chapter-02-housing-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doing Data Science: chapter 2 - Housing Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python code for the exercise on the RealDirect study about the housing dataset (\"*improve the way people sell and buy houses*\").\n",
    "\n",
    "**Author**: Damien Garaud\n",
    "\n",
    "**Project on Github**: https://github.com/garaud/doing_pydata_science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Getting Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clone the official Github project at https://github.com/oreillymedia/doing_data_science and unzip the `dds_datasets.zip` file. You'll find a new ZIP file named `dds_chapter2_rollingsales.zip`. Then, you'll get five XLS files:\n",
    "\n",
    "- `rollingsales_bronx.xls`\n",
    "- `rollingsales_brooklyn.xls`\n",
    "- `rollingsales_manhattan.xls`\n",
    "- `rollingsales_queens.xls`\n",
    "- `rollingsales_statenisland.xls`\n",
    "\n",
    "**Note**: for each carried out task, I'll try to write a **function**. Functions are good."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# useful to explore data with null values\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20.3\n"
     ]
    }
   ],
   "source": [
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_context('talk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: about the missingno package, visit https://github.com/ResidentMario/missingno You can install it with conda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You continue to use pandas for XLS files reading. Suppose you have the `rollingsales_AREA.xls` files in the `data` directory.\n",
    "\n",
    "The relevant row number to extract header is the row no.5 (note: row spreadsheet begins to 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_data(fname):\n",
    "    \"\"\"Read data from an Excel file\n",
    "    \"\"\"\n",
    "    return pd.read_excel(fname, header=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "brooklyn = read_data(\"./data/rollingsales_brooklyn.xls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23373 entries, 0 to 23372\n",
      "Data columns (total 21 columns):\n",
      "BOROUGH                           23373 non-null int64\n",
      "NEIGHBORHOOD                      23373 non-null object\n",
      "BUILDING CLASS CATEGORY           23373 non-null object\n",
      "TAX CLASS AT PRESENT              23373 non-null object\n",
      "BLOCK                             23373 non-null int64\n",
      "LOT                               23373 non-null int64\n",
      "EASE-MENT                         23373 non-null object\n",
      "BUILDING CLASS AT PRESENT         23373 non-null object\n",
      "ADDRESS                           23373 non-null object\n",
      "APART\n",
      "MENT\n",
      "NUMBER                 23373 non-null object\n",
      "ZIP CODE                          23373 non-null int64\n",
      "RESIDENTIAL UNITS                 23373 non-null int64\n",
      "COMMERCIAL UNITS                  23373 non-null int64\n",
      "TOTAL UNITS                       23373 non-null int64\n",
      "LAND SQUARE FEET                  23373 non-null int64\n",
      "GROSS SQUARE FEET                 23373 non-null int64\n",
      "YEAR BUILT                        23373 non-null int64\n",
      "TAX CLASS AT TIME OF SALE         23373 non-null int64\n",
      "BUILDING CLASS AT TIME OF SALE    23373 non-null object\n",
      "SALE\n",
      "PRICE                        23373 non-null int64\n",
      "SALE DATE                         23373 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(12), object(8)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "brooklyn.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "21 columns and several different data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Clean up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quoting: *First challenge: load in and clean up the data. Next, conduct\n",
    "exploratory data analysis in order to find out where there are\n",
    "outliers or missing values, decide how you will treat them, make\n",
    "sure the dates are formatted correctly, make sure values you\n",
    "think are numerical are being treated as such, etc.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename some column names (there are some `\\n`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_columns(df):\n",
    "    \"\"\"Clean some columns name\n",
    "    \"\"\"\n",
    "    df.columns = [x.replace('\\n', ' ').lower() for x in df.columns]\n",
    "    return df.rename_axis({'apart ment number': 'apartment number',\n",
    "                           'ease-ment': 'easement'}, axis=1)\n",
    "    \n",
    "def missing_string(df, colnames):\n",
    "    \"\"\"Strip content and fill empty string with NaN\n",
    "    \"\"\"\n",
    "    df = df.copy()\n",
    "    for col in colnames:\n",
    "        df[col] = df[col].str.strip().apply(lambda x: None if not x else x)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Column `apart ment number` sounds strange and this is an object (i.e. a string). Rename it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "brooklyn = clean_columns(brooklyn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23373 entries, 0 to 23372\n",
      "Data columns (total 21 columns):\n",
      "borough                           23373 non-null int64\n",
      "neighborhood                      23373 non-null object\n",
      "building class category           23373 non-null object\n",
      "tax class at present              23373 non-null object\n",
      "block                             23373 non-null int64\n",
      "lot                               23373 non-null int64\n",
      "easement                          23373 non-null object\n",
      "building class at present         23373 non-null object\n",
      "address                           23373 non-null object\n",
      "apartment number                  23373 non-null object\n",
      "zip code                          23373 non-null int64\n",
      "residential units                 23373 non-null int64\n",
      "commercial units                  23373 non-null int64\n",
      "total units                       23373 non-null int64\n",
      "land square feet                  23373 non-null int64\n",
      "gross square feet                 23373 non-null int64\n",
      "year built                        23373 non-null int64\n",
      "tax class at time of sale         23373 non-null int64\n",
      "building class at time of sale    23373 non-null object\n",
      "sale price                        23373 non-null int64\n",
      "sale date                         23373 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(12), object(8)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "brooklyn.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, sometimes, you can have two digits or one digits and letters. We can normalize that, i.e. discard some trailing whitespaces. And replace every empty string by None."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "brooklyn = missing_string(brooklyn, ['apartment number'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if you have other string field with the same issues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16902     None\n",
       "13101     None\n",
       "6930      None\n",
       "13242     None\n",
       "20436      NaN\n",
       "5554        3L\n",
       "11066     None\n",
       "11513     8 2F\n",
       "11780    13 2E\n",
       "14613     None\n",
       "Name: apartment number, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brooklyn['apartment number'].sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Type of Columns\n",
    "\n",
    "Are integer and string columns qualitative or sequential data?\n",
    "\n",
    "* Districts or zipcodes could be qualitative data\n",
    "* Years or prices could be sequential data (i.e. you can sort them)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def number_unique_values(data):\n",
    "    \"\"\"Count the number of unique values for each column\n",
    "    \n",
    "    Return a DataFrame with the name of the columns, the type and the number of unique values\n",
    "    \"\"\"\n",
    "    res = pd.DataFrame([(name, data[name].dtype.name, data[name].nunique()) for name in data],\n",
    "                      columns=['name', 'type', 'number'])\n",
    "    return res.sort_values(by=['type', 'number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "nuniq = number_unique_values(brooklyn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sale date</td>\n",
       "      <td>datetime64[ns]</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>borough</td>\n",
       "      <td>int64</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>tax class at time of sale</td>\n",
       "      <td>int64</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>commercial units</td>\n",
       "      <td>int64</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>zip code</td>\n",
       "      <td>int64</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>residential units</td>\n",
       "      <td>int64</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>total units</td>\n",
       "      <td>int64</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>year built</td>\n",
       "      <td>int64</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lot</td>\n",
       "      <td>int64</td>\n",
       "      <td>1134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>land square feet</td>\n",
       "      <td>int64</td>\n",
       "      <td>2552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gross square feet</td>\n",
       "      <td>int64</td>\n",
       "      <td>3311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sale price</td>\n",
       "      <td>int64</td>\n",
       "      <td>3318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>block</td>\n",
       "      <td>int64</td>\n",
       "      <td>5455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>easement</td>\n",
       "      <td>object</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tax class at present</td>\n",
       "      <td>object</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>building class category</td>\n",
       "      <td>object</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neighborhood</td>\n",
       "      <td>object</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>building class at time of sale</td>\n",
       "      <td>object</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>building class at present</td>\n",
       "      <td>object</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>apartment number</td>\n",
       "      <td>object</td>\n",
       "      <td>1286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>address</td>\n",
       "      <td>object</td>\n",
       "      <td>18465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name            type  number\n",
       "20                       sale date  datetime64[ns]     373\n",
       "0                          borough           int64       1\n",
       "17       tax class at time of sale           int64       4\n",
       "12                commercial units           int64      27\n",
       "10                        zip code           int64      41\n",
       "11               residential units           int64      91\n",
       "13                     total units           int64      96\n",
       "16                      year built           int64     145\n",
       "5                              lot           int64    1134\n",
       "14                land square feet           int64    2552\n",
       "15               gross square feet           int64    3311\n",
       "19                      sale price           int64    3318\n",
       "4                            block           int64    5455\n",
       "6                         easement          object       1\n",
       "3             tax class at present          object      11\n",
       "2          building class category          object      38\n",
       "1                     neighborhood          object      63\n",
       "18  building class at time of sale          object     131\n",
       "7        building class at present          object     132\n",
       "9                 apartment number          object    1286\n",
       "8                          address          object   18465"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nuniq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note** that *most of the time*, `object` type means `string`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you have a small number of unique values in comparison with the number of rows, it could be qualitative data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a predictive model, the columns with a unique value are irrelevant.\n",
    "I'm specifying two list of columns:\n",
    "- columns with string data\n",
    "- columns with integer data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "intcols = nuniq.query(\"type == 'int64'\")['name'].values.tolist()\n",
    "strcols = nuniq.query(\"type == 'object'\")['name'].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crazy Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data are always messed up. Check if some integer/float columns contain some crazy values, e.g. -999 for a positive float or 0 for years..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def range_columns(data):\n",
    "    \"\"\"Min/max values for each integer columns\n",
    "    \"\"\"\n",
    "    intcols = data.dtypes[data.dtypes == 'int64'].index.tolist()\n",
    "    res = data[intcols].describe().T\n",
    "    return res[[\"min\", \"50%\", \"max\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>min</th>\n",
       "      <th>50%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>borough</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>block</th>\n",
       "      <td>20.0</td>\n",
       "      <td>3839.0</td>\n",
       "      <td>8955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lot</th>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>9039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip code</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11218.0</td>\n",
       "      <td>11416.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residential units</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commercial units</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total units</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land square feet</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>7446955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gross square feet</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>2548000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year built</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax class at time of sale</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sale price</th>\n",
       "      <td>0.0</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>276947000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            min       50%          max\n",
       "borough                     3.0       3.0          3.0\n",
       "block                      20.0    3839.0       8955.0\n",
       "lot                         1.0      48.0       9039.0\n",
       "zip code                    0.0   11218.0      11416.0\n",
       "residential units           0.0       1.0        509.0\n",
       "commercial units            0.0       0.0        222.0\n",
       "total units                 0.0       1.0        509.0\n",
       "land square feet            0.0    1850.0    7446955.0\n",
       "gross square feet           0.0    1760.0    2548000.0\n",
       "year built                  0.0    1925.0       2013.0\n",
       "tax class at time of sale   1.0       1.0          4.0\n",
       "sale price                  0.0  250000.0  276947000.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range_columns(brooklyn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "zip code, sale price, square feet or year equal to `0` is quite strange! Next step: **identify** null values. For some integer values, you can suppose that 0 is a null value. For string values, you can suppose that the empty string is null. You have a dedicated function for the second issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "brooklyn = missing_string(brooklyn, strcols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select the integer columns where you can say: \"zero value is a null value\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['borough',\n",
       " 'tax class at time of sale',\n",
       " 'commercial units',\n",
       " 'zip code',\n",
       " 'residential units',\n",
       " 'total units',\n",
       " 'year built',\n",
       " 'lot',\n",
       " 'land square feet',\n",
       " 'gross square feet',\n",
       " 'sale price',\n",
       " 'block']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intcols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "intcols_to_nullify = ['zip code', 'year built', 'land square feet',\n",
    "                      'gross square feet', 'sale price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip code</th>\n",
       "      <th>year built</th>\n",
       "      <th>land square feet</th>\n",
       "      <th>gross square feet</th>\n",
       "      <th>sale price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>403572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>218010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11238</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>952311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11238</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>842692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11238</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>815288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip code  year built  land square feet  gross square feet  sale price\n",
       "0     11220           0                 0                  0      403572\n",
       "1     11220           0                 0                  0      218010\n",
       "2     11238           0                 0                  0      952311\n",
       "3     11238           0                 0                  0      842692\n",
       "4     11238           0                 0                  0      815288"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brooklyn[intcols_to_nullify].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "brooklyn[intcols_to_nullify] = brooklyn[intcols_to_nullify].replace(0, np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you have a number of non-null object values which is quite different of the total number of rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you have a better representation of your data. As you can see, you have multiple rows where there are some null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23373 entries, 0 to 23372\n",
      "Data columns (total 21 columns):\n",
      "borough                           23373 non-null int64\n",
      "neighborhood                      23365 non-null object\n",
      "building class category           23043 non-null object\n",
      "tax class at present              3779 non-null object\n",
      "block                             23373 non-null int64\n",
      "lot                               23373 non-null int64\n",
      "easement                          0 non-null object\n",
      "building class at present         23272 non-null object\n",
      "address                           23373 non-null object\n",
      "apartment number                  4112 non-null object\n",
      "zip code                          23356 non-null float64\n",
      "residential units                 23373 non-null int64\n",
      "commercial units                  23373 non-null int64\n",
      "total units                       23373 non-null int64\n",
      "land square feet                  15346 non-null float64\n",
      "gross square feet                 14439 non-null float64\n",
      "year built                        20274 non-null float64\n",
      "tax class at time of sale         23373 non-null int64\n",
      "building class at time of sale    23373 non-null object\n",
      "sale price                        14582 non-null float64\n",
      "sale date                         23373 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(5), int64(7), object(8)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "brooklyn.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the `missingno` package which works well with pandas and seaborn to visualize features (i.e. columns) with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z9CEDOVOTteo/lHR3ROwKHEZmNyDptCoAXzLg96u/TtPeiFk3UzLtRpTF00zA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\nYfvqsk6mxxSW4KnnV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KCWhNLQvEcffTT77bff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\nAAAAAABtJnwHAAAAAIA2+/8Bn1JE0Ow9KNoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd0dece748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(brooklyn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also plot the correlation of \"number of missing values\" between each feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k8DdgZEQc5GFMZtZVVfplywuyZh6+ZGZmVquIaF6knIGcbv1yeX8t4AJgD0lDI2IyYC/G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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd07f3d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.heatmap(brooklyn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there is a huge correlation between the 'land square feet' and 'gross square feet' feature. It's not very suprising since when you don't have the value of the first feature, you don't have the second one.\n",
    "\n",
    "For a predictive model which tries to estimate the sale price for instance, you need to remove some lines (where you have these null values) and some features (unique values, etc.). In this study, you can drop `easement` and `borough` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "intcols.remove('borough')\n",
    "brooklyn = brooklyn.drop(['easement', 'borough'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd0e27c550>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2zZmkIYg9cpd9SyBRdhBsBfDSLiuShgCblby2mnXaaHut7bNs707q7noe+EnZ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\n+opgdTtJP7e9cw/Hn0ha3t+LdFz4ZeBc4MCoXNi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yA8YiST8nJW0eSCovPYVUTOv1tnfq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd07f50438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(brooklyn[intcols].corr())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}