Notebooks/Python_DataScience/06_Intermediate_Pandas_DS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python  \n",
    "\n",
    "### Pandas Descriptive Statistics and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U -q seaborn xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Case Study: Voters analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading Dataframe from Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "_A1F6vrCL8ZQ",
    "outputId": "74993dc2-e50b-426e-886a-e4627e9397d7"
   },
   "outputs": [],
   "source": [
    "dfvote = pd.read_excel(os.path.join('../../Data','xls','votesurvey.xls'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dfvote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First steps examining data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary before Stern</th>\n",
       "      <th>Expected salary</th>\n",
       "      <th>Candidate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>60000</td>\n",
       "      <td>110000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>125000</td>\n",
       "      <td>125000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>50000</td>\n",
       "      <td>120000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>56000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>82000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>150000</td>\n",
       "      <td>150000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>55000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>110000</td>\n",
       "      <td>150000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Salary before Stern  Expected salary Candidate\n",
       "0    Male   27                60000           110000      Bush\n",
       "1    Male   30               125000           125000      Bush\n",
       "2    Male   27                50000           120000      Bush\n",
       "3    Male   26                56000           100000      Bush\n",
       "4    Male   27                82000           100000      Bush\n",
       "5    Male   32               150000           150000      Bush\n",
       "6  Female   24                55000           100000      Bush\n",
       "7    Male   30               110000           150000      Bush"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfvote.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary before Stern</th>\n",
       "      <th>Expected salary</th>\n",
       "      <th>Candidate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>25000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Refuse to answer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>5000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Refuse to answer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>45000</td>\n",
       "      <td>180000</td>\n",
       "      <td>Refuse to answer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>100000</td>\n",
       "      <td>150000</td>\n",
       "      <td>Refuse to answer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>48000</td>\n",
       "      <td>120000</td>\n",
       "      <td>Refuse to answer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>45000</td>\n",
       "      <td>80000</td>\n",
       "      <td>Undecided</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>56000</td>\n",
       "      <td>95000</td>\n",
       "      <td>Undecided</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>90000</td>\n",
       "      <td>150000</td>\n",
       "      <td>Undecided</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>65000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Undecided</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>39000</td>\n",
       "      <td>90000</td>\n",
       "      <td>Undecided</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Age  Salary before Stern  Expected salary         Candidate\n",
       "38    Male   29                25000           100000  Refuse to answer\n",
       "39    Male   25                 5000           100000  Refuse to answer\n",
       "40    Male   28                45000           180000  Refuse to answer\n",
       "41    Male   29               100000           150000  Refuse to answer\n",
       "42    Male   27                48000           120000  Refuse to answer\n",
       "43  Female   25                45000            80000         Undecided\n",
       "44  Female   27                56000            95000         Undecided\n",
       "45    Male   28                90000           150000         Undecided\n",
       "46  Female   27                65000           100000         Undecided\n",
       "47    Male   29                39000            90000         Undecided"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfvote.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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 before Stern</th>\n",
       "      <th>Expected salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>27.187500</td>\n",
       "      <td>70145.833333</td>\n",
       "      <td>109166.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.038525</td>\n",
       "      <td>37886.723427</td>\n",
       "      <td>25730.463221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>75000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.750000</td>\n",
       "      <td>48750.000000</td>\n",
       "      <td>90000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>59500.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>80500.000000</td>\n",
       "      <td>121250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>225000.000000</td>\n",
       "      <td>180000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age  Salary before Stern  Expected salary\n",
       "count  48.000000            48.000000        48.000000\n",
       "mean   27.187500         70145.833333    109166.666667\n",
       "std     2.038525         37886.723427     25730.463221\n",
       "min    24.000000          5000.000000     75000.000000\n",
       "25%    25.750000         48750.000000     90000.000000\n",
       "50%    27.000000         59500.000000    100000.000000\n",
       "75%    29.000000         80500.000000    121250.000000\n",
       "max    33.000000        225000.000000    180000.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfvote.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K1wsHQAnL8ZV"
   },
   "source": [
    "### Plotting: Histogram  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "8mjoMA6wL8ZW",
    "outputId": "5b53a59f-ea7a-4963-9dee-2ce0acfbe1e6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using Matplotlib calls\n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "#Create one or more subplots using add_subplot, because you can't create blank figure\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "#Variable\n",
    "ax.hist(dfvote['Age'],bins = 8) # Here you can play with number of bins Labels and Tit\n",
    "\n",
    "plt.title('Age distribution')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('#Citizens')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "THnZpgO-L8ZY",
    "outputId": "725871b6-2c2d-4407-8778-fc032211ff13"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting within Pandas\n",
    "\n",
    "dfvote.Age.hist(figsize=(15,8), bins=8);\n",
    "#dfvote['Age'].hist(figsize=(15,8), bins=8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "ckyox8U5L8Za",
    "outputId": "4ca012e0-594f-4081-8b6c-3b633d4b2234"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using Matplotlib calls\n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.boxplot(dfvote['Age'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 11,
     "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": [
    "# Using Pandas directly\n",
    "\n",
    "dfvote.boxplot(column=['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K7jcd1MbL8ZY"
   },
   "source": [
    "### Box Plot  with many columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Age', 'Salary before Stern', 'Expected salary', 'Candidate'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfvote.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "wRP_I20RL8Zc",
    "outputId": "0f5c7bbf-f077-43c0-83bc-dbe24160c901"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting within Pandas\n",
    "#dfvote[['Age', 'Expected salary']].boxplot(by='Age', figsize=(15,8));\n",
    "dfvote.boxplot(by='Age', figsize=(15,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-wNQLVtZL8Zd"
   },
   "source": [
    "### Violin Plot (using Seaborn)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "_39rh8npL8Ze",
    "outputId": "a476add0-dd75-4287-c4f2-99479a926bf1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "\n",
    "sns.violinplot(x=dfvote['Age'], y=dfvote['Gender'])\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jL_NbWFOL8Zf"
   },
   "source": [
    "### Bar Chart  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary before Stern</th>\n",
       "      <th>Expected salary</th>\n",
       "      <th>Candidate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>60000</td>\n",
       "      <td>110000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>125000</td>\n",
       "      <td>125000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>50000</td>\n",
       "      <td>120000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>56000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>82000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>150000</td>\n",
       "      <td>150000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>55000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>110000</td>\n",
       "      <td>150000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>48000</td>\n",
       "      <td>80000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>120000</td>\n",
       "      <td>80000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Salary before Stern  Expected salary Candidate\n",
       "0    Male   27                60000           110000      Bush\n",
       "1    Male   30               125000           125000      Bush\n",
       "2    Male   27                50000           120000      Bush\n",
       "3    Male   26                56000           100000      Bush\n",
       "4    Male   27                82000           100000      Bush\n",
       "5    Male   32               150000           150000      Bush\n",
       "6  Female   24                55000           100000      Bush\n",
       "7    Male   30               110000           150000      Bush\n",
       "8    Male   26                48000            80000      Bush\n",
       "9    Male   26               120000            80000      Bush"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfvote.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WZHRERO\\AppData\\Local\\Temp\\1\\ipykernel_20164\\1071552671.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  grouped = dfvote.groupby('Gender').mean()\n"
     ]
    },
    {
     "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 before Stern</th>\n",
       "      <th>Expected salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>26.142857</td>\n",
       "      <td>61500.000000</td>\n",
       "      <td>99642.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>27.617647</td>\n",
       "      <td>73705.882353</td>\n",
       "      <td>113088.235294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Salary before Stern  Expected salary\n",
       "Gender                                                 \n",
       "Female  26.142857         61500.000000     99642.857143\n",
       "Male    27.617647         73705.882353    113088.235294"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = dfvote.groupby('Gender').mean()\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Gender'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped[['Salary before Stern','Expected salary']].plot(kind='bar', figsize=(8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "VEM-ndjsL8Zf",
    "outputId": "f2e37e4b-08b6-4de0-dec8-3d5fb33fb585"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Candidate\n",
       "Bush                27.000000\n",
       "Gore                27.277778\n",
       "Refuse to answer    27.300000\n",
       "Undecided           27.200000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = dfvote.groupby('Candidate').Age.mean()\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var.plot(kind='bar', figsize=(12,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mYjoJoRML8Zg"
   },
   "source": [
    "### Line Chart  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "8s4tOFqaL8Zg",
    "outputId": "f76048e0-4016-40f4-ce60-618e202241d8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "ax1 = fig.add_subplot(1,1,1)  #https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html\n",
    "ax1.set_xlabel('Candidate')\n",
    "ax1.set_ylabel('Mean of Ages')\n",
    "ax1.set_title(\"Candidate wise mean of ages\")\n",
    "\n",
    "var.plot(ax=ax1, kind='line');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_JzA2hbCL8Zh"
   },
   "source": [
    "### Stacked Column Chart  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary before Stern</th>\n",
       "      <th>Expected salary</th>\n",
       "      <th>Candidate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>60000</td>\n",
       "      <td>110000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>125000</td>\n",
       "      <td>125000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>50000</td>\n",
       "      <td>120000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>56000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>82000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Bush</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender  Age  Salary before Stern  Expected salary Candidate\n",
       "0   Male   27                60000           110000      Bush\n",
       "1   Male   30               125000           125000      Bush\n",
       "2   Male   27                50000           120000      Bush\n",
       "3   Male   26                56000           100000      Bush\n",
       "4   Male   27                82000           100000      Bush"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfvote.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WZHRERO\\AppData\\Local\\Temp\\1\\ipykernel_20164\\892439179.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  var = dfvote.groupby(['Age','Gender']).sum()\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Salary before Stern</th>\n",
       "      <th>Expected salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>24</th>\n",
       "      <th>Female</th>\n",
       "      <td>114000</td>\n",
       "      <td>190000</td>\n",
       "    </tr>\n",
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       "      <th rowspan=\"2\" valign=\"top\">25</th>\n",
       "      <th>Female</th>\n",
       "      <td>139000</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>405000</td>\n",
       "      <td>750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">26</th>\n",
       "      <th>Female</th>\n",
       "      <td>320000</td>\n",
       "      <td>470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>279000</td>\n",
       "      <td>380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">27</th>\n",
       "      <th>Female</th>\n",
       "      <td>166000</td>\n",
       "      <td>285000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>375000</td>\n",
       "      <td>675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <th>Male</th>\n",
       "      <td>415000</td>\n",
       "      <td>755000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">29</th>\n",
       "      <th>Female</th>\n",
       "      <td>122000</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>437000</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <th>Male</th>\n",
       "      <td>385000</td>\n",
       "      <td>470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <th>Male</th>\n",
       "      <td>150000</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <th>Male</th>\n",
       "      <td>60000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Salary before Stern  Expected salary\n",
       "Age Gender                                      \n",
       "24  Female               114000           190000\n",
       "25  Female               139000           250000\n",
       "    Male                 405000           750000\n",
       "26  Female               320000           470000\n",
       "    Male                 279000           380000\n",
       "27  Female               166000           285000\n",
       "    Male                 375000           675000\n",
       "28  Male                 415000           755000\n",
       "29  Female               122000           200000\n",
       "    Male                 437000           565000\n",
       "30  Male                 385000           470000\n",
       "32  Male                 150000           150000\n",
       "33  Male                  60000           100000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = dfvote.groupby(['Age','Gender']).sum()\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Salary before Stern</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Expected salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>114000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>139000.0</td>\n",
       "      <td>405000.0</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>750000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>320000.0</td>\n",
       "      <td>279000.0</td>\n",
       "      <td>470000.0</td>\n",
       "      <td>380000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>166000.0</td>\n",
       "      <td>375000.0</td>\n",
       "      <td>285000.0</td>\n",
       "      <td>675000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>415000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>755000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>122000.0</td>\n",
       "      <td>437000.0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>565000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>385000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>470000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NaN</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NaN</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Salary before Stern           Expected salary          \n",
       "Gender              Female      Male          Female      Male\n",
       "Age                                                           \n",
       "24                114000.0       NaN        190000.0       NaN\n",
       "25                139000.0  405000.0        250000.0  750000.0\n",
       "26                320000.0  279000.0        470000.0  380000.0\n",
       "27                166000.0  375000.0        285000.0  675000.0\n",
       "28                     NaN  415000.0             NaN  755000.0\n",
       "29                122000.0  437000.0        200000.0  565000.0\n",
       "30                     NaN  385000.0             NaN  470000.0\n",
       "32                     NaN  150000.0             NaN  150000.0\n",
       "33                     NaN   60000.0             NaN  100000.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "fAgDx-11L8Zi",
    "outputId": "c88911de-27d7-4277-fe94-1e4edb3d6912"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Age'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var.unstack().plot(kind='bar', \n",
    "                   stacked=True, \n",
    "                   color=['red','blue','orange','green'], \n",
    "                   grid=False, \n",
    "                   figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZKgC1vcRL8Zj"
   },
   "source": [
    "### Scatter Plot  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "-BdT5bPyL8Zj",
    "outputId": "a7d6ceb1-b001-4404-f715-62a3d16ec8a3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.scatter(x=dfvote['Age'], \n",
    "           y=dfvote['Expected salary'],\n",
    "           s=dfvote['Expected salary']/500) #You can also add more variables here to represent color and size.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "djgdgo3SL8Zk",
    "outputId": "282c87e1-d924-4d90-eb2b-0c5dfbeb804e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfvote.plot.scatter(x='Age',\n",
    "                    y='Expected salary', \n",
    "                    c='Age', \n",
    "                    s=dfvote['Salary before Stern']/500, \n",
    "                    cmap='jet', \n",
    "                    figsize=(15,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tWOVS7LTL8Zp"
   },
   "source": [
    "### Pie chart  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WZHRERO\\AppData\\Local\\Temp\\1\\ipykernel_20164\\1799382071.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  var = dfvote.groupby(['Gender']).mean().stack()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Gender                     \n",
       "Female  Age                        26.142857\n",
       "        Salary before Stern     61500.000000\n",
       "        Expected salary         99642.857143\n",
       "Male    Age                        27.617647\n",
       "        Salary before Stern     73705.882353\n",
       "        Expected salary        113088.235294\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = dfvote.groupby(['Gender']).mean().stack()\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 before Stern</th>\n",
       "      <th>Expected salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>26.142857</td>\n",
       "      <td>61500.000000</td>\n",
       "      <td>99642.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>27.617647</td>\n",
       "      <td>73705.882353</td>\n",
       "      <td>113088.235294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Salary before Stern  Expected salary\n",
       "Gender                                                 \n",
       "Female  26.142857         61500.000000     99642.857143\n",
       "Male    27.617647         73705.882353    113088.235294"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = var.unstack()\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "ynojR8K_L8Zp",
    "outputId": "ce5c8e29-61ce-4965-9252-635a9440e10b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_list = temp['Age']\n",
    "label_list = temp.index\n",
    "\n",
    "#The pie chart is oval by default. To make it a circle use plt.axis(\"equal\")\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.axis(\"equal\")\n",
    "\n",
    "#To show the percentage of each pie slice, pass an output format to the autopctparameter \n",
    "plt.pie(x_list,labels=label_list,autopct=\"%1.1f%%\") \n",
    "plt.title(\"Gender Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second Case Study: Melbourne House Prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading Dataframe from CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "      <th>Distance</th>\n",
       "      <th>CouncilArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>49 Lithgow St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1490000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>1/04/2017</td>\n",
       "      <td>3067</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>59A Turner St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1220000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Marshall</td>\n",
       "      <td>1/04/2017</td>\n",
       "      <td>3067</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>119B Yarra St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1420000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>1/04/2017</td>\n",
       "      <td>3067</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aberfeldie</td>\n",
       "      <td>68 Vida St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1515000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Barry</td>\n",
       "      <td>1/04/2017</td>\n",
       "      <td>3040</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>1543</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Moonee Valley City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Airport West</td>\n",
       "      <td>92 Clydesdale Rd</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>1/04/2017</td>\n",
       "      <td>3042</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>3464</td>\n",
       "      <td>10.4</td>\n",
       "      <td>Moonee Valley City Council</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Suburb           Address  Rooms Type      Price Method   SellerG  \\\n",
       "0    Abbotsford     49 Lithgow St      3    h  1490000.0      S    Jellis   \n",
       "1    Abbotsford     59A Turner St      3    h  1220000.0      S  Marshall   \n",
       "2    Abbotsford     119B Yarra St      3    h  1420000.0      S    Nelson   \n",
       "3    Aberfeldie        68 Vida St      3    h  1515000.0      S     Barry   \n",
       "4  Airport West  92 Clydesdale Rd      2    h   670000.0      S    Nelson   \n",
       "\n",
       "        Date  Postcode             Regionname  Propertycount  Distance  \\\n",
       "0  1/04/2017      3067  Northern Metropolitan           4019       3.0   \n",
       "1  1/04/2017      3067  Northern Metropolitan           4019       3.0   \n",
       "2  1/04/2017      3067  Northern Metropolitan           4019       3.0   \n",
       "3  1/04/2017      3040   Western Metropolitan           1543       7.5   \n",
       "4  1/04/2017      3042   Western Metropolitan           3464      10.4   \n",
       "\n",
       "                  CouncilArea  \n",
       "0          Yarra City Council  \n",
       "1          Yarra City Council  \n",
       "2          Yarra City Council  \n",
       "3  Moonee Valley City Council  \n",
       "4  Moonee Valley City Council  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../Data/csv/MELBOURNE_HOUSE_PRICES_LESS.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "      <th>Distance</th>\n",
       "      <th>CouncilArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63018</th>\n",
       "      <td>Roxburgh Park</td>\n",
       "      <td>3 Carr Pl</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>566000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Raine</td>\n",
       "      <td>31/03/2018</td>\n",
       "      <td>3064</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>5833</td>\n",
       "      <td>20.6</td>\n",
       "      <td>Hume City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63019</th>\n",
       "      <td>Roxburgh Park</td>\n",
       "      <td>9 Parker Ct</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Raine</td>\n",
       "      <td>31/03/2018</td>\n",
       "      <td>3064</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>5833</td>\n",
       "      <td>20.6</td>\n",
       "      <td>Hume City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63020</th>\n",
       "      <td>Roxburgh Park</td>\n",
       "      <td>5 Parkinson Wy</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>545000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Raine</td>\n",
       "      <td>31/03/2018</td>\n",
       "      <td>3064</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>5833</td>\n",
       "      <td>20.6</td>\n",
       "      <td>Hume City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63021</th>\n",
       "      <td>Thomastown</td>\n",
       "      <td>3/1 Travers St</td>\n",
       "      <td>3</td>\n",
       "      <td>u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PI</td>\n",
       "      <td>Barry</td>\n",
       "      <td>31/03/2018</td>\n",
       "      <td>3074</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>7955</td>\n",
       "      <td>15.3</td>\n",
       "      <td>Whittlesea City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63022</th>\n",
       "      <td>Williams Landing</td>\n",
       "      <td>1 Diadem Wy</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SP</td>\n",
       "      <td>Aussie</td>\n",
       "      <td>31/03/2018</td>\n",
       "      <td>3027</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>1999</td>\n",
       "      <td>17.6</td>\n",
       "      <td>Wyndham City Council</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Suburb         Address  Rooms Type     Price Method SellerG  \\\n",
       "63018     Roxburgh Park       3 Carr Pl      3    h  566000.0      S   Raine   \n",
       "63019     Roxburgh Park     9 Parker Ct      3    h  500000.0      S   Raine   \n",
       "63020     Roxburgh Park  5 Parkinson Wy      3    h  545000.0      S   Raine   \n",
       "63021        Thomastown  3/1 Travers St      3    u       NaN     PI   Barry   \n",
       "63022  Williams Landing     1 Diadem Wy      4    h       NaN     SP  Aussie   \n",
       "\n",
       "             Date  Postcode             Regionname  Propertycount  Distance  \\\n",
       "63018  31/03/2018      3064  Northern Metropolitan           5833      20.6   \n",
       "63019  31/03/2018      3064  Northern Metropolitan           5833      20.6   \n",
       "63020  31/03/2018      3064  Northern Metropolitan           5833      20.6   \n",
       "63021  31/03/2018      3074  Northern Metropolitan           7955      15.3   \n",
       "63022  31/03/2018      3027   Western Metropolitan           1999      17.6   \n",
       "\n",
       "                   CouncilArea  \n",
       "63018        Hume City Council  \n",
       "63019        Hume City Council  \n",
       "63020        Hume City Council  \n",
       "63021  Whittlesea City Council  \n",
       "63022     Wyndham City Council  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 63023 entries, 0 to 63022\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Suburb         63023 non-null  object \n",
      " 1   Address        63023 non-null  object \n",
      " 2   Rooms          63023 non-null  int64  \n",
      " 3   Type           63023 non-null  object \n",
      " 4   Price          48433 non-null  float64\n",
      " 5   Method         63023 non-null  object \n",
      " 6   SellerG        63023 non-null  object \n",
      " 7   Date           63023 non-null  object \n",
      " 8   Postcode       63023 non-null  int64  \n",
      " 9   Regionname     63023 non-null  object \n",
      " 10  Propertycount  63023 non-null  int64  \n",
      " 11  Distance       63023 non-null  float64\n",
      " 12  CouncilArea    63023 non-null  object \n",
      "dtypes: float64(2), int64(3), object(8)\n",
      "memory usage: 6.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WZHRERO\\AppData\\Local\\Temp\\1\\ipykernel_20164\\2692884853.py:1: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  df['Date'] = pd.to_datetime(df['Date'])\n"
     ]
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df['Type'] = df['Type'].astype('category')\n",
    "df['Method'] = df['Method'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 63023 entries, 0 to 63022\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Suburb         63023 non-null  object        \n",
      " 1   Address        63023 non-null  object        \n",
      " 2   Rooms          63023 non-null  int64         \n",
      " 3   Type           63023 non-null  category      \n",
      " 4   Price          48433 non-null  float64       \n",
      " 5   Method         63023 non-null  category      \n",
      " 6   SellerG        63023 non-null  object        \n",
      " 7   Date           63023 non-null  datetime64[ns]\n",
      " 8   Postcode       63023 non-null  int64         \n",
      " 9   Regionname     63023 non-null  object        \n",
      " 10  Propertycount  63023 non-null  int64         \n",
      " 11  Distance       63023 non-null  float64       \n",
      " 12  CouncilArea    63023 non-null  object        \n",
      "dtypes: category(2), datetime64[ns](1), float64(2), int64(3), object(5)\n",
      "memory usage: 5.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['h', 't', 'u']\n",
       "Categories (3, object): ['h', 't', 'u']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h    45053\n",
       "u    11655\n",
       "t     6315\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "997898.24\n"
     ]
    }
   ],
   "source": [
    "average = df['Price'].mean()\n",
    "print('{:.2f}'.format(average))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "830000.00\n"
     ]
    }
   ],
   "source": [
    "med = df['Price'].median()\n",
    "print('{:.2f}'.format(med))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "593498.92\n"
     ]
    }
   ],
   "source": [
    "standard_deviation = df['Price'].std()\n",
    "print('{:.2f}'.format(standard_deviation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Propertycount</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>63023.000000</td>\n",
       "      <td>4.843300e+04</td>\n",
       "      <td>63023.000000</td>\n",
       "      <td>63023.000000</td>\n",
       "      <td>63023.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.110595</td>\n",
       "      <td>9.978982e+05</td>\n",
       "      <td>3125.673897</td>\n",
       "      <td>7617.728131</td>\n",
       "      <td>12.684829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.957551</td>\n",
       "      <td>5.934989e+05</td>\n",
       "      <td>125.626877</td>\n",
       "      <td>4424.423167</td>\n",
       "      <td>7.592015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.500000e+04</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.200000e+05</td>\n",
       "      <td>3056.000000</td>\n",
       "      <td>4380.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.300000e+05</td>\n",
       "      <td>3107.000000</td>\n",
       "      <td>6795.000000</td>\n",
       "      <td>11.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.220000e+06</td>\n",
       "      <td>3163.000000</td>\n",
       "      <td>10412.000000</td>\n",
       "      <td>16.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.120000e+07</td>\n",
       "      <td>3980.000000</td>\n",
       "      <td>21650.000000</td>\n",
       "      <td>64.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms         Price      Postcode  Propertycount      Distance\n",
       "count  63023.000000  4.843300e+04  63023.000000   63023.000000  63023.000000\n",
       "mean       3.110595  9.978982e+05   3125.673897    7617.728131     12.684829\n",
       "std        0.957551  5.934989e+05    125.626877    4424.423167      7.592015\n",
       "min        1.000000  8.500000e+04   3000.000000      39.000000      0.000000\n",
       "25%        3.000000  6.200000e+05   3056.000000    4380.000000      7.000000\n",
       "50%        3.000000  8.300000e+05   3107.000000    6795.000000     11.400000\n",
       "75%        4.000000  1.220000e+06   3163.000000   10412.000000     16.700000\n",
       "max       31.000000  1.120000e+07   3980.000000   21650.000000     64.100000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WZHRERO\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\seaborn\\_oldcore.py:1599: UserWarning: Vertical orientation ignored with only `x` specified.\n",
      "  warnings.warn(single_var_warning.format(\"Vertical\", \"x\"))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(8,12))\n",
    "ax = sns.boxplot(x='Price', data=df, orient=\"v\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "ax = sns.boxplot(x='Type', y='Price', data=df, orient=\"v\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Price', ylabel='Count'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8cZSVlcXYsWPjlVdeiZtvvjlmzpyZh2cIAAAAUFoKPkPpueeei/Ly8li6dGkceuih7c69+uqrsXnz5vjABz7Q4bUvvPBCbNq0KaZOndp2bK+99ooJEybEk08+GRERy5cvjylTpkRZ2X9lZ0ceeWSsXr063nrrrR54RgAAAAClreAzlKZPn95uT6Tt/eEPf4iIiDvvvDMeffTR6NevXxx99NFxzjnnRFVVVaxZsyYiIvbff/92140YMaLt3Jo1a2LcuHE7nI+IePPNN2PfffftUt25XC42b97cpWvzpbGxsd2vxSqTycS2lm2xrWVbtPzlo7s+78zYbS3borGxMXK5XLtatj9OYfSWHoad0cOUAn1Mb6eHKQX6mHxqfW28OwUPlHblD3/4Q/Tr1y9GjBgRixYtildffTWuuuqqePHFF+P2229v+8M0YMCAdtdVVFREfX19REQ0NTV1eD4iIpvNdrm2LVu2xMqVK7t8fT6tXr260CXsUkVFRTQ1NsXmTZsj21QZ2aZst32eLY/djh08IOKll16KbDbbrpbtj1NYxd7DsDt6mFKgj+nt9DClQB+TL+/NUTpS1IHSGWecEZ/5zGdi2LBhERExbty42G+//eLv//7v49lnn43KysqIeHcvpdbPI94NigYOHBgREZWVlW0bdG9/PiJi0KBBXa6tvLw8DjrooC5fnw+NjY2xevXqGD16dNv3oxhlMpmofHpzDGqOqKisiIotEYMG57rl886MrRxYGWPHjm1LYVtr2f44hdFbehh2Rg9TCvQxvZ0ephToY/Jp1apVnRpX1IFSv3792sKkVh/84Acj4t2lbK1L3datWxcHHnhg25h169bF+PHjIyKiuro61q1b1+4erV+PHDmyy7VlMpk9CqTyaeDAgUVfa/9+/aN/v/7R7y8f3fV5Z8b279e/3Q/l/js5TuH0hh6GXdHDlAJ9TG+nhykF+ph86Mxyt4gi2JR7V84777z4whe+0O7Ys88+GxERBx10UBx88MExZMiQWLZsWdv5DRs2xPPPPx81NTUREVFTUxNPPfVUbNu2rW3Mr3/96xgzZkzss88+Pf8kAAAAAEpMUQdK/+N//I94/PHH44YbbohXX301/uM//iO+9rWvxQknnBBjx46NAQMGxKxZs2LhwoXxyCOPxAsvvBDnnHNOVFdXx7HHHhsRETNnzoyNGzfG17/+9Vi1alXcf//9cdttt8XcuXML/OwAAAAAeqeiXvL2N3/zN3HttdfGzTffHN/97nejqqoqTjzxxJg/f37bmHnz5sXWrVvjggsuiKampqipqYlbbrklysvLIyJin332ie9973tx2WWXxYwZM2K//faL8847L2bMmFGgZwUAAADQuxVVoHTllVfucOz444+P448/fqfX9O/fP84999w499xzdzrmkEMOiR/84AfdUiMAAABAX1fUS94AAAAAKD4CJQAAAACSCJQAAAAASCJQAgAAACCJQAkAAACAJAIlAAAAAJIIlAAAAABIIlACAAAAIIlACQAAAIAkAiUAAAAAkgiUAAAAAEgiUKLPy2QKXQEAAAD0LmWFLgAKbeiQiqhduiLqGrIxqrqq0OUAAABA0TNDCSKiriEb6+ubon5Tc6FLAQAAgKInUAIAAAAgiUAJAAAAgCQCJQAAAACSCJQAAAAASCJQAgAAACCJQAkAAACAJAIlAAAAAJIIlAAAAABIIlACAAAAIIlACQAAAIAkAiUAAAAAkgiUAAAAAEgiUAIAAAAgiUAJAAAAgCQCJQAAAACSCJQAAAAASCJQAgAAACCJQAkAAACAJAIlAAAAAJIIlAAAAABIIlACAAAAIIlACQAAAIAkAiUAAAAAkgiUAAAAAEgiUAIAAAAgiUAJAAAAgCQCJQAAAACSCJQAAAAASCJQAgAAACCJQAkAAACAJAIlAAAAAJIIlAAAAABIIlACAAAAIIlACQAAAIAkAiUAAAAAkgiUAAAAAEgiUAIAAAAgSVmhC4DeonbpiqhryMawqoqYfdLEQpcDAAAABSNQgk6qa8jG+vqmQpcBAAAABWfJGwAAAABJBEoAAAAAJBEoAQAAAJBEoAQAAABAEoESAAAAAEkESgAAAAAkESgBAAAAkESgBAAAAEASgRIAAAAAScoKXQB9V+3SFVHXkI1R1VWFLgUAAABI0CMzlNasWdMTt6XE1DVkY319U9Rvai50KQAAAECCLgVKH/rQh+KZZ57p8Nzy5cvj+OOP36OiAAAAAChenV7yVltbG5s3b46IiFwuFz/84Q/j0Ucf3WHc008/HQMGDOi+CgEAAAAoKp0OlLLZbNxwww0REZHJZOKHP/zhDmP69esXVVVVccYZZ3RfhQAAAAAUlU4HSmeccUZbUHTwwQfHPffcE4ccckiPFQbFKpMpdAUAAABQWF16l7cXXnihu+uAXmPokIq2d6gbVlURs0+aWOiSAAAAIK+6FChFRPzqV7+KX/ziF9HY2BgtLS3tzmUymbj88sv3uDgoVq3vUAcAAAB9UZcCpdra2rjqqquioqIihg8fHpn3rAF679cAAAAAlI4uBUp33XVXnHjiiXHZZZd5RzcAAACAPqZfVy5666234lOf+pQwCQAAAKAP6lKgNGHChHjxxRe7uxYAAAAAeoEuLXn72te+FvPnz49BgwbFoYceGgMHDtxhzPve9749Lg4AAACA4tOlQOmUU06JlpaW+NrXvrbTDbhXrly5R4UBAAAAUJy6FChdcskl3skNAAAAoI/qUqD0yU9+srvrAAAAAKCX6FKg9OSTT+52TE1NTVduDQAAAECR61KgdOqpp0Ymk4lcLtd27L1L4OyhBAAAAFCauhQo3XHHHTsc27x5cyxfvjx+/OMfx/XXX7/HhQEAAABQnLoUKE2ZMqXD48ccc0wMGjQobrrppli8ePEeFQYAAABAcerX3Tc84ogj4oknnuju20LeeSNDAAAA6FiXZijtyr//+7/H4MGDu/u2kHdDh1RE7dIVUdeQjVHVVYUuBwAAAIpGlwKlz33uczsca2lpiTVr1sSf/vSnOP300/e4MCgGdQ3ZWF/fFHtXVRS6FAAAACgaXQqUtn93t1b9+vWLcePGxdy5c2PmzJl7XBgAAAAAxalLgdKdd97Z3XUAAAAA0Evs0R5Kjz76aDzxxBOxYcOGGD58eBx++OExbdq07qoNAAAAgCLUpUCpubk5zjzzzHjssceif//+MWzYsKirq4vFixfHkUceGYsXL44BAwZ0d60AAAAAFIF+Xbno+uuvj6eeeiquuuqqeOaZZ+Kxxx6L3/3ud3HFFVfEb3/727jpppu6u04AAAAAikSXAqWHHnoozj777DjppJOif//+ERFRVlYWJ598cpx99tnx4IMPdmuRAAAAABSPLgVKb7/9dkyYMKHDcxMmTIi1a9fuUVEAAAAAFK8uBUoHHnhgPPXUUx2ee/LJJ2P//fffo6IAAAAAKF5d2pT705/+dFx55ZVRWVkZn/jEJ2LfffeNt956Kx566KH47ne/G2effXZ31wkAAABAkehSoHTKKafE888/HwsXLoxvfetbbcdzuVzMmDEjvvjFL3ZbgQAAAAAUly4FSs3NzXHZZZfF7Nmz44knnoj6+vrIZDLx3/7bf4uxY8d2d40AAAAAFJGkPZR+//vfx8yZM+PWW2+NiIixY8fGKaecEp/5zGfiO9/5Tnz5y1+Ol19+uUcKBQAAAKA4dDpQev311+Nzn/tcvPXWWzFmzJh258rLy+O8886Ld955Jz7zmc94lzcAAACAEtbpQOnmm2+OvffeO370ox/Fcccd1+7cwIED4wtf+ELce++9UVFREYsXL+72QgEAAAAoDp0OlB5//PGYM2dODB8+fKdj9ttvv5g9e3b86le/6pbiAAAAACg+nQ6U1q1bF6NHj97tuHHjxsWaNWv2pCYAAAAAilinA6Xhw4fHunXrdjuurq4uhg4dukdFAQAAAFC8Oh0o1dTUxP3337/bcQ888EBMmDBhj4oCAAAAoHh1OlA69dRTY9myZXHllVdGNpvd4Xxzc3NcddVV8eijj8ZnP/vZbi0SAAAAgOJR1tmBH/nIR+L888+Pyy+/PH784x/H1KlT44ADDoht27bFG2+8EcuWLYu6urr4x3/8x5g2bVpP1gwAAABAAXU6UIqI+OxnPxsHH3xw3HLLLfHII4+0zVQaPHhwHHXUUTF79uw49NBDe6RQAAAAAIpDUqAUEXH44YfH4YcfHhERb7/9dpSVlcVee+3V7YUBAAAAUJySA6XtDR8+vLvqAAAAAKCX6PSm3AAAAAAQIVACAAAAIJFACQAAAIAkAiUAAAAAkuzRptyQqnbpiqhryMao6qpClwIAAAB0kRlK5FVdQzbW1zdF/abmQpcCAAAAdJFACQAAAIAkAiUAAAAAkgiUAAAAAEgiUAIAAAAgiUAJAAAAgCQCJQAAAACSCJQAAAAASFJUgdLixYvj1FNPbXds5cqVMWvWrJg0aVJMnz497rjjjnbnW1pa4rrrrotp06bFpEmT4vTTT4/XXnst6R4AAAAAdF7RBEp33313XHvtte2O1dXVxWmnnRYHHnhg3HfffXHWWWfFwoUL47777msbc+ONN8aSJUvikksuie9///vR0tISc+bMiebm5k7fAwAAAIDOKyt0AWvXro1vfvObsWzZshg9enS7c/fcc0+Ul5fHxRdfHGVlZTF27Nh45ZVX4uabb46ZM2dGc3Nz1NbWxoIFC+KYY46JiIhrrrkmpk2bFg8//HCccMIJu70HAAAAAGkKPkPpueeei/Ly8li6dGkceuih7c4tX748pkyZEmVl/5V7HXnkkbF69ep466234oUXXohNmzbF1KlT287vtddeMWHChHjyySc7dQ8AAAAA0hR8htL06dNj+vTpHZ5bs2ZNjBs3rt2xESNGRETEm2++GWvWrImIiP3333+HMa3ndnePfffdt0t153K52Lx5c5euzZfGxsZ2vxZaJpOJbS3bYlvLtmj5y0dPf97Tj7OtZVs0NjZGLpcr9Le3JBVbD0MqPUwp0Mf0dnqYUqCPyadcLheZTGa34woeKO1KU1NTDBgwoN2xioqKiIjIZrNtf5g6GlNfX9+pe3TVli1bYuXKlV2+Pp9Wr15d6BIi4t3ve1NjU2zetDmyTZWRbcr2+OfZ8ujRxxk8IOKll17ao15i94qlh6Gr9DClQB/T2+lhSoE+Jl/em6N0pKgDpcrKyrbNtVu1vnAfNGhQVFZWRkREc3Nz2+etYwYOHNipe3RVeXl5HHTQQV2+Ph8aGxtj9erVMXr06LbvRyFlMpmofHpzDGqOqKisiIotEYMG53r0855+nMqBlTF27FgzlHpIsfUwpNLDlAJ9TG+nhykF+ph8WrVqVafGFXWgVF1dHevWrWt3rPXrkSNHxtatW9uOHXjgge3GjB8/vlP36KpMJrNHgVQ+DRw4sGhq7d+vf/Tv1z/6/eWjpz/v6cfp36+/H+h5UEw9DF2hhykF+pjeTg9TCvQx+dCZ5W4RRbAp967U1NTEU089Fdu2bWs79utf/zrGjBkT++yzTxx88MExZMiQWLZsWdv5DRs2xPPPPx81NTWdugcAAAAAaYo6UJo5c2Zs3Lgxvv71r8eqVavi/vvvj9tuuy3mzp0bEe+u6Zs1a1YsXLgwHnnkkXjhhRfinHPOierq6jj22GM7dQ8AAAAA0hT1krd99tknvve978Vll10WM2bMiP322y/OO++8mDFjRtuYefPmxdatW+OCCy6IpqamqKmpiVtuuSXKy8s7fQ8AAAAAOq+oAqUrr7xyh2OHHHJI/OAHP9jpNf37949zzz03zj333J2O2d09AAAAAOi8ol7yBgAAAEDxESgBAAAAkESgBAAAAEASgRIAAAAASQRKAAAAACQRKAEAAACQRKAEAAAAQBKBEgAAAABJBEoAAAAAJBEoAQAAAJBEoAQAAABAEoESAAAAAEkESgAAAAAkESgBAAAAkESgBAAAAEASgRIAAAAASQRKAAAAACQRKAEAAACQRKAEAAAAQJKyQhcApap26Yqoa8jGsKqKmH3SxEKXAwAAAN1GoAQ9pK4hG+vrmwpdBgAAAHQ7S94AAAAASCJQAgAAACCJQAkAAACAJAIlAAAAAJIIlAAAAABIIlACAAAAIIlACQAAAIAkAiUAAAAAkpQVugDoq2qXroi6hmwMq6qI2SdNLHQ5AAAA0GkCJSiQuoZsrK9vKnQZAAAAkMySNwAAAACSmKEERcyyOAAAAIqRQAmKmGVxAAAAFCNL3gAAAABIIlACAAAAIIlACQAAAIAkAiUAAAAAkgiUAAAAAEgiUAIAAAAgSVmhC6D01S5dEXUN2RhVXVXoUgAAAIBuYIYSPa6uIRvr65uiflNzoUsBAAAAuoEZSpBHZmsBAABQCsxQgjwyWwsAAIBSIFACAAAAIIlACQAAAIAkAiUAAAAAkgiUAAAAAEgiUAIAAAAgiUAJAAAAgCQCJQAAAACSlBW6ACgltUtXRF1DNkZVVxW6FAAAAOgxZihBN6pryMb6+qao39Rc6FIAAACgxwiUAAAAAEgiUAIAAAAgiUAJAAAAgCQCJQAAAACSCJQAAAAASFJW6AKA9mqXroi6hmyMqq4qdCkAAADQITOUoMjUNWRjfX1T1G9qLnQpAAAA0CGBEgAAAABJBEoAAAAAJBEoAQAAAJBEoAQAAABAEoESAAAAAEkESgAAAAAkESgBAAAAkESgBHsgkyl0BQAAAJB/ZYUuAHqzoUMqonbpiqhryMao6qpClwMAAAB5YYYS7KG6hmysr2+K+k3NXbreLCcAAAB6GzOUoMDMcgIAAKC3MUMJisCeznICAACAfBIoAQAAAJBEoAQAAABAEoESAAAAAEkESgAAAAAkESgBAAAAkESgBAAAAEASgRIAAAAASQRKAAAAACQRKAEAAACQRKAEAAAAQBKBEgAAAABJygpdALBnapeuiLqGbAyrqojZJ00sdDkAAAD0AQIl6OXqGrKxvr6p0GUAAADQh1jyBgAAAEASgRIAAAAASQRKAAAAACQRKAEAAACQRKAEAAAAQBKBEgAAAABJygpdAKWpdumKqGvIxqjqqkKXAgAAAHQzM5ToEXUN2Vhf3xT1m5oLXQoAAADQzQRK0MMymeK4BwAAAHQXS96ghw0dUhG1S1fEXoMH7PE96hqyMayqImafNLEbKwQAAIA0AiXIg7qGbOS64R7r65u6pR4AAADYE5a8AQAAAJBEoAQAAABAEoESAAAAAEkESgAAAAAkESgBAAAAkESgBL1MJlPoCgAAAOjrygpdAJBm6JCKqF26IuoasjGquqrQ5QAAANAHmaEEvVBdQzbW1zdF/abm5Gtrl66I2qUreqAqAAAA+gozlKCPqWvIFroEAAAAejkzlAAAAABIIlACAAAAIIlACQAAAIAkAiUAAAAAkgiUAAAAAEgiUAIAAAAgiUAJAAAAgCQCJQAAAACSCJQAAAAASCJQAgAAACCJQAkAAACAJAIlAAAAAJIIlAAAAABIIlACAAAAIIlACQAAAIAkAiUAAAAAkvSKQGnt2rUxfvz4HT7uv//+iIhYuXJlzJo1KyZNmhTTp0+PO+64o931LS0tcd1118W0adNi0qRJcfrpp8drr71WiKcCAAAA0OuVFbqAznjhhReioqIifv7zn0cmk2k7XlVVFXV1dXHaaafF9OnT46KLLorf/va3cdFFF8XgwYNj5syZERFx4403xpIlS+LKK6+M6urquPrqq2POnDnx4IMPxoABAwr1tAAAAAB6pV4RKP3hD3+I0aNHx4gRI3Y4d/vtt0d5eXlcfPHFUVZWFmPHjo1XXnklbr755pg5c2Y0NzdHbW1tLFiwII455piIiLjmmmti2rRp8fDDD8cJJ5yQ52cDAAAA0Lv1ikDp97//fYwdO7bDc8uXL48pU6ZEWdl/PZUjjzwyFi9eHG+99Va88cYbsWnTppg6dWrb+b322ismTJgQTz75ZJcDpVwuF5s3b+7StfnS2NjY7td8yWQysa1lW2xr2RYtf/ko1OeFfvy81pLbFk1NTZHL5SIi2n7t6PemtS86GlNMCtXD0F30MKVAH9Pb6WFKgT4mn3K5XLvVYTvTKwKlP/zhDzFs2LD47Gc/Gy+//HKMGjUqzjjjjDj66KNjzZo1MW7cuHbjW2cyvfnmm7FmzZqIiNh///13GNN6riu2bNkSK1eu7PL1+bR69eq8Pl5FRUU0NTbF5k2bI9tUGdmmbME+z5ZHQR8/n7UM3LcyapeuiLfe2RzDhw6MYz48MLLZbIe/NxERL7300g7ni1W+exi6mx6mFOhjejs9TCnQx+RLZ7YHKvpAaevWrfHHP/4xDjrooPjqV78aQ4YMiX/5l3+JL37xi3HrrbdGU1PTDk+0oqIiIiKy2WxbgtvRmPr6+i7XVV5eHgcddFCXr8+HxsbGWL16dYwePToGDhyYt8fNZDJR+fTmGNQcUVFZERVbIgYNzhXk80I/fr5reachG5uaIyqbczF27NgdZiC1/t5ERIfni02hehi6ix6mFOhjejs9TCnQx+TTqlWrOjWu6AOlsrKyWLZsWfTv3z8qKysjImLixInx4osvxi233BKVlZXR3Nzc7prWWReDBg1qu6a5ubnt89Yxe/IHMZPJxKBBg7p8fT4NHDgw77X279c/+vfrH/3+8lGozwv9+IWqpX+//jvt7/79+kdE9Kq/iArRw9Cd9DClQB/T2+lhSoE+Jh86s9wtIqJfD9fRLQYPHtwuDIqI+OAHPxhr166N6urqWLduXbtzrV+PHDmybalbR2NGjhzZg1UDAAAAlKaiD5RefPHFOOyww2LZsmXtjq9YsSIOOuigqKmpiaeeeiq2bdvWdu7Xv/51jBkzJvbZZ584+OCDY8iQIe2u37BhQzz//PNRU1OTt+cBAAAAUCqKPlAaO3ZsfOADH4iLL744li9fHi+99FJcccUV8dvf/jbOOOOMmDlzZmzcuDG+/vWvx6pVq+L++++P2267LebOnRsR7+6dNGvWrFi4cGE88sgj8cILL8Q555wT1dXVceyxxxb42QEAAAD0PkW/h1K/fv1i0aJF8a1vfSvmz58fGzZsiAkTJsStt97a9u5u3/ve9+Kyyy6LGTNmxH777RfnnXdezJgxo+0e8+bNi61bt8YFF1wQTU1NUVNTE7fcckuUl5cX6mkBAAAA9FpFHyhFROy7775xxRVX7PT8IYccEj/4wQ92er5///5x7rnnxrnnntsT5QEAAAD0Kb0iUALyp3bpiqhryMawqoqYfdLEQpcDAABAERIoAe3UNWRjfX1TocsAAACgiBX9ptwAAAAAFBeBEgAAAABJBEoAAAAAJBEoAQAAAJBEoAQlKJMpdAUAAACUMu/yBiVo6JCKqF26IuoasjGsqiJmnzSx0CUBAABQQgRKUKLqGrKxvr6p0GUAAABQgix5AwAAACCJQAkAAACAJAIlAAAAAJIIlAAAAABIIlACAAAAIIlACQAAAIAkAiUAAAAAkgiUAAAAAEgiUAIAAAAgiUAJAAAAgCQCJQAAAACSlBW6AEpH7dIVUdeQjVHVVYUuBQAAAOhBZijRbeoasrG+vinqNzUXuhQAAACgBwmUAAAAAEhiyRuQrHV547Cqiph90sRClwMAAECeCZSAZK3LGwEAAOibLHkDAAAAIIlACQAAAIAkAiUAAAAAkgiUAAAAAEgiUAIAAAAgiUAJAAAAgCQCJQAAAACSCJQAAAAASCJQAgAAACCJQAkAAACAJAIlAAAAAJIIlAAAAABIIlACuiyTKXQFAAAAFEJZoQsA8q+7gqChQyqidumKqGvIxrCqiph90sS2czs7DgAAQO8nUII+aFdBUKq6hmysr2/q9HEAAAB6P4ES9FG7C3wsZwMAAGBnBEpAh7pzFhMAAAClRaAE7JRlawAAAHTEu7wBAAAAkESgBAAAAEASgRIAAAAASQRKAAAAACSxKTd7rHbpithr8IBClwEAAADkiUCJPVbXkI1coYsAAAAA8saSNyhxmUyhKwAAAKDUmKEEJW7okIqoXboi6hqyMaq6qtDlAAAAUALMUII+oK4hG+vrm6J+U3OhSwEAAKAECJQAAAAASCJQAgqmdumKqF26otBlAAAAkMgeSkDB1DVkC10CAAAAXWCGEgAAAABJBEoAAAAAJBEoAQAAAJBEoAQAAABAEoESAAAAAEkESgAAAAAkEShBH5fJFLoCAAAAepuyQhcAFNbQIRVRu3RF1DVkY1R1VaHLAQAAoBcwQwmIuoZsrK9vivpNzd1+bzOgAAAASo8ZSkCP2n4G1LCqiph90sRClwQAAMAeEigBPa51BhQAAAClwZI3oGhlMpmoqKiIjHVzAAAARcUMJaBo3f3wi/HG2nfifS+1xJy/PaTQ5QAAAPAXAiWgaL29oSn+XLc5KgdWFroUAAAAtmPJGwAAAABJBEoAAAAAJBEoAQAAAJBEoAQAAABAEoESAAAAAEkESgAAAAAkESgBu5XJFLoCAAAAiklZoQsAit/QIRVRu3RF1DVkY1R1VaHLAQAAoMDMUAI6pa4hG+vrm6J+U3OhSwEAAKDABEpAQVlOBwAA0PtY8gYU1PbL6YZVVcTskyYWuiQAAAB2Q6AEFFzrcjoAAAB6B4ES0KuZ3QQAAJB/AiWgVzO7CQAAIP9syg0AAABAEjOUgJJnWRwAAED3EijRJa0v0EdVVxW6FNgty+IAAAC6lyVvdEnrC/T6Tc2FLgUAAADIM4ESAAAAAEkESgAAAAAkESgB3SKTKXQFAAAA5ItNuYFuMXRIhc3aAQAA+ggzlIBus7vN2s1iAgAAKA1mKAF5YxYTAABAaRAoAXnVOotp76qKQpfSTmvQNayqImafNLHQ5QAAABQ1gRLQZ+xqyV1r0AUAAMDuCZSAPmP7JXdmIgEAAHSdQAnoU8xEAgAA2HPe5Q0AAACAJGYoAUVjZ3sc7Wrvo3yxVA4AAOC/CJSAorH9HkejqquiNUfauwj2PrJUDgAA4L8IlICi0hrc7F1V0eHxQiuG2VIAAACFJlAC2M7uAiPvFAcAACBQAkpEd80cag2MImKnYVGxzJYCAAAoFIESUBK6c+ZQXUO2GysDAAAoPQIloGSYOQQAAJAfAiWAbmJvJQAAoK8QKAG9SjG/y1rrDKlirhEAAKA7CJSAXmX7vZJGVVcVupwOeSc4AACg1AmUgF6ndSbQ3lUVHZ4vhhlCO9vPqTuCJmEVAABQaAIloOQU8yym7tg43ObjAABAoQmUgJK0p7OYCjnLyQwkAACg2AmUgD5pd7OYCjnLKV8zkARXAABAVwmUgD5rd7OYdne+mHUmLLJ0DgAA6CqBEkAR2N1sqNTZUsIiAACgJwmU6LRi3eQYSkEpz5YCAABKj0CJTvOCFrpPZzb9LuTG4K3sswQAAHREoARQAJ3Z9Lt1zF6DB3Tqnj0xi9DSOQAAoCMCJYAu2tMZRJ2Z9VfXkI1cN94PAACgOwiUALqoM7OMenLZmiVxAABAoQiUAPbA7mYFdSZ06qqevHdn7WxJXGtdw6sq4rQ8BU3CLQAAyB+BErtU6BerUAp6cina7u793llMHf2Z7syYPamrM0FPd4RBndnvqRChU0VFRWSKYToZAAB0oz4TKLW0tMQNN9wQP/zhD6OhoSFqamriG9/4Rrz//e8vdGlFzZ4s0Lu9dxZTR3+mOzNmd3nIrs633m9XwVW+wqCUx+mO2VWZTCb+v+ca4ydPPxv7Dh3UK2ZOmekFAEBn9JlA6cYbb4wlS5bElVdeGdXV1XH11VfHnDlz4sEHH4wBAzr3DkoAvVFnN//uzNK9vQYPiA2bmneYwZTyrnW7Cq468zz2JNxKeZztZ1eNqq5qe96pQcvb9Y2xqTmif7/+e1ZYnhTrTC8AAIpLnwiUmpubo7a2NhYsWBDHHHNMRERcc801MW3atHj44YfjhBNOKGyBAL1A6zvOvbOTIKg7gqvOLL9LCbd2Nsuos8v8tq/3nd3MtNrdjKZdBV3FGNB0ZtbZrhRiH63eoBh/rwEAuiKTy+U6+47UvdYzzzwTf/d3fxc//elPY8yYMW3HTznllBg3blxcdNFFSff7zW9+E7lcLsrLy7u71G6Vy+Vi69atUVZW1uH+HZuzW6OlJRdl/ftFSy7X4edbt7XsdkxLLhf9Mpldni/U58VUl1q6Vsu2bS1RXta/6OpSS889TmSiUz+DOlvLzu7X2cdJvWdExKCKsrafwxsbt0RLLqJ8u+v69cu0jYmId8f85fiQgR3/3dLRz+z33ue9Y7avZfvz771ue621lO1hve+9z85q2dXz6YyU78vO7r+z8535fqXa3feuJx5zT+3u3xNQ7PQwpUAfk09btmyJTCYThx122C7HFce/VHrYmjVrIiJi//33b3d8xIgRbedStP4BLvY/yJlMZpfL+QZXFncgBtDbZTKZ2Gvw7pf0VQ3a/dLrzvzM3t2YztyjM7V0x5ju+juoJ78vPfH3ZL6+L91pd/+egGKnhykF+ph8ymQynco7+kSg1NjYGBGxwx/AioqKqK+vT77f5MmTu6UuAAAAgN6oX6ELyIfKysqIeHcvpe1ls9kYOHBgIUoCAAAA6LX6RKDUutRt3bp17Y6vW7cuRo4cWYiSAAAAAHqtPhEoHXzwwTFkyJBYtmxZ27ENGzbE888/HzU1NQWsDAAAAKD36RN7KA0YMCBmzZoVCxcujOHDh8df/dVfxdVXXx3V1dVx7LHHFro8AAAAgF6lTwRKERHz5s2LrVu3xgUXXBBNTU1RU1MTt9xyS5SXF9+7qQAAAAAUs0wul8sVuggAAAAAeo8+sYcSAAAAAN1HoAQAAABAEoESAAAAAEkESgAAAAAkESgBAAAAkESgBAAAAEASgVIv1tLSEtddd11MmzYtJk2aFKeffnq89tprOx1fV1cXX/nKV6KmpiamTJkSF110UTQ2NuaxYmgvtYdffPHF+OIXvxgf/ehHY+rUqTFv3rx444038lgxtJfaw9tbunRpjB8/Pl5//fUerhJ2LbWPt2zZEt/61rfaxs+aNStWrlyZx4qhvdQeXr9+fXzlK1+JI488Mj760Y/GOeecE2vXrs1jxbBrixcvjlNPPXWXY7y2oxgIlHqxG2+8MZYsWRKXXHJJfP/734+WlpaYM2dONDc3dzh+3rx58corr8Rtt90W3/nOd+I//uM/4sILL8xv0bCdlB6uq6uL0047LSorK+POO++M7373u/H222/HnDlzIpvNFqB6SP853OpPf/pTXHzxxXmqEnYttY8vvPDCuP/+++Pyyy+P++67L4YPHx6nn356NDQ05LlyeFdqD8+fPz/eeOONuPXWW+PWW2+NN954I84666w8Vw0du/vuu+Paa6/d7Tiv7SgKOXqlbDabmzx5cu7uu+9uO1ZfX5875JBDcg8++OAO43/zm9/kxo0bl1u1alXbsV/+8pe58ePH59asWZOXmmF7qT18zz335CZPnpxrbGxsO/bGG2/kxo0bl/vP//zPvNQM20vt4Vbbtm3LnXLKKbnPfe5zuXHjxuVee+21fJQLHUrt41dffTU3fvz43C9+8Yt24//6r//az2IKIrWH6+vrc+PGjcs98sgjbcd+/vOf58aNG5erq6vLR8nQoTVr1uTmzp2bmzRpUu64447LzZo1a6djvbajWJih1Eu98MILsWnTppg6dWrbsb322ismTJgQTz755A7jly9fHvvtt1+MHTu27diUKVMik8nEU089lZeaYXupPTx16tS48cYbo7Kysu1Yv37v/gjbsGFDzxcM75Haw60WLVoUW7Zsiblz5+ajTNil1D7+1a9+FVVVVXH00Ue3G//v//7v7e4B+ZLaw5WVlTF48OB44IEHYuPGjbFx48b48Y9/HGPGjIm99torn6VDO88991yUl5fH0qVL49BDD93lWK/tKBZlhS6ArlmzZk1EROy///7tjo8YMaLt3PbWrl27w9gBAwbE3nvvHW+++WbPFQo7kdrDBxxwQBxwwAHtjt18881RWVkZNTU1PVco7ERqD0dEPPPMM1FbWxv33nuv/TooCql9/PLLL8f73//+ePjhh+Pmm2+OtWvXxoQJE+KrX/1quxc2kC+pPTxgwIC48sor4xvf+EYcccQRkclkYsSIEXHXXXe1/UcVFML06dNj+vTpnRrrtR3Fwk/NXqp1w7UBAwa0O15RUdHhfjKNjY07jN3VeOhpqT38XnfeeWfcddddsWDBghg+fHiP1Ai7ktrDmzdvjgULFsSCBQti9OjR+SgRdiu1jzdu3BivvPJK3HjjjfHlL385brrppigrK4vPfOYzsX79+rzUDNtL7eFcLhcrV66MyZMnx9133x233357vO9974szzzwzNm7cmJeaYU95bUexECj1Uq3Lft672WA2m42BAwd2OL6jjQmz2WwMGjSoZ4qEXUjt4Va5XC6uvfbauPTSS+OMM87Y7TtgQE9J7eFLL700xowZE5/+9KfzUh90Rmofl5WVxcaNG+Oaa66Jo446Kg455JC45pprIiLiRz/6Uc8XDO+R2sP/+q//GnfddVdcffXVcfjhh8eUKVNi0aJF8ac//SnuvffevNQMe8prO4qFQKmXap3iuG7dunbH161bFyNHjtxhfHV19Q5jm5ub45133okRI0b0XKGwE6k9HPHuW1Wfe+65sWjRojj//PNj/vz5PV0m7FRqD993333xn//5nzF58uSYPHlynH766RERccIJJ8SiRYt6vmDoQFf+PVFWVtZueVtlZWW8//3vj9dff71ni4UOpPbw8uXLY8yYMTFkyJC2Y0OHDo0xY8bEK6+80rPFQjfx2o5iIVDqpQ4++OAYMmRILFu2rO3Yhg0b4vnnn+9wP5mamppYs2ZNu78on3jiiYiIOPzww3u+YHiP1B6OiDjvvPPipz/9aXzrW9+KL3zhC3mqFDqW2sMPP/xwPPTQQ/HAAw/EAw88EJdeemlEvLsXmFlLFEpX/j2xdevWePbZZ9uONTU1xWuvvRajRo3KS82wvdQerq6ujldeeaXdsqDNmzfH66+/bjkyvYbXdhQLm3L3UgMGDIhZs2bFwoULY/jw4fFXf/VXcfXVV0d1dXUce+yxsW3btnj77bejqqoqKisr49BDD43DDjsszjnnnLjwwgtj8+bN8Y1vfCNOPvnknc4GgZ6U2sP3339//OQnP4nzzjsvpkyZEn/+85/b7tU6BvIptYff+2K7dbPY973vfbH33nsX4BlAeh8fccQR8bGPfSz+6Z/+KS6++OLYe++947rrrov+/fvH3/7t3xb66dAHpfbwySefHLfcckvMnz8//vEf/zEiIq699tqoqKiIT37ykwV+NtAxr+0oVmYo9WLz5s2LT33qU3HBBRfEKaecEv37949bbrklysvL480334yjjjoqfvKTn0RERCaTiRtuuCEOOOCA+PznPx/z58+Po48+Oi688MLCPgn6tJQefuihhyIi4qqrroqjjjqq3UfrGMi3lB6GYpXax9dff31MmTIlzj777PjUpz4VGzdujDvuuMMbJFAwKT08YsSIWLJkSeRyufj85z8fp512WpSXl8eSJUuiqqqqwM8EOua1HcUqk8vlcoUuAgAAAIDewwwlAAAAAJIIlAAAAABIIlACAAAAIIlACQAAAIAkAiUAAAAAkgiUAAAAAEhSVugCAAAAAOiaxYsXx2OPPRZ33nlnp8YvW7YsPve5z3V47oADDohHHnmkU/cRKAEAAAD0QnfffXdce+21ccQRR3T6msmTJ8djjz3W7thvf/vb+N//+3/HmWee2en7CJQAAHrIqaeeGk888US7Y+Xl5bHvvvvGX//1X8f8+fNj6NChHV77+uuvx9/8zd/EFVdcEZ/85CfzUS4A0EusXbs2vvnNb8ayZcti9OjRSdcOGDAg9ttvv7avN2/eHFdccUXMmDEjZs6c2en7CJQAAHrQhAkT4pvf/Gbb11u2bInnnnsuvv3tb8fKlSvj//2//xeZTGaH60aMGBE/+MEP4sADD8xnuQBAL/Dcc89FeXl5LF26NP7v//2/8ac//and+V/84hdx/fXXx6pVq2LkyJHxiU98Is4888wYMGDADvdatGhRNDY2xj/90z8l1SBQAgDoQUOGDIlJkya1O1ZTUxObNm2K6667Ln73u9/tcD7i3f897Og4AMD06dNj+vTpHZ579NFHY/78+XH++efHxz72sXj11VfjkksuiZdffjm+853vtBv79ttvx2233RZf+cpXYu+9906qwbu8AQAUwMSJEyMi4o033ohTTz01FixYEPPmzYtJkybFaaedFq+//nqMHz8+7r///rZr/vjHP8bZZ58dU6ZMiZqampg7d2689NJLbeez2WxcddVV8fGPfzwmTpwYJ554YvzkJz/J+3MDAApn0aJF8fd///fx6U9/Og488MA46qij4qKLLoqf/vSn8frrr7cbu2TJkqiqqop/+Id/SH4cM5QAAArg5ZdfjoiI97///RER8a//+q9x0kknxU033RQtLS07jF+7dm38wz/8Q4wcOTIuvPDCGDRoUFx//fXx+c9/Ph566KEYOnRonHXWWfGb3/wm5s2bF2PHjo2f/exncc4550Rzc3OcfPLJ+Xx6AECBPP/88/HMM8/Evffe23Ysl8tFRMRLL70UBxxwQNvxBx54IE4++eSorKxMfhyBEgBAD8rlcrF169a2r+vr6+OJJ56Im266KSZPntw2U6m8vDwuuuiitr0N3vs/iLfddls0NzfHrbfe2raR5sEHHxynnHJK/O53v4uysrL45S9/Gddcc038z//5PyMiYtq0adHY2BgLFy6ME044IcrK/NMPAEpdS0tLzJkzJ2bMmLHDue03437hhRfitddeixNPPLFLj+NfFQAAPejJJ5+MD3/4w+2O9evXLz72sY/FxRdf3LYh9wc+8IEON8ps9dRTT8WkSZPa/UOwuro6fvGLX0RExMKFCyOTycTHP/7xdgHW9OnTY+nSpfHiiy/Ghz70oe58agBAEfrgBz8YL7/8cowaNart2LJly+KOO+5om+UcEbF8+fLYZ5994uCDD+7S4wiUAAB60Ic//OG46KKLIiIik8lERUVF7L///jFkyJB24wYPHrzL+7zzzjvtpqh3dD6Xy8Vhhx3W4fl169YJlACgDzj99NNj/vz5ccMNN8QnPvGJWLNmTXz961+PAw44oN1/TD3//PMxfvz4Lj+OQAkAoAcNHjw4PvKRj+zxfaqqquLtt9/e4fjjjz8eBxxwQFRVVcWgQYPijjvu6PD67f+XEgAoXccdd1xcc801sXjx4li0aFHsvffeMX369FiwYEG7cX/+85+T39ltewIlAIBe4Igjjoh77rkn3n777Rg+fHhERKxfvz7mzJkT559/fkyZMiVqa2sjl8vFIYcc0nbdfffdFz/72c/i8ssvL1TpAEAPuvLKK3c4dvzxx8fxxx+/y+u++93v7tHjCpQAAHqBL3zhC/HAAw/EnDlzYu7cuVFeXh433XRTVFdXx4knnhhVVVVRU1MTZ555Zpx55pkxduzYeOaZZ+K6666LadOmtYVQAADdQaAEANAL7L///rFkyZK4+uqr46tf/WoMGDAgPvrRj8Y111wTQ4cOjYiIm2++Ob7zne/E4sWLY/369TFy5Mg47bTT4qyzzipw9QBAqcnkcrlcoYsAAAAAoPfoV+gCAAAAAOhdBEoAAAAAJBEoAQAAAJBEoAQAAABAEoESAAAAAEkESgAAAAAkESgBAAAAkESgBAAAAEASgRIAAAAASQRKAAAAACQRKAEAAACQ5P8HMGKC3qZS/SMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter_data = df.dropna(subset=['Price'])\n",
    "plt.figure(figsize=(14,8))\n",
    "\n",
    "sns.histplot(filter_data['Price'], kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='house_type'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_counts = df['Type'].value_counts()\n",
    "\n",
    "df2 = pd.DataFrame({'house_type': type_counts}, index = ['t', 'h', 'u'])\n",
    "df2.plot.pie(y='house_type', figsize=(10,10), autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='darkgrid')\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "ax = sns.countplot(x='Regionname', data=df)\n",
    "#plt.savefig('./myfig.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "      <th>Distance</th>\n",
       "      <th>CouncilArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>49 Lithgow St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1490000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>3067</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>59A Turner St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1220000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Marshall</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>3067</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>119B Yarra St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1420000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>3067</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aberfeldie</td>\n",
       "      <td>68 Vida St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1515000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Barry</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>3040</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>1543</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Moonee Valley City Council</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Airport West</td>\n",
       "      <td>92 Clydesdale Rd</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>3042</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>3464</td>\n",
       "      <td>10.4</td>\n",
       "      <td>Moonee Valley City Council</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Suburb           Address  Rooms Type      Price Method   SellerG  \\\n",
       "0    Abbotsford     49 Lithgow St      3    h  1490000.0      S    Jellis   \n",
       "1    Abbotsford     59A Turner St      3    h  1220000.0      S  Marshall   \n",
       "2    Abbotsford     119B Yarra St      3    h  1420000.0      S    Nelson   \n",
       "3    Aberfeldie        68 Vida St      3    h  1515000.0      S     Barry   \n",
       "4  Airport West  92 Clydesdale Rd      2    h   670000.0      S    Nelson   \n",
       "\n",
       "        Date  Postcode             Regionname  Propertycount  Distance  \\\n",
       "0 2017-01-04      3067  Northern Metropolitan           4019       3.0   \n",
       "1 2017-01-04      3067  Northern Metropolitan           4019       3.0   \n",
       "2 2017-01-04      3067  Northern Metropolitan           4019       3.0   \n",
       "3 2017-01-04      3040   Western Metropolitan           1543       7.5   \n",
       "4 2017-01-04      3042   Western Metropolitan           3464      10.4   \n",
       "\n",
       "                  CouncilArea  \n",
       "0          Yarra City Council  \n",
       "1          Yarra City Council  \n",
       "2          Yarra City Council  \n",
       "3  Moonee Valley City Council  \n",
       "4  Moonee Valley City Council  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1120x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Price', \n",
    "           y='Postcode', \n",
    "           data=df, \n",
    "           fit_reg=False, \n",
    "           scatter_kws={\"marker\": \"D\", \"s\": 20}, \n",
    "           height=7, \n",
    "           aspect=1.6) \n",
    "plt.title('Scatter Plot of Data without Regression Line')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Post Code')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "a3a5d4f9651fa550758ade4a473ec6b18daeb3d63067db8abd62b64f4493ea9c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}