DataTalks_Visualization V.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "import dataset from https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Titanic_Desaster/train.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Titanic_Desaster/train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set PassengerId as the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "...               ...     ...   \n",
       "887                 0       2   \n",
       "888                 1       1   \n",
       "889                 0       3   \n",
       "890                 1       1   \n",
       "891                 0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "...                                                        ...     ...   ...   \n",
       "887                                      Montvila, Rev. Juozas    male  27.0   \n",
       "888                               Graham, Miss. Margaret Edith  female  19.0   \n",
       "889                   Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN   \n",
       "890                                      Behr, Mr. Karl Howell    male  26.0   \n",
       "891                                        Dooley, Mr. Patrick    male  32.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  \n",
       "...            ...    ...               ...      ...   ...      ...  \n",
       "887              0      0            211536  13.0000   NaN        S  \n",
       "888              0      0            112053  30.0000   B42        S  \n",
       "889              1      2        W./C. 6607  23.4500   NaN        S  \n",
       "890              0      0            111369  30.0000  C148        C  \n",
       "891              0      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(\"PassengerId\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a pie chart presenting the male/female proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "males = (df[\"Sex\"] == \"male\").sum()\n",
    "females = (df[\"Sex\"] == \"female\").sum()\n",
    "\n",
    "proportion = [males, females]\n",
    "\n",
    "plt.pie(proportion, labels = [\"Males\", \"Females\"], shadow = False, colors = [\"blue\", \"red\"], explode = (0.15, 0),\n",
    "       startangle = 90, autopct = \"%1.1f%%\")\n",
    "plt.axis(\"equal\")\n",
    "plt.title(\"Sex proportion\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a scatterplot with the Fare payed and the Age, differ the plot color by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5, 85)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot( x = \"Age\", y = \"Fare\", data = df, hue = \"Sex\", fit_reg = False)\n",
    "g.set(title = \"Age & Fare\")\n",
    "axes = g.axes\n",
    "axes[0,0].set_ylim(-5,)\n",
    "axes[0,0].set_xlim(-5,85)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many people survived?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Survived.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a histogram with the Fare payed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df\n",
    "binsVal = np.arange(0,600,10)\n",
    "binsVal\n",
    "plt.hist(df, bins = binsVal)\n",
    "\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Fare Payed Histrogram')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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
}