data-visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA VISUALISATION\n",
    "If not present , create locally a 'Data' folder containing the datasets<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "\n",
    "# Environment settings\n",
    "data_path = 'Data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_type = {\n",
    "    'PassengerId': str,\n",
    "    'Survived': bool,\n",
    "    'Pclass': 'category', # Ticket class: 1 = 1st, 2 = 2nd, 3 = 3rd\n",
    "    'Name': str,\n",
    "    'Sex': 'category',\n",
    "    'Age': float,\n",
    "    'SibSp': 'uint8', # number of siblings/spouses aboard\n",
    "    'Parch': 'uint8', # number of parents/children aboard\n",
    "    'Ticket': str, # Ticket number\n",
    "    'Fare': float, # Passenger fare\n",
    "    'Cabin': str, # Cabin number\n",
    "    'Embarked': 'category'} # Port of embarkation: C = Cherbourg, Q = Queenstown, S = Southampton\n",
    "\n",
    "# load and serialize dataframes for later use in preprocessing notebook\n",
    "for ds_name in ['test', 'train']: # last one is used in this document\n",
    "    df = pd.read_csv(data_path+ds_name+'.csv', dtype=data_type)\n",
    "    with open(data_path+ds_name+'_dv.obj', 'wb') as file:\n",
    "        pickle.dump(df, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View Data"
   ]
  },
  {
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>False</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",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId  Survived Pclass  \\\n",
       "0           1     False      3   \n",
       "1           2      True      1   \n",
       "2           3      True      3   \n",
       "3           4      True      1   \n",
       "4           5     False      3   \n",
       "5           6     False      3   \n",
       "6           7     False      1   \n",
       "7           8     False      3   \n",
       "8           9      True      3   \n",
       "9          10      True      2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the data\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assumptions and considerations about the available 12 fields:\n",
    "* *PassengerId*, *Ticket*: unlikely to be useful because they can be considered as some sort of ids\n",
    "* *Name*: unlikely to be useful because can be considered as some sort of id, but surname could be useful because of family relationships among the passengers\n",
    "* *Pclass*: can be useful because it locates people on the ship based onthe class fare\n",
    "* *sex*: can be useful because for priorities for lifeboat rescue (female first)\n",
    "* *age*: can be useful because for priorities for lifeboat rescue (young and elder first), adult are stronger than others and more likely to survive\n",
    "* *SibSp*, Parch: can be useful because relatives are likely to help each others\n",
    "* *Fare*: unlikely to be useful because its meaning it's likely to be included in 'Pclass'\n",
    "* *Cabin*: can be useful in relation of where the cabins are positioned on the ship\n",
    "* *Embarked*: unlikely to be useful because all listed embark happened before the disaster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values"
   ]
  },
  {
   "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": [
    "# Missing values\n",
    "\n",
    "# number of rows\n",
    "rows_length = df.shape[0]\n",
    "# fields with missing value count\n",
    "fields_with_nan_length = df.isna().sum().where(lambda x : x > 0).dropna()\n",
    "\n",
    "ax = fields_with_nan_length \\\n",
    ".plot(kind='bar', alpha=0.9, title='Missing values count', table=True)\n",
    "ax.xaxis.set_visible(False) # hide x axis labels\n",
    "\n",
    "\n",
    "# write percentage on top of each bar\n",
    "for i in ax.patches:\n",
    "    ax.text(i.get_x()+.1, i.get_height()+5, \\\n",
    "            str(round((i.get_height()/rows_length)*100, 2))+'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*age*, *cabin*, *embarked* are the only fields with missing values:\n",
    "* *cabin* being way more than 77% is likely to be dropped as information\n",
    "* *age* needs some way to refill data\n",
    "* *embarked* not really a problem since it has only 2 missing entries"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age       SibSp       Parch        Fare\n",
       "count  714.000000  891.000000  891.000000  891.000000\n",
       "mean    29.699118    0.523008    0.381594   32.204208\n",
       "std     14.526497    1.102743    0.806057   49.693429\n",
       "min      0.420000    0.000000    0.000000    0.000000\n",
       "25%     20.125000    0.000000    0.000000    7.910400\n",
       "50%     28.000000    0.000000    0.000000   14.454200\n",
       "75%     38.000000    1.000000    0.000000   31.000000\n",
       "max     80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# some statistics about the numerical data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age is the only numerical feature with NaNs. <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Survival Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specifies the parameters of our graphs\n",
    "plt.figure(figsize=(13,6))\n",
    "plot_params = {\n",
    "    'kind': 'pie',\n",
    "    'startangle': 90,\n",
    "    'autopct': '%1.1f%%',\n",
    "    'colors': ['tomato', 'lightgreen']\n",
    "}\n",
    "\n",
    "\n",
    "# Plots a bar graph of those who surived vs those who did not\n",
    "plt.subplot2grid((2,3), (0,0))\n",
    "df.Survived.value_counts() \\\n",
    ".plot(title='Total survival rate', **plot_params).axis('equal')\n",
    "\n",
    "\n",
    "# Survival rate wrt Sex\n",
    "df_male = df.Survived[df.Sex == 'male'].value_counts(normalize=True, sort=False)\n",
    "df_female = df.Survived[df.Sex == 'female'].value_counts(normalize=True, sort=False)\n",
    "\n",
    "## male survival plot\n",
    "plt.subplot2grid((2,3),(0,1))\n",
    "df_male.plot(title='Male survival rate', **plot_params).axis('equal')\n",
    "\n",
    "## female survival plot\n",
    "plt.subplot2grid((2,3),(0,2))\n",
    "df_female.plot(title='Female survival rate', **plot_params).axis('equal')\n",
    "\n",
    "\n",
    "# Survival rate wrt Pclass\n",
    "for i in range(3):\n",
    "    plt.subplot2grid((2,3),(1,i))\n",
    "    df.Survived[df.Pclass == str(i+1)].value_counts(normalize=True, sort=False) \\\n",
    "    .plot(title=str(i+1)+'° class survival rate', **plot_params).axis('equal')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assumptions and considerations about the survival rates:\n",
    "* *Total survival rate* graph: between half and 3/4 of people did not survive the disaster\n",
    "* *Sex survival rate* graphs: there seems to be a strong correlation with being female and the chance to survive wrt to being a man\n",
    "* *Class survival rate* graphs: there seems to be a clear correlation between an higher class ticket and the chance to survive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,4))\n",
    "\n",
    "# plots a bar graph of the age distribution\n",
    "plt.subplot2grid((1,2), (0,0))\n",
    "df.Age.plot(kind='kde', alpha=0.6, title='Age distribution', xlim=(-20, 100))\n",
    "\n",
    "# plots a bar graph of the class distribution\n",
    "plt.subplot2grid((1,2),(0,1))\n",
    "df.Pclass.value_counts(sort=False).plot(kind='pie', title='Class Distribution', \\\n",
    "                                        autopct='%1.1f%%', counterclock=False, \\\n",
    "                                        startangle=90).axis('equal')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assumptions and considerations:\n",
    "* *Age distribution* graph: as also seen in the numerical analysis the majority of people have around 28 +/- 10 years old\n",
    "* *Class distribution* graphs: the majority of people did embark with a third class ticket while first and class tickets have around the same number of people (with first class tickets being slightly more) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "#Correlation between features\n",
    "df['Pclass'] = pd.to_numeric(df['Pclass']) # hack for seaborn to process categorical data\n",
    "\n",
    "cov_matrix = df.corr(method='pearson')\n",
    "sns.heatmap(cov_matrix, square=True, annot=True, cmap=sns.cubehelix_palette(8)) \\\n",
    ".set_title('Features correlation');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}