Notebooks/Python_DataScience/16_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": [
    "#import pip\n",
    "#pip.main(['install','seaborn'])\n",
    "#pip.main(['install','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','CSV','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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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "markdown",
   "metadata": {
    "id": "K7jcd1MbL8ZY"
   },
   "source": [
    "### Box Plot  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Age', 'Salary before Stern', 'Expected salary', 'Candidate'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfvote.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "wRP_I20RL8Zc",
    "outputId": "0f5c7bbf-f077-43c0-83bc-dbe24160c901"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 13,
   "metadata": {
    "id": "_39rh8npL8Ze",
    "outputId": "a476add0-dd75-4287-c4f2-99479a926bf1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfvote.head(10)"
   ]
  },
  {
   "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>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = dfvote.groupby('Gender').mean()\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped[['Salary before Stern','Expected salary']].plot(kind='bar', figsize=(8,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = dfvote.groupby('Candidate').Age.mean()\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var.plot(kind='bar', figsize=(12,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mYjoJoRML8Zg"
   },
   "source": [
    "### Line Chart  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "8s4tOFqaL8Zg",
    "outputId": "f76048e0-4016-40f4-ce60-618e202241d8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfvote.head()"
   ]
  },
  {
   "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></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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <th>Female</th>\n",
       "      <td>114000</td>\n",
       "      <td>190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = dfvote.groupby(['Age','Gender']).sum()\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "fAgDx-11L8Zi",
    "outputId": "c88911de-27d7-4277-fe94-1e4edb3d6912"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F36Tn5OTokksu0bfffqsmTZooPDxc//3vf/XPf/6zwrn8aXNCo0aNlJKSojFjxpz0skdJSktLkyStWLFC4eHhCg8PV0pKimbMmCFXslBK6cszTwgPD1fz5s19l+XNnTvXN7tWWps2bbR3715fUCsoKNCGDRsqtHvvvfdUUFAgqXjRkv379+vCCy9U06ZN9d133/na+VNbdYYOHer3fV+n2993332nVq1aqaCgQPPmzauw/+yzz9all16qN954Q1JxMCw9k/jZZ58pJibmB/VZGYIaAAAAUE6fPn20YsUKSdKePXv0i1/8QjExMYqNjVX9+vV19913Ky4uTgkJCWrbtq1uvvlmdenSpcJ5/GlT2pAhQ1SvXj316dPnpG0aNWqkhIQE3XHHHb77px5++GEVFBQoNjZW7dq108MPP1zpsX/5y180fvx4xcbGKjs7W4888kiFNg0aNFB6eromTJiguLg4xcfHa+XKlRXaLVq0SDExMYqLi1NKSoqeeuop/eQnP1GPHj20ceNG32Ii/tZWlZycHF1wwQV+tT3d/h5//HFdccUV6tKli9q2bVtpm3nz5unll19WXFyc2rVr51uwRJL+85//+C4bPR3m/FietCYkJSW5mni2BELDHg3wsrlB5CaF5u8Afnz4nANAcGzatElRUVEhrWHNmjWaNm2a5s6dG9R+p06dqvz8fD3++ONB7dfLvv32W40cOdI3g+Vla9eu1dNPP13p56ayz7WZZTnnkio7F/eoAQAAAOUkJiaqR48eNfZ8s8oMHDhQ27Zt04cffhiU/n4szj777B9FSJOKF6IJVMgmqAEAAACVCMRDi3+It956K6j9IfACccnjCdyjBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAABAOUeOHFG3bt1UVFSk48ePa+zYsYqJiVH79u3VsWNHbd++vcrju3fvrpp4FNWpnHfw4MGKjY3VtGnTAlrL5s2b1b17d8XHxysqKkqjR4+WJGVnZ2vhwoUB7etkhg8frjPPPLPMA7bvvfdemZn27dtX5bGRkZHVtunVq5e++eabgNT6Q7HqIwAAADzNAvwYS38eIzxr1iwNGjRIYWFhmj9/vr788kvl5OSoXr16ysvLU5MmTQJaU009BuCrr75SRkaGtm7d6vcxhYWFql+/+pgwduxYjRs3Tv3795ckrVu3TlJxUMvMzNS1114b8D4r89Of/lTvvPOObrnlFh0/flwffvihLrzwwlM6V3m33nqrZs6cqYkTJwbkfD8EM2oAAABAOfPmzfMFkN27d6tVq1aqV6/4W+eIiAg1b95ckjRmzBglJSWpXbt2mjRpUqXnOlmbyMhITZgwQYmJiZoyZYoSExN9+7Zs2VLmfWlz585VfHy8YmJitHr1aknSoUOHlJqaqk6dOikhIUHvvPOOJKlPnz7atWuX4uPjtXz5cmVnZ6tz586KjY3VwIEDfbNF3bt317333qukpCQ988wzysrKUrdu3dShQwelpKRo9+7dFerYvXu3IiIifO/bt2+vY8eO6ZFHHlFaWpri4+OVlpZ20trmzJmjfv36qWfPnkpOTtacOXM0aNAg9e3bV61bt9b999/vx5+UdNNNNyktLU2StGTJEnXp0qVM6BswYIA6dOigdu3a6cUXX6z0HK+++qo6deqk+Ph43X777SoqKpIk9evXT/Pnz/erjkAjqAEAAAClHDt2TJ9//rkiIyMlSb/61a/0t7/9TfHx8brvvvu0du1aX9vJkycrMzNTOTk5Wrp0qXJyciqcr6o2LVq00Jo1azRx4kSFh4crOztbkjR79myNGDGi0voOHz6s7OxszZw50/est8mTJ6tnz55avXq1Fi9erPHjx+vQoUN69913dfnllys7O1tdu3bV0KFD9eSTTyonJ0ft27fXo48+WubrzszM1NixY3XPPfcoPT1dWVlZSk1NrXRGady4cerZs6euueYaTZs2TQcOHFCDBg302GOP6cYbb1R2drZuvPHGk9YmSWvWrFF6erqWLl0qqXg2Li0tTevWrVNaWpp27txZ7Z/Xz372M+3du1fffPON5s+fr5tuuqnM/lmzZikrK0uZmZmaPn269u/fX2b/pk2blJaWpv/85z/Kzs5WWFiY5s2bJ0lq3ry5vv/++wrHBANBDQAAAChl3759atasme99RESENm/erN/97neqV6+ekpOT9cEHH0iSXn/9dSUmJiohIUEbNmzQxo0bK5yvqjY33nij7/WoUaM0e/ZsFRUVKS0tTTfffHOl9Q0ePFiSdPXVV+vbb7/VgQMHtGjRIk2ZMkXx8fHq3r27jh49qh07dpQ5Lj8/XwcOHFC3bt0kScOGDdOyZcsq1LJ582atX79evXv3Vnx8vJ544gnl5eVVqGPEiBHatGmTfvnLX2rJkiXq3Lmzvv/++wrtqqqtd+/eOuecc3xtk5OTFR4erkaNGik6OlpffPFFpWNQ3qBBg7RgwQJ9/PHH6tq1a5l906dPV1xcnDp37qydO3dqy5YtZfZ/8MEHysrKUseOHRUfH68PPvhAn3/+uW//eeedpy+//NKvOgKJe9QAAACAUho3bqyjR4+W2dawYUNdc801uuaaa3T++efr7bff1mWXXaapU6cqIyNDzZs31/Dhwysct3379irblL7X7frrr9ejjz6qnj17qkOHDmrRokWl9Vm5m/bMTM45vfnmm2rTpk2Zfbm5uX5/3Sdqcc6pXbt2WrVqVbXHXHDBBUpNTVVqaqpiYmK0fv36Cm1OVtvHH39c4V6/hg0b+l6HhYWpsLDQr9pvvPFGdejQQcOGDfNdoioVXwr5/vvva9WqVTrzzDN9QbF8fcOGDdPvfve7Ss999OhRNW7c2K86AokZNQAAAKCU5s2bq6ioyPcN/Zo1a3wzKsePH1dOTo4uueQSffvtt2rSpInCw8P13//+V//85z8rnMufNic0atRIKSkpGjNmzEkve5Tkux9rxYoVCg8PV3h4uFJSUjRjxgy5kpVSSl+eeUJ4eLiaN2+u5cuXSyq+1+3E7Fppbdq00d69e31BraCgQBs2bKjQ7r333lNBQYGk4kVL9u/frwsvvFBNmzYtswqjP7VVZ+jQob778SpzySWXaPLkybrzzjvLbM/Pz1fz5s115pln6tNPP9VHH31U4djk5GSlp6drz549kqSvv/7aN5PnnNNXX33luww2mJhRA36k7NEAL4EVRG6SH8ttAQAQQn369NGKFSvUq1cv7dmzR7fddpvvsr5OnTrp7rvvVqNGjZSQkKC2bdvqoosuUpcuXSqcJy4urto2pQ0ZMkRvvfWW+vTpc9I2J/otKCjQrFmzJEkPP/yw7r33XsXGxur48eO69NJL9fe//73CsX/5y190xx136PDhw7rssss0e/bsCm0aNGig9PR0jR07Vvn5+SosLNS9996rdu3alWm3aNEi/frXv1ajRo0kSU899ZR+8pOfqEePHr5LHR988EG/a6tKTk6OLrjggirb3H777RW29e3bV88//7yioqLUpk0bde7cuUKb6OhoPfHEE+rTp4+OHz+uM844Q88995wuueQSZWVlqXPnzqe8IuXpMOfP+qQ1ICkpydXEsyUQGoSG4GPMg48xB4Dg2LRpk6KiokJaw5o1azRt2jTNnTs3qP1OnTpV+fn5evzxx4Par5d9++23GjlypN54442g9/3rX/9a/fr1U3Jy8mmfq7LPtZllOeeSKmvPjBoAAABQTmJionr06FFjzzerzMCBA7Vt2zZ9+OGHQenvx+Lss88OSUiTpJiYmICEtFNBUAMAAAAqcWLp+2B56623gtofqnfbbbeFrG8WEwEAAAAAjyGoAQAAAIDHENQAAAAAwGMIagAAAADgMQQ1AAAAoJwjR46oW7duKioqUm5urho3bqz4+Hjfr1deeaXGa8jNzdVrr732g48bPny40tPTT6vvJUuW6Lrrrjutc/gjLCyszLjm5ubWWF+RkZHat29flW169eqlb775psZq+CFY9REAAADe9lqAn2N5c/XPlpw1a5YGDRrkW5r/8ssvV3Z2dmDrqMaJoHbzzTcHtd9TUVhYeEoPhW7cuHHQx7Uqt956q2bOnKmJEyeGupTqZ9TMbJaZ7TGz9dW062hmhWZ2Q+DKAwAAAIJv3rx56t+/f5VtvvjiC7Vu3Vr79u3T8ePH1bVrVy1atEi5ublq27athgwZoqioKN1www06fPiwJCkrK0vdunVThw4dlJKSot27d0uStm7dql69eikuLk6JiYnatm2bHnjgAS1fvlzx8fGaNm2aioqKNH78eHXs2FGxsbF64YUXJEnOOd19991q06aNevXqpT179lRa7/Tp0xUdHa3Y2FjddNNNkqTVq1fryiuvVEJCgq666ipt3ry5wnEnazNnzhz169dPPXv2VHJysoYOHaq3337bd9yQIUP0zjvv/LCBr2KMunfvrnHjxikpKUlRUVHKyMjQoEGD1Lp1az300EO+4wcMGKAOHTqoXbt2evHFFyvt49VXX1WnTp0UHx+v22+/XUVFRZKkfv36af78+T+45prgz6WPcyT1raqBmYVJelLSogDUBAAAAITMsWPH9PnnnysyMtK3bdu2bWUu0Vu+fLkuueQSTZgwQWPGjNEf/vAHRUdHq0+fPpKkzZs3684779SmTZt09tlna+bMmSooKNA999yj9PR0ZWVlKTU11TdzM2TIEN1111365JNPtHLlSrVq1UpTpkxR165dlZ2drXHjxunll19WeHi4MjIylJGRoZdeeknbt2/XW2+9pc2bN2vjxo165ZVXtHLlykq/rilTpmjt2rXKycnR888/L0lq27atli9frrVr1+qxxx7Tb37zmwrHVdVmzZo1Sk9P19KlSzVy5EjNmTNHkpSfn6+VK1fqf/7nf6oc6yNHjvjGdODAgVWOkSQ1aNBAmZmZuuOOO9S/f38999xzWr9+vebMmaP9+/dLKp4NzcrKUmZmpqZPn+7bfsKmTZuUlpam//znP8rOzlZYWJjmzZsnSWrevLm+//77CseEQrXzk865ZWYWWU2zeyS9KaljIIoCAAAAQmXfvn1q1qxZmW0nu/Rx1KhReuONN/T888+X2X/RRRepS5cukqRbbrlF06dPV9++fbV+/Xr17t1bklRUVKRWrVrpu+++065duzRw4EBJUqNGjSqta9GiRcrJyfHdf5afn68tW7Zo2bJlGjx4sMLCwnTBBReoZ8+elR4fGxurIUOGaMCAARowYIDvHMOGDdOWLVtkZiooKKhwXFVtevfurXPOOUeS1K1bN915553au3ev3nzzTV1//fXVXg5Z/tLH9evXVzpGJ/Tr10+S1L59e7Vr186377LLLtPOnTvVokULTZ8+3ffw8J07d2rLli1q0aKF7xwffPCBsrKy1LFjcXQ5cuSIzjvvPN/+8847T19++WWZY0LhtO9RM7MLJQ2U1EMENQAAAPzINW7cWEePHvWr7eHDh5WXlydJOnjwoJo2bSpJMit7X52ZyTmndu3aadWqVWX2fffdd3715ZzTjBkzlJKSUmb7woUL/Tr+H//4h5YtW6a//e1vmjx5statW6eHH35YPXr00FtvvaXc3Fx17969wnFVtWnSpEmZtkOHDtWrr76qBQsWaPbs2X7VVf5rrGyMTmjYsKEkqV69er7XJ94XFhZqyZIlev/997Vq1SqdeeaZ6t69e4U/S+echg0bpt/97neV9nH06FE1btz4B9ceaIFY9fGPkiY4545X19DMRptZppll7t27NwBdAwAAAIHVvHlzFRUV+RXWJkyYoCFDhuixxx7Tbbfd5tu+Y8cOX9h47bXX9POf/1xt2rTR3r17fdsLCgq0YcMGNW3aVBEREb77u77//nsdPnxYTZs2LRPiUlJS9Kc//ck3o/XZZ5/p0KFDuvrqq5WWlqaioiLt3r1bixcvrlDn8ePHtXPnTvXo0UNPPvmk8vPzdfDgQeXn5+vCCy+UJN9li+X50+aE4cOH649//KMkKTo6WpK0a9cuJScnV3ncCScbI3/l5+erefPmOvPMM/Xpp5/qo48+qtAmOTlZ6enpvnv5vv76a33xxReSikPcV199Veay11AJRFBLkrTAzHIl3SBpppkNqKyhc+5F51yScy6pZcuWAegaAAAACLw+ffpoxYoVvvfl71GbPn26li5dqoyMDF9Ya9CggW8WqU2bNnruuecUFRWlb775RmPGjFGDBg2Unp6uCRMmKC4uTvHx8b77yebOnavp06crNjZWV111lb766ivFxsYqLCxMcXFxmjZtmkaNGqXo6GglJiYqJiZGt99+uwoLCzVw4EC1bt1a0dHRGjp0qK688soKX09RUZFuueUWtW/fXgkJCRo7dqyaNWum+++/Xw8++KASEhJUWFhY6Vj40+aE888/X1FRURoxYoRv2+7du/1eEbKqMfJH3759VVhYqKioKD3wwAPq3LlzhTbR0dF64okn1KdPH8XGxqp3796+BUuysrLUuXPnU1rBMtDMueqXJy25R+3vzrmYatrNKWlX7YMbkpKSXGZmpp9lwuvs0QAvmxtEblL1fwe8iDEPPsYcAIJj06ZNioqKCmkNa9as0bRp0zR37twffGxubq6uu+46rV9f5aLptdLhw4fVvn17rVmzRuHh4ZKkZ599VhdffLHv/jIv+/Wvf61+/fr5PQP4Q1T2uTazLOdcUmXtq42KZjZfUndJ55pZnqRJks6QJOfc86dbMAAAAOA1iYmJ6tGjh4qKinzPUkPV3n//fY0cOVLjxo3zhTRJuvvuu0NY1Q8TExNTIyHtVPiz6uNgf0/mnBt+WtUAAAAAHpGamnpKx0VGRtbJ2bRevXr57vX6sSp9n2GoBeIeNQAAAABAABHUAAAAAMBjCGoAAAAA4DEENQAAAADwGIIaAAAAUM6RI0fUrVs3FRUVKTc3V40bNy7zHLVXXnmlxmvIzc3Va6+99oOPGz58uNLTq31aVpWWLFmi66677rTO4Q8z0y233OJ7X1hYqJYtW1bbtz/1rVu3TsOHDw9EmSER+ie5AQAAAFUI9HMs/Xm25KxZszRo0CDf0vyXX365srOzA1pHdU4EtZtvvjmo/Z6KwsLCU3pIdJMmTbR+/XodOXJEjRs31r///W9deOGFAampffv2ysvL044dO3TxxRcH5JzBxIwaAAAAUM68efPUv3//Ktt88cUXat26tfbt26fjx4+ra9euWrRokXJzc9W2bVsNGTJEUVFRuuGGG3T48GFJUlZWlrp166YOHTooJSVFu3fvliRt3bpVvXr1UlxcnBITE7Vt2zY98MADWr58ueLj4zVt2jQVFRVp/Pjx6tixo2JjY/XCCy9Ikpxzuvvuu9WmTRv16tVLe/bsqbTe6dOnKzo6WrGxsbrpppskSatXr9aVV16phIQEXXXVVdq8eXOF407WZs6cOerXr5969uyp5ORkDR06VG+//bbvuCFDhuidd96pdqyvvfZa/eMf/5AkzZ8/X4MH//+ng/lT36FDh5SamqpOnTopISGhTJ+/+MUvtGDBgmpr8CKCGgAAAFDKsWPH9PnnnysyMtK3bdu2bWUufVy+fLkuueQSTZgwQWPGjNEf/vAHRUdHq0+fPpKkzZs3684779SmTZt09tlna+bMmSooKNA999yj9PR0ZWVlKTU1VRMnTpRUHGruuusuffLJJ1q5cqVatWqlKVOmqGvXrsrOzta4ceP08ssvKzw8XBkZGcrIyNBLL72k7du366233tLmzZu1ceNGvfLKK1q5cmWlX9eUKVO0du1a5eTk6Pnnn5cktW3bVsuXL9fatWv12GOP6Te/+U2F46pqs2bNGqWnp2vp0qUaOXKk5syZI0nKz8/XypUr9T//8z/VjvdNN92kBQsW6OjRo8rJydEVV1zhV98nTJ48WT179tTq1au1ePFijR8/XocOHZIkJSUlafny5dXW4EVc+ggAAACUsm/fPjVr1qzMtpNd+jhq1Ci98cYbev7558vsv+iii9SlSxdJ0i233KLp06erb9++Wr9+vXr37i1JKioqUqtWrfTdd99p165dGjhwoCSpUaNGlda1aNEi5eTk+O4/y8/P15YtW7Rs2TINHjxYYWFhuuCCC9SzZ89Kj4+NjdWQIUM0YMAADRgwwHeOYcOGacuWLTIzFRQUVDiuqja9e/fWOeecI0nq1q2b7rzzTu3du1dvvvmmrr/+er8uh4yNjVVubq7mz5+va6+91u++S4/Lu+++q6lTp0qSjh49qh07digqKkrnnXeevvzyy2pr8CKCGgAAAFBK48aNdfToUb/aHj58WHl5eZKkgwcPqmnTppKKF8kozczknFO7du20atWqMvu+++47v/pyzmnGjBlKSUkps33hwoV+Hf+Pf/xDy5Yt09/+9jdNnjxZ69at08MPP6wePXrorbfeUm5urrp3717huKraNGnSpEzboUOH6tVXX9WCBQs0e/Zsv+qSpH79+un//u//tGTJEu3fv9+vvk9wzunNN99UmzZtKuw7evSoGjdu7HcdXsKljwAAAEApzZs3V1FRkV9hbcKECRoyZIgee+wx3Xbbbb7tO3bs8AWy1157TT//+c/Vpk0b7d2717e9oKBAGzZsUNOmTRUREeG7v+v777/X4cOH1bRp0zIhLiUlRX/60598s0qfffaZDh06pKuvvlppaWkqKirS7t27tXjx4gp1Hj9+XDt37lSPHj305JNPKj8/XwcPHlR+fr5v8Y4Tly2W50+bE4YPH64//vGPkqTo6GhJ0q5du5ScnFzlcampqZo0aZLat2//g/tOSUnRjBkz5FzxIjFr16717fvss88UExNTZd9eRVADAAAAyunTp49WrFjhe1/+HrXp06dr6dKlysjI8IW1Bg0a+GaR2rRpo+eee05RUVH65ptvNGbMGDVo0EDp6emaMGGC4uLiFB8f77ufbO7cuZo+fbpiY2N11VVX6auvvlJsbKzCwsIUFxenadOmadSoUYqOjlZiYqJiYmJ0++23q7CwUAMHDlTr1q0VHR2toUOH6sorr6zw9RQVFemWW25R+/btlZCQoLFjx6pZs2a6//779eCDDyohIUGFhYWVjoU/bU44//zzFRUVpREjRvi27d69u9pLICMiIjR27NhT6vvhhx9WQUGBYmNj1a5dOz388MO+fYsXL/brPjkvshPJM9iSkpJcZmZmSPpG4AV62dxg8meJXi9izIOPMQeA4Ni0aZOioqJCWsOaNWs0bdo0zZ079wcfm5ubq+uuu07r16+vgcq87fDhw2rfvr3WrFmj8PBwSdKzzz6riy++WP369QtqLd9//726deumFStWnNKjAwKtss+1mWU555Iqax/6igEAAACPSUxMVI8ePVRUVOR7lhqq9v7772vkyJEaN26cL6RJ0t133x2Senbs2KEpU6Z4IqSdih9n1QAAAEANS01NPaXjIiMj6+RsWq9evfTFF1+Eugyf1q1bq3Xr1qEu45QR1AAAgA+X+AKAN7CYCAAAADwnVOsoADXhVD7PBDUAAAB4SqNGjbR//37CGmoF55z2799/0geZnwyXPgIAAMBTIiIilJeXp71794a6FCAgGjVqpIiIiB90DEENAAAAnnLGGWfo0ksvDXUZQEhx6SMAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGMIagAAAADgMQQ1AAAAAPAYghoAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGMIagAAAADgMQQ1AAAAAPAYghoAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGMIagAAAADgMQQ1AAAAAPCYaoOamc0ysz1mtv4k+4eYWY6ZrTOzlWYWF/gyAQAAAKDu8GdGbY6kvlXs3y6pm3OuvaTHJb0YgLoAAAAAoM6qX10D59wyM4usYv/KUm8/khQRgLoAAAAAoM4K9D1qIyX9M8DnBAAAAIA6pdoZNX+ZWQ8VB7WfV9FmtKTRknTxxRcHqmsAAAAAqFUCMqNmZrGS/iypv3Nu/8naOededM4lOeeSWrZsGYiuAQAAAKDWOe2gZmYXS/qrpFudc5+dfkkAAAAAULdVe+mjmc2X1F3SuWaWJ2mSpDMkyTn3vKRHJLWQNNPMJKnQOZdUUwUDAAAAQG3nz6qPg6vZP0rSqIBVBAAAAAB1XKBXfQQAAAAAnCaCGgAAAAB4DEENAAAAADwmYM9RAwAg0OxRC3UJp8xNcqEuAQDwI8aMGgAAAAB4DEENAAAAADyGoAYAAAAAHkNQAwAAAACPIagBAAAAgMcQ1AAAAADAYwhqAAAAAOAxBDUAAAAA8BiCGgAAAAB4DEENAAAAADyGoAYAAAAAHkNQAwAAAACPIagBAAAAgMcQ1AAAAADAYwhqAAAAAOAxBDUAAAAA8BiCGgAAAAB4DEENAAAAADyGoAYAAAAAHkNQAwAAAACPIagBAAAAgMcQ1AAAAADAYwhqAAAAAOAxBDUAAAAA8BiCGgAAAAB4DEENAAAAADymfqgLAHBqXOtQVwAAAICawowaAAAAAHgMQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAAAA4DEENQAAAADwGIIaAAAAAHgMQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjqg1qZjbLzPaY2fqT7Dczm25mW80sx8wSA18mAAAAANQd/syozZHUt4r910hqXfJrtKQ/nX5ZAAAAAFB3VRvUnHPLJH1dRZP+kl5xxT6S1MzMWgWqQAAAAACoawJxj9qFknaWep9Xsg0AAAAAcAqCupiImY02s0wzy9y7d28wuwYAAACAH41ABLVdki4q9T6iZFsFzrkXnXNJzrmkli1bBqBrAAAAAKh9AhHU3pU0tGT1x86S8p1zuwNwXgAAAACok+pX18DM5kvqLulcM8uTNEnSGZLknHte0kJJ10raKumwpBE1VSwAAAAA1AXVBjXn3OBq9jtJdwWsIgAAAACo44K6mAgAAAAAoHoENQAAAADwGIIaAAAAAHgMQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAAAA4DEENQAAAADwGIIaAAAAAHgMQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAAAA4DEENQAAAADwGIIaAAAAAHgMQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAAAA4DEENQAAAADwGIIaAAAAAHgMQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAAAA4DEENQAAAADwmPqhLgAAAKAus0ct1CWcMjfJhboEoNZiRg0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAAAA4DEszw8AfnKtQ10BAACoK5hRAwAAAACPIagBAAAAgMf4FdTMrK+ZbTazrWb2QCX7LzazxWa21sxyzOzawJcKAAAAAHVDtUHNzMIkPSfpGknRkgabWXS5Zg9Jet05lyDpJkkzA10oAAAAANQV/syodZK01Tn3uXPumKQFkvqXa+MknV3yOlzSl4ErEQAAAADqFn9WfbxQ0s5S7/MkXVGuzW8lLTKzeyQ1kdQrINUBAAAAQB0UqMVEBkua45yLkHStpLlmVuHcZjbazDLNLHPv3r0B6hoAAAAAahd/gtouSReVeh9Rsq20kZJelyTn3CpJjSSdW/5EzrkXnXNJzrmkli1bnlrFAAAAAFDL+XPpY4ak1mZ2qYoD2k2Sbi7XZoekZElzzCxKxUGNKbM6hAcBAwAAAIFT7Yyac65Q0t2S/iVpk4pXd9xgZo+ZWb+SZvdJus3MPpE0X9Jw55yrqaIBAAAAoDbzZ0ZNzrmFkhaW2/ZIqdcbJXUJbGkAAAAAUDcFajERAAAAAECAENQAAAAAwGMIagAAAADgMQQ1AAAAAPAYghoAAAAAeAxBDQAAAAA8xq/l+QEAQN3gWoe6AgCAxIwaAAAAAHgOQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAAAA4DEENQAAAADwGIIaAAAAAHgMQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAAAA4DEENQAAAADwGIIaAAAAAHgMQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAAAA4DEENQAAAADwGIIaAAAAAHgMQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBjCGoAAAAA4DEENQAAAADwGIIaAAAAAHgMQQ0AAAAAPIagBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBj/ApqZtbXzDab2VYze+AkbX5lZhvNbIOZvRbYMgEAAACg7qhfXQMzC5P0nKTekvIkZZjZu865jaXatJb0oKQuzrlvzOy8mioYAAAAAGo7f2bUOkna6pz73Dl3TNICSf3LtblN0nPOuW8kyTm3J7BlAgAAAEDd4U9Qu1DSzlLv80q2lfYzST8zs/+Y2Udm1jdQBQIAAABAXVPtpY8/4DytJXWXFCFpmZm1d84dKN3IzEZLGi1JF198cYC6BuomG+JCXcIpczeHugIAAABv82dGbZeki0q9jyjZVlqepHedcwXOue2SPlNxcCvDOfeicy7JOZfUsmXLU60ZAAAAAGo1f4JahqTWZnapmTWQdJOkd8u1eVvFs2kys3NVfCnk54ErEwAAAADqjmqDmnOuUNLdkv4laZOk151zG8zsMTPrV9LsX5L2m9lGSYsljXfO7a+pogEAAACgNvPrHjXn3EJJC8tte6TUayfpf0t+AQAAAABOg18PvAYAAAAABA9BDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGP8Wp4fAADUDTbEhbqEU+ZuDnUFABA4zKgBAAAAgMcQ1AAAAADAYwhqAAAAAOAx3KOGgOCeBgAAACBwCGoA4Cd+IAEAAIKFSx8BAAAAwGMIagAAAADgMQQ1AAAAAPAYghoAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGMIagAAAADgMQQ1AAAAAPAYghoAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGMIagAAAADgMQQ1AAAAAPAYghoAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGMIagAAAADgMQQ1AAAAAPAYghoAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGPq+9PIzPpKekZSmKQ/O+emnKTd9ZLSJXV0zmUGrEoAQN30WxfqCk7dpFAXAAD4Mat2Rs3MwiQ9J+kaSdGSBptZdCXtmkr6taSPA10kAAAAANQl/lz62EnSVufc5865Y5IWSOpfSbvHJT0p6WgA6wMAAACAOsefoHahpJ2l3ueVbPMxs0RJFznn/hHA2gAAAACgTjrtxUTMrJ6kpyXd50fb0WaWaWaZe/fuPd2uAQAAAKBW8ieo7ZJ0Uan3ESXbTmgqKUbSEjPLldRZ0rtmllT+RM65F51zSc65pJYtW5561QAAAABQi/kT1DIktTazS82sgaSbJL17YqdzLt85d65zLtI5FynpI0n9WPURAAAAAE5NtUHNOVco6W5J/5K0SdLrzrkNZvaYmfWr6QIBAAAAoK4x50LzjJqkpCSXmcmkW21hFuoKTl2I/gqcNsY8+Bjz4GPMg48xDz7GHKi7zCzLOVfhljEpAIuJAAAAAAACi6AGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGMIagAAAADgMQQ1AAAAAPAYghoAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGMIagAAAADgMQQ1AAAAAPAYghoAAAAAeEz9UBcA4NQ4WahLOA0u1AUAAAB4GjNqAAAAAOAxBDUAAAAA8BiCGgAAAAB4DEENAAAAADymdi4m8tqPeJGFm1lkAQAAAKjramdQAwAAAE7CHv3x/lDfTeKH+nUFlz4CAAAAgMcQ1AAAAADAYwhqAAAAAOAxBDUAAAAA8BiCGgAAAAB4DEENAAAAADyGoAYAAAAAHkNQAwAAAACPIagBAAAAgMcQ1AAAAADAYwhqAAAAAOAxBDUAAAAA8BiCGgAAAAB4DEENAAAAADyGoAYAAAAAHkNQAwAAAACPIagBAAAAgMcQ1AAAAADAYwhqAAAAAOAxBDUAAAAA8BiCGgAAAAB4jF9Bzcz6mtlmM9tqZg9Usv9/zWyjmeWY2QdmdkngSwUAAACAuqHaoGZmYZKek3SNpGhJg80sulyztZKSnHOxktIl/T7QhQIAAABAXeHPjFonSVudc587545JWiCpf+kGzrnFzrnDJW8/khQR2DIBAAAAoO7wJ6hdKGlnqfd5JdtOZqSkf55OUQAAAABQl9UP5MnM7BZJSZK6nWT/aEmjJeniiy8OZNcAAAAAUGv4M6O2S9JFpd5HlGwrw8x6SZooqZ9z7vvKTuSce9E5l+ScS2rZsuWp1AsAAAAAtZ4/QS1DUmszu9TMGki6SdK7pRuYWYKkF1Qc0vYEvkwAAAAAqDuqDWrOuUJJd0v6l6RNkl53zm0ws8fMrF9Js6cknSXpDTPLNrN3T3I6AAAAAEA1/LpHzTm3UNLCctseKfW6V4DrAgAAAIA6y68HXgMAAAAAgoegBgAAAAAeQ1ADAAAAAI8hqAEAAACAxxDUAAAAAMBj/Fr1EQAgOVmoSzgNLtQFAACAH4CgBgAAAKBG2aM/3h92ukmh+WEnlz4CAAAAgMcQ1AAAAADAY7j0EQAAAHXLb3/E9+1OCnUBCBZm1AAAAADAYwhqAAAAAOAxXPqIgGDZcgAAACBwmFEDAAAAAI8hqAEAAACAxxDUAAAAAMBjuEcNAAD4cM8xAHgDM2oAAAAA4DG1c0ZtSKgLOA03h7oAAAAAAKHGjBoAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGMIagAAAADgMQQ1AAAAAPAYghoAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAjyGoAQAAAIDHENQAAAAAwGPqh7oAAAAAALXcb12oKzh1k0LTLTNqAAAAAOAxBDUAAAAA8BiCGgAAAAB4DEENAAAAADyGoAYAAAAAHkNQAwAAAACPIagBAAAAgMcQ1AAAAADAYwhqAAAAAOAxBDUAAAAA8BiCGgAAAAB4jF9Bzcz6mtlmM9tqZg9Usr+hmaWV7P/YzCIDXikAAAAA1BHVBjUzC5P0nKRrJEVLGmxm0eWajZT0jXPup5KmSXoy0IUCAAAAQF3hz4xaJ0lbnXOfO+eOSVogqX+5Nv0l/aXkdbqkZDOzwJUJAAAAAHWHP0HtQkk7S73PK9lWaRvnXKGkfEktAlEgAAAAANQ19YPZmZmNljS65O1BM9sczP4D6FxJ+2rkzExEngxjHnyMefAx5sFXY2POkJ8Un/Pg43MefIx58P1Yx/ySk+3wJ6jtknRRqfcRJdsqa5NnZvUlhUvaX/5EzrkXJb3oR5+eZmaZzrmkUNdRlzDmwceYBx9jHnyMefAx5sHHmAcfYx58tXHM/bn0MUNSazO71MwaSLpJ0rvl2rwraVjJ6xskfeicc4ErEwAAAADqjmpn1JxzhWZ2t6R/SQqTNMs5t8HMHpOU6Zx7V9LLkuaa2VZJX6s4zAEAAAAAToFf96g55xZKWlhu2yOlXh+V9MvAluZpP/rLN3+EGPPgY8yDjzEPPsY8+Bjz4GPMg48xD75aN+bGFYoAAAAA4C3+3KMGAAAAAAgighoAAAAAeAxBDQAAAAA8hqD2A5nZeaGuAahpZtYi1DUAAADUZQS1KpjZOeV+tZC02syam9k5oa6vNjKzvqVeh5vZy2aWY2avmdn5oayttjKzKWZ2bsnrJDP7XNLHZvaFmXULcXm1kpmtMbOHzOzyUNdSV5R8theb2atmdpGZ/dvM8s0sw8wSQl1fbWRmZ5nZY2a2oWSs95rZR2Y2PNS11VZmVt/Mbjez90r+78wxs3+a2R1mdkao66uNzCysZMwfN7Mu5fY9FKq6ajMzO9PM7jez8WbWyMyGm9m7ZvZ7Mzsr1PUFEqs+VsHMjkv6otzmCEl5kpxz7rLgV1W7mdka51xiyes/S/pK0kuSBknq5pwbEMLyaiUzW+eca1/yerGk+51zGWb2M0mvOeeSQlth7WNm2yW9KelXKv6Mz5eU5pz7MqSF1WJmtlrSJEnNJP1e0jjnXLqZJUt6wjl3ZSjrq43M7B1Jb0l6X8Wf9SaSFkh6SNIu59xvQlherWRm8yUdkPQXFX+vIhV/3zJM0jnOuRtDVFqtVfK9ypmSVku6VdJS59z/luzzfU+DwDGz1yXtlNRYUhtJmySlSeon6SfOuVtDWF5AEdSqYGb3Seotabxzbl3Jtu3OuUtDW1ntVS6oZTvn4kvtK/MegWFmmyS1L3m4/UfOuc6l9vlCHAKn3Oe8q6TBKv5hxCZJ851zte5ZMKFmZmudcwklr3c45y6ubB8Cx8w+cc7FlXqf4ZzraGb1JG10zrUNYXm1kpl95pz72Q/dh1NnZjnOudiS1/UlzZR0ror/Xf+If1sC78T3g2ZmknZLauWccyXvPznx51EbcOljFZxzf5A0StIjZva0mTWVRLKtWeeZ2f+WhOSzS/7SncDntWbMlLTQzHpKes/MnjGzbmb2qKTs0JZW+znnljvn7pR0oaQnJTGzUzOOmlkfM/ulJGdmAySp5PLeopBWVnsdMrOfS5KZ9ZP0tSQ5545LsqoOxCn72sx+WRKGJUlmVs/MbpT0TQjrqs0anHjhnCt0zo2W9ImkDyXVqsvwvMYVzzYtLPn9xPta9X16/VAX4HXOuTxJvyz5T+bfKp7eRs15SVLTktd/UfFPpfaa2U9EaKgRzrkZZrZO0hhJP1PxvwutJb0t6YkQllabfVZ+g3OuSNJ7Jb8QeHeo+JLH45JSJI0xszmSdkm6LYR11WZjJL1kZq0lbZA0UpLMrKWk50JZWC12k4p/4POcmR0o2dZM0uKSfQi8TDPr65zz/dvtnHvUzHZJ+lMI66rNMs3sLOfcQedc6omNJfd9fxfCugKOSx+rYWZtVfyT7o9V/FPXy51z68v/pUTglB5z59zBUtsZ8xrCmAcfYx58ZhYl6QIx5kFTMuYXqvgSMMY8CMzsChXPKmyT1FbFs/QbnXMLQ1pYLWZmnVQ8oZNhZtGS+kr6lDGvOScZ880qNcNWGxDUqmBmYyXdpeL7RuIl/do5907JPm4QrQFmdo+ku8WYBw2f8+Djcx58JZ/zOyV9KsY8KBjz4DOzSZKuUfGVEf+W1EnSEhXfb/8v59zk0FVXO1Uy5leoeAaTMa8hdWnMufSxardJ6uCcO2hmkZLSzSzSOfeMuL6+powWYx5sfM6Dj8958N0mKYkxDyrGPPhuUHEobqjiFWUjnHPfmtlUFV8ZVGu+gfUQxjz46syYE9SqVu/EpRrOuVwz667i/2guEf/J1BTGPPgY8+BjzIOPMQ8+xjz4Ckvudz1sZtucc99KknPuiBU/cgiBx5gHX50Zc1bRq9p/zSz+xJuS/3CuU/ECFyxZXjMY8+BjzIOPMQ8+xjz4GPPgO2ZmJxY963Bio5mFq3ghHQQeYx58dWbMuUetCmYWoeLU/lUl+7o45/4TgrJqNcY8+Bjz4GPMg48xDz7GPPjMrKFz7vtKtp+r4mdNrQtBWbUaYx58dWnMCWoAAAAA4DFc+ggAAAAAHkNQAwAAAACPIagBAGotMxtgZq7kAeMAAPxoENQAALXZYEkrSn4HAOBHg6AGAKiVzOwsST+XNFLSTSXb6pnZTDP71Mz+bWYLzeyGkn0dzGypmWWZ2b/MrFUIywcA1HEENQBAbdVf0nvOuc8k7TezDpIGSYqUFC3pVklXSpKZnSFphqQbnHMdJM2SNDkURQMAIEn1Q10AAAA1ZLCkZ0peLyh5X1/SG86545K+MrPFJfvbSIqR9G8zk6QwSbuDWy4AAP8fQQ0AUOuY2TmSekpqb2ZOxcHLSXrrZIdI2uCcuzJIJQIAUCUufQQA1EY3SJrrnLvEORfpnLtI0nZJX0u6vuRetfMldS9pv1lSSzPzXQppZu1CUTgAABJBDQBQOw1WxdmzNyX9RFKepI2SXpW0RlK+c+6YisPdk2b2iaRsSVcFrVoAAMox51yoawAAIGjM7Czn3EEzayFptaQuzrmvQl0XAAClcY8aAKCu+buZNZPUQNLjhDQAgBcxowYAAAAAHsM9agAAAADgMQQ1AAAAAPAYghoAAAAAeAxBDQAAAAA8hqAGAAAAAB5DUAMAAAAAj/l/Ohxczscc7IgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 24,
   "metadata": {
    "id": "-BdT5bPyL8Zj",
    "outputId": "a7d6ceb1-b001-4404-f715-62a3d16ec8a3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 25,
   "metadata": {
    "id": "djgdgo3SL8Zk",
    "outputId": "282c87e1-d924-4d90-eb2b-0c5dfbeb804e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = dfvote.groupby(['Gender']).mean().stack()\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = var.unstack()\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "ynojR8K_L8Zp",
    "outputId": "ce5c8e29-61ce-4965-9252-635a9440e10b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 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": 29,
   "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": 29,
     "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": 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>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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "metadata": {},
   "outputs": [],
   "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": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['h', 't', 'u']\n",
       "Categories (3, object): ['h', 't', 'u']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h    45053\n",
       "u    11655\n",
       "t     6315\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "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": 38,
   "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": 39,
   "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>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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/renato/Documentos/envs/default/lib/python3.8/site-packages/seaborn/_core.py:1326: 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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\n",
      "text/plain": [
       "<Figure size 576x864 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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Count'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='house_type'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 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": 45,
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 806.4x504 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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}