tutorials/titanic/PandasQuickstart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro to Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Data From File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DATA_DIR = \"data/\"\n",
    "\n",
    "# For .read_csv, always use header=0 when you know row 0 is the header row\n",
    "df = pd.read_csv(DATA_DIR+'train.csv', header=0)  #Returns Pandas DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>803</th>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2625</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hamalainen, Master. Viljo</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250649</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>645</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Eugenie</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                             Name     Sex  \\\n",
       "803          804         1       3  Thomas, Master. Assad Alexander    male   \n",
       "755          756         1       2        Hamalainen, Master. Viljo    male   \n",
       "644          645         1       3           Baclini, Miss. Eugenie  female   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket     Fare Cabin Embarked  \n",
       "803  0.42      0      1    2625   8.5167   NaN        C  \n",
       "755  0.67      1      1  250649  14.5000   NaN        S  \n",
       "644  0.75      2      1    2666  19.2583   NaN        C  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()      #Metadata about table\n",
    "df.columns\n",
    "df.values\n",
    "df\n",
    "df.head(2)\n",
    "df.tail(2)\n",
    "df.sort_values(by='Age').head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select by Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age\n",
       "1         1  38.0\n",
       "2         1  26.0\n",
       "5         0   NaN"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.get_loc(\"Age\")     #Get column index by name\n",
    "df.iloc[0]                    #Get row by index\n",
    "df[0:3]                       #Get rows 0-2\n",
    "df.iloc[:,2]                  #Get all column 2 data\n",
    "df.iloc[3,3]                  #Get value in cell (1,1)\n",
    "df.iloc[[1,2,5],[1,5]]        #Get rows 1,2,5 and display columns 1 and 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select by Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Sex\n",
       "0  22.0    male\n",
       "1  38.0  female\n",
       "2  26.0  female"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Survived']                  #Get column by name\n",
    "df['Age'][0:3]                  #Select first 3 rows in Age column\n",
    "df[['Age','Sex']][0:3]          #Select first 3 rows in Age and Sex columns\n",
    "#Alternative\n",
    "#df.loc[0:2,'Age']               #Select first 3 rows in Age column - index INCLUSIVE!\n",
    "#df.loc[0:2,['Sex','Age']]       #Select first 3 rows in Sex and Age columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Updating Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = df.copy()                           #Create copy\n",
    "df2[\"NewColumn\"] = \"DefaultValue\"         #Create new column\n",
    "df2['Sex'] = \"Unknown\"                    #Override all values in column\n",
    "df2.iat[0,0] = 9999                       #Set value by index (row,col)\n",
    "df2.at[3,'Pclass'] = 7777                 #Set value in row 2, col 'Pclass'\n",
    "df2[df2.SibSp == 1] = \"OVERRIDE\"          #Set all values in rows with 1 sibling to OVERRIDE\n",
    "\n",
    "# Set column matching query\n",
    "df.loc[(df.Age.isnull()) & (df.Sex==\"female\") & (df.Pclass==1),'Age'] = \"NEW_VALUE\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Only works on numerical columns\n",
    "#Ignores missing values\n",
    "\n",
    "df.describe()     #Summary stats\n",
    "df.mean()         #Mean for all rows in a column\n",
    "df.mean(1)        #Mean for all cells in a row     **transposed\n",
    "df.sum()          #Sum for all rows in a column\n",
    "df.count()        #Count for all rows in a column (non-null)\n",
    "\n",
    "df['Embarked'].unique()    #Get unique values\n",
    "df['Embarked'].nunique()   #Get count of unique, non-null values\n",
    "df['Embarked'].mode()[0]   #Mode is weird, you need to extract the value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Queries return DataFrames\n",
    "\n",
    "res = df[df.Age < 10]                       #Get rows that match condition\n",
    "assert type(res) == pd.core.frame.DataFrame\n",
    "\n",
    "df[df.Age < 10][['Sex', 'Age']][:3]         #Same, but return only 2 columns and 3 rows\n",
    "\n",
    "df[df['Embarked'].isin(['S','C'])][:3]      #Get rows where Embarked is S or C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Querying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fare        42.257604\n",
       "Age         15.032745\n",
       "Survived     0.473684\n",
       "dtype: float64"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Simple query\n",
    "df[ (df['Sex'] == 'male') & (df['Pclass'] == 1) ]     #Return males in Pclass 1\n",
    "\n",
    "#Complex Queries\n",
    "\n",
    "#Create new column equal to query\n",
    "df['FancyName'] = np.where(\n",
    "    (\n",
    "        (df.Name.str.contains(\"Master.\")) \n",
    "        | (df.Name.str.contains(\"Rev.\")) \n",
    "        | (df.Name.str.contains(\"Dr.\"))\n",
    "        | (df.Name.str.contains(\"Dr.\")) \n",
    "        | (df.Name.str.contains(\"Sir.\"))\n",
    "    ),\n",
    "    True,\n",
    "    False\n",
    ")\n",
    "\n",
    "#Get all rows equal to query\n",
    "upper_class_men = df.loc[ \n",
    "    (df.Sex.str.lower() == \"male\") & (\n",
    "        (df.Name.str.contains(\"Master.\")) \n",
    "        | (df.Name.str.contains(\"Rev.\")) \n",
    "        | (df.Name.str.contains(\"Dr.\"))\n",
    "        | (df.Name.str.contains(\"Dr.\")) \n",
    "        | (df.Name.str.contains(\"Sir.\")) \n",
    "    )\n",
    "][['Fare','Age','Survived']]\n",
    "upper_class_men.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Gender</th>\n",
       "      <th>SurvivalPct</th>\n",
       "      <th>FancyName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>DEFAULT</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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</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",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</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",
       "\n",
       "                                                Name     Sex Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38      1   \n",
       "\n",
       "   Parch     Ticket     Fare    Cabin Embarked  Gender  SurvivalPct FancyName  \n",
       "0      0  A/5 21171   7.2500  DEFAULT        S       1          0.0     False  \n",
       "1      0   PC 17599  71.2833      C85        C       0          0.5     False  "
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pandas uses the value np.nan to represent missing data\n",
    "#These methods return a copy of the DataFrame without modifying it\n",
    "\n",
    "df3 = df.copy()                               #Just to be extra safe ;)\n",
    "df3.isnull().sum()                            #Show # of null values in each Column\n",
    "df3[df3.Age.isnull()][['Name','Age']]         #Show rows where Age is null\n",
    "df3.isnull()                                  #Return True/False if cell is null\n",
    "df3.fillna(value={\"Cabin\":\"DEFAULT_CABIN\"})   #Set null values in column to default value \n",
    "df3.dropna(how='any')                         #Drop rows with missing data in ANY cell\n",
    "df3.fillna(value=\"DEFAULT\").head(2)           #Fill cells missing data w 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Gender</th>\n",
       "      <th>SurvivalPct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>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",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</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",
       "      <td>0</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "\n",
       "                                                Name     Sex  SibSp  Parch  \\\n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female      1      0   \n",
       "2                             Heikkinen, Miss. Laina  female      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female      1      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  Gender  SurvivalPct  \n",
       "1          PC 17599  71.2833   C85        C       0     0.500000  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S       0     0.333333  \n",
       "3            113803  53.1000  C123        S       0     0.250000  "
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df.copy()\n",
    "\n",
    "#New column\n",
    "df4['SurvivalPct'] = df4['Survived'] / df4['PassengerId']   #New column based on values in other columns\n",
    "df4['Gender'] = df4['Sex'].map( lambda x: x[0].upper() )    #New column Gender with M or F\n",
    "df4['Gender'] = df4['Sex'].map( {'male':1,'female':0} )     #Update Gender values to 0 or 1\n",
    "\n",
    "#Drop Row\n",
    "df4 = df4.drop(0)\n",
    "df4[['Name','Sex','Age']].head(3)\n",
    "\n",
    "#Drop Column\n",
    "df4 = df4.drop('Age',1)         # 1 means Column axis\n",
    "\n",
    "df4.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group By"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "C     64\n",
       "Q     27\n",
       "S    140\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Sex').sum()                    #For each column, return sum of all rows that equal 'female'\n",
    "df.groupby(['Sex','Embarked']).mean()      #Group by multiple columns\n",
    "df.groupby('Embarked').Fare.mean()         #Get the mean fare price for each Embarked category\n",
    "df[df['Sex'] == 'female'].groupby('Embarked').Survived.sum()    #Query THEN group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg4AAAFkCAYAAABIPLOYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJHypAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a01f850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg4AAAFkCAYAAABIPLOYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a5cfe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "\n",
    "# Pandas auto-generates buckets\n",
    "df['Age'].hist()\n",
    "pylab.show()\n",
    "\n",
    "# Set specific buckets\n",
    "\n",
    "df['Age'].dropna().hist(bins=16, range=(0,80), alpha=.5)\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhEAAAGpCAYAAAAz72SXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCEmSVIkhQpIkVfL/Aa2l7vP+SPk8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12892f450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sex_group = df[['Sex','Age']].groupby('Sex')\n",
    "sex_group.size()\n",
    "\n",
    "total_passengers = sex_group.mean()\n",
    "myplot = total_passengers.plot(kind='bar', title=\"Mean Age by Sex\")\n",
    "myplot.set_xlabel(\"Sex\")\n",
    "myplot.set_ylabel(\"Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count_nonzero</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>C</th>\n",
       "      <td>1729.00</td>\n",
       "      <td>28.344262</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>291.50</td>\n",
       "      <td>24.291667</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>5165.50</td>\n",
       "      <td>27.771505</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>C</th>\n",
       "      <td>2276.92</td>\n",
       "      <td>32.998841</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>495.00</td>\n",
       "      <td>30.937500</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>11147.25</td>\n",
       "      <td>30.291440</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>21105.17</td>\n",
       "      <td>29.642093</td>\n",
       "      <td>712.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      sum       mean  count_nonzero\n",
       "Sex    Embarked                                    \n",
       "female C          1729.00  28.344262           73.0\n",
       "       Q           291.50  24.291667           36.0\n",
       "       S          5165.50  27.771505          203.0\n",
       "male   C          2276.92  32.998841           95.0\n",
       "       Q           495.00  30.937500           41.0\n",
       "       S         11147.25  30.291440          441.0\n",
       "All              21105.17  29.642093          712.0"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html\n",
    "\n",
    "'''\n",
    "1. Pivot on Age (the value we perform calculations on)\n",
    "2. Group by Sex and Embarked\n",
    "3. Include statistics: sum, mean, and count\n",
    "4. Show totals at bottom!\n",
    "'''\n",
    "pd.pivot_table(df, values='Age', index=['Sex', 'Embarked'], aggfunc=[np.sum, np.mean, np.count_nonzero], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Fare</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Age</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>fare_range</th>\n",
       "      <th>total_of_all_fares</th>\n",
       "      <th>max_fare</th>\n",
       "      <th>min_fare</th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>survival_percent</th>\n",
       "      <th>total_survived</th>\n",
       "      <th>mean_survival</th>\n",
       "      <th>total_passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</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",
       "      <th>female</th>\n",
       "      <td>505.5792</td>\n",
       "      <td>13966.6628</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>6.75</td>\n",
       "      <td>261</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>233</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>512.3292</td>\n",
       "      <td>14727.2865</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>0.00</td>\n",
       "      <td>453</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>109</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fare                                         Age        \\\n",
       "       fare_range total_of_all_fares  max_fare min_fare count   max   \n",
       "Sex                                                                   \n",
       "female   505.5792         13966.6628  512.3292     6.75   261  63.0   \n",
       "male     512.3292         14727.2865  512.3292     0.00   453  80.0   \n",
       "\n",
       "               Survived                                                \n",
       "       survival_percent total_survived mean_survival total_passengers  \n",
       "Sex                                                                    \n",
       "female         0.742038            233      0.742038              314  \n",
       "male           0.188908            109      0.188908              577  "
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Simple Aggregation\n",
    "df.groupby('Embarked').aggregate({'Survived':np.sum, 'Age':np.mean})\n",
    "\n",
    "\n",
    "#Complex Aggregation\n",
    "aggregations = {\n",
    "    'Survived': { # work on the \"Survived\" column\n",
    "        'total_passengers': 'count',  # get count, and call this result 'total_passengers'\n",
    "        'total_survived': 'sum', # get sum, call result 'total_survived'\n",
    "        'mean_survival': 'mean',\n",
    "        'survival_percent': lambda x: float(sum(x)) / len(x)\n",
    "    },\n",
    "    'Fare': {     # Now work on the \"Fare\" column\n",
    "        'max_fare': 'max',   # Find the max, call the result \"max_fare\"\n",
    "        'min_fare': 'min',\n",
    "        'total_of_all_fares': 'sum',\n",
    "        'fare_range': lambda x: max(x) - min(x)  # Calculate the fare range per group\n",
    "    },\n",
    "    'Age': [\"count\", \"max\"]  # Calculate two results for 'Age' column\n",
    "}\n",
    " \n",
    "df.groupby('Sex').agg(aggregations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Write to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.head()\n",
    "df.to_csv(DATA_DIR+\"pandastutorial.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}