26 Drop Rows or Columns with Null Values.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drop Rows or Columns With Null Values\n",
    "By:<a href='https://www.youtube.com/wonkyCode'>WonkyCode</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "dataset = {\n",
    "    \"name\" : [\"Rohit\", \"Harsha\", \"Saran\", \"Abhijith\", np.nan],\n",
    "    \"age\" : [25, 18, np.nan, np.nan, np.nan],\n",
    "    \"height\": [5.6, 5.8, 6.2, np.nan, np.nan],\n",
    "    \"job\": [True, np.nan, np.nan, np.nan, np.nan]\n",
    "}\n",
    "data = pd.DataFrame(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rohit</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Harsha</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Saran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abhijith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       name   age  height   job\n",
       "0     Rohit  25.0     5.6  True\n",
       "1    Harsha  18.0     5.8   NaN\n",
       "2     Saran   NaN     6.2   NaN\n",
       "3  Abhijith   NaN     NaN   NaN\n",
       "4       NaN   NaN     NaN   NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* You can drop rows containing null values using <code>dropna()</code> method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rohit</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>True</td>\n",
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      "text/plain": [
       "    name   age  height   job\n",
       "0  Rohit  25.0     5.6  True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>5.6</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Harsha</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Saran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abhijith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "       name   age  height   job\n",
       "0     Rohit  25.0     5.6  True\n",
       "1    Harsha  18.0     5.8   NaN\n",
       "2     Saran   NaN     6.2   NaN\n",
       "3  Abhijith   NaN     NaN   NaN\n",
       "4       NaN   NaN     NaN   NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Harsha</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Saran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abhijith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "       name   age  height   job\n",
       "0     Rohit  25.0     5.6  True\n",
       "1    Harsha  18.0     5.8   NaN\n",
       "2     Saran   NaN     6.2   NaN\n",
       "3  Abhijith   NaN     NaN   NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.dropna(how=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Harsha</td>\n",
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       "      <th>2</th>\n",
       "      <td>Saran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>Abhijith</td>\n",
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       "      <td>NaN</td>\n",
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       "       name   age  height   job\n",
       "0     Rohit  25.0     5.6  True\n",
       "1    Harsha  18.0     5.8   NaN\n",
       "2     Saran   NaN     6.2   NaN\n",
       "3  Abhijith   NaN     NaN   NaN\n",
       "4       NaN   NaN     NaN   NaN"
      ]
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(how=\"all\", inplace=True)"
   ]
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   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>Rohit</td>\n",
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       "      <td>Harsha</td>\n",
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       "      <td>Saran</td>\n",
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       "       name   age  height   job\n",
       "0     Rohit  25.0     5.6  True\n",
       "1    Harsha  18.0     5.8   NaN\n",
       "2     Saran   NaN     6.2   NaN\n",
       "3  Abhijith   NaN     NaN   NaN"
      ]
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     "execution_count": 8,
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = {\n",
    "    \"name\" : [\"Rohit\", \"Harsha\", \"Saran\", \"Abhijith\", np.nan],\n",
    "    \"age\" : [25, 18, np.nan, np.nan, np.nan],\n",
    "    \"height\": [5.6, 5.8, 6.2, np.nan, np.nan],\n",
    "    \"job\": [True, np.nan, np.nan, np.nan, np.nan]\n",
    "}\n",
    "data = pd.DataFrame(dic)"
   ]
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   "cell_type": "code",
   "execution_count": 10,
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       "       name   age  height   job\n",
       "0     Rohit  25.0     5.6  True\n",
       "1    Harsha  18.0     5.8   NaN\n",
       "2     Saran   NaN     6.2   NaN\n",
       "3  Abhijith   NaN     NaN   NaN\n",
       "4       NaN   NaN     NaN   NaN"
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       "     name   age  height   job\n",
       "0   Rohit  25.0     5.6  True\n",
       "1  Harsha  18.0     5.8   NaN\n",
       "2   Saran   NaN     6.2   NaN"
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(thresh=2) # requires that many non-null values in a row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   "source": [
    "**Useful References:**\n",
    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html\n",
    "* https://www.geeksforgeeks.org/python-pandas-dataframe-dropna/"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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