Missing_values.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a36c3b6-f597-400b-a83e-3bfb04e3ad97",
   "metadata": {},
   "source": [
    "Missing values\n",
    "They occur in a dataset,when some of the information is not stored for a variable.\n",
    "1.Missing completely at random\n",
    "2.Missing at random\n",
    "3.Missing data not at random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d72636b8-c123-4491-8257-a8be9b4ff676",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "49bd928c-ed99-4805-81a2-7daa503752cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73bb67bd-b348-449e-8d33-65c7fec674d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de4a1f4a-0e9b-45cd-8a12-599d568b104e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a89ce5d-be4a-4c2f-af6d-904742695777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3f8ca70-d283-4c09-b96d-61a9b3cc8dfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(182, 15)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Row wise deletion - but it leads to loss of large amount of data\n",
    "df.dropna().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "76ee1817-8ad8-4fae-afa0-8deacbfef96b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>sibsp</th>\n",
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       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
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       "      <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>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex  sibsp  parch     fare   class    who  \\\n",
       "0           0       3    male      1      0   7.2500   Third    man   \n",
       "1           1       1  female      1      0  71.2833   First  woman   \n",
       "2           1       3  female      0      0   7.9250   Third  woman   \n",
       "3           1       1  female      1      0  53.1000   First  woman   \n",
       "4           0       3    male      0      0   8.0500   Third    man   \n",
       "..        ...     ...     ...    ...    ...      ...     ...    ...   \n",
       "886         0       2    male      0      0  13.0000  Second    man   \n",
       "887         1       1  female      0      0  30.0000   First  woman   \n",
       "888         0       3  female      1      2  23.4500   Third  woman   \n",
       "889         1       1    male      0      0  30.0000   First    man   \n",
       "890         0       3    male      0      0   7.7500   Third    man   \n",
       "\n",
       "     adult_male alive  alone  \n",
       "0          True    no  False  \n",
       "1         False   yes  False  \n",
       "2         False   yes   True  \n",
       "3         False   yes  False  \n",
       "4          True    no   True  \n",
       "..          ...   ...    ...  \n",
       "886        True    no   True  \n",
       "887       False   yes   True  \n",
       "888       False    no  False  \n",
       "889        True   yes   True  \n",
       "890        True    no   True  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Column wise deletion\n",
    "df.dropna(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "696708d8-4330-4513-acb7-9a99a797d0af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Next technique is imputation missing values\n",
    "#1.Mean value imputation - replacing null values in the age with the mean value.It works well when we have a normall distributed data\n",
    "\n",
    "df['age_mean'] = df['age'].fillna(df['age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89d68bde-5c33-47c9-b551-f255063e55d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_mean</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
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       "      <td>27.0</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>29.699118</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age_mean   age\n",
       "0    22.000000  22.0\n",
       "1    38.000000  38.0\n",
       "2    26.000000  26.0\n",
       "3    35.000000  35.0\n",
       "4    35.000000  35.0\n",
       "..         ...   ...\n",
       "886  27.000000  27.0\n",
       "887  19.000000  19.0\n",
       "888  29.699118   NaN\n",
       "889  26.000000  26.0\n",
       "890  32.000000  32.0\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['age_mean','age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4af289d1-6a16-4eb0-ba4d-fef2658ff6a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2.Median value imputation - If we have right skewed or left skewed , then we will also be having outliers in that case we use this\n",
    "df['age_median'] = df['age'].fillna(df['age'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74b94b5c-a6b9-4c5b-bf44-2f72d02e2a56",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     age_median   age\n",
       "0          22.0  22.0\n",
       "1          38.0  38.0\n",
       "2          26.0  26.0\n",
       "3          35.0  35.0\n",
       "4          35.0  35.0\n",
       "..          ...   ...\n",
       "886        27.0  27.0\n",
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       "888        28.0   NaN\n",
       "889        26.0  26.0\n",
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       "\n",
       "[891 rows x 2 columns]"
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     "execution_count": 18,
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "54208689-4cd8-4741-a0ab-5781dd0f74d8",
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   "outputs": [],
   "source": [
    "#3.Mode value imputation - used with categorical values\n",
    "#taking categorical data - embarked from the dataset\n",
    "\n",
    "df['embarked_mode']=df['embarked'].fillna(df['embarked'].mode())"
   ]
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   "cell_type": "code",
   "execution_count": 20,
   "id": "d5b8ef1f-f526-413e-8593-cfa69287d732",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    embarked_mode embarked\n",
       "0               S        S\n",
       "1               C        C\n",
       "2               S        S\n",
       "3               S        S\n",
       "4               S        S\n",
       "..            ...      ...\n",
       "886             S        S\n",
       "887             S        S\n",
       "888             S        S\n",
       "889             C        C\n",
       "890             Q        Q\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['embarked_mode','embarked']]"
   ]
  }
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