Pandas for Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Desktop/CAT/Data Science with Python/Datasets/Data.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>44.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>35.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>NaN</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>48.0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>50.0</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>37.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age   Salary Purchased\n",
       "Country                         \n",
       "France   44.0  72000.0        No\n",
       "Spain    27.0  48000.0       Yes\n",
       "Germany  30.0  54000.0        No\n",
       "Spain    38.0  61000.0        No\n",
       "Germany  40.0      NaN       Yes\n",
       "France   35.0  58000.0       Yes\n",
       "Spain     NaN  52000.0        No\n",
       "France   48.0  79000.0       Yes\n",
       "Germany  50.0  83000.0        No\n",
       "France   37.0  67000.0       Yes"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('bmh')\n",
    "df['Age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('dark_background')\n",
    "df['Age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EsPerKdq2lXNuvOfg5THXe3JunP78fNd1nPc4gjk1DXPqmXNubxz6vtd6vR5XgJecT9xkCgAwx9k1AIA54gMAMEd8AADmiA8AwBzxAQCYIz4AAHPEBwBgjvgAAMwRHwCAuf8Du3a8WAn7PUwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "df['Age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Country'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.area(alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>44.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age   Salary Purchased\n",
       "Country                         \n",
       "France   44.0  72000.0        No\n",
       "Spain    27.0  48000.0       Yes\n",
       "Germany  30.0  54000.0        No\n",
       "Spain    38.0  61000.0        No\n",
       "Germany  40.0      NaN       Yes"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Country'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Country'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAEFCAYAAACyxaPNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXAUlEQVR4nO3df3AU9f3H8dclMdxgMCQXSHolSglQzTAjhFAgLT9iNHW0pZlqpSrTQcogEzQo0BEyFG1tpjigEQraH8SAte1A7dQ6dYp0qgUKLYSSIMq0EqgYmoTIXSD8CnDZ+/7R4b5cfpBNuNv7hDwfM87ks/fZ3fd+7kNe7t7exhUMBoMCAMBQcbEuAACAayGoAABGI6gAAEYjqAAARiOoAABGI6gAAEYjqAAARkuIdQG9UV9fH+sSHOX1evvdMfcWY2UP42QP42RPpMbJ6/V2upwzKgCA0QgqAIDRCCoAgNEIKgCA0QgqAIDRHLnr75VXXtH+/fuVnJysF198scPrwWBQlZWVqq6u1oABA1RcXKwRI0Y4URoAwHCOnFFNnz5dpaWlXb5eXV2txsZGrV27VvPmzdOGDRucKAsA0Ac4ElTZ2dlKSkrq8vV9+/Zp6tSpcrlcGj16tM6dO6fm5mYnSgMAGM6Iz6j8fr/S0tJCbY/HI7/fH8OKAACmMOLJFJ39kWGXy9Vl/66+vWxX3f25oZ8z39l3XdtywtX1Sn2jZieFjc87+657fvR233bfl1i+n1f2XReDfTspkmPs5Hy6Xk7+bnPy350RQeXxeHTy5MlQ2+fzKSUlpcv+kXykSV98PEpfrNlJsRqf3u43lu9nf5lLvT3OvvwIJafrvuEfoZSbm6sdO3YoGAzq448/1sCBA68ZVACA/sORM6qXX35Zhw4d0pkzZzR//nw99NBDCgQCkqTCwkKNGzdO+/fvV0lJiRITE1VcXOxEWQCAPsCRoHrqqaeu+brL5dLcuXOdKAUA0McYcekPAICuEFQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoyU4taOamhpVVlbKsiwVFBSoqKgo7PXz589r7dq18vl8amtr09e//nXl5+c7VR4AwFCOBJVlWaqoqNDy5cvl8Xi0bNky5ebmatiwYaE+W7du1bBhw7R06VK1tLRo4cKFmjJlihISHMtSAICBHLn0V1tbq4yMDKWnpyshIUF5eXmqqqoK6+NyudTa2qpgMKjW1lYlJSUpLo4rkwDQ3zmSBH6/Xx6PJ9T2eDzy+/1hfe69917997//1eOPP67FixfrscceI6gAAM5c+gsGgx2WuVyusPaBAwd02223acWKFTpx4oSef/553X777Ro4cGCHdb1e73XVUxfBbTmhrl27L9TspFiOT2/mkin1Or1vJ0XyOPvSGDn5u83JueRIUHk8Hvl8vlDb5/MpJSUlrM/777+voqIiuVwuZWRkaOjQoaqvr9fIkSM7bK++vj5itUVyW07pizU7KVbj09v9xvL97C9zqbfH6fV6++wYOV13JPbXVdg5cm0tKytLDQ0NampqUiAQ0O7du5WbmxvWJy0tTQcPHpQknTp1SvX19Ro6dKgT5QEADObIGVV8fLzmzJmjsrIyWZal/Px8ZWZmatu2bZKkwsJCPfDAA3rllVe0ePFiSdKjjz6qW265xYnyAAAGc+ze75ycHOXk5IQtKywsDP2cmpqq5cuXO1UOAKCP4LY6AIDRCCoAgNEIKgCA0QgqAIDRCCoAgNEIKgCA0QgqAIDRCCoAgNEIKgCA0QgqAIDRCCoAgNEIKgCA0QgqAIDRCCoAgNEIKgCA0QgqAIDRCCoAgNEIKgCA0QgqAIDRCCoAgNEIKgCA0QgqAIDRCCoAgNEIKgCA0QgqAIDRCCoAgNEIKgCA0QgqAIDRbAfVvn371NbWFs1aAADowHZQbd68WfPmzVNFRYUOHz4czZoAAAhJsNtx1apV+uSTT7Rz5069+OKLGjBggKZOnaopU6Zo6NCh0awRANCP2Q4qSRo+fLiGDx+uWbNm6eDBg/rlL3+pLVu26Pbbb9fdd9+tL3/5y4qL42MvAEDk9CioJKmxsVE7d+7Uzp075XK5NHPmTKWlpWnr1q3as2ePlixZEo06AQD9lO2g2rp1q3bu3KnGxkZNnjxZTzzxhEaPHh16feLEiZo7d25UigQA9F+2g6qmpkZf+9rXNGHCBCUkdFxtwIABnE0BACLOdlAtWrRIcXFxYSEVCAQUDAZ10003SZLuvPPOLtevqalRZWWlLMtSQUGBioqKOvT56KOPtHHjRrW1tWnQoEH6wQ9+0INDAQDciGzf+VBWVqajR4+GLTt69KjKysq6XdeyLFVUVKi0tFTl5eXatWuXjh8/Htbn3Llz2rBhg5555hm99NJLWrRokd3SAAA3MNtBdezYMY0aNSps2ciRI3Xs2LFu162trVVGRobS09OVkJCgvLw8VVVVhfX529/+pokTJyotLU2SlJycbLc0AMANzPalv5tvvlmnT5/W4MGDQ8tOnz6tAQMGdLuu3++Xx+MJtT0eT4cvDTc0NCgQCOi5557ThQsXdN9992natGl2ywMA3KBsB9XEiRO1Zs0aPfbYY0pPT9eJEye0adMmTZ48udt1g8Fgh2Uulyus3dbWpv/85z/6/ve/r0uXLmn58uUaNWqUvF5vh3U7W9YTdRHclhPq2rX7Qs1OiuX49GYumVKv0/t2UiSPsy+NkZO/25ycS7aD6tvf/rZef/11lZaW6vLly0pMTNT06dP18MMPd7uux+ORz+cLtX0+n1JSUjr0GTRokNxut9xut+644w4dO3as04Ovr6+3W3a3Irktp/TFmp0Uq/Hp7X5j+X72l7nU2+P0er19doycrjsS++sq7GwHVWJioubOnavvfve7OnPmjAYNGtThrKgrWVlZamhoUFNTk1JTU7V7926VlJSE9cnNzdVrr72mtrY2BQIB1dbW6v7777dbHgDgBtWjJ1OcP39e9fX1am1tDVs+ZsyYa64XHx+vOXPmqKysTJZlKT8/X5mZmdq2bZskqbCwUMOGDdPYsWO1ZMkSxcXF6a677tKtt97aw8MBANxobAfVX//6V1VUVMjtdisxMTG03OVyad26dd2un5OTo5ycnLBlhYWFYe0ZM2ZoxowZdksCAPQDtoPqN7/5jRYtWqRx48ZFsx4AAMLY/h6VZVnXfPIEAADRYDuovvGNb+h3v/udLMuKZj0AAISxfenvnXfe0alTp/T2228rKSkp7LVXX3014oUBACD1IKiefPLJaNYBAECnbAdVdnZ2NOsAAKBTtoPq8uXLevPNN7Vr1y6dOXNGmzZt0oEDB9TQ0KB77703mjUCAPox2zdTbNq0SXV1dSopKQk9keLqL+0CABANts+o9u7dq7Vr18rtdoeCKjU1VX6/P2rFAQBg+4wqISGhw63pLS0tGjRoUMSLAgDgCttBNWnSJK1bt05NTU2SpObmZlVUVCgvLy9qxQEAYDuoHnnkEQ0dOlSLFy/W+fPnVVJSopSUFH3rW9+KZn0AgH7O9mdUCQkJmj17tmbPnh265Gf3z3wAANBbtoPqxIkTYe0LFy6Efk5PT49cRQAAXMV2ULX/Q4dX27x5c0SKAQCgPdtB1T6MTp06pd/+9re64447Il4UAABX2L6Zor3Bgwdr9uzZ+vWvfx3JegAACNProJKk+vp6Xbx4MVK1AADQge1LfytWrAi7y+/ixYuqq6vTgw8+GJXCAACQehBUd911V1jb7Xbrtttu0+c+97mIFwUAwBW2g2r69OlRLAMAgM71+q6/rsycObPXxQAA0J7toGpoaNCePXs0cuRIpaWl6eTJk6qtrdXEiROVmJgYzRoBAP2Y7aCSpIULF2rSpEmh9p49e/T3v/9dxcXFES8MAACpB7enV1dX60tf+lLYsgkTJqi6ujriRQEAcIXtoMrIyNDWrVvDlr377rvKyMiIeFEAAFxh+9Lf/PnztXr1ar399tuhv+wbHx+vxYsXR7M+AEA/ZzuovvCFL2jNmjU6fPiwmpubNXjwYI0ePVoJCT36mAsAgB7p9SOUsrOzFQgE1NraGsl6AAAIY/t06NNPP9ULL7ygm266ST6fT3l5eTp06JC2b9+up59+Opo1AgD6MdtnVL/4xS80c+ZMvfzyy6HLfdnZ2frXv/4VteIAALAdVMePH9eUKVPClrndbl26dCniRQEAcIXtoBoyZIiOHj0atqy2tpbb0wEAUWX7M6qZM2dq5cqVuueeexQIBPT73/9ef/7zn/X4449Hsz4AQD9n+4xq/PjxWrZsmVpaWpSdna3PPvtMS5Ys0Z133hnN+gAA/ZytMyrLsrRw4UK99NJLmjt3brRrAgAgxNYZVVxcnOLi4nT58uVe76impkYLFy7Uk08+qbfeeqvLfrW1tZo5c6b+8Y9/9HpfAIAbh+1Lf/fdd5/Ky8t16NAhNTY26sSJE6H/umNZlioqKlRaWqry8nLt2rVLx48f77Tfr371K40dO7ZHBwEAuHF1e+nv1KlTGjx4sF577TVJ0gcffNChT3d/VPHK3YHp6emSpLy8PFVVVWnYsGFh/f70pz9p4sSJOnLkiO0DAADc2LoNqoULF2rTpk2hMFq1apW+973v9Wgnfr9fHo8n1PZ4PDp8+HCHPnv37tWzzz6rV1999Zrb83q9Pdp/e3UR3JYT6tq1+0LNTorl+PRmLplSr9P7dlIkj7MvjZGTv9ucnEvdBlUwGAxrHzp0qMc7ab8NSXK5XGHtjRs36tFHH1VcXPdXI+vr63tcgxPbckpfrNlJsRqf3u43lu9nf5lLvT1Or9fbZ8fI6bojsb+uwq7boGofKL3h8Xjk8/lCbZ/Pp5SUlLA+R44c0Zo1ayRJLS0tqq6uVlxcXIc/1ggA6F+6Daq2tjZ9+OGHobZlWWFtSRozZsw1t5GVlaWGhgY1NTUpNTVVu3fvVklJSVif9evXh/08fvx4QgoA0H1QJScnh31mlJSUFNZ2uVxat27dNbcRHx+vOXPmqKysTJZlKT8/X5mZmdq2bZskqbCwsLf1AwBucN0G1dVnOtcjJydHOTk5Ycu6CqgFCxZEZJ8AgL6v1384EQAAJxBUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjEVQAAKMRVAAAoxFUAACjJTi1o5qaGlVWVsqyLBUUFKioqCjs9Z07d+oPf/iDJMntdmvu3LkaPny4U+UBAAzlyBmVZVmqqKhQaWmpysvLtWvXLh0/fjysz9ChQ/Xcc89p9erVeuCBB/Tzn//cidIAAIZzJKhqa2uVkZGh9PR0JSQkKC8vT1VVVWF9vvjFLyopKUmSNGrUKPl8PidKAwAYzpGg8vv98ng8obbH45Hf7++y/3vvvadx48Y5URoAwHCOfEYVDAY7LHO5XJ32/fDDD/X+++/rhz/8YZfb83q911VPXQS35YS6du2+ULOTYjk+vZlLptTr9L6dFMnj7Etj5OTvNifnkiNB5fF4wi7l+Xw+paSkdOh37Ngx/exnP9OyZcs0aNCgLrdXX18fsdoiuS2n9MWanRSr8entfmP5fvaXudTb4/R6vX12jJyuOxL76yrsHLn0l5WVpYaGBjU1NSkQCGj37t3Kzc0N63Py5EmtXr1aTzzxRJ/6PxgAQHQ5ckYVHx+vOXPmqKysTJZlKT8/X5mZmdq2bZskqbCwUG+++abOnj2rDRs2hNZZuXKlE+UBAAzm2PeocnJylJOTE7assLAw9PP8+fM1f/58p8oBAPQRPJkCAGA0ggoAYDSCCgBgNIIKAGA0ggoAYDSCCgBgNIIKAGA0ggoAYDSCCgBgNIIKAGA0ggoAYDSCCgBgNIIKAGA0ggoAYDSCCgBgNIIKAGA0ggoAYDSCCgBgNIIKAGA0ggoAYDSCCgBgNIIKAGA0ggoAYDSCCgBgNIIKAGA0ggoAYDSCCgBgNIIKAGA0ggoAYDSCCgBgNIIKAGA0ggoAYDSCCgBgNIIKAGA0ggoAYLQEp3ZUU1OjyspKWZalgoICFRUVhb0eDAZVWVmp6upqDRgwQMXFxRoxYoRT5QEADOXIGZVlWaqoqFBpaanKy8u1a9cuHT9+PKxPdXW1GhsbtXbtWs2bN08bNmxwojQAgOEcCara2lplZGQoPT1dCQkJysvLU1VVVVifffv2aerUqXK5XBo9erTOnTun5uZmJ8oDABjMkaDy+/3yeDyhtsfjkd/v79AnLS3tmn0AAP2PI59RBYPBDstcLleP+1zh9Xqvr6B39l3f+k7ra/U6rd34XPf8uI59R22dSOkvcymCx+nofLpeTr6/Dv67c+SMyuPxyOfzhdo+n08pKSkd+pw8efKafQAA/Y8jQZWVlaWGhgY1NTUpEAho9+7dys3NDeuTm5urHTt2KBgM6uOPP9bAgQMJKgCAXMHOrrlFwf79+7Vp0yZZlqX8/Hx985vf1LZt2yRJhYWFCgaDqqio0IEDB5SYmKji4mJlZWU5URoAwGCOBRXsuXTpkp599lkFAgG1tbVp0qRJeuihh3T27FmVl5frs88+05AhQ/T0008rKSkp1uXGTFfjtGXLFv3lL3/RLbfcIkl6+OGHlZOTE+NqY8+yLC1dulSpqalaunQp86kL7ceJ+dS5BQsWyO12Ky4uTvHx8Vq5cmVU5xRBZZhgMKiLFy/K7XYrEAhoxYoVmj17tvbu3aukpCQVFRXprbfe0tmzZzVr1qxYlxszXY1TTU2N3G63ZsyYEesSjfLHP/5RR44c0YULF7R06VK98cYbzKdOtB+nLVu2MJ86sWDBAv34xz8OBbikqM4pHqFkGJfLJbfbLUlqa2tTW1ubXC6XqqqqNG3aNEnStGnTOnwPrb/papzQkc/n0/79+1VQUBBaxnzqqLNxgn3RnFOOPUIJ9lmWpWeeeUaNjY366le/qlGjRun06dOhm0tSUlLU0tIS4ypjr7Nxqq6u1rvvvqsdO3ZoxIgR+s53vtPvL2lt3LhRs2bN0oULF0LLmE8ddTZOkphPXSgrK5Mk3XPPPbr77rujOqcIKgPFxcVp1apVOnfunFavXq1PP/001iUZqbNxKiws1IMPPihJ2rx5s15//XUVFxfHuNLY+ec//6nk5GSNGDFCH330UazLMVZX48R86tzzzz+v1NRUnT59Wj/60Y+i/l0zgspgN998s7Kzs1VTU6Pk5GQ1NzcrJSVFzc3NYdeG+7urx+nqzxIKCgr0wgsvxLCy2Pv3v/+tffv2qbq6WpcuXdKFCxe0du1a5lM7XY1TSUlJqA/z6f+lpqZKkpKTkzVhwgTV1tZGdU7xGZVhWlpadO7cOUn/u7Pt4MGD+vznP6/c3Fxt375dkrR9+3ZNmDAhlmXGXFfjdPXzIffu3avMzMxYlWiERx55RD/96U+1fv16PfXUUxozZoxKSkqYT+10NU7Mp45aW1tDl0dbW1v1wQcf6NZbb43qnOKMyjDNzc1av369LMtSMBjU5MmTNX78eI0ePVrl5eV67733lJaWpkWLFsW61Jjqapx+8pOf6JNPPpHL5dKQIUM0b968WJdqpKKiIuaTDW+88QbzqZ3Tp09r9erVkv53I9NXvvIVjR07VllZWVGbU9yeDgAwGpf+AABGI6gAAEYjqAAARiOoAABGI6gAAEYjqAAARiOoAABGI6gAAEb7P8X5hjxLQIknAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].plot.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Salary'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='Age', y='Salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.box()"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}