{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with NaN values #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from plydata import call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>w</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     w    x    y   z\n",
       "0  1.0  NaN  NaN NaN\n",
       "1  2.0  2.0  NaN NaN\n",
       "2  NaN  NaN  NaN NaN\n",
       "3  4.0  4.0  NaN NaN\n",
       "4  5.0  5.0  5.0 NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'w': [1, 2, np.nan, 4, 5],\n",
    "    'x': [np.nan, 2, np.nan, 4, 5],\n",
    "    'y': [np.nan] * 4 + [5],\n",
    "    'z': [np.nan] * 5\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop NaN Values ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop rows with any NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>x</th>\n",
       "      <th>y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [w, x, y, z]\n",
       "Index: []"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df >> call(pd.DataFrame.dropna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>x</th>\n",
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       "      <th>z</th>\n",
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       "  </thead>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [w, x, y, z]\n",
       "Index: []"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can call method on the dataframe with '.method_name'\n",
    "df >> call('.dropna')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop rows with all NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>z</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     w    x    y   z\n",
       "0  1.0  NaN  NaN NaN\n",
       "1  2.0  2.0  NaN NaN\n",
       "3  4.0  4.0  NaN NaN\n",
       "4  5.0  5.0  5.0 NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df >> call('.dropna', how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop rows with NaN values in the x column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>y</th>\n",
       "      <th>z</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     w    x    y   z\n",
       "1  2.0  2.0  NaN NaN\n",
       "3  4.0  4.0  NaN NaN\n",
       "4  5.0  5.0  5.0 NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df >> call('.dropna', subset=['x'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop and keep rows atleast 3 non-NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     w    x    y   z\n",
       "4  5.0  5.0  5.0 NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df >> call('.dropna', thresh=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop columns with all NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "     w    x    y\n",
       "0  1.0  NaN  NaN\n",
       "1  2.0  2.0  NaN\n",
       "2  NaN  NaN  NaN\n",
       "3  4.0  4.0  NaN\n",
       "4  5.0  5.0  5.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df >> call('.dropna', axis=1, how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop columns with any NaN values in row 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>2.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     w    x\n",
       "0  1.0  NaN\n",
       "1  2.0  2.0\n",
       "2  NaN  NaN\n",
       "3  4.0  4.0\n",
       "4  5.0  5.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df >> call('.dropna', axis=1, subset=[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fill NaN values ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace all NaN values with -1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     w    x    y    z\n",
       "0  1.0 -1.0 -1.0 -1.0\n",
       "1  2.0  2.0 -1.0 -1.0\n",
       "2 -1.0 -1.0 -1.0 -1.0\n",
       "3  4.0  4.0 -1.0 -1.0\n",
       "4  5.0  5.0  5.0 -1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df >> call(pd.DataFrame.fillna, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
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      "text/plain": [
       "     w    x    y    z\n",
       "0  1.0 -1.0 -1.0 -1.0\n",
       "1  2.0  2.0 -1.0 -1.0\n",
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     "execution_count": 11,
     "metadata": {},
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   "source": [
    "df >> call('.fillna', -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace all NaN values with the first non-NaN value *above in column*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "     w    x    y   z\n",
       "0  1.0  NaN  NaN NaN\n",
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   "source": [
    "df >> call('.fillna', method='ffill')"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace all NaN values with the first non-NaN value *below in column*"
   ]
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  {
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   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "     w    x    y   z\n",
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   "source": [
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   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace atmost 2 NaN values with the first non-NaN value *below in column*"
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "     w    x    y   z\n",
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  },
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   "metadata": {},
   "source": [
    "Replace all NaN values with the first non-NaN value to the *left in the row*"
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  {
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   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "     w    x    y    z\n",
       "0  1.0  1.0  1.0  1.0\n",
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace all NaN values with the first non-NaN value to the *right in the row*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "     w    x    y   z\n",
       "0  1.0  NaN  NaN NaN\n",
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       "2  NaN  NaN  NaN NaN\n",
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   "source": [
    "df >> call('.fillna', method='bfill', axis=1)"
   ]
  }
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