akshay.ipynb


{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.Collecting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of columns\n",
    "len(titanic_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of rows\n",
    "len(titanic_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>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",
       "    <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>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>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",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</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",
       "  </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",
       "5            6         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",
       "5                                   Moran, Mr. James    male   NaN      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  \n",
       "5      0            330877   8.4583   NaN        Q  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of passengers in the data is: 891\n"
     ]
    }
   ],
   "source": [
    "print('total number of passengers in the data is:', str(len(titanic_data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Analysing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2618cd4dd30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "sns.countplot(x='Survived',data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2618d33fc88>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "sns.countplot(x='Pclass',data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2618ce64358>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "sns.countplot(y='Pclass',data=titanic_data)\n",
    "#note: we changed 'x' to 'y' inside countplot parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2618d020a20>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Pclass',data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2618d18d630>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='white')\n",
    "sns.countplot(data=titanic_data,x='Survived',hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2618d26c4a8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=titanic_data,x='Survived',hue='Pclass')\n",
    "# 0 for passengers who didn't survived\n",
    "# 1 for passeners who survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2618e58abe0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_data['Fare'].plot.hist(figsize=(10,5),bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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"
     ]
    }
   ],
   "source": [
    "titanic_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2618e577f98>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='darkgrid')\n",
    "sns.countplot(data=titanic_data,x='SibSp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2618e692668>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=titanic_data,x='Parch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin Embarked\n",
       "0   NaN        S\n",
       "1   C85        C\n",
       "2   NaN        S\n",
       "3  C123        S\n",
       "4   NaN        S"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data[['Cabin','Embarked']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fare Cabin Embarked\n",
       "0   7.2500   NaN        S\n",
       "1  71.2833   C85        C\n",
       "2   7.9250   NaN        S\n",
       "3  53.1000  C123        S\n",
       "4   8.0500   NaN        S"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.loc[:,'Fare':].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>Ticket</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>861</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "5          False     False   False  False  False   True  False  False   False   \n",
       "6          False     False   False  False  False  False  False  False   False   \n",
       "7          False     False   False  False  False  False  False  False   False   \n",
       "8          False     False   False  False  False  False  False  False   False   \n",
       "9          False     False   False  False  False  False  False  False   False   \n",
       "10         False     False   False  False  False  False  False  False   False   \n",
       "11         False     False   False  False  False  False  False  False   False   \n",
       "12         False     False   False  False  False  False  False  False   False   \n",
       "13         False     False   False  False  False  False  False  False   False   \n",
       "14         False     False   False  False  False  False  False  False   False   \n",
       "15         False     False   False  False  False  False  False  False   False   \n",
       "16         False     False   False  False  False  False  False  False   False   \n",
       "17         False     False   False  False  False   True  False  False   False   \n",
       "18         False     False   False  False  False  False  False  False   False   \n",
       "19         False     False   False  False  False   True  False  False   False   \n",
       "20         False     False   False  False  False  False  False  False   False   \n",
       "21         False     False   False  False  False  False  False  False   False   \n",
       "22         False     False   False  False  False  False  False  False   False   \n",
       "23         False     False   False  False  False  False  False  False   False   \n",
       "24         False     False   False  False  False  False  False  False   False   \n",
       "25         False     False   False  False  False  False  False  False   False   \n",
       "26         False     False   False  False  False   True  False  False   False   \n",
       "27         False     False   False  False  False  False  False  False   False   \n",
       "28         False     False   False  False  False   True  False  False   False   \n",
       "29         False     False   False  False  False   True  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "861        False     False   False  False  False  False  False  False   False   \n",
       "862        False     False   False  False  False  False  False  False   False   \n",
       "863        False     False   False  False  False   True  False  False   False   \n",
       "864        False     False   False  False  False  False  False  False   False   \n",
       "865        False     False   False  False  False  False  False  False   False   \n",
       "866        False     False   False  False  False  False  False  False   False   \n",
       "867        False     False   False  False  False  False  False  False   False   \n",
       "868        False     False   False  False  False   True  False  False   False   \n",
       "869        False     False   False  False  False  False  False  False   False   \n",
       "870        False     False   False  False  False  False  False  False   False   \n",
       "871        False     False   False  False  False  False  False  False   False   \n",
       "872        False     False   False  False  False  False  False  False   False   \n",
       "873        False     False   False  False  False  False  False  False   False   \n",
       "874        False     False   False  False  False  False  False  False   False   \n",
       "875        False     False   False  False  False  False  False  False   False   \n",
       "876        False     False   False  False  False  False  False  False   False   \n",
       "877        False     False   False  False  False  False  False  False   False   \n",
       "878        False     False   False  False  False   True  False  False   False   \n",
       "879        False     False   False  False  False  False  False  False   False   \n",
       "880        False     False   False  False  False  False  False  False   False   \n",
       "881        False     False   False  False  False  False  False  False   False   \n",
       "882        False     False   False  False  False  False  False  False   False   \n",
       "883        False     False   False  False  False  False  False  False   False   \n",
       "884        False     False   False  False  False  False  False  False   False   \n",
       "885        False     False   False  False  False  False  False  False   False   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "5    False   True     False  \n",
       "6    False  False     False  \n",
       "7    False   True     False  \n",
       "8    False   True     False  \n",
       "9    False   True     False  \n",
       "10   False  False     False  \n",
       "11   False  False     False  \n",
       "12   False   True     False  \n",
       "13   False   True     False  \n",
       "14   False   True     False  \n",
       "15   False   True     False  \n",
       "16   False   True     False  \n",
       "17   False   True     False  \n",
       "18   False   True     False  \n",
       "19   False   True     False  \n",
       "20   False   True     False  \n",
       "21   False  False     False  \n",
       "22   False   True     False  \n",
       "23   False  False     False  \n",
       "24   False   True     False  \n",
       "25   False   True     False  \n",
       "26   False   True     False  \n",
       "27   False  False     False  \n",
       "28   False   True     False  \n",
       "29   False   True     False  \n",
       "..     ...    ...       ...  \n",
       "861  False   True     False  \n",
       "862  False  False     False  \n",
       "863  False   True     False  \n",
       "864  False   True     False  \n",
       "865  False   True     False  \n",
       "866  False   True     False  \n",
       "867  False  False     False  \n",
       "868  False   True     False  \n",
       "869  False   True     False  \n",
       "870  False   True     False  \n",
       "871  False  False     False  \n",
       "872  False  False     False  \n",
       "873  False   True     False  \n",
       "874  False   True     False  \n",
       "875  False   True     False  \n",
       "876  False   True     False  \n",
       "877  False   True     False  \n",
       "878  False   True     False  \n",
       "879  False  False     False  \n",
       "880  False   True     False  \n",
       "881  False   True     False  \n",
       "882  False   True     False  \n",
       "883  False   True     False  \n",
       "884  False   True     False  \n",
       "885  False   True     False  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26190004860>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic_data.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2619009dda0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic_data.isnull(),yticklabels=False,cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TESTING heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2619010c828>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV8AAAEBCAYAAADfMaYGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAFf9JREFUeJzt3Xu0ZGV55/Fvne4Gmm61TQQMtwiJPiYuuXhjFIgIHRgv3KKAEhcycgmjYegIMV5wusGQIajQujToErBRQ9BWI44kylVGEQEFxIHwhDAtFxsGoacBaehu4Mwfu1oO7eHUqctbe5/y+2HtRdWuU7/9rtOnnnrr3W/ttzU+Po4kabjG6m6AJP02svhKUg0svpJUA4uvJNXA4itJNbD4SlINLL6SVAOLryTVwOIrSTWw+EpSDSy+klQDi68k1WD2MA+26EdXFLmKz3bzniwRywNry7w3LT/ufxfJHX/ZC4vkAmx7yLZFcleteqpI7pZblOtXbLP5E0Vyf3p3mTYfucvjRXIBzrqkXAm5631vaPXz/Lnbv2Pa9eaxu/6pr2P1YqjFV5KGpdVq9gd7i6+kkdRq+KiqxVfSSLLnK0k1sPhKUg1arVl1N2FKFl9JI8meryTVYMYX34h4KfA2YFvgKWAl8J3M/HHhtklSz5o+22HK1kXEe4AL23evB25o3/58RJxYsmGS1I9Wa2zaWx069XxPAHbNzDUTd0bEmVSF+BOlGiZJ/Zjpww5PAHMm2T8XWD/45kjSYIzN8NkOpwE3RsTlwL3AOLA1sDfw4cJtk6SeNb3nO2XrMvMCYA/g+8AaYG379p6ZeeFUz5WkOs30MV8ycyXwxSG0RZIGpuk9X+f5ShpRFl9JGrqxsWaXt2a3TpJ61PQvWVh8JY0kx3wlqQat1tBXBuqKxVfSSLLnO0E+vEmR3BsfKPMOt3ZtkfU+ufKqHYvk/p+Hy32j54YH13T+oR7s87q1RXLP+NlziuQCfH6PuUVyL7pzVZHcTWeV+TsGOOC1ze2/OeYrSTVwtoMk1cCeryTVwTFfSRo+T7hJUg2caiZJNXDMV5Jq0Bqb2RdTl6SZqdkdX4uvpBHlmK8k1WAmF9+I2H6qxzPzrsE2R5IGZIYPO1wMvBhYCWz8NjIOlLlIgST1aXxsBvd8gd2pFsx8T2ZePYT2SNJgFCq+EbEYOLR99+LMfH8vOZ1WL34YOAZ4Vy/hklSbVmv62zRFxEJgX2BXYBfglRFxcC/Nm87qxdcB1/USLkm1KdPxvRc4MTPXAUTEvwFTnht7Ns52kDSauhh2iIgFwIJJHlqdmas33MnMWyY858VUww+799S8Xp4kSY3X3bDDImDFJNuiyaIj4mXApcBfZ+btvTTPnq+k0TSrq3GHpcCySfav3nhHROwOfB1YlJkX9tQ2LL6SRlUXtbc9tPAbhXZjEbEd8E3gsMy8oue2YfGVNKLGy3zD7SRgM+DMiNiw77OZ+dlugyy+kkZTgXm+mXkCcMIgsoZafLfZ/IkiuZf+eH2R3Fe8ZrMiuW/+xmQnVfs3d265b/T83xsfLpJ71gvmF8k979BHi+QCfHXF44Vy5xXJ3fv3yrQX4Nq75xTL5rV9Pr/ZX3Cz5ytpRM3kC+tI0ozV3WyHobP4ShpN9nwlqQYWX0mqQcO/v2vxlTSa7PlK0vCNe8JNkmrQ8J5vx1GRiDgwIo6PiD/YaP+x5ZolSX1qdbHVYMriGxGnA8cDLwGujoh3Tnj4uJINk6S+jLWmv9XRvA6Pvxn4z5l5PLAn8NGIOKT9WLP79JJ+uxVYRmiQOhXfFtUqxbQvGPwW4JMRsdeG/ZLUSDN52AFYDnwvIl4Dv15C4xDgq8AfTPVESarV7LHpbzXotHrxKcAS4JEJ+64GXgl8oWjLJKkP463pb3WYzurFl0+y726eZW0jSWqEmk6kTZfzfCWNpobP87X4ShpN9nwlqQZeWEeSajCr2dXX4itpJBVavXhgLL6SRlOzO77DLb67b7m2SO45H9qhSO6LTr6jSO6Jh88qknvPo2VyAX4873lFcu++58kiuR/4/nOK5AJccvDqIrnfv2/TIrm3rC63wvBmZRb4HgxPuElSDRx2kKQaeDF1SRq+cYcdJKkGFl9JqoFjvpJUA6eaSVINZnrPNyJeDDyamSsj4mhgJ+AHmfnV4q2TpF7VdJH06eq0gOZfAd8FromI84C3A7cBR0XER4bQPknqyXirNe2tDp16vu8G/hjYCrgFeEFmPh4R5wDXAx8t3D5J6k3Bjm9EPBf4IfCWzPx5LxmdmjcGrM3MO4GPZ+bjEx5zvFhScxVavTgidgN+ALykn+Z1KqBfB66KiDdk5pL2gXcGPg98pZ8DS1JRXczzjYgFwIJJHlqdmRtfzOMY4L3Al3pvXOcFNP87cHJmTrz6yePA4sw8tZ8DS1JRY63pb9WalCsm2X5jrcrMPDozv99v86azgOb/2uh+AtnvgSWppPHuru2wFFg2yf4yl7DDcVtJo6qLsdz20EKxQjsZi6+k0eS1HSSpBs2uvRZfSaNprPAX3DLzRf083+IraSSVLr79svhKGkmtmX5hnUH6h5vmFcl9+e/cXiT3VXuXWYTxH64pEgvA/i9/okju789fXyR3v93K5F50x9wiuQD7XfT8Irnz55cpFn+0YF2RXIBHHhkvlt2vhtdee76jpFThlWYii68k1aDlmK8kDZ89X0mqwSx7vpI0fPZ8JakGTjWTpBp4wk2SatDwjm93qxxFxCdKNUSSBmlsbPpbHZ6159terXhjB0TE8wEy893FWiVJfWr4FSWnHHZYBRwBnMbTFxneB7iqdKMkqV8zdtghM08C3gG8HbgzM88HVmXm+e3bktRYhRYvHphOC2heDrwZeE9EfByYNZRWSVKfWmOtaW916DjUnJmrMvNQ4DbgvvJNkqT+Nb3nO+2pZpl5DnBOwbZI0sB4MXVJqsFMnu0gSTNW02c7WHwljSS/XixJNbDnK0k18KpmklQDZztM8NrtyqxU+2cXzC+S+8kDHi2Su83LnyqSe8k9mxbJBbjul2Wy588us/rtypVPFskFOPvANUVy711Tplrc9lC5l/mCBc2tcA3v+NrzlTSanGomSTWw+EpSDcZaZYa0BsXiK2kkzbbnK0nDZ89XkmrgmK8k1aC5k+AqFl9JI6lUzzciDgdOBuYASzPzM73kTFl8I+LVmXl9+/Y+wJuA9cA/Z+a1vRxQkoahVWDMNyK2oVrX8pXAWuCHEXFlZt7abVannu/ngFdExHuB44BzgRbwuYg4JzM/3e0BJWkYupntEBELgAWTPLQ6M1dPuL8QuCIzV7Wf9zXgbcCpXbdvmj93DLBXZj7YPuA5wPWAxVdSI3U522ERsHiS/acASybc3xq4d8L9e4HXdNs26Fx850TEGPAgVRd7g3VAmQsUSNIAdDnmuxRYNsn+1RvdHwMmVvUWPdbCTsX3AeCu9u1PA0dGxN7AGcDyXg4oScPQzWyH9tDCxoV2MvcAe064/0JgZTft2mDK4puZbwCIiACe3969FlicmRf3ckBJGoZCsx0uA5ZExBbAo8BbgWN7CZrWmG9m5oTbV/dyIEkaphLfcMvMX0TEh4ErgU2AczLzul6ynOcraSSVurZDZl4AXNBvjsVX0kjy2g6SVAOv7SBJNbD4SlINvLCOJNVg9phjvr/21hc9ViT3m9fNK5J72o/KrIq8rswizmy2WbnPWQ/88okiuce9dG3nH+rBRb+aUyQXYPGPnlMkd/68Mv9+H975oSK5AA+ta27/srktq9jzlTSSHPOVpBqUuKTkIFl8JY0ke76SVAPHfCWpBs52kKQaOOwgSTWYVXcDOrD4ShpJM/7COhGxH3BtZq6OiCOo1iv6SWZ+oXjrJKlHTR92mPKEYEQsBT4EbBYRHwXeCdwCHBwRnxxC+ySpJ2Ot6W+1tK/D438K7J2Z9wFvBvbPzLOBg4F9SzdOkno1Z2z6Wx06DTusAbakWh75bmAe1Rpu84AyX/aXpAGY6WO+pwLXR8SFwArgqoi4DNiPagVjSWqkGT3mm5n/k2qZ5JVUi8VdAzwCHJmZy4q3TpJ6NKuLrQ4dZztk5grgzCG0RZIGpuk9X+f5ShpJc/x6sSQNnz1fSaqBxVeSamDxlaQazJrh83wlaUbyYuoTfOmOzYvkbnLVXUVyX/Lfti+Se9gOa4rkzi7413bCe+8vkjv7jVsVyX3e1psUyQW4744yKy7v/ydl/gG/9vO5RXIB/uW6gp/td+/v6SVfD4Ngz1fSSHLYQZJq4Ak3SaqBxVeSalBH8W1f9/zJzFzS6WctvpJG0jC/XhwRz6O6Bs47mOYVHy2+kkbSkCc7HAjcDnxiuk+w+EoaSd0MO0TEAmDBJA+tzszVnZ6fmV9s5yyZ7jGnLL4R8SlgcWb+v+kGSlITzOpuzHcRsHiS/acASzbciYhDgLM2+pnbMnNhl83r2PM9AtgvIj6Ymd/oNlyS6tLlMkJLgWWT7H9GrzczlwPLe2/V0zoV3xXAnwNnR8TfUA0ofyszHxvEwSWplG6GHdpDCx2HFwapU/Edz8xbgddHxELgWOCTEfHvwD2ZeXjxFkpSD2bP8Hm+v25+Zl4GXBYRc4CdgB1LNkyS+tGqofhOZ37vBp2K76cnCV8P/KS9SVIjNbzjO3Xxzcxzh9UQSRqkOnq+3XCer6SR1PArSlp8JY2mlpeUlKTh86pmklSDhtdei6+k0WTPV5Jq0PDaO9zi+x8PzSmS+9RW84rkzp9dZsD+hgfL/B4A9txqXZHcXRbtUCR3323LLEb5ocfLnWwZu//RIrnf+fsy327d5IAXFckF2Gqb5s4pcKqZhqZU4ZVmoua+LVQsvpJGkmO+klSDhtdei6+k0eSXLCSpBvZ8JakGznaQpBp0uYbb0Fl8JY2khtfezsU3IvYGHsvMayLiRGAv4Hrg9Mx0YqmkRprRww4RcQbwJ8CciFgBPAWcDewPfAY4pngLJakHDa+9HXu+bwR2BjYF7gK2zsz1EfGvwE2lGydJvWr6lyw6fQOvBTwPeAEwD3hue/9cYJOC7ZKkvrS62OrQqed7OvAfVO17P3BpRFwGLATOK9w2SerZWMO/ZDFlzzczvwxsC2yfmZ8G3gXcD/xNZn5sCO2TpJ60WtPf6tBxtkNmPjbh9s+AnxVtkSQNQMOHfJ3nK2k0eUlJSarBjJ7nK0kzVavhfV+Lr6SR1GpZfCWpBs0ed7D4ShpJLYvv09asKTPpeYt9tiySu828x4vknn7guUVyl/3Xo4rkAvz9oWuK5H7s5jJ/gpe8/aEiuQAf+PGCIrkn71LmY/L+f1tmVWSAdT9/uFg2h+7RZ4DFV5KGzjFfSarBMGc7RMTuwFlU17x5EHh3Zt451XOa/dYgST1qdfHfAPwjcHRm7tK+/alOT7D4ShpRY11svYuITYGTM/Pm9q6bge07Pc9hB0kjqdXFV9wiYgEw2ZnU1Zk55RnLzFwLfLmdMwYsAb7Z6ZgWX0kjqqvhhEXA4kn2n0JVTAGIiEOoxnYnui0zF0bEJsD5VHX17zod0OIraSR1OZa7FFg2yf5n9HozczmwfOMfioj5wLeoTrYdmJnrOx3Q4itpJLWYNe2fbQ8t9DMh+stUC08cl5lPTecJ01m9+CDgIOCFwDrgDuCrmXlNHw2VpKK6GfPtR0TsChwI3ArcEBEAKzPzTVM9r9PqxR8E/hPwHeAA4EdUBfi8iDgzMz8/gLZLUgHDKb6ZeWMvB+s0x+Iw4KDMPBs4GFiYmR+nKsjv67qVkjQkLcamvdWh01E3AzZv354L/G779q+AaY1rSFI9mr1+cacx32XA1RHxXWA/4AsRsT1wEXBB4bZJUs+afm2HTqsXnw6cCPwSeF9mLgVWAUdk5mlDaJ8k9aTpww7TWb34cuDyCfd/hSsYS2o8LykpSUPnxdQlqQbDmufbK4uvpBHV7BNuFl9JI8ml4yWpBg47SFItmt3zbY2Pl1lRWJL07Jr91iBJI8riK0k1sPhKUg0svpJUA4uvJNXA4itJNbD4SlINLL6SVAOLryTVoHFfL46Iw4GTgTnA0sz8zACznwv8EHhLZv58gLmLgUPbdy/OzPcPKPdU4G3AOHBuZp45iNwJ+R8HXpCZRw4w80pgS2B9e9dfZOa1A8jdH1gMzAMuycwT+s1s5x4N/OWEXTsAX8rMv3yWp3ST/U7gg+27/5qZJ/Wb2c79APBfgLXAV/pdVWbj10VELATOpFq38SuZefIgctv75lCthv7RzPxeP+2e6RrV842IbYDTgD2AXYBjI+KPB5S9G/AD4CWDyJuQuxDYF9iVqs2vjIiDB5D7emBvYCfgVcDxERH95k7I3wd416Dy2pktqt/vzpm5S3sbROHdEfgscBDV7+MVEfHGfnMBMvOcDW0F/hy4H1jSb25EbA58Cng9sDOwZ/tvpd/chcDhwKup/uZ2i4g/6yPvGa+LiJgLnAccCPwR8OpefteTvd7af7/fA17Xa3tHSaOKL7AQuCIzV2Xmo8DXqHp+g3AM8F5g5YDyNrgXODEz12XmeuDfgO37Dc3Mq4A3ZOYTVD3J2cCj/eYCRMTvUL3J/d0g8iZGt/9/SUT8NCL67j22HUzVA7un/Ts+DOi7qE/ibOBDmfnAALJmUb2+5lF9ipsDPDaA3F2B72bmw5n5JFUv8qA+8jZ+XbwGuD0zV7T/9r4MHDKAXICjgI9R5t9uxmnasMPWVMVsg3up/hj6lplHAwyw87gh95YNtyPixVTDD7sPKHt9RJwCnAQsB34xiFzgc8CHge0GlLfB86nW+zueqth8LyIyMy/tM/cPgXUR8S2qN7ZvAx/pM/MZ2j3KuZm5fBB5mflIRHwEuA1YA1xF9RG8XzcAZ0XE/2jnHkAfnahJXheTvQa3HUAuG4bjImJRb60dLU3r+Y5RjW9u0AKeqqktXYmIlwGXAn+dmbcPKjczFwNbUBXKY/rNa49x3t1eGHWgMvOazDwiMx9q9x7PBd40gOjZVJ+KjgJeC+zGgIdMgL+gGucciIjYCXg38PtUBe1JqjfRvrT/3ZZRfXz/DtVH+3X95k4wY1+DM03Tiu89wO9NuP9CBj9MMHARsTtVj+8DmXn+gDJfGhG7AGTmGuAbVOOd/ToM2DcibgJOBQ6IiLMGkEtE7NEeS96gxdMn3vpxH3BZZv4yMx8D/pkBfSICiIhNqMZmvzWoTGA/4PLMvD8z11IVzL36DY2I5wBfz8ydMnMvqpNud/SbO8GMfA3ORE0bdrgMWBIRW1CNb74VOLbeJk0tIrYDvgkclplXDDB6R+CUiNiDqidyINWJkL5k5p9uuB0RRwJ7ZeZf9ZvbtgA4NSJeRzXs8C7guAHkfhs4PyIWAI8Ab6T6nQ/KTsC/t88zDMpPgTMiYh7V8MD+wPUDyN0B+GJEvIpqPPmo9jYo11KdG/tDYAXVyb2+/+70mxrV883MX1CNRV4J3ARckJnX1duqjk4CNgPOjIib2lvfBScz/wW4GLgR+Anww8y8sN/ckjLz2zyzzedl5jUDyL0WOIPqI/atwJ3AF/rNnWBHqh7fwGTmJcA/Uf0ebqZ6Mzp9ALk3A19vZ15HNR3z6n5zJ+Q/DhzZPsatVGPWXxtUvp7mShaSVING9Xwl6beFxVeSamDxlaQaWHwlqQYWX0mqgcVXkmpg8ZWkGlh8JakG/x8XdWHuwdaCQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normal_data=np.random.randn(10,12)\n",
    "sns.heatmap(normal_data,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x261901c8e48>"
      ]
     },
     "execution_count": 68,
     "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": [
    "sns.boxplot(data=titanic_data,x='PassengerId',orient='v',width=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26190208dd8>"
      ]
     },
     "execution_count": 69,
     "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": [
    "sns.boxplot(data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x261902ab550>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='white')\n",
    "sns.boxplot(data=titanic_data,x='Pclass',y='Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>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",
       "    <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>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>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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "testing drop command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.arange(12).reshape(3,4),columns=['A', 'B', 'C', 'D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B   C   D\n",
       "0  0  1   2   3\n",
       "1  4  5   6   7\n",
       "2  8  9  10  11"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n",
    "# pd.DataFrame(np.arange(12).reshape(3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C  D\n",
       "0  0  1  2  3\n",
       "1  4  5  6  7"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to drop entire row, axix=0 by default\n",
    "df.drop([2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B   D\n",
       "0  0  1   3\n",
       "1  4  5   7\n",
       "2  8  9  11"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to drop columns use axis=1\n",
    "df.drop(['C'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping Cabin column\n",
    "titanic_data.drop(['Cabin'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <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>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>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>S</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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Embarked]\n",
       "Index: []"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.dropna(inplace=False,thresh=50,how='any')\n",
    "#here use thresh carefully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 11)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data=pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop(['Cabin'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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>Embarked</th>\n",
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       "      <th>0</th>\n",
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       "      <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>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>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>S</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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x261903742e8>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic_data.isna(),cmap='Blues',cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x261903d0ac8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic_data.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   female  male\n",
       "0       0     1\n",
       "1       1     0\n",
       "2       1     0\n",
       "3       1     0\n",
       "4       0     1"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(titanic_data['Sex']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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       "   male\n",
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       "3     0\n",
       "4     1"
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     },
     "execution_count": 90,
     "metadata": {},
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   ],
   "source": [
    "sex = pd.get_dummies(titanic_data['Sex'],drop_first=True)\n",
    "sex.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
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      "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  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "\n",
       "   Parch     Ticket     Fare Embarked  \n",
       "0      0  A/5 21171   7.2500        S  \n",
       "1      0   PC 17599  71.2833        C  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   C  Q  S\n",
       "0  0  0  1\n",
       "1  1  0  0\n",
       "2  0  0  1\n",
       "3  0  0  1"
      ]
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     "execution_count": 92,
     "metadata": {},
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    }
   ],
   "source": [
    "embark = pd.get_dummies(titanic_data['Embarked'])\n",
    "embark.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1"
      ]
     },
     "execution_count": 93,
     "metadata": {},
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   ],
   "source": [
    "embark = pd.get_dummies(titanic_data['Embarked'],drop_first=True)\n",
    "embark.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   1  2  3\n",
       "0  0  0  1\n",
       "1  1  0  0\n",
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     },
     "execution_count": 94,
     "metadata": {},
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   "source": [
    "Pcl = pd.get_dummies(titanic_data['Pclass'])\n",
    "Pcl.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   2  3\n",
       "0  0  1\n",
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     "execution_count": 95,
     "metadata": {},
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   "source": [
    "Pcl = pd.get_dummies(titanic_data['Pclass'],drop_first=True)\n",
    "Pcl.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data  = pd.concat([titanic_data,sex,embark,Pcl],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Name</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <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>C</td>\n",
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       "      <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>S</td>\n",
       "      <td>0</td>\n",
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       "      <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>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "\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",
       "\n",
       "   Parch            Ticket     Fare Embarked  male  Q  S  2  3  \n",
       "0      0         A/5 21171   7.2500        S     1  0  1  0  1  \n",
       "1      0          PC 17599  71.2833        C     0  0  0  0  0  \n",
       "2      0  STON/O2. 3101282   7.9250        S     0  0  1  0  1  \n",
       "3      0            113803  53.1000        S     0  0  1  0  0  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(4)\n",
    "#note that last 5 columns are added in titanic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop(['Sex','Embarked','Pclass','PassengerId','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Name'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-99-47212bd0317a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtitanic_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Name'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3695\u001b[0m                                            \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3696\u001b[0m                                            \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3697\u001b[1;33m                                            errors=errors)\n\u001b[0m\u001b[0;32m   3698\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3699\u001b[0m     @rewrite_axis_style_signature('mapper', [('copy', True),\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3109\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3110\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3111\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3112\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3141\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3142\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3143\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3144\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   4402\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'ignore'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4403\u001b[0m                 raise KeyError(\n\u001b[1;32m-> 4404\u001b[1;33m                     '{} not found in axis'.format(labels[mask]))\n\u001b[0m\u001b[0;32m   4405\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4406\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['Name'] not found in axis\""
     ]
    }
   ],
   "source": [
    "titanic_data.drop(['Name'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Train and Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x is the feature variable, independent variable\n",
    "x = titanic_data.drop('Survived',axis=1)\n",
    "y = titanic_data['Survived']\n",
    "# y is the value you need to predict, dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  Parch     Fare  male  Q  S  2  3\n",
       "0  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3  35.0      1      0  53.1000     0  0  1  0  0\n",
       "4  35.0      0      0   8.0500     1  0  1  0  1"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'             precision    recall  f1-score   support\\n\\n          0       0.81      0.83      0.82       126\\n          1       0.75      0.72      0.73        88\\n\\navg / total       0.78      0.79      0.78       214\\n'"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[105,  21],\n",
       "       [ 25,  63]], dtype=int64)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### using confusion matrix we can also caluclate the accuracy as follows:\n",
    "\n",
    "accuracy = (105+63)/(105+21+25+63)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7850467289719626"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "model=DecisionTreeClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7617021276595745"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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