EDA_Univariate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.Countplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='count', ylabel='Embarked'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Embarked'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B.piechart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uPUfDuWOwlp6GMevaDj83UUct2pKLw+f8dwm8LSwZLzh0tgrvbM4RHcM3OJ3Qx3ZH+Ohp0MV2R0j/8TD2uw41e78FADQUnUT17q8QecOTUKlUbj1F7YH/QRuVDH1Crw49N5E7bA4nnl1+EHY/X6W4FJaMxBwOJ57/4qDfL5NdoDGGQxvVrclt2sgk2KtLAAANZw7DYarC2YX34vT8m3F6/s2wVxejYuO/ULDwvjYf32E1w/TTFhj7XdvsY209N5G7Dp6twntbeSXblnAmI7HPfjyDA368N9nF9Il9YC0vaHKbtfwsAkJjAADBGVchMKVfk48Xf/YigvuOhTHzmjYfv+7o93DarQjue1WHn5uoM/6x7jiu6xuHlCjurfhr3JKRUFWdFfPXHhMdw6eEDrkFDeeOoeqHz2CtOAfTkU2o3b8GxoE3AgA0hlDoolOavEEdAE1wOLSRXRsfp/Sb11Gx+YNmj1974DsE9RwGjSG0w89N1BlmqwPPrTgoOobP4ZaMhF7/3zGUmyyiY/gUfXw6oif+AZWbP0Tltv8iICwW4WMfhLGFLY/W2KpLAFXT10jW8rNoKDiCmMmvSPrcRJfyQ24ZvthbgIkDurZ9Zz/BI/4l8lNhNW5663sOA4n8TFxoIDY+MwYGnUZ0FJ/A5TKJvPTlYRYMkR8qqjZjIfcmbcSSkcDKvWexM4+XUibyV4u25OAcz9QMgCXjcaYGG+au5hmWifyZ2erAa9zpBwBLxuPe23oK56sbRMcgIsFW7jvLMwGAJeNRVfVW/Ot7HpBFRK4TaM5bzcs1s2Q86L2tuag229q+IxH5ha0nSrH1hH+fUYIl4yGVdRb8e1ue6BhE5GPmr/Hv2QxLxkPe3ZKL2gZuxRBRUwfPVmHTsWLRMYRhyXhAWW0DPtyeJzoGEfmotzf673EzLBkPeGdzDuosvDoeEbVsZ145dvnpsXMsmU4qrjHj4+zTomMQkY/7Pz+99DpLppPe23oKZqtDdAwi8nGbjpX45XEzLJlOqLPYsHRnvugYRCQT/jibYcl0wvLdBTwuhojabfWhQuSW1IqO4VUsGTc5nU58wD3KiKgDHE5g4Sb/2pphybhpy4lS5JSYRMcgIpn5cv85VPjRxQxZMm7697ZToiMQkQxZbA4s31MgOobXsGTckFtSi83H/ft8RETkviV+tMMQS8YNH27PAy9aTUTuyi0xITu3THQMr2DJdFCN2Yrle86KjkFEMrdkh39szbBkOuibA4U8ESYRddqaw0Uo94MdAFgyHfTFXm7FEFHnWWwOLN+t/B0AWDIdcLay3m9PckdEnvdfP9gBgCXTASv3nuXAn4g8JrdU+TsAsGQ64Mt9XCojIs/65sA50REkxZJpp8PnqnD8vH+dc4iIpLfm0Hk4HMpdImHJtNNKDvyJSAKltQ3YqeBZL0umHRwOJ77ar+xNWiISZ/XBQtERJMOSaYfs3DKcr24QHYOIFGrN4SI4FbpXEUumHb47cl50BCJSsPPVDdh9ukJ0DEmwZNph07Fi0RGISOG+PVgkOoIkWDJtOFVqQl5ZnegYRKRwaw4VKnLJjCXTho1HuRVDRNI7V2XGgYIq0TE8jiXTho1cKiMiL/n+ZKnoCB7HkmlFvcWOHaeUu/86EfmW7TksGb+yPacUFptDdAwi8hM/5lWgwWYXHcOjWDKt4FIZEXlTg82huF2ZWTKt2HSsRHQEIvIz208q66zMLJlLOFdZj4KKetExiMjPKG0uw5K5hL35laIjEJEfOlBQpahLvLNkLmFPvrLWRYlIHmwOJ3aeUs6SGUvmEvayZIhIkB9yWDKKZrE5cOhctegYROSnlHTkP0umBYfOVfH4GCIS5khhtWLOY8aSaQGH/kQkUo3ZhvxyZZyYlyXTAg79iUi0Q2eVsWTPkmnBPm7JEJFgh88pYy7DkrlIVb0VZyt5ECYRiaWUnY9YMhfJKakVHYGICEe4JaNMOcUsGSISr7TWgqIqs+gYncaSuUhuqUl0BCIiAMqYy7BkLsItGSLyFacU8KKXJXMRzmSIyFco4UzwLJlfsdkdijkAiojk74wC/h6xZH4lv7wOVrsyTuVARPLHLRmFySmR//onESnHmQpuySgKl8qIyJfUWewoq20QHaNT3CqZsWPHorKystnt1dXVGDt2bGczCXO+Wv77pBORspyR+ZKZWyWzadMmWCyWZrebzWZs3bq106FEYckQka8pkPmSWUBH7nzgwIHG/x85cgRFRUWN79vtdqxZswaJiYmeS+dlLBki8jVnyuW9JdOhkunfvz9UKhVUKlWLy2IGgwFvvfWWx8J5W3GNvNc+iUh55P7it0Mlc+rUKTidTqSlpWHnzp2Ijo5u/JhOp0NMTAw0Go3HQ3pLKUuGiHxMdb1VdIRO6VDJJCcnAwAcDuVdmthmd6CmwSY6BhFRE9VmPyqZXzt+/Dg2bdqE4uLiZqXz4osvdjqYt5XXWaCQS2oTkYJU18v7xa9bJbN48WI8+uijiIqKQlxcHFQqVePHVCqVLEumwiTvVwtEpExV/rRcdsFf/vIXvPrqq5gzZ46n8whTUdd8l2wiItHkvlzm1nEyFRUVmDRpkqezCFVvtYuOQETUjNy3ZNwqmUmTJuG7777zdBahLDbl7cxARPJXZ7HDZpfv3ye3lst69OiBF154AdnZ2cjMzIRWq23y8SeeeMIj4bzJKuMfIhEpW7XZhohgnegYblE5nR3fpyo1NfXSD6hSITc3t1OhRFixpwBPfbZfdAwiomY2zxqD5Mhg0THc4taWzKlTpzydQzgulxGRr7I55Ht8BU/1/zMulxGRr3LIuGTc2pK57777Wv34+++/71YYkRq4JUNEPkrGHeNeyVRUVDR532q14tChQ6isrJTt9WQs3JIhIh9ll3HLuFUyX3zxRbPbHA4HZsyYgbS0tE6HEsFqk+8PkXxLuNaGdV3fQ5ClXHQUUgi1+n0AoaJjuMXtc5ddTK1WY+bMmRgzZgxmz57tqYf1ml+dGYeoU1Ykr0BkwRbRMUhJVPI9f5lHB/85OTmw2eT5zQjUch8I6ry5aQeRWrBSdAxSGpV8L6Hi1pbMU0891eR9p9OJwsJCrFq1CtOmTfNIMG8L1Mr3h0i+4erIctxZ8oboGKREavn+fXKrZPbu3dvkfbVajejoaLz++utt7nnmq1gy1BnhWhve1r0FlUne12MnH6WS70qLWyWzceNGT+cQjiVDnbEieQX0BcdExyCl0mjbvo+P6tTgv6SkBMeOHYNKpUJ6enqTyzHLjYElQ27iHIYkF9hFdAK3ubUNZjKZcN999yE+Ph6jRo3CyJEjkZCQgPvvvx91dfJcLmDJkDs4hyHJqQOAQHnuvgy4WTJPPfUUNm/ejK+//hqVlZWorKzEl19+ic2bN+Ppp5/2dEav4N5l1FGNcxirPF9YkUzIeCsGcHO5bPny5Vi2bBnGjBnTeNsNN9wAg8GAyZMnY+HChZ7K5zWcyVBHcQ5DXmEIF52gU9x6+V5XV4fY2Nhmt8fExMh2uSxY77HjUskPcA5DXmPoIjpBp7hVMsOHD8dLL70Es9nceFt9fT3+9Kc/Yfjw4R4L503RIXrREUgmOIchr5L5loxbL98XLFiA8ePHo2vXrujXrx9UKhX27dsHvV4v28syG/UBCNZpYLLYRUchH8bjYcjr/LFkMjMzceLECXzyySc4evQonE4n7rzzTtx1110wGAyezug1saGByC01iY5BPoxzGPI6fyyZuXPnIjY2Fg8++GCT299//32UlJRgzpw5HgnnbTGhepYMXRLnMCSEzEvGrZnMu+++i969eze7vW/fvnjnnXc6HUqU2NBA0RHIR3EOQ8IYIkQn6BS3SqaoqAjx8fHNbo+OjkZhYWGnQ4nCkqGW8HgYEio8WXSCTnGrZJKSkrBt27Zmt2/btg0JCQmdDiVKDPcwoxasSF4BfQXnMCRIhDwvBHmBWzOZBx54AE8++SSsVmvj5ZbXr1+P2bNny/aIf4BbMtQc5zAklEoNdJH3loxbJTN79myUl5djxowZsFgsAIDAwEDMmTMHzz33nEcDelNcGEuGfsE5DAkX2hUI0IlO0Skqp9Pp9sXta2tr8dNPP8FgMKBnz57Q6+W93FRS04Ahr64THYN8QLjWhuyoV7lMRmKljgamfSU6Rad06lwqRqMRQ4YM8VQW4aJD9AgP0qKizio6CgnG42HIJ8h8HgO4OfhXsp4xIaIjkGCcw5DPiEgVnaDTWDIX6RlrFB2BBOIchnwKt2SUp2cMS8Zf8XgY8jksGeVJj+Vymb/i8TDkW1RAOJfLFKcHl8v8Eucw5HOiegK6INEpOo0lc5GYkEB0CdKKjkFexDkM+aTEQaITeARLpgWcy/gPzmHIZ7FklCsjMUx0BPISzmHIZyUOFJ3AI1gyLRicLO9Ta1P7cA5DPkujB2IzRafwCJZMCwanyPsiQdQ2zmHIp8VlyP6cZRewZFoQGxqIpAj5XkaaWsc5DPk8hcxjAJbMJXHJTLk4hyGfx5JRvkHJXDJTIs5hSBZYMso3JIVbMkrDOQzJQmAYENlDdAqPYclcQnqsEaGBnboSAvkQzmFINtLGACqV6BQew5K5BJVKhYFcMlMMzmFINnpcIzqBR7FkWnFl9yjREcgDOIchWWHJ+I+xl8WIjkCdxDkMyUpsBhCaIDqFR7FkWtE92ojUqGDRMchNnMOQ7ChsKwZgybRpbG9uzcgV5zAkOz3HiU7gcSyZNlzNJTNZ4hyGZEcfCiQNFZ3C41gybbg8JYK7MssM5zAkS6mjAI3yrmXFkmlDgEaN0b24NSMXnMOQbClwqQxgybTLNVwykw3OYUieVEDPa0WHkARLph3GpMdAo1bOEbhKxTkMyVbylYrbdfkClkw7hAVpcUX3SNExqBWcw5CsZU0WnUAyLJl2um1gV9ER6BI4hyFZ0+iBvr8RnUIyLJl2uq5vHIx67mXmiziHIVlLv8515mWFYsm0k0GnwfiMONEx6CKcw5Ds9btTdAJJsWQ64LZBXDLzJZzDkOwZIhS7V9kFLJkOGJoaga7hBtExCJzDkEL0/Y0iD8D8NZZMB6hUKtzKHQB8AucwpAhZd4hOIDmWTAfdNjBRdAS/xzkMKUKXZKDbMNEpJMeS6aDkyGAMSeEVM0XhHIYUY+A9ohN4BUvGDb8d2k10BL/EOQwpRoABGHyf6BRewZJxw42ZCYgO0YuO4Xc4hyHF6HcnEBQhOoVXsGTcoAtQ4y5uzXgV5zCkHCpg2AzRIbyGJeOmu4YmQ6fht88bOIchRelxNRCdLjqF1/CvpJuiQ/SY0E+ZZ031JZzDkOL40VYMwJLplIdGpYmOoHicw5CiRF/m2pLxIyyZTugVF4IxvaJFx1AszmFIcYY9KjqB17FkOolbM9LgHIYUJyjKL47wvxhLppOu6B6FfkldRMdQFM5hSJEufxDQBopO4XUsGQ94apz/7CniDZzDkOIYIvxu4H8BS8YDRqdHY2iqfxxYJTXOYUiRRswEAkNFpxCCJeMhs67rJTqC7HEOQ4oUkgBc/pDoFMKwZDxkcEoEruKeZm7jHIYUa/Qsv5zFXMCS8aBnrusFlUp0CnniHIYUKTwVGOAfZ1u+FJaMB/VNCMMNmfGiY8gO5zCkWFf9AdAEiE4hFEvGw54elw6Nmpsz7cU5DClWbAaQebvoFMKxZDwsLdqI23mJ5nbhHIYUbewfwfVzlowknr4uHSF6/95Ebg/OYUixkoYBvcaLTuET+JdQAjEhgZg5Lh1//uaI6Cg+i3OYjlu4y4KFP1qQV+kAAPSN0eDFUTqM76kFAExfWY8P91ubfM7QRA2yHwi+5GMu3m3BRwesOFRsBwAMitfgr1cH4vJETYv3n7u1Ac9vaMDvh+qw4Hr/3WOqVSoNcMNrolP4DJaMRKZdkYJluwtwpLBadBSfwzmMe7qGqjDvGj16RLgWID7cZ8UtS+ux92E1+sa4SuH6Hhr8+xZD4+foNK0v12w6bcOUDC2uSApEYAAwf5sF135swuEZRiSGNl3o2HXWjkV7LMiK5QJIq4bcD8RniU7hM/jbIhGNWoVXfpPBJdmLcA7jvgm9tLihpxbpkRqkR2rw6tWBMOqA7AJ74330GhXijOrGtwhD67+A/7k1CDOG6NA/ToPeURosnhAIhxNYf8rW5H61FifuWlGPxRMMCA/kL/UlBUe7ZjHUiCUjoUHJ4Zg8KEl0DJ/COYxn2B1OLD1khckKDE/6ZWlrU54NMa/VIP2tWjz4VT2KTY4OPW6dFbA60KycHvvWjBt7BuCaNC5+tGrcK0BgmOgUPoW/MRJ7dnxvfHekCBV11rbvrHCcw3TewfN2DP+XCWYbYNQBX9xhQJ9oV8mM7xGASX0CkNxFjVMVDrywsQFjP6zD7oeCoQ9o39bHs+vMSAxRNSmTpYes2FNox64HLz3bIQApI4H+U0Sn8DnckpFYeLAOs6/vLTqGcJzDeEavKDX2PWJE9gPBeHSwDtNWmnGkxLVcdkeGFjema5ERo8GEXlqsvisIx8scWHXC1sajuszf1oD/HrJixR1BCPy5lM5UOfD7NWZ8MtHQeBu1ICAQmMDf75ZwS8YL7hyShM9+PIO9+ZWiowjROIcxcQ7TWTqNCj0iXH/sBydosOucHW9kW/DuBEOz+8aHqJHcRY0TZW0vmf19ewP+urUB6+4JRlbsL8tvuwvtKDY5MWiRqfE2uxPYctqOf+60oOGPITz4GABGPQNEdhedwiexZLxApVJh/m1ZuOmt79Fg69gauRKsSF4BfQHnMFJwAmiwt/yxsjoHzlQ5EB/Segm8tq0Bf9nagLV3B2FwQtNdl69ODcDBR5suk937ZT16R2kw50odCwYAYvoAVz4pOoXPYsl4Sc/YEDw7vjf+9LV/HTvDOYznPL/ejPE9ApAUpkZNg2vwvynPjjV36VFrceLlTQ247bIAxIeokVfpwPPrGxAVpMLE3trGx7jni3okhqgw9xrXMS7ztzXghY0NWHKrASld1Ciqdb0IMupUMOpUCNGrkBHTtHiCtSpEGprf7pfUWuCWfwIabdv39VMsGS+afkUKNhwtxtYTpaKjeAXnMJ51vtaJqV/Uo7DWiTC9Clmxaqy5Kwjjugeg3urEwWI7PtpvRaXZifgQFa5KCcCntxsQov9layO/ygG16pdR7Nu7LLDYgds/r2/yXC+N1uHlMTzYsk1XPQckDhKdwqepnE6nU3QIf1JUZcZ1C7agql7Ze5uFa23IjnqVuyuTcqWMBO75ClBz/6nW8LvjZXFhgXh1YoboGJLj8TCkaIZw4NZFLJh24HdIgJuyEjBxQKLoGJLhHIYUb8KbQGiC6BSywJIR5E+39EVil+a7ncod5zCkeAOnAX1uFp1CNlgygoQGavH65H6K2gWU5yUjxYtKB66fJzqFrLBkBBqWFolZ1/USHcNjOIchRdPogNv+BeiCRCeRFZaMYI+M7o4bs+JFx+g0zmFI8cb9mafwdwNLxge8dnsWeseFiI7hNs5hSPH63wUMe1R0ClliyfiAIF0A3p06CGEG+R01zDkMKV7SUOCmBaJTyBZLxkckRwbjzSkDILf9ADiHIUUL6wbc8R8gQCc6iWyxZHzI6PRoPH2tfHYE4ByGFE0bDEz5L2CMFp1E1lgyPuaxq3rghsw40THaxDkMKZvKdUR/nPLPziE1lowPen1Sf/RP6iI6xiVxDkOKN/YPwGU3iU6hCCwZH2TQafDv6UPQPdo3L3fLOQwpWuYkYNQs0SkUgyXjo8KDdfjo/qGIC/Wt061zDkOKljQUuPmfolMoCkvGhyV2MeCj+y/3mV2bOYchRYvvB9z1OaD1rRd2cseS8XHpsSF4b9pgBGrF/qg4hyFFi74MmLoSCAwTnURxWDIyMCQlAm9NGSj0ZJqcw5BiRaQB96wEgiJEJ1EkloxMjOsTi78KutgZ5zCkWGFJrqtbhvj+YQNyxZKRkTuGdMMrt/SFyosbNJzDkGIZY4F7vgS6JIlOomgsGZmZOjwFcydmeuX0M5zDkGIZIlwzmMjuopMoHktGhu68vBv+Pkn6C55xDkOKpA8Dpq4AYvuITuIXWDIydevArlhwR38ESFQ0nMOQIgXHANO/ARIGiE7iNwJEByD3TeiXAK1GjSf+uxcWu8Njj8s5DClSeAow9QvX3mTkNdySkbnrM+LwztSB0AV45kfJOQwpUmwmcN93LBgBWDIKMLZ3LN6fNgRGfec3TDmHIcVJvhK4dxUQEis6iV9iySjEiJ5R+PyR4UgIc/+UGJzDkOL0vgm4ewWP5BdI5XQ6naJDkOcU15jxwIc/4kBBVYc+7+rIcrxnmc1lMlKOAVOBCW8Aao3oJH6NWzIKExMSiE8fGo7r+rZ/aYBzGFKcEU8Bt/yTBeMDuCWjUA6HE/PWHMWiLblt3ndjj8+4TEbKoA0Cbn4LyLxddBL6GUtG4ZbsyMeLXx6CzdHyj3lu2kFMOTfXy6mIJBCeAtzxH14y2cewZPzA1hMl+N2Svaiqtza5nXMYUozuVwO3/wswhItOQhdhyfiJM+V1eGzJnsYdAsK1NmRHvcrdlUn+RjwFjH0BUHPE7ItYMn6kwWbHK98cwSfZ+ZzDkPzpjMBv3gb63CI6CbWCJeOHDh46gMyvxgOWWtFRiNwT2cM1f4npLToJtYEl46/KcoBl9wGF+0QnIeqY/ncB188DAkNFJ6F2YMn4M5sFWPcykP02AP4akI8LjnYdXNn7RtFJqANYMgQc/w746nGgtkh0EqKW9b7JVTDBUaKTUAexZMilvhL43wvAno9EJyH6hT4MGD8P6P9b0UnITSwZaip3M/D174GKU6KTkL9LHQXc8jbQJUl0EuoElgw1Z60HNvwFyF4IOO2i05C/CTAA17wEDH0EUEl7iXGSHkuGLu3sHtes5vwh0UnIX1x2M3Ddq0CXbqKTkIewZKh1divw/QJgy2uAvUF0GlKqqF7A+L8B3a8SnYQ8jCVD7VOWA6x7Cfjpa9FJSEn0ocDoOcDQhwGNVnQakgBLhjomPxv47o9AwS7RSUjWVEC/KcA1L/OyyArHkiH3HP4CWPcn7oVGHRffH7jhNSDpctFJyAtYMuQ+mwXY9R6wZT5QXyE6Dfm6iO7AmGeBjNt5xmQ/wpKhzquvBLb+HdixiDsHUHNh3YDRs13LY5oA0WnIy1gy5DnV54Af/g/Y/QHP8ExAWBIw4klgwD1AgE50GhKEJUOeV1/hWkbb8S5gKhGdhrwtPBUY+TTQ707uMUYsGZKQ1Qzs+wTY/hZQkSc6DUktpi9w5e+BzNsBtUZ0GvIRLBmSnsPu2htt2wKg6KDoNORJai3Q52ZgyINA8nDRacgHsWTIu3I2uGY2x1YDdovoNOSu0K7AoOnAoGmAMUZ0GvJhLBkSw1QGHPgU2PsJUHxYdBpqFxWQNtq11dJrPJfEqF1YMiTe2T3A3o+Bg8uBhirRaehixjgg4zZg8H1AVA/RaUhmWDLkO6z1rnOj7f0YOLUVvCS0QMExrllL31uBbsMlO3jS6XTi4YcfxrJly1BRUYG9e/eif//+kjxXa/Ly8pCamirs+ZWMR0aR79AagKzJrrfqQuD4atfsJnczD/L0hqAo4LIJQMatQPIIrxyVv2bNGnzwwQfYtGkT0tLSEBXFyysrDUuGfFNovGt5ZvB9QEOta4eBY98Cx9cC9eWi0ylHUCTQ+0bXFkvqKK/PWXJychAfH48rrrjCq89L3sMTCJHv0xtdSzcT3wFmnQSmfwsM/x0QkSY6mfxog4Ee44Br/wI8vBWYlQPc/JbrOi5eLpjp06fj8ccfR35+PlQqFVJSUuB0OjF//nykpaXBYDCgX79+WLZsWePnbNq0CSqVCmvXrsWAAQNgMBgwduxYFBcXY/Xq1bjssssQGhqKKVOmoK6urvHz1qxZgxEjRqBLly6IjIzETTfdhJycnFbzHTlyBDfccAOMRiNiY2MxdepUlJaWSvb9UCrOZEjeKs8AZ3YA+T+4LkNQfARwOkSn8h1qLdB1iGuvsNTRQNfBPnMUflVVFd58800sWrQIu3btgkajwYIFC7BixQosWLAAPXv2xJYtW/DII49g7dq1GD16NDZt2oSrrroKw4YNw9///ncEBQVh8uTJSExMhF6vx7x581BbW4uJEydi1qxZmDNnDgBg+fLlUKlUyMzMhMlkwosvvoi8vDzs27cParW62UymsLAQWVlZePDBB3HPPfegvr4ec+bMgc1mw4YNGwR/5+SFJUPKYq4CzuwCzmS7SufsbsBa1/bnKYUxDojPAuKyXAdHdhsO6IJFp7qkBQsWYMGCBcjLy4PJZEJUVBQ2bNiA4cN/ObDzgQceQF1dHZYsWdJYMuvWrcPVV18NAJg3bx6ee+455OTkIC3NtXX7yCOPIC8vD2vWrGnxeUtKShATE4ODBw8iIyOjWcm8+OKL2LFjB9auXdv4OQUFBUhKSsKxY8eQnp4u4XdFWTiTIWUJDAN6XuN6A1yXjy464DrTQMlxoOQoUHocqCqArPdeU6ldp86PzwLiMl2lEpcFGKNFJ3PbkSNHYDabMW7cuCa3WywWDBgwoMltWVlZjf+PjY1FUFBQY8FcuG3nzp2N7+fk5OCFF15AdnY2SktL4XC4tnbz8/ORkZHRLMvu3buxceNGGI3GZh/LyclhyXQAS4aUTaMFEge53n7NYnKVza+Lp+QYUJnvO3uyaXRAaILrbMZhSUBYV6BLEhB9GRDbF9AFiU7oURf+8K9atQqJiYlNPqbX65u8r9X+suSnUqmavH/htguPBwATJkxAUlISFi9ejISEBDgcDmRkZMBiafmsEw6HAxMmTMDf/va3Zh+Lj4/v2Bfm51gy5J90wUDCANfbxerKgZrCn9+KgNrzrtvqyn55M1e5tpIcNte/dkvT/zfZSlIB2iDXDgy6YEBnBPQhv/xfF+y61n1I3M9F0s31rzEWUKm89R0Rrk+fPtDr9cjPz8fo0aM99rhlZWX46aef8O6772LkyJEAgO+//77Vzxk4cCCWL1+OlJQUBATwz2Rn8LtHdLGgCNdbbF/3H8NhdxWO0+4qGD8qC3eFhITgmWeewcyZM+FwODBixAhUV1dj+/btMBqNmDZtmluPGx4ejsjISCxatAjx8fHIz8/Hs88+2+rnPPbYY1i8eDGmTJmCWbNmISoqCidPnsTSpUuxePFiaDQ8pU57sWSIpKDW8NxebnjllVcQExODuXPnIjc3F126dMHAgQPx/PPPu/2YarUaS5cuxRNPPIGMjAz06tULb775JsaMGXPJz0lISMC2bdswZ84cXHfddWhoaEBycjKuv/56qHnp6A7h3mVERCQZVjIREUmGJUNERJJhyRARkWRYMkREJBmWDBERSYYlQ0REkmHJEBGRZFgyREQkGZYMERFJhiVDRESSYckQEZFkWDJERCQZlgwREUmGJUNERJJhyRARkWRYMkREJBmWDBERSYYlQ0REkmHJEBGRZFgyREQkGZYMERFJhiVDRESSYckQEZFkWDJERCQZlgwREUmGJUNERJJhyRARkWRYMkREJBmWDBERSYYlQ0REkmHJEBGRZFgyREQkGZYMERFJhiVDRESSYckQEZFkWDJERCQZlgwREUmGJUNERJJhyRARkWRYMkREJBmWDBERSYYlQ0REkmHJEBGRZFgyREQkmf8PqJIzrIR0K1oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sex'].value_counts().plot(kind='pie',autopct='%.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numerical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([100., 346., 188.,  69.,  11.]),\n",
       " array([ 0.42 , 16.336, 32.252, 48.168, 64.084, 80.   ]),\n",
       " <BarContainer object of 5 artists>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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55hkFg0ENGjRIW7du1ezZsyVJH330kXJzc7V3715NnTr1S79vKBSSx+NRMBhURkbGjW4fMTZ0+Z5Yb+GWcHbNtFhvAQAkRff7+6ZegxIMBiVJmZmZkqSmpiYFAgGVlpY6a9xut8aNG6cjR45IkhoaGnTp0qWINX6/XwUFBc6aq4XDYYVCoYgDAAAkrhsOFGOMKioqNHbsWBUUFEiSAoGAJMnr9Uas9Xq9zrVAIKDU1FQNGDDgumuuVlVVJY/H4xy5ubk3um0AABAHbjhQFixYoHfeeUf/+q//2uOay+WKuG2M6XHual+0ZsWKFQoGg87R3Nx8o9sGAABx4IYCZeHChdq9e7cOHTqkwYMHO+d9Pp8k9XgkpLW11XlUxefzqaurS21tbdddczW3262MjIyIAwAAJK6oAsUYowULFmjnzp06ePCg8vPzI67n5+fL5/OptrbWOdfV1aW6ujqVlJRIkoqKipSSkhKxpqWlRSdPnnTWAACAW1tyNIvnz5+v7du36xe/+IXS09OdR0o8Ho/69esnl8ul8vJyrV69WsOGDdOwYcO0evVq9e/fX0888YSzdt68eVq8eLEGDhyozMxMLVmyRIWFhZo8eXLvTwgAAOJOVIGyYcMGSdL48eMjzm/atElz586VJC1dulSdnZ167rnn1NbWplGjRmn//v1KT0931q9bt07JycmaNWuWOjs7NWnSJG3evFlJSUk3Nw0AAEgIN/U5KLHC56AkBj4H5evB56AAsMXX9jkoAAAAfYFAAQAA1iFQAACAdQgUAABgHQIFAABYJ6q3GQOIP7xb6uvBu6WA3sUjKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6UQfKW2+9pYceekh+v18ul0u7du2KuD537ly5XK6IY/To0RFrwuGwFi5cqKysLKWlpWnGjBk6d+7cTQ0CAAASR9SBcvHiRY0cOVLr16+/7poHH3xQLS0tzrF3796I6+Xl5aqpqVF1dbXq6+vV0dGh6dOnq7u7O/oJAABAwkmO9gvKyspUVlb2hWvcbrd8Pt81rwWDQW3cuFFbt27V5MmTJUnbtm1Tbm6uDhw4oKlTp0a7JQAAkGD65DUohw8fVnZ2tu6++249/fTTam1tda41NDTo0qVLKi0tdc75/X4VFBToyJEjfbEdAAAQZ6J+BOXLlJWV6Tvf+Y7y8vLU1NSkv//7v9fEiRPV0NAgt9utQCCg1NRUDRgwIOLrvF6vAoHANe8zHA4rHA47t0OhUG9vGwAAWKTXA2X27NnOPxcUFKi4uFh5eXnas2ePZs6ced2vM8bI5XJd81pVVZVeeOGF3t4qAACwVJ+/zTgnJ0d5eXk6c+aMJMnn86mrq0ttbW0R61pbW+X1eq95HytWrFAwGHSO5ubmvt42AACIoT4PlPPnz6u5uVk5OTmSpKKiIqWkpKi2ttZZ09LSopMnT6qkpOSa9+F2u5WRkRFxAACAxBX1UzwdHR167733nNtNTU06ceKEMjMzlZmZqcrKSj366KPKycnR2bNntXLlSmVlZemRRx6RJHk8Hs2bN0+LFy/WwIEDlZmZqSVLlqiwsNB5Vw8AALi1RR0ox48f14QJE5zbFRUVkqQ5c+Zow4YNamxs1JYtW3ThwgXl5ORowoQJ2rFjh9LT052vWbdunZKTkzVr1ix1dnZq0qRJ2rx5s5KSknphJAAAEO9cxhgT601EKxQKyePxKBgM8nRPHBu6fE+stwD0mrNrpsV6C4D1ovn9zd/iAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYJ+pAeeutt/TQQw/J7/fL5XJp165dEdeNMaqsrJTf71e/fv00fvx4nTp1KmJNOBzWwoULlZWVpbS0NM2YMUPnzp27qUEAAEDiiDpQLl68qJEjR2r9+vXXvP7SSy9p7dq1Wr9+vY4dOyafz6cpU6aovb3dWVNeXq6amhpVV1ervr5eHR0dmj59urq7u298EgAAkDCSo/2CsrIylZWVXfOaMUYvv/yyVq1apZkzZ0qSXnvtNXm9Xm3fvl3PPPOMgsGgNm7cqK1bt2ry5MmSpG3btik3N1cHDhzQ1KlTb2IcAACQCHr1NShNTU0KBAIqLS11zrndbo0bN05HjhyRJDU0NOjSpUsRa/x+vwoKCpw1AADg1hb1IyhfJBAISJK8Xm/Eea/Xqw8++MBZk5qaqgEDBvRYc+XrrxYOhxUOh53boVCoN7cNAAAs06uBcoXL5Yq4bYzpce5qX7SmqqpKL7zwQq/t78sMXb7na/teAACgp159isfn80lSj0dCWltbnUdVfD6furq61NbWdt01V1uxYoWCwaBzNDc39+a2AQCAZXo1UPLz8+Xz+VRbW+uc6+rqUl1dnUpKSiRJRUVFSklJiVjT0tKikydPOmuu5na7lZGREXEAAIDEFfVTPB0dHXrvvfec201NTTpx4oQyMzM1ZMgQlZeXa/Xq1Ro2bJiGDRum1atXq3///nriiSckSR6PR/PmzdPixYs1cOBAZWZmasmSJSosLHTe1QMAAG5tUQfK8ePHNWHCBOd2RUWFJGnOnDnavHmzli5dqs7OTj333HNqa2vTqFGjtH//fqWnpztfs27dOiUnJ2vWrFnq7OzUpEmTtHnzZiUlJfXCSAAAIN65jDEm1puIVigUksfjUTAY7JOne3iRLIBonV0zLdZbAKwXze9v/hYPAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwTnKsNwAAiWDo8j2x3sIt4+yaabHeAr4GPIICAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADr9HqgVFZWyuVyRRw+n8+5boxRZWWl/H6/+vXrp/Hjx+vUqVO9vQ0AABDH+uQRlPvuu08tLS3O0djY6Fx76aWXtHbtWq1fv17Hjh2Tz+fTlClT1N7e3hdbAQAAcahPAiU5OVk+n885Bg0aJOnzR09efvllrVq1SjNnzlRBQYFee+01ffrpp9q+fXtfbAUAAMShPgmUM2fOyO/3Kz8/X4899pjef/99SVJTU5MCgYBKS0udtW63W+PGjdORI0eue3/hcFihUCjiAAAAiavXA2XUqFHasmWLfvnLX+rVV19VIBBQSUmJzp8/r0AgIEnyer0RX+P1ep1r11JVVSWPx+Mcubm5vb1tAABgkV4PlLKyMj366KMqLCzU5MmTtWfPHknSa6+95qxxuVwRX2OM6XHuD61YsULBYNA5mpube3vbAADAIn3+NuO0tDQVFhbqzJkzzrt5rn60pLW1tcejKn/I7XYrIyMj4gAAAImrzwMlHA7rN7/5jXJycpSfny+fz6fa2lrneldXl+rq6lRSUtLXWwEAAHEiubfvcMmSJXrooYc0ZMgQtba26sUXX1QoFNKcOXPkcrlUXl6u1atXa9iwYRo2bJhWr16t/v3764knnujtrQAAgDjV64Fy7tw5Pf744/rkk080aNAgjR49WkePHlVeXp4kaenSpers7NRzzz2ntrY2jRo1Svv371d6enpvbwUAAMQplzHGxHoT0QqFQvJ4PAoGg33yepShy/f0+n0CAHrH2TXTYr0F3KBofn/zt3gAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdZJjvQEAAKIxdPmeWG/hlnB2zbSYfn8eQQEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdWIaKD/72c+Un5+v22+/XUVFRfrVr34Vy+0AAABLxCxQduzYofLycq1atUpvv/22/uIv/kJlZWX68MMPY7UlAABgiZgFytq1azVv3jx9//vf1ze/+U29/PLLys3N1YYNG2K1JQAAYImYfJJsV1eXGhoatHz58ojzpaWlOnLkSI/14XBY4XDYuR0MBiVJoVCoT/Z3Ofxpn9wvAADxoi9+x165T2PMl66NSaB88skn6u7ultfrjTjv9XoVCAR6rK+qqtILL7zQ43xubm6f7REAgFuZ5+W+u+/29nZ5PJ4vXBPTv8XjcrkibhtjepyTpBUrVqiiosK5ffnyZf3ud7/TwIEDr7n+RoRCIeXm5qq5uVkZGRm9cp82SfT5pMSfMdHnk5gxEST6fBIz3gxjjNrb2+X3+790bUwCJSsrS0lJST0eLWltbe3xqIokud1uud3uiHN/9Ed/1Cd7y8jISNh/4aTEn09K/BkTfT6JGRNBos8nMeON+rJHTq6IyYtkU1NTVVRUpNra2ojztbW1KikpicWWAACARWL2FE9FRYWefPJJFRcXa8yYMfr5z3+uDz/8UM8++2ystgQAACwRs0CZPXu2zp8/rx/+8IdqaWlRQUGB9u7dq7y8vJjsx+126/nnn+/xVFKiSPT5pMSfMdHnk5gxEST6fBIzfl1c5qu81wcAAOBrxN/iAQAA1iFQAACAdQgUAABgHQIFAABYh0CR9LOf/Uz5+fm6/fbbVVRUpF/96lex3tINe+utt/TQQw/J7/fL5XJp165dEdeNMaqsrJTf71e/fv00fvx4nTp1KjabvQFVVVW6//77lZ6eruzsbD388MM6ffp0xJp4n3HDhg0aMWKE8wFJY8aM0Ztvvulcj/f5rlZVVSWXy6Xy8nLnXLzPWFlZKZfLFXH4fD7nerzPd8X//u//6rvf/a4GDhyo/v3760/+5E/U0NDgXI/nOYcOHdrjZ+hyuTR//nxJ8T3bFZ999pn+7u/+Tvn5+erXr5/uvPNO/fCHP9Tly5edNTGd09ziqqurTUpKinn11VfNu+++axYtWmTS0tLMBx98EOut3ZC9e/eaVatWmTfeeMNIMjU1NRHX16xZY9LT080bb7xhGhsbzezZs01OTo4JhUKx2XCUpk6dajZt2mROnjxpTpw4YaZNm2aGDBliOjo6nDXxPuPu3bvNnj17zOnTp83p06fNypUrTUpKijl58qQxJv7n+0O//vWvzdChQ82IESPMokWLnPPxPuPzzz9v7rvvPtPS0uIcra2tzvV4n88YY373u9+ZvLw8M3fuXPNf//VfpqmpyRw4cMC89957zpp4nrO1tTXi51dbW2skmUOHDhlj4nu2K1588UUzcOBA8x//8R+mqanJ/Nu//Zu54447zMsvv+ysieWct3yg/Nmf/Zl59tlnI87de++9Zvny5THaUe+5OlAuX75sfD6fWbNmjXPu97//vfF4POaf/umfYrDDm9fa2mokmbq6OmNMYs5ojDEDBgww//Iv/5JQ87W3t5thw4aZ2tpaM27cOCdQEmHG559/3owcOfKa1xJhPmOMWbZsmRk7dux1ryfKnFcsWrTIfOMb3zCXL19OmNmmTZtmnnrqqYhzM2fONN/97neNMbH/Gd7ST/F0dXWpoaFBpaWlEedLS0t15MiRGO2q7zQ1NSkQCETM63a7NW7cuLidNxgMSpIyMzMlJd6M3d3dqq6u1sWLFzVmzJiEmm/+/PmaNm2aJk+eHHE+UWY8c+aM/H6/8vPz9dhjj+n999+XlDjz7d69W8XFxfrOd76j7Oxsfetb39Krr77qXE+UOaXPf1ds27ZNTz31lFwuV8LMNnbsWP3nf/6nfvvb30qS/ud//kf19fX6y7/8S0mx/xnG9K8Zx9onn3yi7u7uHn+g0Ov19vhDhongykzXmveDDz6IxZZuijFGFRUVGjt2rAoKCiQlzoyNjY0aM2aMfv/73+uOO+5QTU2Nhg8f7vxPId7nq66u1n//93/r2LFjPa4lws9w1KhR2rJli+6++259/PHHevHFF1VSUqJTp04lxHyS9P7772vDhg2qqKjQypUr9etf/1p//dd/Lbfbre9973sJM6ck7dq1SxcuXNDcuXMlJca/o5K0bNkyBYNB3XvvvUpKSlJ3d7d+9KMf6fHHH5cU+zlv6UC5wuVyRdw2xvQ4l0gSZd4FCxbonXfeUX19fY9r8T7jPffcoxMnTujChQt64403NGfOHNXV1TnX43m+5uZmLVq0SPv379ftt99+3XXxPGNZWZnzz4WFhRozZoy+8Y1v6LXXXtPo0aMlxfd8knT58mUVFxdr9erVkqRvfetbOnXqlDZs2KDvfe97zrp4n1OSNm7cqLKyMvn9/ojz8T7bjh07tG3bNm3fvl333XefTpw4ofLycvn9fs2ZM8dZF6s5b+mneLKyspSUlNTj0ZLW1tYexZgIrryLIBHmXbhwoXbv3q1Dhw5p8ODBzvlEmTE1NVV33XWXiouLVVVVpZEjR+qVV15JiPkaGhrU2tqqoqIiJScnKzk5WXV1dfqHf/gHJScnO3PE84xXS0tLU2Fhoc6cOZMQP0NJysnJ0fDhwyPOffOb39SHH34oKXH+W/zggw904MABff/733fOJcpsf/u3f6vly5frscceU2FhoZ588kn9zd/8jaqqqiTFfs5bOlBSU1NVVFSk2traiPO1tbUqKSmJ0a76Tn5+vnw+X8S8XV1dqquri5t5jTFasGCBdu7cqYMHDyo/Pz/ieiLMeC3GGIXD4YSYb9KkSWpsbNSJEyeco7i4WH/1V3+lEydO6M4774z7Ga8WDof1m9/8Rjk5OQnxM5SkP//zP+/xFv/f/va3zh98TZQ5N23apOzsbE2bNs05lyizffrpp7rttsgMSEpKct5mHPM5+/xluJa78jbjjRs3mnfffdeUl5ebtLQ0c/bs2Vhv7Ya0t7ebt99+27z99ttGklm7dq15++23nbdNr1mzxng8HrNz507T2NhoHn/88bh6a9wPfvAD4/F4zOHDhyPeAvjpp586a+J9xhUrVpi33nrLNDU1mXfeecesXLnS3HbbbWb//v3GmPif71r+8F08xsT/jIsXLzaHDx8277//vjl69KiZPn26SU9Pd/6/Eu/zGfP5W8STk5PNj370I3PmzBnz+uuvm/79+5tt27Y5a+J9zu7ubjNkyBCzbNmyHtfifTZjjJkzZ4754z/+Y+dtxjt37jRZWVlm6dKlzppYznnLB4oxxvzjP/6jycvLM6mpqeZP//RPnbesxqNDhw4ZST2OOXPmGGM+f9vY888/b3w+n3G73eaBBx4wjY2Nsd10FK41mySzadMmZ028z/jUU085/z4OGjTITJo0yYkTY+J/vmu5OlDifcYrnxWRkpJi/H6/mTlzpjl16pRzPd7nu+Lf//3fTUFBgXG73ebee+81P//5zyOux/ucv/zlL40kc/r06R7X4n02Y4wJhUJm0aJFZsiQIeb22283d955p1m1apUJh8POmljO6TLGmL5/nAYAAOCru6VfgwIAAOxEoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALDO/wMIis02S0bbKQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Age'],bins= 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.Distplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_12204\\3255828239.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['Age'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Density'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Fare'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Age'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512.3292"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38910778230082704"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}