddb6ce1ced",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@time_return\n",
+    "def numpy_matmul(a,b):\n",
+    "    return (np.matmul(a,b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "cdbacc07-0035-4223-85ef-480c296c26f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.48"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SIZE = 1000\n",
+    "a = np.random.beta(1.0,2.0,size=(SIZE,SIZE))\n",
+    "b = np.random.beta(1.0,2.0,size=(SIZE,SIZE))\n",
+    "numpy_matmul(a,b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "33a26139-375f-406d-92ff-f2fffc958b26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "111.301"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SIZE = 2000\n",
+    "a = np.random.beta(1.0,2.0,size=(SIZE,SIZE))\n",
+    "b = np.random.beta(1.0,2.0,size=(SIZE,SIZE))\n",
+    "numpy_matmul(a,b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "8b62edde-40ac-4d10-abc6-7fc77b057916",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix multiplication of size (500x500) took 3.0 milliseconds\n",
+      "Matrix multiplication of size (1000x1000) took 17.031 milliseconds\n",
+      "Matrix multiplication of size (2000x2000) took 111.501 milliseconds\n",
+      "Matrix multiplication of size (3000x3000) took 359.307 milliseconds\n",
+      "Matrix multiplication of size (4000x4000) took 835.614 milliseconds\n",
+      "Matrix multiplication of size (5000x5000) took 1611.042 milliseconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "SIZE = [500,1000,2000,3000,4000,5000]\n",
+    "for s in SIZE:\n",
+    "    a = np.random.beta(1.0,2.0,size=(s,s))\n",
+    "    b = np.random.beta(1.0,2.0,size=(s,s))\n",
+    "    t = numpy_matmul(a,b)\n",
+    "    print(f\"Matrix multiplication of size ({s}x{s}) took {t} milliseconds\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68f93384-d0c4-45d8-bf44-ac2657089d2d",
+   "metadata": {},
+   "source": [
+    "## Throwing an ML estimator into the mix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "28dc6ecb-4a34-473d-b3a4-a607afd4e8de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def time_estimator(func):\n",
+    "    @wraps(func)\n",
+    "    def wrap(*args, **kw):\n",
+    "        ts = time()\n",
+    "        result = func(*args, **kw)\n",
+    "        te = time()\n",
+    "        tdelta = round(1000*(te-ts),3)\n",
+    "        return (tdelta, result)\n",
+    "    return wrap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "6cbe46a4-0a4d-4cb8-866e-14646442e918",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@time_estimator\n",
+    "def classifier_accuracy(estimator,x,y):\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(x, y, \n",
+    "                                                    test_size=0.33, \n",
+    "                                                    random_state=42)\n",
+    "    estimator.fit(X_train,y_train)\n",
+    "    score = estimator.score(X_test,y_test)\n",
+    "    return round(score,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "50dd6f15-7df4-4627-a62f-0f55e5e90d74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = make_classification(n_samples=1000,n_features=20,n_informative=20,n_redundant=0,\n",
+    "                           flip_y=0.05,class_sep=1.5)\n",
+    "x,y = data[0],data[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "4da09873-f5c3-4edf-8347-c995d95eb043",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_model = LogisticRegressionCV()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "39c5e844-b61c-4763-9ccb-c9d058e7be63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(312.083, 0.836)"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classifier_accuracy(log_model,x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b987ae48-52e9-449b-9fd3-663459465097",
+   "metadata": {},
+   "source": [
+    "## Change the data and record execution time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "f21f6487-9c84-48cf-8d43-414b8aa88428",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SIZE = [1000+500*i for i in range(21)]\n",
+    "log_model = LogisticRegressionCV()\n",
+    "model_time, model_acc = [],[]\n",
+    "\n",
+    "for s in SIZE:\n",
+    "    data = make_classification(n_samples=s,n_features=20,n_informative=20,n_redundant=0,\n",
+    "                               flip_y=0.05,class_sep=1.5)\n",
+    "    x,y = data[0],data[1]\n",
+    "    m_time, m_acc = classifier_accuracy(log_model,x,y)\n",
+    "    model_time.append(m_time)\n",
+    "    model_acc.append(m_acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "593abe3d-4d07-404e-87c5-7374379689cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots(1,2,figsize=(12,4))\n",
+    "ax[0].scatter(SIZE,model_acc,edgecolor='k',s=100)\n",
+    "ax[0].plot(SIZE,model_acc)\n",
+    "ax[0].set_title(\"Accuracy score with data size\",fontsize=15)\n",
+    "ax[0].set_xlabel(\"Data size\",fontsize=14)\n",
+    "ax[0].grid(True)\n",
+    "ax[1].scatter(SIZE,model_time,edgecolor='k',s=100)\n",
+    "ax[1].plot(SIZE,model_time)\n",
+    "ax[1].set_title(\"Training time (msec) with data size\",fontsize=15)\n",
+    "ax[1].set_xlabel(\"Data size\",fontsize=14)\n",
+    "ax[1].grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "102ece6b-e0be-4ff3-8ee2-7fa3012c3741",
+   "metadata": {},
+   "source": [
+    "## Change the model and optimize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "id": "52a0dca9-879c-4f8c-9942-8960a734a210",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_trees = [5*x for x in range(1,21)]\n",
+    "model_time, model_acc = [],[]\n",
+    "data = make_classification(n_samples=1000, n_features=20, \n",
+    "                           n_informative=20, n_redundant=0, \n",
+    "                           flip_y=0.05,class_sep=1.0)\n",
+    "x,y = data[0],data[1]\n",
+    "for n in num_trees:\n",
+    "    rf_model = RandomForestClassifier(n_estimators=n)\n",
+    "    m_time, m_acc = classifier_accuracy(rf_model,x,y)\n",
+    "    model_time.append(m_time)\n",
+    "    model_acc.append(m_acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "id": "ce3efbe4-e817-4296-9227-75bd3905e41d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots(1,2,figsize=(12,4))\n",
+    "ax[0].scatter(num_trees,model_acc,edgecolor='k',s=100)\n",
+    "ax[0].plot(num_trees,model_acc)\n",
+    "ax[0].set_title(\"Accuracy score with Random Forest\",fontsize=15)\n",
+    "ax[0].set_xlabel(\"Number of trees\",fontsize=14)\n",
+    "ax[0].grid(True)\n",
+    "ax[1].scatter(num_trees,model_time,edgecolor='k',s=100)\n",
+    "ax[1].plot(num_trees,model_time)\n",
+    "ax[1].set_title(\"Training time (msec) with Random Forest\",fontsize=15)\n",
+    "ax[1].set_xlabel(\"Number of trees\",fontsize=14)\n",
+    "ax[1].grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "id": "e2db0158-f14b-46d9-81fe-f5698ceb9be7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_time = np.array(model_time)\n",
+    "model_acc = np.array(model_acc)\n",
+    "model_opt = model_acc + 1/model_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "id": "9e2568d9-9ec5-4a6f-8540-6a286c6252b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(dpi=120)\n",
+    "plt.title(\"Model optimization with number of trees\", fontsize=15)\n",
+    "plt.plot(num_trees,model_opt)\n",
+    "plt.scatter(num_trees,model_opt,s=100,edgecolor='k')\n",
+    "plt.xlabel(\"Number of trees\",fontsize=14)\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}