diff --git a/Sony - NumPy.ipynb b/Sony - NumPy.ipynb
new file mode 100644
index 0000000..a45af03
--- /dev/null
+++ b/Sony - NumPy.ipynb	
@@ -0,0 +1,527 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l = [1,'Hello',2.2,5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 'Hello', 2.2, 5]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l1 = [1.1,l]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.1, [1, 'Hello', 2.2, 5]]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create NumPy Array from Python List"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([1,2,3,4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4,)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.random.randint(10, 100, (5,5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[50, 50, 97, 20, 33],\n",
+       "       [68, 40, 74, 33, 79],\n",
+       "       [33, 96, 16, 85, 75],\n",
+       "       [42, 49, 23, 35, 32],\n",
+       "       [96, 66, 82, 71, 89]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 5)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.ndim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "97"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.26237485, -0.26237485,  0.37960774,  0.91294525,  0.99991186],\n",
+       "       [-0.89792768,  0.74511316, -0.98514626,  0.99991186, -0.44411267],\n",
+       "       [ 0.99991186,  0.98358775, -0.28790332, -0.17607562, -0.38778164],\n",
+       "       [-0.91652155, -0.95375265, -0.8462204 , -0.42818267,  0.55142668],\n",
+       "       [ 0.98358775, -0.02655115,  0.31322878,  0.95105465,  0.86006941]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sin(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.01042192, -0.00055567, -0.01778947,  0.03854819,  0.00548865],\n",
+       "       [ 0.00590693,  0.01348381,  0.00298488,  0.03875234, -0.03060777],\n",
+       "       [ 0.01528493, -0.00900967,  0.00351046, -0.02767716,  0.00932299],\n",
+       "       [ 0.00293678, -0.04170059,  0.00849387, -0.04720859,  0.04574234],\n",
+       "       [-0.00956437,  0.03216795,  0.00696477, -0.00715668, -0.01706728]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.inv(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.array([1,2,3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.random.randint(10, 100, (5,5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[63, 55, 44, 34, 67],\n",
+       "       [30, 11, 68, 32, 17],\n",
+       "       [82, 16, 96, 96, 89],\n",
+       "       [80, 58, 22, 40, 82],\n",
+       "       [40, 71, 62, 91, 91]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Maximum value across each row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([67, 68, 96, 82, 91])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.max(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([82, 71, 96, 96, 91])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.max(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Understanding Vectors & Scalers\n",
+    "* Scaler value has to be broadcasted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([1,2,3,4,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6,  7,  8,  9, 10])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a + 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Genrating Data from subset of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['hi', 'hola', 'hi', 'hi', 'hi', 'hi', 'hi', 'hi'], dtype='<U5')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.choose([1,2,1,1,1,1,1,1],choices=['hello','hi','hola','hey'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skimage.io import imread"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = imread('/home/awantik/Pictures/child-fedex.jpg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(194, 260, 3)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Sony - Pandas & Basic Visualization.ipynb b/Sony - Pandas & Basic Visualization.ipynb
new file mode 100644
index 0000000..4d42d7d
--- /dev/null
+++ b/Sony - Pandas & Basic Visualization.ipynb	
@@ -0,0 +1,1026 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s1 = pd.Series(['ho','ag','ra','maa'],index=['a','b','c','d'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a     ho\n",
+       "b     ag\n",
+       "c     ra\n",
+       "d    maa\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s2 = pd.Series([1,11,22,2],index=['a','b','c','d'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a     ho\n",
+       "b     ag\n",
+       "c     ra\n",
+       "d    maa\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a     1\n",
+       "b    11\n",
+       "c    22\n",
+       "d     2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame({'S1':s1,'S2':s2})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hr_data = pd.read_csv('https://raw.githubusercontent.com/edyoda/data-science-complete-tutorial/master/Data/HR_comma_sep.csv.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 14999 entries, 0 to 14998\n",
+      "Data columns (total 10 columns):\n",
+      "satisfaction_level       14999 non-null float64\n",
+      "last_evaluation          14999 non-null float64\n",
+      "number_project           14999 non-null int64\n",
+      "average_montly_hours     14999 non-null int64\n",
+      "time_spend_company       14999 non-null int64\n",
+      "Work_accident            14999 non-null int64\n",
+      "left                     14999 non-null int64\n",
+      "promotion_last_5years    14999 non-null int64\n",
+      "sales                    14999 non-null object\n",
+      "salary                   14999 non-null object\n",
+      "dtypes: float64(2), int64(6), object(2)\n",
+      "memory usage: 1.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "hr_data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>satisfaction_level</th>\n",
+       "      <th>last_evaluation</th>\n",
+       "      <th>number_project</th>\n",
+       "      <th>average_montly_hours</th>\n",
+       "      <th>time_spend_company</th>\n",
+       "      <th>Work_accident</th>\n",
+       "      <th>left</th>\n",
+       "      <th>promotion_last_5years</th>\n",
+       "      <th>sales</th>\n",
+       "      <th>salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>2</td>\n",
+       "      <td>157</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>5</td>\n",
+       "      <td>262</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>medium</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.11</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>7</td>\n",
+       "      <td>272</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>medium</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.72</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>5</td>\n",
+       "      <td>223</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.37</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>2</td>\n",
+       "      <td>159</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
+       "0                0.38             0.53               2                   157   \n",
+       "1                0.80             0.86               5                   262   \n",
+       "2                0.11             0.88               7                   272   \n",
+       "3                0.72             0.87               5                   223   \n",
+       "4                0.37             0.52               2                   159   \n",
+       "\n",
+       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
+       "0                   3              0     1                      0  sales   \n",
+       "1                   6              0     1                      0  sales   \n",
+       "2                   4              0     1                      0  sales   \n",
+       "3                   5              0     1                      0  sales   \n",
+       "4                   3              0     1                      0  sales   \n",
+       "\n",
+       "   salary  \n",
+       "0     low  \n",
+       "1  medium  \n",
+       "2  medium  \n",
+       "3     low  \n",
+       "4     low  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hr_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>satisfaction_level</th>\n",
+       "      <th>last_evaluation</th>\n",
+       "      <th>number_project</th>\n",
+       "      <th>average_montly_hours</th>\n",
+       "      <th>time_spend_company</th>\n",
+       "      <th>Work_accident</th>\n",
+       "      <th>left</th>\n",
+       "      <th>promotion_last_5years</th>\n",
+       "      <th>sales</th>\n",
+       "      <th>salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8739</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>4</td>\n",
+       "      <td>137</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>technical</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9255</th>\n",
+       "      <td>0.71</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>3</td>\n",
+       "      <td>161</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11265</th>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>3</td>\n",
+       "      <td>142</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>product_mng</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>0.46</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>2</td>\n",
+       "      <td>146</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>IT</td>\n",
+       "      <td>medium</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10867</th>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>4</td>\n",
+       "      <td>173</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>accounting</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       satisfaction_level  last_evaluation  number_project  \\\n",
+       "8739                 0.69             0.82               4   \n",
+       "9255                 0.71             0.48               3   \n",
+       "11265                0.18             0.75               3   \n",
+       "1741                 0.46             0.50               2   \n",
+       "10867                0.80             0.72               4   \n",
+       "\n",
+       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
+       "8739                    137                   2              1     0   \n",
+       "9255                    161                   3              0     0   \n",
+       "11265                   142                   2              0     0   \n",
+       "1741                    146                   3              0     1   \n",
+       "10867                   173                   3              0     0   \n",
+       "\n",
+       "       promotion_last_5years        sales  salary  \n",
+       "8739                       0    technical     low  \n",
+       "9255                       0        sales     low  \n",
+       "11265                      0  product_mng     low  \n",
+       "1741                       0           IT  medium  \n",
+       "10867                      0   accounting     low  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hr_data.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Identify the columns of object types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_hr_data = hr_data.select_dtypes(include=['object'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['sales', 'salary'], dtype='object')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_hr_data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0          sales\n",
+       "1          sales\n",
+       "2          sales\n",
+       "3          sales\n",
+       "4          sales\n",
+       "          ...   \n",
+       "14994    support\n",
+       "14995    support\n",
+       "14996    support\n",
+       "14997    support\n",
+       "14998    support\n",
+       "Name: sales, Length: 14999, dtype: object"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_hr_data.sales"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>sales</th>\n",
+       "      <th>salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sales</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sales</td>\n",
+       "      <td>medium</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sales</td>\n",
+       "      <td>medium</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sales</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sales</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sales  salary\n",
+       "0  sales     low\n",
+       "1  sales  medium\n",
+       "2  sales  medium\n",
+       "3  sales     low\n",
+       "4  sales     low"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_hr_data[['sales','salary']].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['sales', 'accounting', 'hr', 'technical', 'support', 'management',\n",
+       "       'IT', 'product_mng', 'marketing', 'RandD'], dtype=object)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_hr_data.sales.unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We realise that sales is wrong column name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hr_data.rename(columns={'sales':'department'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>satisfaction_level</th>\n",
+       "      <th>last_evaluation</th>\n",
+       "      <th>number_project</th>\n",
+       "      <th>average_montly_hours</th>\n",
+       "      <th>time_spend_company</th>\n",
+       "      <th>Work_accident</th>\n",
+       "      <th>left</th>\n",
+       "      <th>promotion_last_5years</th>\n",
+       "      <th>department</th>\n",
+       "      <th>salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>2</td>\n",
+       "      <td>157</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>5</td>\n",
+       "      <td>262</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>medium</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.11</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>7</td>\n",
+       "      <td>272</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>medium</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.72</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>5</td>\n",
+       "      <td>223</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.37</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>2</td>\n",
+       "      <td>159</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sales</td>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
+       "0                0.38             0.53               2                   157   \n",
+       "1                0.80             0.86               5                   262   \n",
+       "2                0.11             0.88               7                   272   \n",
+       "3                0.72             0.87               5                   223   \n",
+       "4                0.37             0.52               2                   159   \n",
+       "\n",
+       "   time_spend_company  Work_accident  left  promotion_last_5years department  \\\n",
+       "0                   3              0     1                      0      sales   \n",
+       "1                   6              0     1                      0      sales   \n",
+       "2                   4              0     1                      0      sales   \n",
+       "3                   5              0     1                      0      sales   \n",
+       "4                   3              0     1                      0      sales   \n",
+       "\n",
+       "   salary  \n",
+       "0     low  \n",
+       "1  medium  \n",
+       "2  medium  \n",
+       "3     low  \n",
+       "4     low  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hr_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Understand salary distribution in the company"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "low       7316\n",
+       "medium    6446\n",
+       "high      1237\n",
+       "Name: salary, dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hr_data.salary.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Salary Prediction\n",
+    "* If you are building a model for prediction of salary., \n",
+    "* Feature data will be (avg_montly_hrs, left ...) and traget data will be salary\n",
+    "* 3 class classification model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Employee distribution across dept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sales          4140\n",
+       "technical      2720\n",
+       "support        2229\n",
+       "IT             1227\n",
+       "product_mng     902\n",
+       "marketing       858\n",
+       "RandD           787\n",
+       "accounting      767\n",
+       "hr              739\n",
+       "management      630\n",
+       "Name: department, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hr_data.department.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Which is the most satisfied team"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "department\n",
+       "IT             0.618142\n",
+       "RandD          0.619822\n",
+       "accounting     0.582151\n",
+       "hr             0.598809\n",
+       "management     0.621349\n",
+       "marketing      0.618601\n",
+       "product_mng    0.619634\n",
+       "sales          0.614447\n",
+       "support        0.618300\n",
+       "technical      0.607897\n",
+       "Name: satisfaction_level, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hr_data.groupby(['department']).satisfaction_level.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Identify ideal number of projects "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "number_project\n",
+       "2    0.478769\n",
+       "3    0.687670\n",
+       "4    0.695132\n",
+       "5    0.678888\n",
+       "6    0.273458\n",
+       "7    0.118711\n",
+       "Name: satisfaction_level, dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hr_data.groupby(['number_project']).satisfaction_level.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "number_project  left\n",
+       "2               0        821\n",
+       "                1       1567\n",
+       "3               0       3983\n",
+       "                1         72\n",
+       "4               0       3956\n",
+       "                1        409\n",
+       "5               0       2149\n",
+       "                1        612\n",
+       "6               0        519\n",
+       "                1        655\n",
+       "7               1        256\n",
+       "Name: satisfaction_level, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hr_data.groupby(['number_project','left']).satisfaction_level.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### relationship between avg_working_hrs, satisfaction & left"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'avg_montly_hrs')"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x=hr_data.satisfaction_level, y=hr_data.average_montly_hours, c=hr_data.left,s=5, alpha=.5)\n",
+    "plt.colorbar()\n",
+    "plt.xlabel('satisfaction_level')\n",
+    "plt.ylabel('avg_montly_hrs')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inferances\n",
+    "* Yellow means people who have left the organization\n",
+    "* Purple means people who are still with the company"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}