PANDAS_TUTORIAL_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "55f6d5d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a913770",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b8a0ada5",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>Subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Business Data Collection - BDC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Business Data Collection - BDC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Business Data Collection - BDC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Business Data Collection - BDC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Business Data Collection - BDC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Magnitude                         Subject\n",
       "0          0  Business Data Collection - BDC\n",
       "1          0  Business Data Collection - BDC\n",
       "2          0  Business Data Collection - BDC\n",
       "3          0  Business Data Collection - BDC\n",
       "4          0  Business Data Collection - BDC"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f14f1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('https://raw.githubusercontent.com/ashishpatel26/Titanic-Machine-Learning-from-Disaster/master/Submission_titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd2cd482",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>893</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  PassengerId  Sex  Age  Fare  Cabin  Embarked  Title  \\\n",
       "0           0          892    0    2     0    2.0         2      0   \n",
       "1           1          893    1    3     0    2.0         0      2   \n",
       "2           2          894    0    3     0    2.0         2      0   \n",
       "3           3          895    0    2     0    2.0         0      0   \n",
       "4           4          896    1    1     0    2.0         0      2   \n",
       "\n",
       "   FamilySize  Survived  \n",
       "0         0.0         0  \n",
       "1         0.4         1  \n",
       "2         0.0         0  \n",
       "3         0.0         0  \n",
       "4         0.8         1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bfcf02e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0       int64\n",
       "PassengerId      int64\n",
       "Sex              int64\n",
       "Age              int64\n",
       "Fare             int64\n",
       "Cabin          float64\n",
       "Embarked         int64\n",
       "Title            int64\n",
       "FamilySize     float64\n",
       "Survived         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "269aaa88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>208.500000</td>\n",
       "      <td>1100.500000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>1.763158</td>\n",
       "      <td>0.770335</td>\n",
       "      <td>1.687081</td>\n",
       "      <td>0.464115</td>\n",
       "      <td>0.732057</td>\n",
       "      <td>0.335885</td>\n",
       "      <td>0.385167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.481622</td>\n",
       "      <td>0.971479</td>\n",
       "      <td>0.998743</td>\n",
       "      <td>0.563371</td>\n",
       "      <td>0.685516</td>\n",
       "      <td>0.972019</td>\n",
       "      <td>0.607629</td>\n",
       "      <td>0.487218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>104.250000</td>\n",
       "      <td>996.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>208.500000</td>\n",
       "      <td>1100.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>312.750000</td>\n",
       "      <td>1204.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>417.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       Unnamed: 0  PassengerId         Sex         Age        Fare  \\\n",
       "count  418.000000   418.000000  418.000000  418.000000  418.000000   \n",
       "mean   208.500000  1100.500000    0.363636    1.763158    0.770335   \n",
       "std    120.810458   120.810458    0.481622    0.971479    0.998743   \n",
       "min      0.000000   892.000000    0.000000    0.000000    0.000000   \n",
       "25%    104.250000   996.250000    0.000000    1.000000    0.000000   \n",
       "50%    208.500000  1100.500000    0.000000    2.000000    0.000000   \n",
       "75%    312.750000  1204.750000    1.000000    3.000000    2.000000   \n",
       "max    417.000000  1309.000000    1.000000    4.000000    3.000000   \n",
       "\n",
       "            Cabin    Embarked       Title  FamilySize    Survived  \n",
       "count  418.000000  418.000000  418.000000  418.000000  418.000000  \n",
       "mean     1.687081    0.464115    0.732057    0.335885    0.385167  \n",
       "std      0.563371    0.685516    0.972019    0.607629    0.487218  \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "25%      1.600000    0.000000    0.000000    0.000000    0.000000  \n",
       "50%      2.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "75%      2.000000    1.000000    1.000000    0.400000    1.000000  \n",
       "max      2.400000    2.000000    3.000000    4.000000    1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a1709cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0     False\n",
       "PassengerId    False\n",
       "Sex            False\n",
       "Age            False\n",
       "Fare           False\n",
       "Cabin           True\n",
       "Embarked       False\n",
       "Title          False\n",
       "FamilySize      True\n",
       "Survived       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes=='float'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c654e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin         float64\n",
       "FamilySize    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes[df.dtypes=='float']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c1ea5e9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cabin', 'FamilySize'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes[df.dtypes=='float'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "269416c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df[df.dtypes[df.dtypes=='float'].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "44c071e3",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cabin</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.687081</td>\n",
       "      <td>0.335885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.563371</td>\n",
       "      <td>0.607629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>4.000000</td>\n",
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      ],
      "text/plain": [
       "            Cabin  FamilySize\n",
       "count  418.000000  418.000000\n",
       "mean     1.687081    0.335885\n",
       "std      0.563371    0.607629\n",
       "min      0.000000    0.000000\n",
       "25%      1.600000    0.000000\n",
       "50%      2.000000    0.000000\n",
       "75%      2.000000    0.400000\n",
       "max      2.400000    4.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1cf9574c",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
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       "   Unnamed: 0  PassengerId  Sex  Age  Fare  Cabin  Embarked  Title  \\\n",
       "0           0          892    0    2     0    2.0         2      0   \n",
       "1           1          893    1    3     0    2.0         0      2   \n",
       "2           2          894    0    3     0    2.0         2      0   \n",
       "3           3          895    0    2     0    2.0         0      0   \n",
       "4           4          896    1    1     0    2.0         0      2   \n",
       "\n",
       "   FamilySize  Survived  \n",
       "0         0.0         0  \n",
       "1         0.4         1  \n",
       "2         0.0         0  \n",
       "3         0.0         0  \n",
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     "metadata": {},
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   "id": "a8c7bbc5",
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     "execution_count": 41,
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  },
  {
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   "id": "ab8457a8",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'PassengerId'], dtype='object')"
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     "execution_count": 44,
     "metadata": {},
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       "     Sex  Age  Fare\n",
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       "   Unnamed: 0  PassengerId  Sex  Age  Fare  Cabin  Embarked  Title  \\\n",
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   "id": "bcf7e2d7",
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  {
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   "id": "6d01daba",
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   "outputs": [
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      "C:\\Users\\Naman Jain\\anaconda3\\lib\\site-packages\\pandas\\io\\formats\\format.py:1405: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
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