DataTalks_Visualization III.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], \n",
    "            'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], \n",
    "            'female': [0, 1, 1, 0, 1],\n",
    "            'age': [42, 52, 36, 24, 73], \n",
    "            'preTestScore': [4, 24, 31, 2, 3],\n",
    "            'postTestScore': [25, 94, 57, 62, 70]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>female</th>\n",
       "      <th>age</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Tina</td>\n",
       "      <td>Ali</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name last_name  female  age  preTestScore  postTestScore\n",
       "0      Jason    Miller       0   42             4             25\n",
       "1      Molly  Jacobson       1   52            24             94\n",
       "2       Tina       Ali       1   36            31             57\n",
       "3       Jake    Milner       0   24             2             62\n",
       "4        Amy     Cooze       1   73             3             70"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(raw_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a Scatterplot of preTestScore and postTestScore, with the size of each point determined by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'preTestScore')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.preTestScore, df.postTestScore, s = df.age)\n",
    "plt.title(\"preTestScore x postTestScore\")\n",
    "plt.xlabel('preTestScore')\n",
    "plt.ylabel('preTestScore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This time the size should be 4.5 times the postTestScore and the color determined by sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'preTestScore')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.preTestScore, df.postTestScore, s = df.postTestScore*4.5, c = df.female)\n",
    "plt.title(\"preTestScore x postTestScore\")\n",
    "plt.xlabel('preTestScore')\n",
    "plt.ylabel('preTestScore')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}