pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manually fill DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column By Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Sale</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>Ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150</td>\n",
       "      <td>Hassan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200</td>\n",
       "      <td>Javad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>Maryam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sale    Name\n",
       "0   100     Ali\n",
       "1   150  Hassan\n",
       "2   200   Javad\n",
       "3    30  Maryam"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({'Sale': [100, 150, 200, 30],\n",
    "                     'Name': [\"Ali\", 'Hassan', 'Javad', 'Maryam']})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row by Row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>Ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150</td>\n",
       "      <td>Hassan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200</td>\n",
       "      <td>Maryam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>Javad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0       1\n",
       "0  100     Ali\n",
       "1  150  Hassan\n",
       "2  200  Maryam\n",
       "3   30   Javad"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame([[100, \"Ali\"],\n",
    "                     [150, \"Hassan\"],\n",
    "                     [200, 'Maryam'],\n",
    "                     [30,  'Javad']], header=[])\n",
    "data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3               4\n",
       "0    5.1  3.5  1.4  0.2     Iris-setosa\n",
       "1    4.9  3.0  1.4  0.2     Iris-setosa\n",
       "2    4.7  3.2  1.3  0.2     Iris-setosa\n",
       "3    4.6  3.1  1.5  0.2     Iris-setosa\n",
       "4    5.0  3.6  1.4  0.2     Iris-setosa\n",
       "..   ...  ...  ...  ...             ...\n",
       "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
       "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
       "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
       "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
       "149  5.9  3.0  5.1  1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = '../../../Datasets/iris.data'\n",
    "pd.read_csv(file, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w1</th>\n",
       "      <th>w2</th>\n",
       "      <th>h1</th>\n",
       "      <th>h2</th>\n",
       "      <th>s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      w1   w2   h1   h2               s\n",
       "0    5.1  3.5  1.4  0.2     Iris-setosa\n",
       "1    4.9  3.0  1.4  0.2     Iris-setosa\n",
       "2    4.7  3.2  1.3  0.2     Iris-setosa\n",
       "3    4.6  3.1  1.5  0.2     Iris-setosa\n",
       "4    5.0  3.6  1.4  0.2     Iris-setosa\n",
       "..   ...  ...  ...  ...             ...\n",
       "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
       "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
       "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
       "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
       "149  5.9  3.0  5.1  1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = '../../../Datasets/iris.data'\n",
    "pd.read_csv(file, names=['w1', 'w2', 'h1', 'h2', 's'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>HURLEY</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>na</td>\n",
       "      <td>2</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT\n",
       "0  100001000.0   104.0      PUTNAM            Y            3        1  1000\n",
       "1  100002000.0   197.0   LEXINGTON            N            3      1.5    --\n",
       "2  100003000.0     NaN   LEXINGTON            N          NaN        1   850\n",
       "3  100004000.0   201.0    BERKELEY           12            1      NaN   700\n",
       "4          NaN   203.0    BERKELEY            Y            3        2  1600\n",
       "5  100006000.0   207.0    BERKELEY            Y          NaN        1   800\n",
       "6  100007000.0     NaN  WASHINGTON          NaN            2   HURLEY   950\n",
       "7  100008000.0   213.0     TREMONT            Y            1        1   NaN\n",
       "8  100009000.0   215.0     TREMONT            Y           na        2  1800"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = '../../../Datasets/property data.csv'\n",
    "pd.read_csv(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>HURLEY</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0        1  1000.0\n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0      1.5     NaN\n",
       "2  100003000.0     NaN   LEXINGTON            N           NaN        1   850.0\n",
       "3  100004000.0   201.0    BERKELEY           12           1.0      NaN   700.0\n",
       "4          NaN   203.0    BERKELEY            Y           3.0        2  1600.0\n",
       "5  100006000.0   207.0    BERKELEY            Y           NaN        1   800.0\n",
       "6  100007000.0     NaN  WASHINGTON          NaN           2.0   HURLEY   950.0\n",
       "7  100008000.0   213.0     TREMONT            Y           1.0        1     NaN\n",
       "8  100009000.0   215.0     TREMONT            Y           NaN        2  1800.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = '../../../Datasets/property data.csv'\n",
    "data = pd.read_csv(file, na_values=['na', '--'])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subscript \n",
    "means column extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1000.0\n",
       "1       NaN\n",
       "2     850.0\n",
       "3     700.0\n",
       "4    1600.0\n",
       "5     800.0\n",
       "6     950.0\n",
       "7       NaN\n",
       "8    1800.0\n",
       "Name: SQ_FT, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SQ_FT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------\n",
    "Very bad type of reading:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "850.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SQ_FT'][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>SQ_FT</th>\n",
       "      <th>NUM_BATH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>850.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>700.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1600.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>800.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>950.0</td>\n",
       "      <td>HURLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1800.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SQ_FT NUM_BATH\n",
       "0  1000.0        1\n",
       "1     NaN      1.5\n",
       "2   850.0        1\n",
       "3   700.0      NaN\n",
       "4  1600.0        2\n",
       "5   800.0        1\n",
       "6   950.0   HURLEY\n",
       "7     NaN        1\n",
       "8  1800.0        2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get multiple columns\n",
    "data[['SQ_FT', 'NUM_BATH']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "850.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SQ_FT       850\n",
       "NUM_BATH      1\n",
       "Name: 2, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Best type of cell reading\n",
    "display(data.loc[2, 'SQ_FT'])\n",
    "print(\"------------------\")\n",
    "display(data.loc[2, ['SQ_FT', 'NUM_BATH']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "850.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Row 3rd, Column 7th\n",
    "# 7th column = 'SQ_FT'\n",
    "data.iloc[2, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>HURLEY</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0        1  1000.0\n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0      1.5     NaN\n",
       "2  100003000.0     NaN   LEXINGTON            N           NaN        1   900.0\n",
       "3  100004000.0   201.0    BERKELEY           12           1.0      NaN   700.0\n",
       "4          NaN   203.0    BERKELEY            Y           3.0        2  1600.0\n",
       "5  100006000.0   207.0    BERKELEY            Y           NaN        1   800.0\n",
       "6  100007000.0     NaN  WASHINGTON          NaN           2.0   HURLEY   950.0\n",
       "7  100008000.0   213.0     TREMONT            Y           1.0        1     NaN\n",
       "8  100009000.0   215.0     TREMONT            Y           NaN        2  1800.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[2, 'SQ_FT'] = 900\n",
    "data"
   ]
  },
  {
   "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>PID</th>\n",
       "      <th>ST_NUM</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>SQ_FT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.000050e+08</td>\n",
       "      <td>191.428571</td>\n",
       "      <td>2.166667</td>\n",
       "      <td>1107.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.927700e+03</td>\n",
       "      <td>39.080503</td>\n",
       "      <td>0.983192</td>\n",
       "      <td>420.741975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000010e+08</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000028e+08</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000050e+08</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000072e+08</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000090e+08</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PID      ST_NUM  NUM_BEDROOMS        SQ_FT\n",
       "count  8.000000e+00    7.000000      6.000000     7.000000\n",
       "mean   1.000050e+08  191.428571      2.166667  1107.142857\n",
       "std    2.927700e+03   39.080503      0.983192   420.741975\n",
       "min    1.000010e+08  104.000000      1.000000   700.000000\n",
       "25%    1.000028e+08  199.000000      1.250000   850.000000\n",
       "50%    1.000050e+08  203.000000      2.500000   950.000000\n",
       "75%    1.000072e+08  210.000000      3.000000  1300.000000\n",
       "max    1.000090e+08  215.000000      3.000000  1800.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   PID           8 non-null      float64\n",
      " 1   ST_NUM        7 non-null      float64\n",
      " 2   ST_NAME       9 non-null      object \n",
      " 3   OWN_OCCUPIED  8 non-null      object \n",
      " 4   NUM_BEDROOMS  6 non-null      float64\n",
      " 5   NUM_BATH      8 non-null      object \n",
      " 6   SQ_FT         7 non-null      float64\n",
      "dtypes: float64(4), object(3)\n",
      "memory usage: 632.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count            9\n",
       "unique           5\n",
       "top       BERKELEY\n",
       "freq             3\n",
       "Name: ST_NAME, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ST_NAME'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     8\n",
       "unique    3\n",
       "top       Y\n",
       "freq      5\n",
       "Name: OWN_OCCUPIED, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:, 3].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.000000</td>\n",
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       "                   PID    ST_NUM  NUM_BEDROOMS     SQ_FT\n",
       "PID           1.000000  0.727655     -0.737804  0.594505\n",
       "ST_NUM        0.727655  1.000000     -0.475893  0.271982\n",
       "NUM_BEDROOMS -0.737804 -0.475893      1.000000  0.764082\n",
       "SQ_FT         0.594505  0.271982      0.764082  1.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "data.corr()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>7</td>\n",
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       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
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       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
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       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1800.0</td>\n",
       "      <td>9</td>\n",
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      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH  \\\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0        1   \n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0      1.5   \n",
       "2  100003000.0     NaN   LEXINGTON            N           NaN        1   \n",
       "3  100004000.0   201.0    BERKELEY           12           1.0      NaN   \n",
       "4          NaN   203.0    BERKELEY            Y           3.0        2   \n",
       "5  100006000.0   207.0    BERKELEY            Y           NaN        1   \n",
       "6  100007000.0     NaN  WASHINGTON          NaN           2.0   HURLEY   \n",
       "7  100008000.0   213.0     TREMONT            Y           1.0        1   \n",
       "8  100009000.0   215.0     TREMONT            Y           NaN        2   \n",
       "\n",
       "    SQ_FT  my_column  \n",
       "0  1000.0          1  \n",
       "1     NaN          2  \n",
       "2   900.0          3  \n",
       "3   700.0          4  \n",
       "4  1600.0          5  \n",
       "5   800.0          6  \n",
       "6   950.0          7  \n",
       "7     NaN          8  \n",
       "8  1800.0          9  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['my_column'] = [1,2,3,4,5,6,7,8,9]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "           PID     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT  \\\n",
       "0  100001000.0      PUTNAM            Y           3.0        1  1000.0   \n",
       "1  100002000.0   LEXINGTON            N           3.0      1.5     NaN   \n",
       "2  100003000.0   LEXINGTON            N           NaN        1   900.0   \n",
       "3  100004000.0    BERKELEY           12           1.0      NaN   700.0   \n",
       "4          NaN    BERKELEY            Y           3.0        2  1600.0   \n",
       "5  100006000.0    BERKELEY            Y           NaN        1   800.0   \n",
       "6  100007000.0  WASHINGTON          NaN           2.0   HURLEY   950.0   \n",
       "7  100008000.0     TREMONT            Y           1.0        1     NaN   \n",
       "8  100009000.0     TREMONT            Y           NaN        2  1800.0   \n",
       "\n",
       "   my_column  \n",
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       "3          4  \n",
       "4          5  \n",
       "5          6  \n",
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       "7          8  \n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH  \\\n",
       "0  100001000.0   104.0      PUTNAM            Y           3.0        1   \n",
       "1  100002000.0   197.0   LEXINGTON            N           3.0      1.5   \n",
       "2  100003000.0     NaN   LEXINGTON            N           NaN        1   \n",
       "3  100004000.0   201.0    BERKELEY           12           1.0      NaN   \n",
       "4          NaN   203.0    BERKELEY            Y           3.0        2   \n",
       "5  100006000.0   207.0    BERKELEY            Y           NaN        1   \n",
       "6  100007000.0     NaN  WASHINGTON          NaN           2.0   HURLEY   \n",
       "7  100008000.0   213.0     TREMONT            Y           1.0        1   \n",
       "8  100009000.0   215.0     TREMONT            Y           NaN        2   \n",
       "\n",
       "    SQ_FT  my_column  \n",
       "0  1000.0          1  \n",
       "1     NaN          2  \n",
       "2   900.0          3  \n",
       "3   700.0          4  \n",
       "4  1600.0          5  \n",
       "5   800.0          6  \n",
       "6   950.0          7  \n",
       "7     NaN          8  \n",
       "8  1800.0          9  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data.drop('ST_NUM', axis=1, ))\n",
    "display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "      <th>my_column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>900.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>HURLEY</td>\n",
       "      <td>950.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT  \\\n",
       "0  100001000.0      PUTNAM            Y           3.0        1  1000.0   \n",
       "1  100002000.0   LEXINGTON            N           3.0      1.5     NaN   \n",
       "2  100003000.0   LEXINGTON            N           NaN        1   900.0   \n",
       "3  100004000.0    BERKELEY           12           1.0      NaN   700.0   \n",
       "4          NaN    BERKELEY            Y           3.0        2  1600.0   \n",
       "5  100006000.0    BERKELEY            Y           NaN        1   800.0   \n",
       "6  100007000.0  WASHINGTON          NaN           2.0   HURLEY   950.0   \n",
       "7  100008000.0     TREMONT            Y           1.0        1     NaN   \n",
       "8  100009000.0     TREMONT            Y           NaN        2  1800.0   \n",
       "\n",
       "   my_column  \n",
       "0          1  \n",
       "1          2  \n",
       "2          3  \n",
       "3          4  \n",
       "4          5  \n",
       "5          6  \n",
       "6          7  \n",
       "7          8  \n",
       "8          9  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.drop('ST_NUM', axis=1, inplace=True)\n",
    "display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
       "      <th>SQ_FT</th>\n",
       "      <th>my_column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PID  ST_NAME  OWN_OCCUPIED  NUM_BEDROOMS  NUM_BATH  SQ_FT  my_column\n",
       "0  False    False         False         False     False  False      False\n",
       "1  False    False         False         False     False   True      False\n",
       "2  False    False         False          True     False  False      False\n",
       "3  False    False         False         False      True  False      False\n",
       "4   True    False         False         False     False  False      False\n",
       "5  False    False         False          True     False  False      False\n",
       "6  False    False          True         False     False  False      False\n",
       "7  False    False         False         False     False   True      False\n",
       "8  False    False         False          True     False  False      False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID             1\n",
       "ST_NAME         0\n",
       "OWN_OCCUPIED    1\n",
       "NUM_BEDROOMS    3\n",
       "NUM_BATH        1\n",
       "SQ_FT           2\n",
       "my_column       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "5    1\n",
       "6    1\n",
       "7    1\n",
       "8    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100001000.0, 'PUTNAM', 'Y', 3.0, '1', 1000.0, 1],\n",
       "       [100002000.0, 'LEXINGTON', 'N', 3.0, '1.5', nan, 2],\n",
       "       [100003000.0, 'LEXINGTON', 'N', nan, '1', 900.0, 3],\n",
       "       [100004000.0, 'BERKELEY', '12', 1.0, nan, 700.0, 4],\n",
       "       [nan, 'BERKELEY', 'Y', 3.0, '2', 1600.0, 5],\n",
       "       [100006000.0, 'BERKELEY', 'Y', nan, '1', 800.0, 6],\n",
       "       [100007000.0, 'WASHINGTON', nan, 2.0, 'HURLEY', 950.0, 7],\n",
       "       [100008000.0, 'TREMONT', 'Y', 1.0, '1', nan, 8],\n",
       "       [100009000.0, 'TREMONT', 'Y', nan, '2', 1800.0, 9]], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PID', 'ST_NAME', 'OWN_OCCUPIED', 'NUM_BEDROOMS', 'NUM_BATH', 'SQ_FT',\n",
       "       'my_column'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>ST_NAME</th>\n",
       "      <th>OWN_OCCUPIED</th>\n",
       "      <th>NUM_BEDROOMS</th>\n",
       "      <th>NUM_BATH</th>\n",
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       "      <th>my_column</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001000.0</td>\n",
       "      <td>PUTNAM</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002000.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003000.0</td>\n",
       "      <td>LEXINGTON</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>900.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004000.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100006000.0</td>\n",
       "      <td>BERKELEY</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100007000.0</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>HURLEY</td>\n",
       "      <td>950.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100008000.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100009000.0</td>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PID     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT  \\\n",
       "0  100001000.0      PUTNAM            Y           3.0        1  1000.0   \n",
       "1  100002000.0   LEXINGTON            N           3.0      1.5     NaN   \n",
       "2  100003000.0   LEXINGTON            N           NaN        1   900.0   \n",
       "3  100004000.0    BERKELEY           12           1.0      NaN   700.0   \n",
       "4          NaN    BERKELEY            Y           3.0        2  1600.0   \n",
       "5  100006000.0    BERKELEY            Y           NaN        1   800.0   \n",
       "6  100007000.0  WASHINGTON          NaN           2.0   HURLEY   950.0   \n",
       "7  100008000.0     TREMONT            Y           1.0        1     NaN   \n",
       "8  100009000.0     TREMONT            Y           NaN        2  1800.0   \n",
       "\n",
       "   my_column  \n",
       "0          1  \n",
       "1          2  \n",
       "2          3  \n",
       "3          4  \n",
       "4          5  \n",
       "5          6  \n",
       "6          7  \n",
       "7          8  \n",
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     "execution_count": 37,
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  {
   "cell_type": "code",
   "execution_count": 39,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 7)"
      ]
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     "execution_count": 39,
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     "output_type": "execute_result"
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    "data.shape"
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  {
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   "execution_count": 40,
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   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "         PID     ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT  \\\n",
       "1  100001000      PUTNAM            Y           3.0        1  1000.0   \n",
       "2  100002000   LEXINGTON            N           3.0      1.5     NaN   \n",
       "3  100003000   LEXINGTON            N           NaN        1   900.0   \n",
       "4  100004000    BERKELEY           12           1.0      NaN   700.0   \n",
       "5  100005000    BERKELEY            Y           3.0        2  1600.0   \n",
       "6  100006000    BERKELEY            Y           NaN        1   800.0   \n",
       "7  100007000  WASHINGTON          NaN           2.0   HURLEY   950.0   \n",
       "8  100008000     TREMONT            Y           1.0        1     NaN   \n",
       "9  100009000     TREMONT            Y           NaN        2  1800.0   \n",
       "\n",
       "   my_column  \n",
       "1          1  \n",
       "2          2  \n",
       "3          3  \n",
       "4          4  \n",
       "5          5  \n",
       "6          6  \n",
       "7          7  \n",
       "8          8  \n",
       "9          9  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PID'] = data['PID'].interpolate().astype('int')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT  my_column\n",
       "1      PUTNAM            Y           3.0        1  1000.0          1\n",
       "2   LEXINGTON            N           3.0      1.5     NaN          2\n",
       "3   LEXINGTON            N           NaN        1   900.0          3\n",
       "4    BERKELEY           12           1.0      NaN   700.0          4\n",
       "5    BERKELEY            Y           3.0        2  1600.0          5\n",
       "6    BERKELEY            Y           NaN        1   800.0          6\n",
       "7  WASHINGTON          NaN           2.0   HURLEY   950.0          7\n",
       "8     TREMONT            Y           1.0        1     NaN          8\n",
       "9     TREMONT            Y           NaN        2  1800.0          9"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('PID', axis=1, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT  my_column\n",
       "1      PUTNAM            Y           3.0        1  1000.0          1\n",
       "2   LEXINGTON            N           3.0      1.5     NaN          2\n",
       "3   LEXINGTON            N           NaN        1   900.0          3\n",
       "4    BERKELEY          NaN           1.0      NaN   700.0          4\n",
       "5    BERKELEY            Y           3.0        2  1600.0          5\n",
       "6    BERKELEY            Y           NaN        1   800.0          6\n",
       "7  WASHINGTON          NaN           2.0   HURLEY   950.0          7\n",
       "8     TREMONT            Y           1.0        1     NaN          8\n",
       "9     TREMONT            Y           NaN        2  1800.0          9"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[~data['OWN_OCCUPIED'].isin(['Y', 'N']), 'OWN_OCCUPIED'] = np.nan\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "    ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT  my_column\n",
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       "5  BERKELEY            Y           3.0        2  1600.0          5"
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     "metadata": {},
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   "source": [
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>HURLEY</td>\n",
       "      <td>950.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TREMONT</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ST_NAME OWN_OCCUPIED  NUM_BEDROOMS NUM_BATH   SQ_FT  my_column\n",
       "1      PUTNAM            Y           3.0        1  1000.0          1\n",
       "2   LEXINGTON            N           3.0      1.5     NaN          2\n",
       "3   LEXINGTON            N           NaN        1   900.0          3\n",
       "4    BERKELEY          NaN           1.0      NaN   700.0          4\n",
       "5    BERKELEY            Y           3.0        2  1600.0          5\n",
       "6    BERKELEY            Y           NaN        1   800.0          6\n",
       "7  WASHINGTON          NaN           2.0   HURLEY   950.0          7\n",
       "8     TREMONT            Y           1.0        1     NaN          8\n",
       "9     TREMONT            Y           NaN        2  1800.0          9"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('cleaned_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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