{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "welsh-marking",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "surgical-recipe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'a':[1, 2, 3, np.nan, np.nan], 'b':[1, 8, 5, 10, np.nan]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bacterial-model",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>5.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a     b\n",
       "0  1.0   1.0\n",
       "1  2.0   8.0\n",
       "2  3.0   5.0\n",
       "3  NaN  10.0\n",
       "4  NaN   NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "brave-roommate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2\n",
       "b    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum() #broj nedostajucih po kolini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "undefined-cedar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "b    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "twenty-photographer",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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      "text/plain": [
       "       a      b\n",
       "0  False  False\n",
       "1  False  False\n",
       "2  False  False\n",
       "3   True  False\n",
       "4   True   True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "loose-sierra",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
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       "      <th>3</th>\n",
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      "text/plain": [
       "       a      b\n",
       "0  False  False\n",
       "1  False  False\n",
       "2  False  False\n",
       "3   True  False\n",
       "4   True   True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "entire-coordinator",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "korean-thumbnail",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "     a    b\n",
       "0  1.0  1.0\n",
       "1  2.0  8.0\n",
       "2  3.0  5.0"
      ]
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "determined-delay",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "     a     b\n",
       "0  1.0   1.0\n",
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     "metadata": {},
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   "source": [
    "df.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aerial-share",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "     a     b\n",
       "0  1.0   1.0\n",
       "1  2.0   8.0\n",
       "2  3.0   5.0\n",
       "3  NaN  10.0\n",
       "4  NaN   NaN"
      ]
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     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "automated-genealogy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "     a    b\n",
       "1  2.0  8.0\n",
       "2  3.0  5.0\n",
       "4  NaN  NaN"
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     "execution_count": 23,
     "metadata": {},
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   "source": [
    "df.drop([0,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "binding-polish",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      b\n",
       "0   1.0\n",
       "1   8.0\n",
       "2   5.0\n",
       "3  10.0\n",
       "4   NaN"
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.drop(['a'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "unauthorized-westminster",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "     a     b\n",
       "2  3.0   5.0\n",
       "3  NaN  10.0\n",
       "4  NaN   NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[df['a']<3].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "computational-knight",
   "metadata": {},
   "outputs": [
    {
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       "      <td>8.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "     a     b\n",
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       "3  NaN  10.0\n",
       "4  NaN   NaN"
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   "execution_count": 29,
   "id": "documentary-peeing",
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   "outputs": [
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       "      a     b\n",
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       "4  25.0  25.0"
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    "df.fillna(25)"
   ]
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  {
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   "execution_count": 30,
   "id": "classical-hospital",
   "metadata": {},
   "outputs": [
    {
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       "      a     b\n",
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     "execution_count": 30,
     "metadata": {},
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   "source": [
    "df.replace(np.nan, 25)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "spoken-cherry",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    2.0\n",
       "4    2.0\n",
       "Name: a, dtype: float64"
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['a'].replace(np.nan, df['a'].mean())"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "french-occasions",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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