Black Friday -  Analysing Age & Marital Status.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1c0acf39",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d1720a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BlackFriday.csv')\n",
    "\n",
    "del df['Product_Category_2']\n",
    "del df['Product_Category_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9b992a81",
   "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>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F  0-17          10             A   \n",
       "1  1000001  P00248942      F  0-17          10             A   \n",
       "2  1000001  P00087842      F  0-17          10             A   \n",
       "3  1000001  P00085442      F  0-17          10             A   \n",
       "4  1000002  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  Purchase  \n",
       "0                          2               0                   3      8370  \n",
       "1                          2               0                   1     15200  \n",
       "2                          2               0                  12      1422  \n",
       "3                          2               0                  12      1057  \n",
       "4                         4+               0                   8      7969  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f0144760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Purchase Distribution by Age'}, xlabel='Age'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Age').size().plot(kind = 'bar', figsize = (12, 6), title = 'Purchase Distribution by Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7ab48797",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = []\n",
    "for i in df['Age'].unique():\n",
    "    lst.append([i, df[df['Age'] == i]['Product_ID'].nunique()])\n",
    "    \n",
    "data = pd.DataFrame(lst , columns = ['Age','Products'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f274a258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.bar(x = 'Age', figsize = (8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "8947785f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Amount Spend by Age'}, xlabel='Age'>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Age').sum()['Purchase'].plot(kind = 'bar', figsize = (12, 6), title = 'Amount Spend by Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "73aed420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Amount Spend by Age'}, xlabel='Age'>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Age').mean()['Purchase'].plot(kind = 'bar', figsize = (12, 6), title = 'Amount Spend by Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "bbdb3f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Purchase'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ONQwTu7wZH93puj1StsP85ReZ85f5swh4GGha1jkDKGuycDa3owuISDxwNUZ6sQ6JyX3wgolZv6ONbQyF6o8nyieLZ9nObUiUmEzR1B421aNbig5FZAG+Ck377DzHooku4qMxk8oNRXmzXm7vBJ84dwNfBoqBDcC3VHV7K+dmAktV9Yxmxy8DfqGqft0EY24E943ed7T1e4GeX9qpM5573FN/07uelfEejfgpazDxigw6HmcLODY81Lg07kCO49FhUSpugP/JnL+s3c1LVXUDtwFvAwXAa22I+xXgY2CCiBwWkaZBLtfj5/QcYnAEL5iYdQfwB3/Pd9s4tGS67dAbU2WqisTcDa81Hik7/slltXXnmG1HI6pUXu7MO7FTR0RVIEcrXFaUN+tts41oSix+oX8QyMnxXobfsML7pecf9eydvtW7IVRGRRJrghAbHixUcf/IdWdhDIgb4G6zDWhOTAm8YGLWBRjB+QGT5Gb8bUu95z39hHvLmfu8W4NsWkSxJSkpYhycnvLM+fgt7/kRM5voIl/NnL/sNLONaEpMCZwAR+/W6NnAWb9c4rUvXOheP6pU9wbDqEjjcITUDf/Qc8bKR9zXR2XwSzvcZbYBTYmZZ/CCiVm9gBKCGO+t4D04kDWPXBM3qqy3dMYhITJR9Ww8cMiVpJgWfVKs/dZPc/xhsmKLtUGmGhhYlDerE66GwSeW/rg3EORkDgK2kWVMe3KRp/+vXvasNHMPPaiIxH2WlLTPrO7rNGnXpY5HTo9BcQP0JAD/9FATS3/gLk/P20Ig6YwDOuOvf/Qk3vm6Z0WyU0OfzT/EfJySbMrNyqNSeqnj4V51JEe0w00XudZsAxqJCYEXTMw6Fzg71P0I9LywQC967nFP/Y3velZF8x76huTksD+bqVJ3vfO+8mIGDAl332Fmdub8Ze2HB4aJmBA4ENZslzZlwKyNOv35Rz1H56z1fkQULmQUJoQ3NlwVvdd9y2cbdGJWOPs1iR7ALLONgNgR+NfM6DTey/Bvf+C98IVHPbujbQ+92iajtHOBDp3i754Zq172XDI1XP1FABExTY/6VfSCiVlZQPOAeVOoTmbL76+02baOskVFMcPXDx85MMblDnmZ3K3ezNVfc/5vp7fDjr/5e+oLNxCXms7Q7xn1BypWvUDd3nUgQlxqb/rNvIv4nv1atK3a8Do1W94BgYQBmfSfeRcSHm/YOmBAUd6swHNrBZFYGMEvNduARno2cNZ9r0bPHvq65OQjoe7jhPb89CrnA10auXvYL2HgN+4/5Viv869h6C1/YujNT5Iy5jwq17R0z3ZXH6dq038YfOMTxo3B66W2YFVXTAmEVGB2uDprC0vgIWBAFVPynvWMfvhp90cDK7TYbHvaItSx4U6N35/jeGyUm/guJbxMHn4GcSk9TzlmS/piR1RdDbSZpM7rQd1O1OtB3Q7ievTtiimBYvo0PWJcFjtDwcSsBOAis+1oDQFbZhkXPrnI49g+Ulb+/grbGVVp0nIOaSI7khJDVgTCq5Rf7nzQVkmP3qHqo3zV89RuW44tKZVB33ywxe/je/an15SrKF50MxKfSPKos0kZFVav2IvD2VlrRPsIPhVjxTJiabKHnnDn656VkbSHfiwubnjHZwWOKs4fuOYdKNRhIX2+7zP9u2T8+DnSTruI6k1LW/ze01BD3Z51DJv7NBm5z6MuBzXbPwilSc3pmzl/2fhwdticaBd4xE3P20Kg14UFRhz6je95VsZ5tO3Ea2HCKzL4pM0WdIeXJ9xfX/++99xJwb5uW6SddhF1uz9qcbyhaDPx6YOIS01H4uJJHX8BjuKCcJnViKk7B9Eu8K+YbUCg2JQBszbojBce9ZTMWetdY/Ye+sYg1Q1v5H3P2Sv+6Lk65EUKXCe/WNqo27uOhL4tk6/G9xqA88guvK4GVJWGA1tI6BeSSUt7mJqTP6BtMhEZDywCBqnqGSJyJjBHVX8bKgPbomBiVm/gOBBpCfEDwhHPrr9cZqtebbdNNqP/a6pqVi44cTIoOdAOeAeuneF84vxg1+w+9sbDOA5uxVNfRVxqb9Kn3UDDvo24Th4GsRHfawB9v5pLfM/+uKtPcOKtPzLIt+pesfolaneuRmw2EgeNod9ldyBdW/MLlM1FebNC7mXZFoEKfCXwM+DPqnq279i25nmjwkHBxKyvYKS+iQnM2kMf43Sueb24tMsZTGs0ecdkx6JRDSRFhItmBOEB0ovyZtWa0XmgU/RUVV3f7Jg7WMYEyEST+g0JTfbQ12WWamG4+j0cH99x1YUOcKvtyCWOR/tZ4m6VOOA8szoPVODHRWQMPhdHEfk6Rgy2GUwwqd+QMqCK8x961jPq4afdH4ZjD90hkukER2fbq1LzdeeC6lL6RmSm1gjBtIW2QAWeC/wZmCgixRjZK34UbKP8JCYFDp/voU97cpGn/30ve1b2rNO2E6h3uTOJ35qUtL8zTVXx/sz9wx2bdWzM/l8EiegQuK+m0iXAAGCiqk5T1aKQWNYxMf+lEkiyH9AZf/uDJ/6Of3tWJDk1JM9xa1OSj3em3YueS1b/wzNjSrDtiUFMy9MWkMBF5E4R6YXhSP+EiHwiImHfqiqYmJVG52o6RSUCvabt0IvyH/fUffc9z6pg76GvT0nyBtrmE+/YVfe5b4m2CiRmYdp3NdAp+i2qWoWx/zwQuBlzyvOOp1MVsqMbmzJg9gad/sKjnpKvBTEOfW9CQu9Azi/T9E3fcP46WmqHRQKpmfOXhdUJvpFABd4oqpnAs6q6BXOEFvPT8/aI9zLiO7449Oxt3o1dvV6VzZbpb2x4gyYUftnx6FgPcVEdx2ACppRBDlTgm0TkHQyBvy0iPYGAp3dBoFsLvJEkNxNu/4938tO/d2+27/du6/SFRHoVJcQf6ug0r8rxy5x5idWkpXe6r+5LVAj8e8B84DxVrQMSMabp4cYSeBN61jPpvle9Z/zpKff6zu6hdxQbrorjRtfPS4p0SNh9PWOEyBS4iCSLyHoR2QJsBa4DxovIdOA+YKmIbBeRhzu4zk9FREWkv+/nTBGpb1I6dXEAdkdE4v5IY2AlUx561pPZmT30tSnJ7e6FP+j+1sbV3jOjIlNNhGKKwP15jnIAOapaIyI/BJ7A2PsuBM4EVqnqxSLSpkeUiAzHiPxqHthQqKqTOmF354pjdwME4nx76I5tI2Xl76+02atTpcMFnu1JiW16ob3pmbLiL57ZFwXV0O5HZI7gatAYw3w7sAsoBfYA3wfKfOeVtXOZJ4B7CF6Sv5A/A95bUsK0vXuYs79lfYBnTp7gtF07KXe37aXrUeXqov386HCHj7YhIdA99GNxca1+AQu9Q9b82HXXRSExsnsRmQIHEJE4EdkMZAHvYOyDTwAygdkislJEWvW3FZE5QLFvxb05o0TkU1/7QJLyhXwEvyo9nb9ktHzcLHG5+Li2jiHx7U9+XigvZ0xiUqjM8xt/99A9IkMrbLbypseqNHXrTOeDsVIY0GxMceX1S+Cq6vFNpf8LfAlYA4wFvgM0Rpi9JnJqmKCIpAL3Ar9q5bIlwAhfVNpPgJd9TjT+EHKBT05NJT2u5Z/nobIy5g0Y0O7eYKnLxcraGq5Jj5zF5iZ76Ee+1kYc+sbkpAON710adzjH8ehgB4mm1S+LMcKSyrU5gbqqzgbeA/YB24C/AVf6Isy8QH8Reda3aPYmMAYYBWwRkSKMaconIjJYVR2qRq0vVd2E8Uzvb3obU750y2uqGRgfz8Tk9rvPKyvjpwMGYotAV5x4LyO/84H3S88/5tk9rdke+scpyZUAqlRd6Xyg4Ti9B5hjZUxiit+AP6voA0Skt+99CnAJxnP36xjebIN9iSASgeOqerOqTlLVmaq6VVUHqmqmqmYCh4FzVLXUd90433VHA+MwbhztUjAxy5Q/VL3Xy59PnOD2/v3bPW9FTQ194+M4vYObgNkku5hwR7M99E+Tk+JV8dzhum33dh011mwbY4ywZploxB+xDAHyfWLsj7HA9XOMBbNhwFyMwgM3amCuk9OBB0TEjREUP1fVr6gpU/5Qh1xOil0urioyAq+Out1cc6CIJSMzGdDkefyT+jo+qKlhVc1eHKrUer3cc+QIDw8daobZHeLbQ6cs3bvu6Tk27189Mz/6j/dL0822KwYxZWAKNKPLXuD8xqm1GfjqgFeGo69il5MfHT7MG6NGt/jdJYV7+fvITPq0s9i2vq6WZ0+eZFEri3WRhIKWDjp/496MM8u/NHh8aq+EXqZFP8UqCkdH5k0P+9810LvKIcIkrnYIy53wp0eKWV9XR4XHw8WFe7mtX3+u6d271XPL3C7uKy3lzxEu5OZ4bIm1+0bN3nR42Izhaos/z1X77soVR9ZmzRmR22ATW2ROOaKXajM69WsEF5Gf+N6ejrE9towmWUBU9fGQWNcKvmIHUVu2NxKoT+57ZNe463ef7HvaJHzrKwANFYs3oXXnDkwesf2iwdePExFTVn5jlL0Zednjwt2pv6NhY92Yg75XIiYt+2ftLHAVTMyqbmKThZ+c7D1++67x11fWpwycgsgpI7SquwGtOw2grOHg6Tsr163K6j3VehYPHqbkLvRL4Kp6f8dnhZXjWAL3C6/Y3MXDZmzYlzmrpyc+pc3st17Xvh3A504tn5WvnD4kZfSHvZMGhjzHeTfBlEIXgWZ0eVeaTOlEpI+ImJG62LRFvmjBFZ9SWTDhhpUrpv++bM/Yr1/QnrgBPI5tVc2PvVfywrlur2tX6KzsVlSY0WmgC1YDVLWi8QdVLW8vyCSEWAJvg5rUIft3Tfjmocpeo89FxO+USl53cYsIPY+6U94pfjbl8owfVIpI5LjlRSdBrSDjL4EK3CMiI1T1IICIjCR4ASSB0KkkgbFMWf9Jn+wZ+3WPI6n3ZERGBdJWvbXHwdVqjH21u3zE+uNvrp/Sf+Z5zV2RLQLClKijQAX+P8CHvgonYDir3Bpck/zCGsEBr8Q7ikZ+Zf3B4ZcO9sYldjooxOPcuRsjxqBVimq2TRmWOm5lRtp4K8li54lsgYuIDcOL7RyMPM8C3K2qZoym3VrgjsRex3aPu3b7sf5nnY7YAonCaxWPs8DT0Tkflf1f9pzhuZtS4nuc29X+uimRPUVXVa+I3KaqrwEtizGHl24p8Mqembt2Tfjm8Zq0YZMRuShY11XPsTF+nGZ7q/iZUVeMuK3YJrZuk7I6iET2CO7jXRH5KbAE+DyBgJ8+5MGkNMz9mYYi3pLBUzcWjr4y0ZXYYxJBzkfndR/bD+rXM7vTW993RemrOy4e/M3+ImJ+sHt0ERUCv8X3b26TYwq0dNYOLZ3PIBoluOOSavaN+tqm4qHZmWqLD1n1EI9z20GMkF6/ONZw6LSCyrWrT+t9QZcfDboRtRl52eEeBIEABa7q350+DOzBmEGkmW1IsKlP7n945/jrC8v7TJwUyDZXZ/E49wZcEXRr+arsISmjP+yTNMhygvEPc/J2EaDAReS7rR1X1eeDY45/ZO0s8BZMzNpCOyu/0caJPllbd4+/rqY+uf8URMKSv0vV60arszrT9r2SFyZfNeKOnfG2xJgq4xwiokPgnFrnOBn4MvAJEFaB+/iUKBe4V2zuw8MuXr8/c2ZvT3xy2FMSe90HdmBkxg28rXqS3y5+Lm1mxg8qmno3WrTKp2Z1HOgU/famP/u8m14IqkX+s9mkfruMKz6tfM/Yqz8rHTRlPGIz7SblcWzr0nNhjbt8+Prjb26Y0n/mZMsJpl0+MqvjrsZW12GkWjID0+6KnaUmbei+nRO+VVzVMzMgN9JQ4XUf6NfVaxTVbDtvaOrYlcPTJpj+eSIUxUhSagqBPoP/hy9cU20YdY9fC7ZRfrINIwQv4ovgHR1w7qY9Y69WZ2L6uRj550xHvQ2VqDMoGUbWlL2ePWd47saU+B6Tg3G9GGNXRl62aa7VgYrj0Sbv3cABVT0cRHv8JmtngaNgYlannyFDjccW31A08vINhzJyhnjjEiPO+8vj2r0TOD9Il7O9Vfz0mCtG3HbYJq0XUOgK897M4/3CNfRL7cP738s/5XeL173C71YsYsvtb9A3tXeLthcsupa0xBTibHHE2eJ488a/Btu8jjBteg5+ClxEkjGSK47FqE/2tKqaEsDejM1EmMAbEnsf3T3u2p3H+9vPCIYbaajwOLa3W4ssUJzehj4flLxamjPkW45gO8F8w34ZN51zFXct+99Tjh+pOsrqoo0M69V+TYHXvvmHVsUfJkwVuL/x4PnAZAxxXw48FjKLAmO12QY0UtFrdMG68+79aM0Fv+1zfMBZMxBbl59vQ4l6jo4M9jWPOw5n7aj4eH2wrzt1+CR6p7SsdXH/+3/i3ot/hJhSot5vIn8EB05TVTuAiDwNBP0/sZP8B6PgQqBlkIOCIt4jQy5cXzh6Tqo7IS2iZhLt4fWUHwZv0AUOsK1idfbQ1NGr+yQNDuns5Z09HzK4Z39OG9h++nYRuOG1eQjCDZPmcMOkOaE0qzllGXnZu8PZYXP8Ffjn6WZU1R0pOyJZOwuOFkzMWgdcEM5+3XFJ1YWjr/jkyJBpo9UWNzWcfQcDj3P7fkJYDO+9khfPu3LEHQUJtsROOdF0RL2rgSc/foGXrut4IvmvG55icM/+HK8t51tLfsKYfiOYOnxSKMxqDdNWzxvxd+Q7S0SqfK9q4MzG9yLSItVPmPl3uDqqSxlw6JOz7li5atpjWjxsxgy1xUVXnmQfXufukO48eNWT/E7xsz1VtbzjswOnqKKYQ5UlfPWZW7hg0bWUVB/j8ue+T1lNyyDDwT2NSjT90/pw2fhsNh8pCIVJbbGy41NCi79JF+NCbUgXeB3IC2UHx/uevmX3uGvrG5L7TcGodR61qKpXvRUhdy+tcVdkrDu2dOP5A2af48slEDSyBoxh8+1vfP7zBYuuZdmNf2mxkFbnrMerSo+kVOqc9azav4E7L7wpmKa0hxfztpA/J+L3kDsia2fBroKJWbsIdhil2FwHh1+y/sCIr/b1xCefFcxrm4m6i3di+C+EnAO1OyYPSxu3cnjaxC45weS+cT9rD37KyfpKzlt4DfOm3cz1Z81u9dzS6uPc89ZDPP+NRzhWV84P/nUvAB6vhytOu4SLRwdrZ7BDVmTkZR8JV2dtEVDpokilYGLWQ8A9wbiWMyHt5J6xX//s6MDJWYjNlJrOocRZ+9YKr3PHRWHsUr82/MebUuN7djcnmO9n5GU/bbYRpqw+h4DXu3qB6h4ZhRvOuWf1h196KOXooCkXxaK4Abyu/b3D3KW8XfzMGK96THGIMgkH8A+zjYAYmKL7WIeR5aVF6t/2UNCjA8/btHfMVeJMSj8Xo555zKLqrEXrw14Az3CCeaU0Z8gNDT6nqVhnWUZettk1/IAYGcGzdhZ4CWAU99gS6/aOvmL1yum/37fjtJsm+8Qd83idhQWYVHLquKM4a3vFRxvM6NsEXjbbgEZiZQQH+DOGO22bNCT1Kdk17rrdJ/qdcSYiEetGGio8zm21HZ8VOrZXfJQ9NHXM6r5JQ2L5b1+J+UlJPycmFtkaKZiY9RGtJIEoTx+3Y9f46yvqUgedh0iCCaZFBA3lf9wDbrPCewGwEee4cuQd+0LlBBMBPJORl/09s41oJJZGcICn8AlcEU/xsOz1+zK/1sOdkBr2bCmRhnqrj5otbgAvnqS3i5/tOSvj1pMi0tdse0LAS2Yb0JRYG8ETXfGpOwpHX3G4ZPAFY9QW/NDFaMVdv+4jd8NHF5ptRyMj0rI2Th3wtaA7wZjM1oy87IiKSYipETxrZ4Fz4Q/fewGxLTDblkZeXPEI2w6spWdKb+699tRt0fe2vMbra/9M3nf/RY+UU2v7ldeU8fwHeVTVlSMiXJg1i4vt13TaDo9zZ6fbhoKDtQWTh6WOWzGiR9ZFZtsSRB4224DmxNLd00BsCzFSSUUEU8d/ldyZD7Y4Xl5Txs7Dm+jTo/XirDaJ4+qpc7nvumf56ZV/YtX2f1NSXtRpO9R7sv2wKxP4+NgbM+rcVbGysn4AeNVsI5oTcwLPXZxzHHjWbDsaGTv0TFKTW8Yy/3PNU1w59dY2Y5nT0/oxfMB4AJITUxnceyQVtZ3L/ON1l+4BjUTHHXm7+NlxXvWYllY4iDyWkZcdCUlQTiHmBO7jMaDDgnpm8VnRGnqn9Sejn39+NSeqSzl8Yi+ZAzu38OxxbDPdJ7otnN6G3stLXqlV1QazbekCRwG/3FJFpEhEtorIZhHZ6Du2QESKfcc2i8jMYBkWkwLPXZyzH3jFbDtaw+lq4O1PX2LW5Jv8Ot/hqudv7yzgmgt+TEpi5wq5eFyFEV0B5oSjeOK2ig83mm1HF8jLyMsO5LHwYlWdpKpN/fOf8B2bpKpvNm/guwncFKhhMSlwH/9DBD2LN3Ks6ggnqkp58B+38quXvkVF7TEe+tdcqupapij3eNz89Z0FTB73ZSaN7pxviKrbgdaG3T01UHZUrJl2wnEkYlJwBUAxsNhsI9oiZgWeuzjnEPCQ2XY0Z1i/0eTd+E8euOFlHrjhZXqnDeDnVy+mV+qpW8KqyksrH2Vw7xF8+cxvdLo/r6toB5DaRbPDwvIjL09xeZ07zLYjQP43Iy87kMcLBd4RkU0icmuT47eJyGci8oyI9AmWcTErcB+PYKxumsaz7/2Wx16/naOVh/jli9exZmeL2dfnVNQe56k3fwHAvtJtrN/zLruPfMqD/7iVB/9xK9sPrgu4f49zW0QEPfiD4QTzTLoJ5ag7SxHwtwDbXKiq52AkL80VkenAIoxAp0lACb6kpiJib3wux3DDfqDJc7pfST1jytGlNRbOXf4NIiCzhlk0lD+5A1wRP0VvyvC0iZsuGDDn7ChwgpmZkZf93842FpEFQI2qPtrkWCawVFXPaOXcIlV9LpA+Iv0P2GVyF+f8HVhhth1moN76cnBFXfXPQ7U7zz1YW7DKbDs64JVAxS0iaSLSs/E98BVgm4gMaXLaVRhVe4JCzAvcx51E8LZZqPA4C3YSpf/Ha4/9Z0aduypS0nM35wTGdypQBgEfisgWjNTjy1T1LeBh39bZZ8DFwN3BMjTmp+iNLJy7fBEdhJPGGo6ql1ar52jUhmYm2JIqrxxxe4VN4kKSw70L3JSRl53f8WnmE1O+6B3wS+A6IGgrlJGOespGBdpmyfot7Cgpo0dSIj+77NRciSt2FrL0s53cf8WlpCWdmjeirKqGF9d+UfD1RE0dXz1jPNPHB2zC57i8jvT3S14qvWTId+pFJKXTFwou70aLuCFKp2+dIXdxzgngh2bbES68nhNFoAFH000elcEPpk9pcbyirp7dR4/TO7V1nQ3s1YOffCWbn3wlm7sumUZifBxnDOu6d+xJR8mEreWrN3X5QsGhjiibBXYbgcPnC24R65QQTDyObQc7027MgH6kJrbMifHvzTuYfVaWX1XA9pQdp19aKn3TgrP9XlD58bTjDcWRsOj264y87H1mGxEI3UrgPu4GPjPbiFDjde0JWoXP7cVHSU9JZmjvlkEzrbH54BEmjRgarO4B+KDk5akur2N7UC8aGJ8AT5jYf6fodgLPXZzTAFwLmJqfLJSoej3qrQrK9pjT7eG9gr189fTxfp3v9njZfuQoZw0f0vHJAeDFm/hW8bN9VLVlfaLQUwl8KyMvO+p2YrqdwAFyF+fsAnLNtiNUeN2HdgDpHZ7oBydqajlZW8fj76zmd0uXU1nfwBPvrqaqvnXvzJ2lZWT0SadnclBLhANQ564cuqbs3wdU1Rv0i7eNF0Pcu8LYZ9DolgIHyF2ckw88b7YdocDj2Bq0UW5I717cf8Wl3Ds7h3tn55Cekszdl2bTK6X19OahmJ435XDdrnMO1G4P5/P4vRl52W37F0c43VbgPn4MROWduT287gN++Sm3xosff8qT76/hWHUtv/nP+6zb1/ZaXWV9A39b9YUvitPtYffR49iHBVR/ImDWHVs2o9ZdGQ4nmFcz8rJDWtgy1HQbR5e2WDh3+ZnAx0RJxFVHqDqqHBULU4lxH4cEW1LlFSNur4gLnRPMp8C0AOO8I47uPoKTuzjnMwz/X6fZtgQDj3P3TmJc3GA4wSw/8pJDVUMhwGPAldEubrAEDkDu4px3gG9jLKhENR7njnqzbQgXJ50l47eWr/q04zMDwgV8PSMvu1N+BJGGJXAfPieYqPJSag11l44w24ZwUlC59sIgO8HcnpGXHQlONUHBEngTchfn/BWYb7YdncXrqTwCns47f0cpQXSCuTMjL/vPQbhOxGAJvBm5i3MeIgIT2PuDx7m90GwbzMBwgnmmr6p2Lq+0kUbpxxl52X8Mpl2RgCXwVshdnPNz4K9m2xEoXueuOLNtMIs6d9WQj8peP6SqgXqbKXBrRl72olDYZTaWwNtmLrDEbCP8RVVVveUTzLbDTIrrdp9dVLMtkMysXuDmjLzsQPOqRQ2WwNsgd3GOF7gB+IvZtviDeo7sBDrt4BIrrD/+5oxaV6U/2Sk9wHejKba7M3R7Rxd/WDh3+X3AA2bb0R6u2rdXeJzbLzLbjkjA5wRTHidxmW2c4ga+nZGXHTUztM5ijeB+kLs45zfA9zC+GBGJx7XPv1jOboDL60h//8iLrjacYBqA67qDuMESuN/kLs55BpgJVJhsSgtUXfVo/elm2xFJlDtLx31WvrK5E8whDPfTf5lhkxlYAg+A3MU57wLnA7vNtqUpXlfhDiD48ZlRzs7KdRceazjU6LTyITA5Iy87UtI/hYWoEriIxInIpyKytMmx20Vkl4hsF5FW96/bq94oIr8Qkb2+a3y1IxtyF+fsxhD5O8H4TMHA49heY7YNkcoHJa9eUO0qfxDIycjLLjPbnnATbUEJdwIFQC8AEbkYuAI4U1UdIjKwnbZPNK0g4Wt/GnA9cDowFHhPRMZ3tJeauzinYuHc5TMxChzeB7RMYhZGvO7i0MZnRi/ViveHWY/NichKs+EgakZwEckAZnFqLagfAXmq6gBQ1UDv0FcAr6qqQ1X3A3uBlilFWyF3cY7Ht/h2PkGsRBEo6q09Bm7/8il1L7YAk+ctWdptxQ1RJHDg98A9nBrxNR7IFpF1IrJSRM5rp31r1RuHYSy8NHLYd8xvchfnfApMxnBvDXs0mse5Yw/4ley0u6AYxfymzluyNKLWSswgKgQuIrOBMlVtvkASj1HIYCrwM+A1EWnty95q9UZaF0bAjgG5i3McPvfWbIxZQNjwOAuiPsQ1iHwGZM9bsvTH85YsDaSkb8wSFQIHLgTmiEgR8CqQIyIvYoy4/1KD9RgjaH8Reda3mPYmgKoeVVWPL1nfX/liGn4YGN6knwzgSGeNzF2cswbjJvIUnbhRdAb1nBgbjn4inGqMdNjnzFuy9COzjYkkos6TTUQuAn6qqrNFZC4wVFV/JSLjgfeBEdrsQ4nIEFUt8b2/GzhfVa8XkdOBlzEEP9TXflwnAhZasHDu8kuBhcC4rl6rLbzuskJn9YtjQnX9KOFVYN68JUs7fWOOZaJtFb05zwDPiMg2jJRLNzYXt4+HRWQSxqhahK+EkapuF5HXgB0YXmq5wRA3GHvmC+cuPw24BfgVAT7b+4PHue0wxqNHd2QXkDtvydL3zTYkkom6ETwaWTh3eTJwO0Yyib7Bum5DxV82oDXtLSzGInXA74BH5y1ZGhN59EKJJfAwsnDu8nTgpxjPi2lduZaqx+Wo+IMD6BEM26KAkxiPPH+at2Rpt3NY6SyWwE1g4dzlgzDKGd8KJHZweqt4nPu2uGpfPyuohkUmRcDjwNPzliyN+iyn4cYSuIksnLt8OEaU2i2cuprfIc6af6/0ugpndHxm1PIJ8Ajw93lLlkZdTbBIwRJ4BLBw7nIbcBnwA2A2fix+NpT/aTs4YzGC7G3gEWvxLDhYAo8wFs5dPgS4Cfg+MLq1c9RbX+GoXNSL6PFj6IjPgH9gjNY7zTYmlrAEHqEsnLtcgByMKfxMmlQLdTdsXuuuXz7VLNuCxEYMUf9z3pKlYfX+605YAo8CFs5dHo/hzTcLmOmoevmEekqnm2xWoChGDbh/Yoj6gMn2dAssgUchj113xTDwXAzMAC4CItFd1QNsxRD1x8D7lrdZ+LEEHgM8dt3sYRjuthMwIuwaXwPCZIITI05/C8bz9CZgw7wlS2vD1L9FG1gCj2Eeu252b04V/ARgCJACJLfybzKnLtzVY1TaLPP92/R9478HgZ3zlix1hfwDWQSMJXCLU3jsutmJGEL3WCNw9GMJ3MIihomVfVQLC4tWsARuEXJEpEhEtvqScGz0HfuGLxOuV0Qmt9O21Yy4IpIpIvVNji8O1+eJJqI9Htwieri4WXnfbcDVgD/1uFtkxPVRqKqTgmFcrGIJ3MIUVLUAoPUUehbBwpqiW4QDBd4RkU0icmsn2reWERdglK8QxkoRyQ6SrTGFJXCLcHChqp4DXA7kikggbrZtZcQtwci/dzbwE+BlEbEKMDbDErhFyFHVI75/y4D/o53iEv5mxPUVqzjhe78JKMRw5rFogiVwi5AiImki0rPxPfAV2qkEo6o3q+okVW1cLR/S5NdXNbYVkQEiEud7Pxoje+2+0HyK6MVaZLMINYOA//MtpsUDL6vqWyJyFfAkhr/8MhHZrKqtFX9sNSMuMB14QETcGIEtc1X1ZEg/SRRiebJZWMQw1hTdwiKGsQRuYRHDWAK3sIhhLIFbWMQwlsAtLGIYS+AWFjGMJXALixjGEriFRQxjCdzCIoaxBG5hEcNYArewiGEsgVtYxDCWwC0sYhhL4BYWMYwlcAuLGMYSuIVFDGMJ3MIihvl/QlR6pp8xV8YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Age').mean()['Purchase'].plot(kind = 'pie', autopct = '%0.1f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "eddf2927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='None'>"
      ]
     },
     "execution_count": 121,
     "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.groupby('Marital_Status').size().plot(kind = 'pie', autopct = '%0.1f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "fa6bc690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='None'>"
      ]
     },
     "execution_count": 122,
     "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.groupby('Gender').size().plot(kind = 'pie', autopct = '%0.1f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb343dac",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}