Handling with missing DATA _PythonBoss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.DataFrame({\"Name\":[\"Shashi\",\"kishan\",\"Rahul\",\"Neeraj\",np.NaN,\"Sushant\"],\n",
    "                  \"Qualification\":[\"B.Tech\",np.NaN,\"BBA\",\"BCA\",\"MBA\",\"MCA\"],\n",
    "                  \"Roll No\":[11407,11408,np.NaN,11410,np.NaN,11234],\n",
    "                  \"Percentage\":[79.0,45,68,np.NaN,78,89]\n",
    "                 })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Name Qualification  Roll No  Percentage\n",
      "0   Shashi        B.Tech  11407.0        79.0\n",
      "1   kishan           NaN  11408.0        45.0\n",
      "2    Rahul           BBA      NaN        68.0\n",
      "3   Neeraj           BCA  11410.0         NaN\n",
      "4      NaN           MBA      NaN        78.0\n",
      "5  Sushant           MCA  11234.0        89.0\n"
     ]
    }
   ],
   "source": [
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Roll No</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shashi</td>\n",
       "      <td>B.Tech</td>\n",
       "      <td>11407.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kishan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11408.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahul</td>\n",
       "      <td>BBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name Qualification  Roll No  Percentage\n",
       "0  Shashi        B.Tech  11407.0        79.0\n",
       "1  kishan           NaN  11408.0        45.0\n",
       "2   Rahul           BBA      NaN        68.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Shashi</td>\n",
       "      <td>kishan</td>\n",
       "      <td>Rahul</td>\n",
       "      <td>Neeraj</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sushant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qualification</th>\n",
       "      <td>B.Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BBA</td>\n",
       "      <td>BCA</td>\n",
       "      <td>MBA</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roll No</th>\n",
       "      <td>11407</td>\n",
       "      <td>11408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percentage</th>\n",
       "      <td>79</td>\n",
       "      <td>45</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0       1      2       3    4        5\n",
       "Name           Shashi  kishan  Rahul  Neeraj  NaN  Sushant\n",
       "Qualification  B.Tech     NaN    BBA     BCA  MBA      MCA\n",
       "Roll No         11407   11408    NaN   11410  NaN    11234\n",
       "Percentage         79      45     68     NaN   78       89"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             1\n",
       "Qualification    1\n",
       "Roll No          2\n",
       "Percentage       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df1.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df1[\"Roll No\"].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Roll No</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shashi</td>\n",
       "      <td>B.Tech</td>\n",
       "      <td>11407.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kishan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11408.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahul</td>\n",
       "      <td>BBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Neeraj</td>\n",
       "      <td>BCA</td>\n",
       "      <td>11410.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>MBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sushant</td>\n",
       "      <td>MCA</td>\n",
       "      <td>11234.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Qualification  Roll No  Percentage\n",
       "0   Shashi        B.Tech  11407.0        79.0\n",
       "1   kishan           NaN  11408.0        45.0\n",
       "2    Rahul           BBA      NaN        68.0\n",
       "3   Neeraj           BCA  11410.0         NaN\n",
       "4      NaN           MBA      NaN        78.0\n",
       "5  Sushant           MCA  11234.0        89.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " np.sum(np.sum(df1.isnull())/df1.shape[0]>.20) #to find the no of columns with more than 10% of data missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6,)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"Roll No\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Roll No       33.333333\n",
       "Percentage    16.666667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df1[[\"Roll No\",\"Percentage\"]].isnull())/df1[[\"Roll No\",\"Percentage\"]].shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             16.666667\n",
       "Qualification    16.666667\n",
       "Roll No          33.333333\n",
       "Percentage       16.666667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df1.isnull())/df1.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [0, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    79.0\n",
       "1    45.0\n",
       "2    68.0\n",
       "3    78.0\n",
       "4    78.0\n",
       "5    89.0\n",
       "Name: Percentage, dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"Percentage\"].fillna(df1[\"Percentage\"].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.66666667])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df1[\"Percentage\"].isnull())/df1[\"Percentage\"].shape*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Roll No'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df1.columns[df1.isnull().mean()>0.20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.count of 0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "Name: Roll No, dtype: bool>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"Roll No\"].isna().count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             5\n",
       "Qualification    5\n",
       "Roll No          4\n",
       "Percentage       5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Roll No</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shashi</td>\n",
       "      <td>B.Tech</td>\n",
       "      <td>11407.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kishan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11408.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rahul</td>\n",
       "      <td>BBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Neeraj</td>\n",
       "      <td>BCA</td>\n",
       "      <td>11410.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>MBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sushant</td>\n",
       "      <td>MCA</td>\n",
       "      <td>11234.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Qualification  Roll No  Percentage\n",
       "0   Shashi        B.Tech  11407.0        79.0\n",
       "1   kishan           NaN  11408.0        45.0\n",
       "2    Rahul           BBA      NaN        68.0\n",
       "3   Neeraj           BCA  11410.0         NaN\n",
       "4      NaN           MBA      NaN        78.0\n",
       "5  Sushant           MCA  11234.0        89.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             True\n",
       "Qualification    True\n",
       "Roll No          True\n",
       "Percentage       True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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       "      Name Qualification  Roll No  Percentage\n",
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       "      Name Qualification  Roll No  Percentage\n",
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       "3   Neeraj           BCA  11410.0         NaN\n",
       "4      NaN           MBA      NaN        78.0\n",
       "5  Sushant           MCA  11234.0        89.0"
      ]
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   "execution_count": 48,
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      "text/plain": [
       "      Name Qualification  Roll No Percentage\n",
       "0   Shashi        B.Tech    11407         79\n",
       "1   kishan       DEFAULT    11408         45\n",
       "2    Rahul           BBA  DEFAULT         68\n",
       "3   Neeraj           BCA    11410    DEFAULT\n",
       "4  DEFAULT           MBA  DEFAULT         78\n",
       "5  Sushant           MCA    11234         89"
      ]
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    "df1.fillna(value='DEFAULT')"
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
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    {
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       "0    11407.00\n",
       "1    11408.00\n",
       "2    11364.75\n",
       "3    11410.00\n",
       "4    11364.75\n",
       "5    11234.00\n",
       "Name: Roll No, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   ]
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