Örnek Analizler/Eksik Veri Analizi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Eksik Veri Analizi\n",
    "\n",
    "* Veri setindeki eksikliğin yapısal bir eksiklik olup olmadığı bilinmesi gerekiyor.\n",
    "* Yapısal olması durumu, kredi kartı olmayan birisinin kresi kartı toplam harcaması NA olur.\n",
    "* Rastlantısal ise veri girişlerinde ya da aktarımında oluşan eksikliklerdir.\n",
    "* NA her zaman eksiklik olduğu anlamına gelmez."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     V1    V2    V3\n",
       "0   1.0   7.0   NaN\n",
       "1   3.0   NaN  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   NaN   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   NaN   7.0\n",
       "6   NaN   NaN   NaN\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
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     "execution_count": 2,
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    }
   ],
   "source": [
    "V1 = np.array([1,3,6,np.NaN,7,1,np.NaN,9,15])\n",
    "V2 = np.array([7,np.NaN,5,8,12,np.NaN,np.NaN,2,3])\n",
    "V3 = np.array([np.NaN,12,5,6,14,7,np.NaN,2,31])\n",
    "\n",
    "df = pd.DataFrame({\"V1\" : V1,\n",
    "                   \"V2\" : V2,\n",
    "                   \"V3\" : V3})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Kaçar tane eksik değer var?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V1    2\n",
       "V2    3\n",
       "V3    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Boş olmayan değerlerin sayısı kaçtır?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V1    7\n",
       "V2    6\n",
       "V3    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Veri setinde toplam kaç eksik değer var?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Eksik değer olan satırlara bakalım.\n",
    "* **any:** En az bir tane eksik değer varsa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>V1</th>\n",
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       "      <th>V3</th>\n",
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       "      <td>3.0</td>\n",
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       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
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       "    V1   V2    V3\n",
       "0  1.0  7.0   NaN\n",
       "1  3.0  NaN  12.0\n",
       "3  NaN  8.0   6.0\n",
       "5  1.0  NaN   7.0\n",
       "6  NaN  NaN   NaN"
      ]
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   "source": [
    "df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Eksik veri olmayan satırlara bakalım.\n",
    "* **all:** Satırda hepsi dolu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>V1</th>\n",
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       "      <th>V3</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
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      "text/plain": [
       "     V1    V2    V3\n",
       "2   6.0   5.0   5.0\n",
       "4   7.0  12.0  14.0\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
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     "execution_count": 7,
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   "source": [
    "df[df.notnull().all(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1. Eksik Gözlemlerin Kaldırılması"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
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      "text/plain": [
       "     V1    V2    V3\n",
       "2   6.0   5.0   5.0\n",
       "4   7.0  12.0  14.0\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.dropna(inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. Basit Değer Atama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>3.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
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       "     V1    V2    V3\n",
       "0   1.0   7.0   NaN\n",
       "1   3.0   NaN  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   NaN   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   NaN   7.0\n",
       "6   NaN   NaN   NaN\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "V1 = np.array([1,3,6,np.NaN,7,1,np.NaN,9,15])\n",
    "V2 = np.array([7,np.NaN,5,8,12,np.NaN,np.NaN,2,3])\n",
    "V3 = np.array([np.NaN,12,5,6,14,7,np.NaN,2,31])\n",
    "\n",
    "df = pd.DataFrame({\"V1\" : V1,\n",
    "                   \"V2\" : V2,\n",
    "                   \"V3\" : V3})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>12.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8.000000</td>\n",
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       "      <td>14.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>31.0</td>\n",
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      ],
      "text/plain": [
       "     V1         V2    V3\n",
       "0   1.0   7.000000  11.0\n",
       "1   3.0   6.166667  12.0\n",
       "2   6.0   5.000000   5.0\n",
       "3   6.0   8.000000   6.0\n",
       "4   7.0  12.000000  14.0\n",
       "5   1.0   6.166667   7.0\n",
       "6   6.0   6.166667  11.0\n",
       "7   9.0   2.000000   2.0\n",
       "8  15.0   3.000000  31.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns\n",
    "\n",
    "for col in columns:\n",
    "    df[col] = df[col].fillna(df[col].mean())\n",
    "    \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3\n",
       "0   1.0   7.0   NaN\n",
       "1   3.0   NaN  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   NaN   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   NaN   7.0\n",
       "6   NaN   NaN   NaN\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V1 = np.array([1,3,6,np.NaN,7,1,np.NaN,9,15])\n",
    "V2 = np.array([7,np.NaN,5,8,12,np.NaN,np.NaN,2,3])\n",
    "V3 = np.array([np.NaN,12,5,6,14,7,np.NaN,2,31])\n",
    "\n",
    "df = pd.DataFrame({\"V1\" : V1,\n",
    "                   \"V2\" : V2,\n",
    "                   \"V3\" : V3})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3\n",
       "0   1.0   7.0   0.0\n",
       "1   3.0   0.0  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   0.0   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   0.0   7.0\n",
       "6   0.0   0.0   0.0\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in columns:\n",
    "    df[col] = df[col].fillna(0)\n",
    "    \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3\n",
       "0   1.0   7.0   NaN\n",
       "1   3.0   NaN  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   NaN   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   NaN   7.0\n",
       "6   NaN   NaN   NaN\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V1 = np.array([1,3,6,np.NaN,7,1,np.NaN,9,15])\n",
    "V2 = np.array([7,np.NaN,5,8,12,np.NaN,np.NaN,2,3])\n",
    "V3 = np.array([np.NaN,12,5,6,14,7,np.NaN,2,31])\n",
    "\n",
    "df = pd.DataFrame({\"V1\" : V1,\n",
    "                   \"V2\" : V2,\n",
    "                   \"V3\" : V3})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1         V2    V3\n",
       "0   1.0   7.000000  11.0\n",
       "1   3.0   6.166667  12.0\n",
       "2   6.0   5.000000   5.0\n",
       "3   6.0   8.000000   6.0\n",
       "4   7.0  12.000000  14.0\n",
       "5   1.0   6.166667   7.0\n",
       "6   6.0   6.166667  11.0\n",
       "7   9.0   2.000000   2.0\n",
       "8  15.0   3.000000  31.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.apply(lambda x : x.fillna(x.mean()), axis = 0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Eksik Veri Yapısının Görselleştirilmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3\n",
       "0   1.0   7.0   NaN\n",
       "1   3.0   NaN  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   NaN   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   NaN   7.0\n",
       "6   NaN   NaN   NaN\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V1 = np.array([1,3,6,np.NaN,7,1,np.NaN,9,15])\n",
    "V2 = np.array([7,np.NaN,5,8,12,np.NaN,np.NaN,2,3])\n",
    "V3 = np.array([np.NaN,12,5,6,14,7,np.NaN,2,31])\n",
    "\n",
    "df = pd.DataFrame({\"V1\" : V1,\n",
    "                   \"V2\" : V2,\n",
    "                   \"V3\" : V3})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"planets\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method              0\n",
       "number              0\n",
       "orbital_period     43\n",
       "mass              522\n",
       "distance          227\n",
       "year                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* orbital_period ne zaman eksikse mass değişkeni de eksik oluyor.\n",
    "* Aralarında bir ilişki olduğunu görüyoruz.\n",
    "* mass'deki eksiklerin bazıları orbital_period değişkenine bağlı olarak gerçekleşmiştir.\n",
    "* mass ve distance arasında da bağımlılık var fakat diğerleri kadar yoktur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Isı haritası ile bakalım.\n",
    "* Değişkenlerin birbirlerini nasıl etkilediğini görürüz.\n",
    "* 1'e ne kadar yakınsa birbirlerini o kadar çok etkilemektedirler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.heatmap(df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3. Silme Yöntemleri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3\n",
       "0   1.0   7.0   NaN\n",
       "1   3.0   NaN  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   NaN   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   NaN   7.0\n",
       "6   NaN   NaN   NaN\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V1 = np.array([1,3,6,np.NaN,7,1,np.NaN,9,15])\n",
    "V2 = np.array([7,np.NaN,5,8,12,np.NaN,np.NaN,2,3])\n",
    "V3 = np.array([np.NaN,12,5,6,14,7,np.NaN,2,31])\n",
    "\n",
    "df = pd.DataFrame({\"V1\" : V1,\n",
    "                   \"V2\" : V2,\n",
    "                   \"V3\" : V3})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* En az bir tane eksik veri olan gözlemlerin kaldırılması."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3\n",
       "2   6.0   5.0   5.0\n",
       "4   7.0  12.0  14.0\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Tüm gözlemlerin aynı anda NaN olması durumunda kaldırılması."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3\n",
       "0   1.0   7.0   NaN\n",
       "1   3.0   NaN  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   NaN   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   NaN   7.0\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(how = \"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* En az bir NaN değer olan gözlemlerin kaldırılması."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
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       "      <th>8</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [0, 1, 2, 3, 4, 5, 6, 7, 8]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Tüm değerleri NaN olan gözlemlerin kaldırılması."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "      <th>V1</th>\n",
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       "      <th>V3</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <td>12.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</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>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3\n",
       "0   1.0   7.0   NaN\n",
       "1   3.0   NaN  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   NaN   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   NaN   7.0\n",
       "6   NaN   NaN   NaN\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis = 1, how = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3  V4\n",
       "0   1.0   7.0   NaN NaN\n",
       "1   3.0   NaN  12.0 NaN\n",
       "2   6.0   5.0   5.0 NaN\n",
       "3   NaN   8.0   6.0 NaN\n",
       "4   7.0  12.0  14.0 NaN\n",
       "5   1.0   NaN   7.0 NaN\n",
       "6   NaN   NaN   NaN NaN\n",
       "7   9.0   2.0   2.0 NaN\n",
       "8  15.0   3.0  31.0 NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"V4\"] = np.NaN\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>V1</th>\n",
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       "      <th>V3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3\n",
       "0   1.0   7.0   NaN\n",
       "1   3.0   NaN  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   NaN   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   NaN   7.0\n",
       "6   NaN   NaN   NaN\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis = 1, how = \"all\", inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4. Basit Değer Atama Yöntemleri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1    V2    V3\n",
       "0   1.0   7.0   NaN\n",
       "1   3.0   NaN  12.0\n",
       "2   6.0   5.0   5.0\n",
       "3   NaN   8.0   6.0\n",
       "4   7.0  12.0  14.0\n",
       "5   1.0   NaN   7.0\n",
       "6   NaN   NaN   NaN\n",
       "7   9.0   2.0   2.0\n",
       "8  15.0   3.0  31.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V1 = np.array([1,3,6,np.NaN,7,1,np.NaN,9,15])\n",
    "V2 = np.array([7,np.NaN,5,8,12,np.NaN,np.NaN,2,3])\n",
    "V3 = np.array([np.NaN,12,5,6,14,7,np.NaN,2,31])\n",
    "\n",
    "df = pd.DataFrame({\"V1\" : V1,\n",
    "                   \"V2\" : V2,\n",
    "                   \"V3\" : V3})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.0\n",
       "1     3.0\n",
       "2     6.0\n",
       "3     0.0\n",
       "4     7.0\n",
       "5     1.0\n",
       "6     0.0\n",
       "7     9.0\n",
       "8    15.0\n",
       "Name: V1, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"V1\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.0\n",
       "1     3.0\n",
       "2     6.0\n",
       "3     6.0\n",
       "4     7.0\n",
       "5     1.0\n",
       "6     6.0\n",
       "7     9.0\n",
       "8    15.0\n",
       "Name: V1, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"V1\"].fillna(df[\"V1\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Tüm değişkenlere aynı işlem yapılsın."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     V1         V2    V3\n",
       "0   1.0   7.000000  11.0\n",
       "1   3.0   6.166667  12.0\n",
       "2   6.0   5.000000   5.0\n",
       "3   6.0   8.000000   6.0\n",
       "4   7.0  12.000000  14.0\n",
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       "6   6.0   6.166667  11.0\n",
       "7   9.0   2.000000   2.0\n",
       "8  15.0   3.000000  31.0"
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     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.apply(lambda x : x.fillna(x.mean()), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>31.0</td>\n",
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      "text/plain": [
       "     V1         V2    V3\n",
       "0   1.0   7.000000  11.0\n",
       "1   3.0   6.166667  12.0\n",
       "2   6.0   5.000000   5.0\n",
       "3   6.0   8.000000   6.0\n",
       "4   7.0  12.000000  14.0\n",
       "5   1.0   6.166667   7.0\n",
       "6   6.0   6.166667  11.0\n",
       "7   9.0   2.000000   2.0\n",
       "8  15.0   3.000000  31.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(df.mean()[:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Dağılımları **düzgün / simetrik / normal dağılımda** ise bunları ortalama ile doldurmak mantıklı olur.\n",
    "* Çarpıklık söz konusu ise ortalama ile doldurmak mantıklı olmaz.\n",
    "* Şimdi ilk iki değişkeni ortalama ile son değişkeni medyan ile dolduruyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <td>15.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>31.0</td>\n",
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      "text/plain": [
       "     V1         V2    V3\n",
       "0   1.0   7.000000   NaN\n",
       "1   3.0   6.166667  12.0\n",
       "2   6.0   5.000000   5.0\n",
       "3   6.0   8.000000   6.0\n",
       "4   7.0  12.000000  14.0\n",
       "5   1.0   6.166667   7.0\n",
       "6   6.0   6.166667   NaN\n",
       "7   9.0   2.000000   2.0\n",
       "8  15.0   3.000000  31.0"
      ]
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     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(df.mean()[\"V1\":\"V2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     7.0\n",
       "1    12.0\n",
       "2     5.0\n",
       "3     6.0\n",
       "4    14.0\n",
       "5     7.0\n",
       "6     7.0\n",
       "7     2.0\n",
       "8    31.0\n",
       "Name: V3, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"V3\"].fillna(df[\"V3\"].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
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      ],
      "text/plain": [
       "     V1         V2    V3\n",
       "0   1.0   7.000000  11.0\n",
       "1   3.0   6.166667  12.0\n",
       "2   6.0   5.000000   5.0\n",
       "3   6.0   8.000000   6.0\n",
       "4   7.0  12.000000  14.0\n",
       "5   1.0   6.166667   7.0\n",
       "6   6.0   6.166667  11.0\n",
       "7   9.0   2.000000   2.0\n",
       "8  15.0   3.000000  31.0"
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     "execution_count": 36,
     "metadata": {},
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   "source": [
    "df.where(pd.notna(df), df.mean(), axis = \"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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