{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "500c7d67",
   "metadata": {},
   "source": [
    "## Prepare: Handling Missing Values\n",
    "### ChemML implements 4 strategies to handle missing values and interpolate, replace or remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "756c0668",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from chemml.preprocessing import MissingValues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0a79423",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>col3</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>nan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>inf</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nan</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>missing</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      col1 col2  col3\n",
       "0        1    1     1\n",
       "1        2  nan     2\n",
       "2        3  inf     3\n",
       "3      nan    2     4\n",
       "4  missing    3     5\n",
       "5        4    4     6\n",
       "6        5    5     7\n",
       "7      NaN    6     8"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df['col1'] = [1,2,3,'nan','missing',4,5,np.nan]\n",
    "df['col2'] = [1,'nan',np.inf,2,3,4,5,6]\n",
    "df['col3'] = [1,2,3,4,5,6,7,8]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c6e2867",
   "metadata": {},
   "source": [
    "### Strategy 1: Ignoring Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e226f63",
   "metadata": {},
   "outputs": [
    {
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       "      <th>col3</th>\n",
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2  col3\n",
       "0   1.0   1.0     1\n",
       "5   4.0   4.0     6\n",
       "6   5.0   5.0     7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = MissingValues(df, strategy='ignore_row',string_as_null=True,inf_as_null=True,missing_values=None)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1feaa3e9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>inf</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nan</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>missing</td>\n",
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       "      <th>5</th>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "      col1 col2  col3\n",
       "0        1    1     1\n",
       "1        2  nan     2\n",
       "2        3  inf     3\n",
       "3      nan    2     4\n",
       "4  missing    3     5\n",
       "5        4    4     6\n",
       "6        5    5     7\n",
       "7      NaN    6     8"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df['col1'] = [1,2,3,'nan','missing',4,5,np.nan]\n",
    "df['col2'] = [1,'nan',np.inf,2,3,4,5,6]\n",
    "df['col3'] = [1,2,3,4,5,6,7,8]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf77747",
   "metadata": {},
   "source": [
    "### Strategy 2: Replacing With Zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c288e5dc",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
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       "      <th>6</th>\n",
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       "   col1  col2  col3\n",
       "0   1.0   1.0     1\n",
       "1   2.0   0.0     2\n",
       "2   3.0   0.0     3\n",
       "3   0.0   2.0     4\n",
       "4   0.0   3.0     5\n",
       "5   4.0   4.0     6\n",
       "6   5.0   5.0     7\n",
       "7   0.0   6.0     8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = MissingValues(df, strategy='zero',string_as_null=True,inf_as_null=True,missing_values=None)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b198c6c",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      col1 col2\n",
       "0        1    1\n",
       "1        2  nan\n",
       "2        3  inf\n",
       "3      nan    2\n",
       "4  missing    3\n",
       "5        4    4\n",
       "6        5    5\n",
       "7      NaN    6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df['col1'] = [1,2,3,'nan','missing',4,5,np.nan]\n",
    "df['col2'] = [1,'nan',np.inf,2,3,4,5,6]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d8e4701",
   "metadata": {},
   "source": [
    "### Strategy 3: Interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61136d0a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>6</th>\n",
       "      <td>5.000000</td>\n",
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       "      <th>7</th>\n",
       "      <td>5.000000</td>\n",
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      "text/plain": [
       "       col1      col2\n",
       "0  1.000000  1.000000\n",
       "1  2.000000  1.333333\n",
       "2  3.000000  1.666667\n",
       "3  3.333333  2.000000\n",
       "4  3.666667  3.000000\n",
       "5  4.000000  4.000000\n",
       "6  5.000000  5.000000\n",
       "7  5.000000  6.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = MissingValues(df,strategy='interpolate',string_as_null=True,inf_as_null=True,missing_values=None)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e2aa9d1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      col1 col2  col3\n",
       "0        1    1     1\n",
       "1        2  nan     2\n",
       "2        3  inf     3\n",
       "3      nan    2     4\n",
       "4  missing    3     5\n",
       "5        4    4     6\n",
       "6        5    5     7\n",
       "7      NaN    6     8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df['col1'] = [1,2,3,'nan','missing',4,5,np.nan]\n",
    "df['col2'] = [1,'nan',np.inf,2,3,4,5,6]\n",
    "df['col3'] = [1,2,3,4,5,6,7,8]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03457de6",
   "metadata": {},
   "source": [
    "### Strategy 3: Ignore Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5242f679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col3\n",
       "0     1\n",
       "1     2\n",
       "2     3\n",
       "3     4\n",
       "4     5\n",
       "5     6\n",
       "6     7\n",
       "7     8"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = MissingValues(df, strategy='ignore_column',string_as_null=True,inf_as_null=True,missing_values=None)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae618f2b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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