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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Cleaning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## \"Housekeeping\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List all the Python packages used with their respective version."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-09-03 16:10:26 CEST\n",
"\n",
"CPython 3.6.5\n",
"IPython 6.5.0\n",
"\n",
"numpy 1.15.1\n",
"pandas 0.23.4\n"
]
}
],
"source": [
"% load_ext watermark\n",
"% watermark -d -t -v -z -p numpy,pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import all the third-party (scientific) libraries needed."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import missingno as msno\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The *utils.py* module defines helper dictionaries, lists, and functions that help with parsing the data types correctly, look up column descriptions, and refer to groups of data columns.\n",
"\n",
"**Note:** the suffix \\_*COLUMNS* indicates a dictionary with all meta information on the provided data file and \\_*VARIABLES* a list with only the column names (i.e., the keys of the respective \\_*COLUMNS* dictionary)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from utils import (\n",
" ALL_COLUMNS,\n",
" ALL_VARIABLES,\n",
" CONTINUOUS_COLUMNS,\n",
" CONTINUOUS_VARIABLES,\n",
" DISCRETE_COLUMNS,\n",
" DISCRETE_VARIABLES,\n",
" INDEX_COLUMNS,\n",
" LABEL_COLUMNS, # groups nominal and ordinal\n",
" LABEL_TYPES,\n",
" NOMINAL_COLUMNS,\n",
" NOMINAL_VARIABLES,\n",
" NUMERIC_VARIABLES, # groups continuous and discrete\n",
" ORDINAL_COLUMNS,\n",
" ORDINAL_VARIABLES,\n",
" TARGET_VARIABLES, # = Sale Price\n",
" correct_column_names,\n",
" print_column_list,\n",
" update_column_descriptions,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Allow cells to be run with a \"%%black\"\n",
"# prefix to make the code look beautiful.\n",
"% load_ext blackcellmagic"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"% matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Show all data columns.\n",
"pd.set_option(\"display.max_columns\", 100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The original data are available for [download](https://www.amstat.org/publications/jse/v19n3/decock/AmesHousing.xls) and a detailed description of the data types for each column can be found [here](https://www.amstat.org/publications/jse/v19n3/decock/DataDocumentation.txt). These meta data go into the `dtype` argument of the `read_excel` function below to parse the data correctly. There are four different generic data types defined that are casted as follows:\n",
"\n",
"- continuous -> np.float64\n",
"- discrete -> actually np.int64 but np.float64 because of missing values\n",
"- nominal -> object (str)\n",
"- ordinal -> object (str), the order can be looked up in the above mentioned *ALL_COLUMNS* dictionary\n",
"\n",
"**Note 1:** the data come with a lot of \"NA\" text strings that do **not** indicate missing data but, for example, the absence of a basement or a parking lot (see the linked data description).\n",
"\n",
"**Note 2:** the mappings from column names to data types are encoded in the \"utils.py\" module that defines the aforementioned helper dictionaries / lists.\n",
"\n",
"**Note 3:** the Excel file with all the data is either loaded from the local dictionary (= \"cache\") or obtained fresh from the source."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# To avoid redundancy.\n",
"kwargs = {\n",
" \"dtype\": { # Ensure each column is parsed as the correct data type.\n",
" column: ( # This creates a mapping from column name to data type.\n",
" object if mapping_info[\"type\"] in LABEL_TYPES else np.float64\n",
" )\n",
" for (column, mapping_info) in ALL_COLUMNS.items()\n",
" },\n",
" \"na_values\": \"\", # By default, pandas treats NA strings as missing,\n",
" \"keep_default_na\": False, # which is not the correct meaning here.\n",
"}\n",
"\n",
"try:\n",
" df = pd.read_excel(\"data/data_raw.xls\", **kwargs)\n",
"except FileNotFoundError:\n",
" df = pd.read_excel(\n",
" \"https://www.amstat.org/publications/jse/v19n3/decock/AmesHousing.xls\", **kwargs\n",
" )\n",
" # Cache the obtained file.\n",
" df.to_excel(\"data/data_raw.xls\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Some columns names differ between the Excel file and\n",
"# the data description file. Correct that with the values\n",
"# in the Excel file.\n",
"correct_column_names(df.columns)\n",
"# Use a compound index and keep both\n",
"# identifying columns in the DataFrame.\n",
"df = df.set_index(INDEX_COLUMNS)\n",
"# Put the provided columns into the same\n",
"# order as in the encoded description file.\n",
"# Note that the target variable \"SalePrice\"\n",
"# is not in the description file.\n",
"df = df[ALL_VARIABLES + TARGET_VARIABLES]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Spelling Mistakes & Data Types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some textual values appear differently in the provided data file as compared to the specification. These inconsistencies are manually repaired."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Repair spelling and whitespace mistakes.\n",
"df[\"Bldg Type\"] = df[\"Bldg Type\"].replace(to_replace=\"2fmCon\", value=\"2FmCon\")\n",
"df[\"Bldg Type\"] = df[\"Bldg Type\"].replace(to_replace=\"Duplex\", value=\"Duplx\")\n",
"df[\"Bldg Type\"] = df[\"Bldg Type\"].replace(to_replace=\"Twnhs\", value=\"TwnhsI\")\n",
"df[\"Exterior 2nd\"] = df[\"Exterior 2nd\"].replace(to_replace=\"Brk Cmn\", value=\"BrkComm\")\n",
"df[\"Exterior 2nd\"] = df[\"Exterior 2nd\"].replace(to_replace=\"CmentBd\", value=\"CemntBd\")\n",
"df[\"Exterior 2nd\"] = df[\"Exterior 2nd\"].replace(to_replace=\"Wd Shng\", value=\"WdShing\")\n",
"df[\"MS Zoning\"] = df[\"MS Zoning\"].replace(to_replace=\"A (agr)\", value=\"A\")\n",
"df[\"MS Zoning\"] = df[\"MS Zoning\"].replace(to_replace=\"C (all)\", value=\"C\")\n",
"df[\"MS Zoning\"] = df[\"MS Zoning\"].replace(to_replace=\"I (all)\", value=\"I\")\n",
"df[\"Neighborhood\"] = df[\"Neighborhood\"].replace(to_replace=\"NAmes\", value=\"Names\")\n",
"df[\"Sale Type\"] = df[\"Sale Type\"].replace(to_replace=\"WD \", value=\"WD\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Ensure that the remaining textual values in the data file are a subset\n",
"# of the values allowed in the specification.\n",
"for column, mapping_info in LABEL_COLUMNS.items():\n",
" # Note that .unique() returns a numpy array with integer dtype in cases\n",
" # where the provided data can be casted as such (e.g., \"Overall Qual\" column).\n",
" values_in_data = set(str(x) for x in df[column].unique() if x is not np.NaN)\n",
" values_in_description = set(mapping_info[\"lookups\"].keys())\n",
" assert values_in_data <= values_in_description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interestingly, all numeric columns (i.e. also \"continuous\" variables) come with only integer values."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Show that all \"continuous\" variables come as integers.\n",
"for column in NUMERIC_VARIABLES + TARGET_VARIABLES:\n",
" not_null = df[column].notnull()\n",
" mask = (\n",
" df.loc[not_null, column].astype(np.int64)\n",
" != df.loc[not_null, column].astype(np.float64)\n",
" )\n",
" assert not mask.any()\n",
"# Cast discrete fields as integers where possible,\n",
"# i.e., all columns without missing values.\n",
"for column in DISCRETE_VARIABLES:\n",
" try:\n",
" df[column] = df[column].astype(np.int64)\n",
" except ValueError:\n",
" mask = df[column].notnull()\n",
" df.loc[mask, column].astype(np.int64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Raw Data Overview"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The overall shape of the data is a 2930 rows x 80 columns matrix."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2930, 80)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Continuous Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The continuous columns are truly continuous in the sense that each column has at least 14 unique value realizations."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"for column in CONTINUOUS_VARIABLES:\n",
" mask = df[column].notnull()\n",
" num_realizations = len(list(x for x in df.loc[mask, column].unique()))\n",
" assert num_realizations > 13"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A brief description of the variables:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1st Flr SF First Floor square feet\n",
"2nd Flr SF Second floor square feet\n",
"3Ssn Porch Three season porch area in square feet\n",
"Bsmt Unf SF Unfinished square feet of basement area\n",
"BsmtFin SF 1 Type 1 finished square feet\n",
"BsmtFin SF 2 Type 2 finished square feet\n",
"Enclosed Porch Enclosed porch area in square feet\n",
"Garage Area Size of garage in square feet\n",
"Gr Liv Area Above grade (ground) living area square feet\n",
"Lot Area Lot size in square feet\n",
"Lot Frontage Linear feet of street connected to property\n",
"Low Qual Fin SF Low quality finished square feet (all floors)\n",
"Mas Vnr Area Masonry veneer area in square feet\n",
"Misc Val $Value of miscellaneous feature\n",
"Open Porch SF Open porch area in square feet\n",
"Pool Area Pool area in square feet\n",
"Screen Porch Screen porch area in square feet\n",
"Total Bsmt SF Total square feet of basement area\n",
"Wood Deck SF Wood deck area in square feet\n"
]
}
],
"source": [
"print_column_list(CONTINUOUS_COLUMNS)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>1st Flr SF</th>\n",
" <th>2nd Flr SF</th>\n",
" <th>3Ssn Porch</th>\n",
" <th>Bsmt Unf SF</th>\n",
" <th>BsmtFin SF 1</th>\n",
" <th>BsmtFin SF 2</th>\n",
" <th>Enclosed Porch</th>\n",
" <th>Garage Area</th>\n",
" <th>Gr Liv Area</th>\n",
" <th>Lot Area</th>\n",
" <th>Lot Frontage</th>\n",
" <th>Low Qual Fin SF</th>\n",
" <th>Mas Vnr Area</th>\n",
" <th>Misc Val</th>\n",
" <th>Open Porch SF</th>\n",
" <th>Pool Area</th>\n",
" <th>Screen Porch</th>\n",
" <th>Total Bsmt SF</th>\n",
" <th>Wood Deck SF</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Order</th>\n",
" <th>PID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <th>526301100</th>\n",
" <td>1656.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>441.0</td>\n",
" <td>639.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>528.0</td>\n",
" <td>1656.0</td>\n",
" <td>31770.0</td>\n",
" <td>141.0</td>\n",
" <td>0.0</td>\n",
" <td>112.0</td>\n",
" <td>0.0</td>\n",
" <td>62.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1080.0</td>\n",
" <td>210.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <th>526350040</th>\n",
" <td>896.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>270.0</td>\n",
" <td>468.0</td>\n",
" <td>144.0</td>\n",
" <td>0.0</td>\n",
" <td>730.0</td>\n",
" <td>896.0</td>\n",
" <td>11622.0</td>\n",
" <td>80.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>120.0</td>\n",
" <td>882.0</td>\n",
" <td>140.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>526351010</th>\n",
" <td>1329.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>406.0</td>\n",
" <td>923.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>312.0</td>\n",
" <td>1329.0</td>\n",
" <td>14267.0</td>\n",
" <td>81.0</td>\n",
" <td>0.0</td>\n",
" <td>108.0</td>\n",
" <td>12500.0</td>\n",
" <td>36.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1329.0</td>\n",
" <td>393.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <th>526353030</th>\n",
" <td>2110.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1045.0</td>\n",
" <td>1065.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>522.0</td>\n",
" <td>2110.0</td>\n",
" <td>11160.0</td>\n",
" <td>93.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2110.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <th>527105010</th>\n",
" <td>928.0</td>\n",
" <td>701.0</td>\n",
" <td>0</td>\n",
" <td>137.0</td>\n",
" <td>791.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>482.0</td>\n",
" <td>1629.0</td>\n",
" <td>13830.0</td>\n",
" <td>74.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>34.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>928.0</td>\n",
" <td>212.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1st Flr SF 2nd Flr SF 3Ssn Porch Bsmt Unf SF \\\n",
"Order PID \n",
"1 526301100 1656.0 0.0 0 441.0 \n",
"2 526350040 896.0 0.0 0 270.0 \n",
"3 526351010 1329.0 0.0 0 406.0 \n",
"4 526353030 2110.0 0.0 0 1045.0 \n",
"5 527105010 928.0 701.0 0 137.0 \n",
"\n",
" BsmtFin SF 1 BsmtFin SF 2 Enclosed Porch Garage Area \\\n",
"Order PID \n",
"1 526301100 639.0 0.0 0.0 528.0 \n",
"2 526350040 468.0 144.0 0.0 730.0 \n",
"3 526351010 923.0 0.0 0.0 312.0 \n",
"4 526353030 1065.0 0.0 0.0 522.0 \n",
"5 527105010 791.0 0.0 0.0 482.0 \n",
"\n",
" Gr Liv Area Lot Area Lot Frontage Low Qual Fin SF \\\n",
"Order PID \n",
"1 526301100 1656.0 31770.0 141.0 0.0 \n",
"2 526350040 896.0 11622.0 80.0 0.0 \n",
"3 526351010 1329.0 14267.0 81.0 0.0 \n",
"4 526353030 2110.0 11160.0 93.0 0.0 \n",
"5 527105010 1629.0 13830.0 74.0 0.0 \n",
"\n",
" Mas Vnr Area Misc Val Open Porch SF Pool Area \\\n",
"Order PID \n",
"1 526301100 112.0 0.0 62.0 0.0 \n",
"2 526350040 0.0 0.0 0.0 0.0 \n",
"3 526351010 108.0 12500.0 36.0 0.0 \n",
"4 526353030 0.0 0.0 0.0 0.0 \n",
"5 527105010 0.0 0.0 34.0 0.0 \n",
"\n",
" Screen Porch Total Bsmt SF Wood Deck SF \n",
"Order PID \n",
"1 526301100 0.0 1080.0 210.0 \n",
"2 526350040 120.0 882.0 140.0 \n",
"3 526351010 0.0 1329.0 393.0 \n",
"4 526353030 0.0 2110.0 0.0 \n",
"5 527105010 0.0 928.0 212.0 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[CONTINUOUS_VARIABLES].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Except for the column *Lot Frontage* the columns with missing data only have a couple of missing values (i.e., < 1% of all the rows)."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"MultiIndex: 2930 entries, (1, 526301100) to (2930, 924151050)\n",
"Data columns (total 19 columns):\n",
"1st Flr SF 2930 non-null float64\n",
"2nd Flr SF 2930 non-null float64\n",
"3Ssn Porch 2930 non-null int64\n",
"Bsmt Unf SF 2929 non-null float64\n",
"BsmtFin SF 1 2929 non-null float64\n",
"BsmtFin SF 2 2929 non-null float64\n",
"Enclosed Porch 2930 non-null float64\n",
"Garage Area 2929 non-null float64\n",
"Gr Liv Area 2930 non-null float64\n",
"Lot Area 2930 non-null float64\n",
"Lot Frontage 2440 non-null float64\n",
"Low Qual Fin SF 2930 non-null float64\n",
"Mas Vnr Area 2907 non-null float64\n",
"Misc Val 2930 non-null float64\n",
"Open Porch SF 2930 non-null float64\n",
"Pool Area 2930 non-null float64\n",
"Screen Porch 2930 non-null float64\n",
"Total Bsmt SF 2929 non-null float64\n",
"Wood Deck SF 2930 non-null float64\n",
"dtypes: float64(18), int64(1)\n",
"memory usage: 572.3 KB\n"
]
}
],
"source": [
"df[CONTINUOUS_VARIABLES].info()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# The columns with a lot of missing\n",
"# values will be treated seperately below.\n",
"missing_a_lot = [\"Lot Frontage\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Discrete Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The discrete columns have between 2 and 15 unique realizations each if year numbers are excluded from the analysis."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"for column in DISCRETE_VARIABLES:\n",
" mask = df[column].notnull()\n",
" num_realizations = len(list(x for x in df.loc[mask, column].unique()))\n",
" if column not in (\"Year Built\", \"Year Remod/Add\", \"Garage Yr Blt\"):\n",
" assert num_realizations < 15\n",
" assert num_realizations > 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A brief description of the variables:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bedroom AbvGr Bedrooms above grade (does NOT include basement bedrooms)\n",
"Bsmt Full Bath Basement full bathrooms\n",
"Bsmt Half Bath Basement half bathrooms\n",
"Fireplaces Number of fireplaces\n",
"Full Bath Full bathrooms above grade\n",
"Garage Cars Size of garage in car capacity\n",
"Garage Yr Blt Year garage was built\n",
"Half Bath Half baths above grade\n",
"Kitchen AbvGr Kitchens above grade\n",
"Mo Sold Month Sold (MM)\n",
"TotRms AbvGrd Total rooms above grade (does not include bathrooms)\n",
"Year Built Original construction date\n",
"Year Remod/Add Remodel date (same as construction date if no remodeling or additions)\n",
"Yr Sold Year Sold (YYYY)\n"
]
}
],
"source": [
"print_column_list(DISCRETE_COLUMNS)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:** columns with missing values are implicitly casted to a *float64* type an the *int64* type has no concept of a NaN (=\"Not a number\") value."
]
},
{
"cell_type": "code",
"execution_count": 20,
"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></th>\n",
" <th>Bedroom AbvGr</th>\n",
" <th>Bsmt Full Bath</th>\n",
" <th>Bsmt Half Bath</th>\n",
" <th>Fireplaces</th>\n",
" <th>Full Bath</th>\n",
" <th>Garage Cars</th>\n",
" <th>Garage Yr Blt</th>\n",
" <th>Half Bath</th>\n",
" <th>Kitchen AbvGr</th>\n",
" <th>Mo Sold</th>\n",
" <th>TotRms AbvGrd</th>\n",
" <th>Year Built</th>\n",
" <th>Year Remod/Add</th>\n",
" <th>Yr Sold</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Order</th>\n",
" <th>PID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <th>526301100</th>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>1960.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>1960</td>\n",
" <td>1960</td>\n",
" <td>2010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <th>526350040</th>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>1961.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1961</td>\n",
" <td>1961</td>\n",
" <td>2010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>526351010</th>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>1958.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>1958</td>\n",
" <td>1958</td>\n",
" <td>2010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <th>526353030</th>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" <td>1968.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>1968</td>\n",
" <td>1968</td>\n",
" <td>2010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <th>527105010</th>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" <td>1997.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>1997</td>\n",
" <td>1998</td>\n",
" <td>2010</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Bedroom AbvGr Bsmt Full Bath Bsmt Half Bath Fireplaces \\\n",
"Order PID \n",
"1 526301100 3 1.0 0.0 2 \n",
"2 526350040 2 0.0 0.0 0 \n",
"3 526351010 3 0.0 0.0 0 \n",
"4 526353030 3 1.0 0.0 2 \n",
"5 527105010 3 0.0 0.0 1 \n",
"\n",
" Full Bath Garage Cars Garage Yr Blt Half Bath \\\n",
"Order PID \n",
"1 526301100 1 2.0 1960.0 0 \n",
"2 526350040 1 1.0 1961.0 0 \n",
"3 526351010 1 1.0 1958.0 1 \n",
"4 526353030 2 2.0 1968.0 1 \n",
"5 527105010 2 2.0 1997.0 1 \n",
"\n",
" Kitchen AbvGr Mo Sold TotRms AbvGrd Year Built \\\n",
"Order PID \n",
"1 526301100 1 5 7 1960 \n",
"2 526350040 1 6 5 1961 \n",
"3 526351010 1 6 6 1958 \n",
"4 526353030 1 4 8 1968 \n",
"5 527105010 1 3 6 1997 \n",
"\n",
" Year Remod/Add Yr Sold \n",
"Order PID \n",
"1 526301100 1960 2010 \n",
"2 526350040 1961 2010 \n",
"3 526351010 1958 2010 \n",
"4 526353030 1968 2010 \n",
"5 527105010 1998 2010 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[DISCRETE_VARIABLES].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Except for the *Garage Yr Blt* column no variable has a significant number of missing values (i.e., > 1% of all rows)."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"MultiIndex: 2930 entries, (1, 526301100) to (2930, 924151050)\n",
"Data columns (total 14 columns):\n",
"Bedroom AbvGr 2930 non-null int64\n",
"Bsmt Full Bath 2928 non-null float64\n",
"Bsmt Half Bath 2928 non-null float64\n",
"Fireplaces 2930 non-null int64\n",
"Full Bath 2930 non-null int64\n",
"Garage Cars 2929 non-null float64\n",
"Garage Yr Blt 2771 non-null float64\n",
"Half Bath 2930 non-null int64\n",
"Kitchen AbvGr 2930 non-null int64\n",
"Mo Sold 2930 non-null int64\n",
"TotRms AbvGrd 2930 non-null int64\n",
"Year Built 2930 non-null int64\n",
"Year Remod/Add 2930 non-null int64\n",
"Yr Sold 2930 non-null int64\n",
"dtypes: float64(4), int64(10)\n",
"memory usage: 457.8 KB\n"
]
}
],
"source": [
"df[DISCRETE_VARIABLES].info()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"missing_a_lot.append(\"Garage Yr Blt\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nominal Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Except for the total of 28 neighborhoods, the nominal columns come with anywhere between 1 and 18 different labels each."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"for column in NOMINAL_VARIABLES:\n",
" mask = df[column].notnull()\n",
" num_realizations = len(list(x for x in df.loc[mask, column].unique()))\n",
" if column not in (\"Neighborhood\"):\n",
" assert num_realizations < 18\n",
" assert num_realizations > 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A brief description of the variables:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Alley Type of alley access to property\n",
"Bldg Type Type of dwelling\n",
"Central Air Central air conditioning\n",
"Condition 1 Proximity to various conditions\n",
"Condition 2 Proximity to various conditions (if more than one is present)\n",
"Exterior 1st Exterior covering on house\n",
"Exterior 2nd Exterior covering on house (if more than one material)\n",
"Foundation Type of foundation\n",
"Garage Type Garage location\n",
"Heating Type of heating\n",
"House Style Style of dwelling\n",
"Land Contour Flatness of the property\n",
"Lot Config Lot configuration\n",
"MS SubClass Identifies the type of dwelling involved in the sale.\n",
"MS Zoning Identifies the general zoning classification of the sale.\n",
"Mas Vnr Type Masonry veneer type\n",
"Misc Feature Miscellaneous feature not covered in other categories\n",
"Neighborhood Physical locations within Ames city limits (map available)\n",
"Roof Matl Roof material\n",
"Roof Style Type of roof\n",
"Sale Condition Condition of sale\n",
"Sale Type Type of sale\n",
"Street Type of road access to property\n"
]
}
],
"source": [
"print_column_list(NOMINAL_COLUMNS)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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></th>\n",
" <th>Alley</th>\n",
" <th>Bldg Type</th>\n",
" <th>Central Air</th>\n",
" <th>Condition 1</th>\n",
" <th>Condition 2</th>\n",
" <th>Exterior 1st</th>\n",
" <th>Exterior 2nd</th>\n",
" <th>Foundation</th>\n",
" <th>Garage Type</th>\n",
" <th>Heating</th>\n",
" <th>House Style</th>\n",
" <th>Land Contour</th>\n",
" <th>Lot Config</th>\n",
" <th>MS SubClass</th>\n",
" <th>MS Zoning</th>\n",
" <th>Mas Vnr Type</th>\n",
" <th>Misc Feature</th>\n",
" <th>Neighborhood</th>\n",
" <th>Roof Matl</th>\n",
" <th>Roof Style</th>\n",
" <th>Sale Condition</th>\n",
" <th>Sale Type</th>\n",
" <th>Street</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Order</th>\n",
" <th>PID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <th>526301100</th>\n",
" <td>NA</td>\n",
" <td>1Fam</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>BrkFace</td>\n",
" <td>Plywood</td>\n",
" <td>CBlock</td>\n",
" <td>Attchd</td>\n",
" <td>GasA</td>\n",
" <td>1Story</td>\n",
" <td>Lvl</td>\n",
" <td>Corner</td>\n",
" <td>020</td>\n",
" <td>RL</td>\n",
" <td>Stone</td>\n",
" <td>NA</td>\n",
" <td>Names</td>\n",
" <td>CompShg</td>\n",
" <td>Hip</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <th>526350040</th>\n",
" <td>NA</td>\n",
" <td>1Fam</td>\n",
" <td>Y</td>\n",
" <td>Feedr</td>\n",
" <td>Norm</td>\n",
" <td>VinylSd</td>\n",
" <td>VinylSd</td>\n",
" <td>CBlock</td>\n",
" <td>Attchd</td>\n",
" <td>GasA</td>\n",
" <td>1Story</td>\n",
" <td>Lvl</td>\n",
" <td>Inside</td>\n",
" <td>020</td>\n",
" <td>RH</td>\n",
" <td>None</td>\n",
" <td>NA</td>\n",
" <td>Names</td>\n",
" <td>CompShg</td>\n",
" <td>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>526351010</th>\n",
" <td>NA</td>\n",
" <td>1Fam</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>Wd Sdng</td>\n",
" <td>Wd Sdng</td>\n",
" <td>CBlock</td>\n",
" <td>Attchd</td>\n",
" <td>GasA</td>\n",
" <td>1Story</td>\n",
" <td>Lvl</td>\n",
" <td>Corner</td>\n",
" <td>020</td>\n",
" <td>RL</td>\n",
" <td>BrkFace</td>\n",
" <td>Gar2</td>\n",
" <td>Names</td>\n",
" <td>CompShg</td>\n",
" <td>Hip</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <th>526353030</th>\n",
" <td>NA</td>\n",
" <td>1Fam</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>BrkFace</td>\n",
" <td>BrkFace</td>\n",
" <td>CBlock</td>\n",
" <td>Attchd</td>\n",
" <td>GasA</td>\n",
" <td>1Story</td>\n",
" <td>Lvl</td>\n",
" <td>Corner</td>\n",
" <td>020</td>\n",
" <td>RL</td>\n",
" <td>None</td>\n",
" <td>NA</td>\n",
" <td>Names</td>\n",
" <td>CompShg</td>\n",
" <td>Hip</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <th>527105010</th>\n",
" <td>NA</td>\n",
" <td>1Fam</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>VinylSd</td>\n",
" <td>VinylSd</td>\n",
" <td>PConc</td>\n",
" <td>Attchd</td>\n",
" <td>GasA</td>\n",
" <td>2Story</td>\n",
" <td>Lvl</td>\n",
" <td>Inside</td>\n",
" <td>060</td>\n",
" <td>RL</td>\n",
" <td>None</td>\n",
" <td>NA</td>\n",
" <td>Gilbert</td>\n",
" <td>CompShg</td>\n",
" <td>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <th>527105030</th>\n",
" <td>NA</td>\n",
" <td>1Fam</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>VinylSd</td>\n",
" <td>VinylSd</td>\n",
" <td>PConc</td>\n",
" <td>Attchd</td>\n",
" <td>GasA</td>\n",
" <td>2Story</td>\n",
" <td>Lvl</td>\n",
" <td>Inside</td>\n",
" <td>060</td>\n",
" <td>RL</td>\n",
" <td>BrkFace</td>\n",
" <td>NA</td>\n",
" <td>Gilbert</td>\n",
" <td>CompShg</td>\n",
" <td>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <th>527127150</th>\n",
" <td>NA</td>\n",
" <td>TwnhsE</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>CemntBd</td>\n",
" <td>CemntBd</td>\n",
" <td>PConc</td>\n",
" <td>Attchd</td>\n",
" <td>GasA</td>\n",
" <td>1Story</td>\n",
" <td>Lvl</td>\n",
" <td>Inside</td>\n",
" <td>120</td>\n",
" <td>RL</td>\n",
" <td>None</td>\n",
" <td>NA</td>\n",
" <td>StoneBr</td>\n",
" <td>CompShg</td>\n",
" <td>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <th>527145080</th>\n",
" <td>NA</td>\n",
" <td>TwnhsE</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>HdBoard</td>\n",
" <td>HdBoard</td>\n",
" <td>PConc</td>\n",
" <td>Attchd</td>\n",
" <td>GasA</td>\n",
" <td>1Story</td>\n",
" <td>HLS</td>\n",
" <td>Inside</td>\n",
" <td>120</td>\n",
" <td>RL</td>\n",
" <td>None</td>\n",
" <td>NA</td>\n",
" <td>StoneBr</td>\n",
" <td>CompShg</td>\n",
" <td>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <th>527146030</th>\n",
" <td>NA</td>\n",
" <td>TwnhsE</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>CemntBd</td>\n",
" <td>CemntBd</td>\n",
" <td>PConc</td>\n",
" <td>Attchd</td>\n",
" <td>GasA</td>\n",
" <td>1Story</td>\n",
" <td>Lvl</td>\n",
" <td>Inside</td>\n",
" <td>120</td>\n",
" <td>RL</td>\n",
" <td>None</td>\n",
" <td>NA</td>\n",
" <td>StoneBr</td>\n",
" <td>CompShg</td>\n",
" <td>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <th>527162130</th>\n",
" <td>NA</td>\n",
" <td>1Fam</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>VinylSd</td>\n",
" <td>VinylSd</td>\n",
" <td>PConc</td>\n",
" <td>Attchd</td>\n",
" <td>GasA</td>\n",
" <td>2Story</td>\n",
" <td>Lvl</td>\n",
" <td>Inside</td>\n",
" <td>060</td>\n",
" <td>RL</td>\n",
" <td>None</td>\n",
" <td>NA</td>\n",
" <td>Gilbert</td>\n",
" <td>CompShg</td>\n",
" <td>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Alley Bldg Type Central Air Condition 1 Condition 2 \\\n",
"Order PID \n",
"1 526301100 NA 1Fam Y Norm Norm \n",
"2 526350040 NA 1Fam Y Feedr Norm \n",
"3 526351010 NA 1Fam Y Norm Norm \n",
"4 526353030 NA 1Fam Y Norm Norm \n",
"5 527105010 NA 1Fam Y Norm Norm \n",
"6 527105030 NA 1Fam Y Norm Norm \n",
"7 527127150 NA TwnhsE Y Norm Norm \n",
"8 527145080 NA TwnhsE Y Norm Norm \n",
"9 527146030 NA TwnhsE Y Norm Norm \n",
"10 527162130 NA 1Fam Y Norm Norm \n",
"\n",
" Exterior 1st Exterior 2nd Foundation Garage Type Heating \\\n",
"Order PID \n",
"1 526301100 BrkFace Plywood CBlock Attchd GasA \n",
"2 526350040 VinylSd VinylSd CBlock Attchd GasA \n",
"3 526351010 Wd Sdng Wd Sdng CBlock Attchd GasA \n",
"4 526353030 BrkFace BrkFace CBlock Attchd GasA \n",
"5 527105010 VinylSd VinylSd PConc Attchd GasA \n",
"6 527105030 VinylSd VinylSd PConc Attchd GasA \n",
"7 527127150 CemntBd CemntBd PConc Attchd GasA \n",
"8 527145080 HdBoard HdBoard PConc Attchd GasA \n",
"9 527146030 CemntBd CemntBd PConc Attchd GasA \n",
"10 527162130 VinylSd VinylSd PConc Attchd GasA \n",
"\n",
" House Style Land Contour Lot Config MS SubClass MS Zoning \\\n",
"Order PID \n",
"1 526301100 1Story Lvl Corner 020 RL \n",
"2 526350040 1Story Lvl Inside 020 RH \n",
"3 526351010 1Story Lvl Corner 020 RL \n",
"4 526353030 1Story Lvl Corner 020 RL \n",
"5 527105010 2Story Lvl Inside 060 RL \n",
"6 527105030 2Story Lvl Inside 060 RL \n",
"7 527127150 1Story Lvl Inside 120 RL \n",
"8 527145080 1Story HLS Inside 120 RL \n",
"9 527146030 1Story Lvl Inside 120 RL \n",
"10 527162130 2Story Lvl Inside 060 RL \n",
"\n",
" Mas Vnr Type Misc Feature Neighborhood Roof Matl Roof Style \\\n",
"Order PID \n",
"1 526301100 Stone NA Names CompShg Hip \n",
"2 526350040 None NA Names CompShg Gable \n",
"3 526351010 BrkFace Gar2 Names CompShg Hip \n",
"4 526353030 None NA Names CompShg Hip \n",
"5 527105010 None NA Gilbert CompShg Gable \n",
"6 527105030 BrkFace NA Gilbert CompShg Gable \n",
"7 527127150 None NA StoneBr CompShg Gable \n",
"8 527145080 None NA StoneBr CompShg Gable \n",
"9 527146030 None NA StoneBr CompShg Gable \n",
"10 527162130 None NA Gilbert CompShg Gable \n",
"\n",
" Sale Condition Sale Type Street \n",
"Order PID \n",
"1 526301100 Normal WD Pave \n",
"2 526350040 Normal WD Pave \n",
"3 526351010 Normal WD Pave \n",
"4 526353030 Normal WD Pave \n",
"5 527105010 Normal WD Pave \n",
"6 527105030 Normal WD Pave \n",
"7 527127150 Normal WD Pave \n",
"8 527145080 Normal WD Pave \n",
"9 527146030 Normal WD Pave \n",
"10 527162130 Normal WD Pave "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[NOMINAL_VARIABLES].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the nominal variables there is only a neglectable number of missing values."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"MultiIndex: 2930 entries, (1, 526301100) to (2930, 924151050)\n",
"Data columns (total 23 columns):\n",
"Alley 2930 non-null object\n",
"Bldg Type 2930 non-null object\n",
"Central Air 2930 non-null object\n",
"Condition 1 2930 non-null object\n",
"Condition 2 2930 non-null object\n",
"Exterior 1st 2930 non-null object\n",
"Exterior 2nd 2930 non-null object\n",
"Foundation 2930 non-null object\n",
"Garage Type 2930 non-null object\n",
"Heating 2930 non-null object\n",
"House Style 2930 non-null object\n",
"Land Contour 2930 non-null object\n",
"Lot Config 2930 non-null object\n",
"MS SubClass 2930 non-null object\n",
"MS Zoning 2930 non-null object\n",
"Mas Vnr Type 2907 non-null object\n",
"Misc Feature 2930 non-null object\n",
"Neighborhood 2930 non-null object\n",
"Roof Matl 2930 non-null object\n",
"Roof Style 2930 non-null object\n",
"Sale Condition 2930 non-null object\n",
"Sale Type 2930 non-null object\n",
"Street 2930 non-null object\n",
"dtypes: object(23)\n",
"memory usage: 663.8+ KB\n"
]
}
],
"source": [
"df[NOMINAL_VARIABLES].info(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ordinal Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ordinal columns come with anywhere between 2 and 11 distinct labels each."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"for column in ORDINAL_VARIABLES:\n",
" mask = df[column].notnull()\n",
" num_realizations = len(list(x for x in df.loc[mask, column].unique()))\n",
" assert 2 < num_realizations < 11"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A brief description of the variables:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bsmt Cond Evaluates the general condition of the basement\n",
"Bsmt Exposure Refers to walkout or garden level walls\n",
"Bsmt Qual Evaluates the height of the basement\n",
"BsmtFin Type 1 Rating of basement finished area\n",
"BsmtFin Type 2 Rating of basement finished area (if multiple types)\n",
"Electrical Electrical system\n",
"Exter Cond Evaluates the present condition of the material on the exterior\n",
"Exter Qual Evaluates the quality of the material on the exterior\n",
"Fence Fence quality\n",
"Fireplace Qu Fireplace quality\n",
"Functional Home functionality (Assume typical unless deductions are warranted)\n",
"Garage Cond Garage condition\n",
"Garage Finish Interior finish of the garage\n",
"Garage Qual Garage quality\n",
"Heating QC Heating quality and condition\n",
"Kitchen Qual Kitchen quality\n",
"Land Slope Slope of property\n",
"Lot Shape General shape of property\n",
"Overall Cond Rates the overall condition of the house\n",
"Overall Qual Rates the overall material and finish of the house\n",
"Paved Drive Paved driveway\n",
"Pool QC Pool quality\n",
"Utilities Type of utilities available\n"
]
}
],
"source": [
"print_column_list(ORDINAL_COLUMNS)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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></th>\n",
" <th>Bsmt Cond</th>\n",
" <th>Bsmt Exposure</th>\n",
" <th>Bsmt Qual</th>\n",
" <th>BsmtFin Type 1</th>\n",
" <th>BsmtFin Type 2</th>\n",
" <th>Electrical</th>\n",
" <th>Exter Cond</th>\n",
" <th>Exter Qual</th>\n",
" <th>Fence</th>\n",
" <th>Fireplace Qu</th>\n",
" <th>Functional</th>\n",
" <th>Garage Cond</th>\n",
" <th>Garage Finish</th>\n",
" <th>Garage Qual</th>\n",
" <th>Heating QC</th>\n",
" <th>Kitchen Qual</th>\n",
" <th>Land Slope</th>\n",
" <th>Lot Shape</th>\n",
" <th>Overall Cond</th>\n",
" <th>Overall Qual</th>\n",
" <th>Paved Drive</th>\n",
" <th>Pool QC</th>\n",
" <th>Utilities</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Order</th>\n",
" <th>PID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <th>526301100</th>\n",
" <td>Gd</td>\n",
" <td>Gd</td>\n",
" <td>TA</td>\n",
" <td>BLQ</td>\n",
" <td>Unf</td>\n",
" <td>SBrkr</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>NA</td>\n",
" <td>Gd</td>\n",
" <td>Typ</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Fa</td>\n",
" <td>TA</td>\n",
" <td>Gtl</td>\n",
" <td>IR1</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>P</td>\n",
" <td>NA</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <th>526350040</th>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>TA</td>\n",
" <td>Rec</td>\n",
" <td>LwQ</td>\n",
" <td>SBrkr</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>MnPrv</td>\n",
" <td>NA</td>\n",
" <td>Typ</td>\n",
" <td>TA</td>\n",
" <td>Unf</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>Gtl</td>\n",
" <td>Reg</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>Y</td>\n",
" <td>NA</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>526351010</th>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>TA</td>\n",
" <td>ALQ</td>\n",
" <td>Unf</td>\n",
" <td>SBrkr</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>NA</td>\n",
" <td>NA</td>\n",
" <td>Typ</td>\n",
" <td>TA</td>\n",
" <td>Unf</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>Gtl</td>\n",
" <td>IR1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>Y</td>\n",
" <td>NA</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <th>526353030</th>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>TA</td>\n",
" <td>ALQ</td>\n",
" <td>Unf</td>\n",
" <td>SBrkr</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>NA</td>\n",
" <td>TA</td>\n",
" <td>Typ</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Ex</td>\n",
" <td>Ex</td>\n",
" <td>Gtl</td>\n",
" <td>Reg</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>Y</td>\n",
" <td>NA</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <th>527105010</th>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>Gd</td>\n",
" <td>GLQ</td>\n",
" <td>Unf</td>\n",
" <td>SBrkr</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>MnPrv</td>\n",
" <td>TA</td>\n",
" <td>Typ</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>TA</td>\n",
" <td>Gtl</td>\n",
" <td>IR1</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>Y</td>\n",
" <td>NA</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <th>527105030</th>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>TA</td>\n",
" <td>GLQ</td>\n",
" <td>Unf</td>\n",
" <td>SBrkr</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>NA</td>\n",
" <td>Gd</td>\n",
" <td>Typ</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Ex</td>\n",
" <td>Gd</td>\n",
" <td>Gtl</td>\n",
" <td>IR1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>Y</td>\n",
" <td>NA</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <th>527127150</th>\n",
" <td>TA</td>\n",
" <td>Mn</td>\n",
" <td>Gd</td>\n",
" <td>GLQ</td>\n",
" <td>Unf</td>\n",
" <td>SBrkr</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>NA</td>\n",
" <td>NA</td>\n",
" <td>Typ</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Ex</td>\n",
" <td>Gd</td>\n",
" <td>Gtl</td>\n",
" <td>Reg</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>Y</td>\n",
" <td>NA</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <th>527145080</th>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>Gd</td>\n",
" <td>ALQ</td>\n",
" <td>Unf</td>\n",
" <td>SBrkr</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>NA</td>\n",
" <td>NA</td>\n",
" <td>Typ</td>\n",
" <td>TA</td>\n",
" <td>RFn</td>\n",
" <td>TA</td>\n",
" <td>Ex</td>\n",
" <td>Gd</td>\n",
" <td>Gtl</td>\n",
" <td>IR1</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>Y</td>\n",
" <td>NA</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <th>527146030</th>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>Gd</td>\n",
" <td>GLQ</td>\n",
" <td>Unf</td>\n",
" <td>SBrkr</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>NA</td>\n",
" <td>TA</td>\n",
" <td>Typ</td>\n",
" <td>TA</td>\n",
" <td>RFn</td>\n",
" <td>TA</td>\n",
" <td>Ex</td>\n",
" <td>Gd</td>\n",
" <td>Gtl</td>\n",
" <td>IR1</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>Y</td>\n",
" <td>NA</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <th>527162130</th>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>TA</td>\n",
" <td>Unf</td>\n",
" <td>Unf</td>\n",
" <td>SBrkr</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>NA</td>\n",
" <td>TA</td>\n",
" <td>Typ</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>Gd</td>\n",
" <td>Gtl</td>\n",
" <td>Reg</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>Y</td>\n",
" <td>NA</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Bsmt Cond Bsmt Exposure Bsmt Qual BsmtFin Type 1 \\\n",
"Order PID \n",
"1 526301100 Gd Gd TA BLQ \n",
"2 526350040 TA No TA Rec \n",
"3 526351010 TA No TA ALQ \n",
"4 526353030 TA No TA ALQ \n",
"5 527105010 TA No Gd GLQ \n",
"6 527105030 TA No TA GLQ \n",
"7 527127150 TA Mn Gd GLQ \n",
"8 527145080 TA No Gd ALQ \n",
"9 527146030 TA No Gd GLQ \n",
"10 527162130 TA No TA Unf \n",
"\n",
" BsmtFin Type 2 Electrical Exter Cond Exter Qual Fence \\\n",
"Order PID \n",
"1 526301100 Unf SBrkr TA TA NA \n",
"2 526350040 LwQ SBrkr TA TA MnPrv \n",
"3 526351010 Unf SBrkr TA TA NA \n",
"4 526353030 Unf SBrkr TA Gd NA \n",
"5 527105010 Unf SBrkr TA TA MnPrv \n",
"6 527105030 Unf SBrkr TA TA NA \n",
"7 527127150 Unf SBrkr TA Gd NA \n",
"8 527145080 Unf SBrkr TA Gd NA \n",
"9 527146030 Unf SBrkr TA Gd NA \n",
"10 527162130 Unf SBrkr TA TA NA \n",
"\n",
" Fireplace Qu Functional Garage Cond Garage Finish Garage Qual \\\n",
"Order PID \n",
"1 526301100 Gd Typ TA Fin TA \n",
"2 526350040 NA Typ TA Unf TA \n",
"3 526351010 NA Typ TA Unf TA \n",
"4 526353030 TA Typ TA Fin TA \n",
"5 527105010 TA Typ TA Fin TA \n",
"6 527105030 Gd Typ TA Fin TA \n",
"7 527127150 NA Typ TA Fin TA \n",
"8 527145080 NA Typ TA RFn TA \n",
"9 527146030 TA Typ TA RFn TA \n",
"10 527162130 TA Typ TA Fin TA \n",
"\n",
" Heating QC Kitchen Qual Land Slope Lot Shape Overall Cond \\\n",
"Order PID \n",
"1 526301100 Fa TA Gtl IR1 5 \n",
"2 526350040 TA TA Gtl Reg 6 \n",
"3 526351010 TA Gd Gtl IR1 6 \n",
"4 526353030 Ex Ex Gtl Reg 5 \n",
"5 527105010 Gd TA Gtl IR1 5 \n",
"6 527105030 Ex Gd Gtl IR1 6 \n",
"7 527127150 Ex Gd Gtl Reg 5 \n",
"8 527145080 Ex Gd Gtl IR1 5 \n",
"9 527146030 Ex Gd Gtl IR1 5 \n",
"10 527162130 Gd Gd Gtl Reg 5 \n",
"\n",
" Overall Qual Paved Drive Pool QC Utilities \n",
"Order PID \n",
"1 526301100 6 P NA AllPub \n",
"2 526350040 5 Y NA AllPub \n",
"3 526351010 6 Y NA AllPub \n",
"4 526353030 7 Y NA AllPub \n",
"5 527105010 5 Y NA AllPub \n",
"6 527105030 6 Y NA AllPub \n",
"7 527127150 8 Y NA AllPub \n",
"8 527145080 8 Y NA AllPub \n",
"9 527146030 8 Y NA AllPub \n",
"10 527162130 7 Y NA AllPub "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[ORDINAL_VARIABLES].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the ordinal variables there is only a neglectable number of missing values."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"MultiIndex: 2930 entries, (1, 526301100) to (2930, 924151050)\n",
"Data columns (total 23 columns):\n",
"Bsmt Cond 2929 non-null object\n",
"Bsmt Exposure 2926 non-null object\n",
"Bsmt Qual 2929 non-null object\n",
"BsmtFin Type 1 2929 non-null object\n",
"BsmtFin Type 2 2928 non-null object\n",
"Electrical 2929 non-null object\n",
"Exter Cond 2930 non-null object\n",
"Exter Qual 2930 non-null object\n",
"Fence 2930 non-null object\n",
"Fireplace Qu 2930 non-null object\n",
"Functional 2930 non-null object\n",
"Garage Cond 2929 non-null object\n",
"Garage Finish 2928 non-null object\n",
"Garage Qual 2929 non-null object\n",
"Heating QC 2930 non-null object\n",
"Kitchen Qual 2930 non-null object\n",
"Land Slope 2930 non-null object\n",
"Lot Shape 2930 non-null object\n",
"Overall Cond 2930 non-null object\n",
"Overall Qual 2930 non-null object\n",
"Paved Drive 2930 non-null object\n",
"Pool QC 2930 non-null object\n",
"Utilities 2930 non-null object\n",
"dtypes: object(23)\n",
"memory usage: 663.8+ KB\n"
]
}
],
"source": [
"df[ORDINAL_VARIABLES].info(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Missing Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizations"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1800x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"msno.matrix(df[CONTINUOUS_VARIABLES]);"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Y1aamprqOAAAAK9aOHTu6jjBUlOYAAAAAACPsta99bTZv3px77rnnkPfUWtM0Tfr9fpqmSdM0efjhh5cx5fJRmgMAAAAAjLD169fnAx/4wCGvHyjMDy7NL7vsslxxxRXLmHR5KM0BAAAAAEbY7bffnre97W1dxxgaY10HAAAAAACgO8985jO7jjBU7DQHAAAAABhhJ510UmZnZ5e8bmZmJtPT00c/UMeU5gAAAAAAI6zWms9+9rPZtWvXktZ9+ctfHlCibinNAQAAAABG2NzcXN75znd2HWNoKM0BAAAAAEbYGWeckde97nXZuXPnktbddtttuf/++weUqjtKcwAAAACAEXbMMcfkzW9+85LXrdaZ5mNdBwAAAAAAgGGhNAcAAAAAgJbSHAAAAAAAWkpzAAAAAABoKc0BAAAAAKC1pusAAACr3cTERObm5rqOser1er2uIwAAwIp077335vWvf33XMYaG0hwAYMB6vV4mJye7jrGqTU1NdR0BAABWrF27dnUdYagYzwIAAAAAMMIeeOCBriMMFaU5AAAAAMAIe+ihh7qOMFSMZwEAAAAAGGHnnHNOTj/99DzyyCPfdq3WmqZp0u/3F15N06Rpmlx33XW54YYbOkg8WEpzAAAAAIAR96xnPWvJa2699dYBJOme8SwAAAAAANBSmgMAAAAAQEtpDgAAAAAALTPNAQAAAABG2H333Zff+Z3fybZt2xbO1VqTJKWUhfcHHDj30EMPLWvO5aI0BwAAAAAYYb1eL7fcckvXMYaG0hwAAAAAYIS96lWvysTERB599NE0TZN+v79wXPz+4OONN96Ym266qev4R53SHAAAAABghJVSctJJJy153Ze//OUBpOmeB4ECAAAAAEBLaQ4AAAAAAC2lOQAAAAAAtJTmAAAAAADQ8iBQAAAAAIARtmfPnrz//e/Pjh07Fs7VWpPMPyT0wPsDDpzbunXrsuZcLkpzAAAAAIAR9vnPfz433nhj1zGGhtIcAAAAAGCEnXHGGfnwhz+cPXv2HPKeWmtqren3++n3+2maJn/5l3+5Kst2pTkAAAAAwIh73vOet+Q1W7ZsGUCS7nkQKAAAAAAAtJTmAAAAAADQUpoDAAAAAEBLaQ4AAAAAAC2lOQAAAAAAtNZ0HQAAYLWbmJjI3Nxc1zFWvV6v13UEAABYke6+++787M/+bNcxhobSHABgwHq9XiYnJ7uOsapNTU11HQEAAFas3bt3dx1hqCjNAQAGzE7z5WGnOQAAPDFPfepTu44wVJTmAAADZqf54NlpDgAAT9zll1/edYShojQHABgwO82Xh53mAADwxJx55pm5/vrru44xNJTmAAADZqf54NlpDgAAT1wppesIQ0VpDgAAAAAwwl7+8pdndnb2Ma/VWtM0Tfr9/rccm6bJ5Zdfno997GPLnHbwxroOAAAAAAAwrEopP1hK+Xgp5d5SSi2l/Nyia8eUUt5bSvlCKeWbpZSvllI+Wkp5boeRj6pSSsbHx3Psscdm3bp1eepTn5r169fnhBNOyHHHHdd1vIFQmgMAAAAAHNrTktyR5L8k2XPQtackeWmSd7fHVyd5TpLrSymmfKxQ/uMAAAAAAA6h1npdkuuSpJQyfdC1XUn+7eJzpZRfSLI5yalJbl+elBxNSnMAAAAAgKPn+Pb4jeX80X6/n02bNuXOO+/MySefnA0bNmR8fPyofPfiueaLZ5rv3bv3qHz/sFGaAwAAAAAcBaWUY5P8bpJP1FrvWa7f7ff7ueCCC7J169bs3bs369aty6mnnppLL700Y2NjaZomSdI0TWqtCyX4gePnP//5XHzxxcsVd+gpzQEAAAAAnqR2hvn/TPIdSX5sOX9706ZN2bp1a/bsmR+5vmfPntxyyy05++yzlzPGqqE0BwAAAAB4EtrC/PIkL07yilrr15fz9++8885VOyqlC0pzAAAAAIAnqJRyTJIrkrwo84X5fcud4eSTT866desWdponybp163LRRRdl48aNj7v+3nvvzetf//pBRlxRlOYAAAAAAIdQSnlakue3H8eSPLeUMpHkwSQ7klyZ5GVJfjRJLaWc1N67q9a65+DvG4QNGzbk1FNPzZYtW/LII49k7dq1eeELX5gNGzYc0fpnP/vZmZ2dXfLvzszMZHp6esnrhp3SHAAAAADg0E5PsrhRfkf7mklycZJXt+c/f9C6NyWZHnC2JMn4+HguvfTSbNq0Kdu2bcvzn//8bNiwIePj48vx86uO0hwAAAAA4BBqrXNJymFuOdy1ZTM+Pp6NGzce0TgWDm+s6wAAAAAAADAs7DQHAAAAABhhjzzySD74wQ/mvvv+6RmmtdYkSSll4f0BB85t3rx5WXMuF6U5AAAAAMAI+9u//dt88pOf7DrG0DCeBQAAAABghL3oRS/qOsJQUZoDAAAAAIywmZmZriMMFaU5AAAAAMAIO+uss7qOMFSU5gAAAAAA0FKaAwAAAACMsKZpuo4wVNZ0HQAAAAAAgO689KUvzezs7MLnWmuapkm/30+/3194f/DxyiuvzFVXXdVh8sFQmgMAAAAAsKCUkvHx8YyPjx/2vuOPP36ZEi0v41kAAAAAAKClNAcAAAAAgJbSHAAAAAAAWkpzAAAAAABoKc0BAAAAAKClNAcAAAAAgJbSHAAAAAAAWmu6DgAAsNpNTExkbm6u6xirXq/X6zoCAACsSNu2bctb3vKWrmMMDaU5AMCA9Xq9TE5Odh1jVZuamuo6AgAArFi7d+/uOsJQMZ4FAAAAAGCE7du3r+sIQ8VOcwAAAACAEbZhw4Zcf/31aZomtdb0+/00TZN+v/8t7w8+XnPNNbn22mu7jn/UKc0BAAAAAEbc2rVrl7zmGc94xgCSdM94FgAAAAAAaCnNAQAAAACgpTQHAAAAAICW0hwAAAAAAFpKcwAAAAAAaCnNAQAAAACgpTQHAAAAAICW0hwAAAAAAFpKcwAAAAAAaCnNAQAAAACgpTQHAAAAAICW0hwAAAAAAFpKcwAAAAAAaCnNAQAAAACgpTQHAAAAAIDWmq4DAAAAAADQnZ07d+anf/qnu44xNOw0BwAAAAAYYZdddlnXEYaKneYAAAAAACPs/PPPz7p167Jz584kSa01TdN8y6vf76ff7y+8b5omd9xxR8fJB0NpDgAAAAAwwo455pice+65S143MzOT6enpox+oY0pzAAAAAAAO6cDO88W7zZumyd69e7uONhBKcwAAAACAEXb77bfnbW97W9cxhoYHgQIAAAAAjLBnPvOZKaV0HWNo2GkOAAAAADDCTjrppNx0001LXrdaZ5rbaQ4AAAAAAC2lOQAAAAAAtJTmAAAAAADQUpoDAAAAAEBLaQ4AAAAAAC2lOQAAAAAAtJTmAAAAAADQUpoDAAAAAEBrTdcBAABWu4mJiczNzXUdY9Xr9XpdRwAAgBVp27Ztectb3tJ1jKGhNAcAGLBer5fJycmuY6xqU1NTXUcAAIAVa3x8vOsIQ0VpDgAAAAAwwr73e783s7OzS143MzOT6enpox+oY2aaAwAAAABAS2kOAAAAAAAtpTkAAAAAALSU5gAAAAAA0FKaAwAAAABAS2kOAAAAAAAtpTkAAAAAALSU5gAAAAAA0FKaAwAAAABAa03XAQAAVruJiYnMzc11HWPV6/V6XUcAAIAV6a677sqb3vSmrmMMDaU5AMCA9Xq9TE5Odh1jVZuamuo6AgAArFj/8A//0HWEoWI8CwAAAADACPvmN7/ZdYShojQHAAAAABhhtdauIwwV41kAAAAAAEbYaaedlh/6oR/Kjh07lrTui1/84oASdUtpDgAAAAAwwo477rhceOGFS143MzOT6enpox+oY8azAAAAAABAS2kOAAAAAAAt41kAAAAAAEitNU3TpN/vf8vxwPsDrwPndu3a1XXkgVCaAwAAAACMsI9//OP5wAc+0HWMoWE8CwAAAADACNu7d2/XEYaKneYAAAM2MTGRubm5rmOser1er+sIAACwIp122mldRxgqSnMAgAHr9XqZnJzsOsaqNjU11XUEAABYsUopXUcYKsazAAAAAACMsG984xtdRxgqSnMAAAAAgBH2qU99qusIQ0VpDgAAAAAwws4666yuIwwVM80BAAAAAEbYi1/84szOzh72nqZp0jRN+v3+wvGyyy7LFVdcsUwpl4/SHAAAAACAwxobG8vY2FjWrPmnSnndunUdJhoc41kAAAAAAKClNAcAAAAAgJbSHAAAAAAAWkpzAAAAAABoKc0BAAAAAKC15vFvAQDgyZiYmMjc3FzXMVa9Xq/XdQQAAFiRtm/fnje+8Y1dxxgaSnMAgAHr9XqZnJzsOsaqNjU11XUEAABYsfbs2dN1hKGiNAcAAAAAGGEveMELMjs7u+R1MzMzmZ6ePvqBOmamOQAAAAAAtJTmAAAAAADQUpoDAAAAAEBLaQ4AAAAAAC2lOQAAAAAAtJTmAAAAAADQUpoDAAAAAEBLaQ4AAAAAAC2lOQAAAAAAtJTmAAAAAADQUpoDAAAAAEBrTdcBAABWu4mJiczNzXUdY9Xr9XpdRwAAgBVpy5YtOf/887uOMTSU5gAAA9br9TI5Odl1jFVtamqq6wgAALBi3XTTTV1HGCpKcwAAAACAEfaGN7whW7duzfbt2xfO1VqTJKWU1FrT7/cXXk3TdBV1WSjNAQAAAABG2MMPP5wtW7Z0HWNoKM0BAAbMTPPlYaY5AAA8MbfeemvXEYaK0hwAYMDMNB88M80BAOCJ2717d9cRhspY1wEAAAAAAOjOKaec0nWEoaI0BwAAAAAYYccee2zXEYaK8SwAAAAAACPshS98YWZnZ4/4/lprmqbJzMxMPvKRjwwwWTfsNAcAAAAA4IiVUjI+Pp7x8fGuowyEnfJQmnQAAAdXSURBVOYAAAAAACNs586deeMb35g9e/Z0HWUo2GkOAAAAADDCPvOZzyjMF1GaAwAAAACMsK1bt3YdYagYzwIAAAAAMMLOO++8nHDCCdm5c+cR3X/gQaBbtmw54jUridIcAAAAAGCEHX/88Tn33HOXvG5mZibT09NHP1DHjmg8SynlxFLKm0spV5dStpVS9pRSdpVS/qaU8p9KKd/2PaWU9aWUd5dSvlhK2VtK+UYp5YZSyisP8Rs/Xkr5WHv/N9rfuLOUcnkp5fTDZHt6KWWqlHJXKeWRUsqOUsr/KKV895H/MwAAAAAAwJHvNP+pJB9K8tUks0m2J3lWkn+f5I+T/Egp5adqrTVJSinfmeRvkrwwyeYkf5jkaUleneTGUsqba60fPug3Xp3kZUk+l2RHkn1Jnp/kJ5K8tpTy87XWP168oJRyYpJPJzklyU1JrkjyL5K8Kck5pZSNtda/P8K/EQAAAABg5Dz00EO5+OKLc/fddy9pXZejWUopP5jk7UlOS/JdSd5Ua50+xL3/LcnPJ/mVWuv7Hu+7j7Q0/3KSH0vyyVprs+jHfiPJpiSvyXyB/uftpYszX5hfleS1tdb9i+7/f0k+WEq5odZ6z6LfOK/Wuvcx/qAXZ75If18p5U9rrfsWXX5P5gvz99daf3nRmrcl+a9J/iDJDx/h3wgAAAAAMHJ6vV5uueWWrmMs1dOS3JHkT9vXYyql/GSSDZnfqH1Ejqg0r7XedIjz95VS/jDJu5O8Iv9Umv9Ee7zoQGHe3v+1Usr7k3wgyX9M8s5F176tMG/P315K2ZpkIskzk9ybJKWUpyV5Q5JvZr6kX+z3kvxSkleVUp5ntzkA0KWJiYnMzc11HWPV6/V6XUcAAIAV6ckW5p/5zGfy8pe/POPj40cp0eOrtV6X5LokKaVMP9Y9pZR/nvnN1Wcn+Ysj/e6j8SDQR9vj/kXnTmqPj1VWHzj3yiwqzQ+llHJKkhckeSDz42EO+NdJjkvyl7XWhxavqbU2pZQbMr/l/qxD5AAAWBa9Xi+Tk5Ndx1jVpqamuo4AAAArVinlSa1/17velVNPPTWXXnrpshbnh1NKWZPk8iTvqrVuXcrf+KRK8/aHf7b9eP2iSw8k+WdJvjfJloOWPa89vuAQ33l2kjOSHNuu/9H20psXj4ZZtP7Lh4h3Z3s85TB/AgDAwNlpvjzsNAcAgCfmVa96Va6++uonvH7Pnj3ZsmVLNm3alI0bNx7FZE/KO5I8UGv90FIXlvbZnU9IKeV9SX45yXW11nMWnf/vSd6c5M+S/Idaa789/8zMzzR/bpJ9tda1j/Gdv53kVxedui/Jz9Vabzjovt/I/FiYd9daf/MxvuctSf4oyR/VWn/hCf+RAAAAAAAsOOussy7M/MjssUWnmyS/NTs7+67lzlNK2Z3krQceBFpKeUWSy5JM1Fp3tufuSvJ7R/NBoI8V5G2ZL8y/mPnZ4otdlORVSX4ySa+U8n+SPDXJqzM/k/y5mf9H/Da11l9L8mullKdmfpf425P8RSnlwlrru59oXgAAAAAAnrzZ2dlLklzSdY7DeEXmJ6F8ddFYlvEk7y2lTNZav/twi8cOd/FQSilvzfwA9S1Jzqq1Prj4eq31q0leluT3k6xP8p+TnJPkY0l+qr3ta4f7jVrrN2utt9ZaX5fkhiSXlFJetuiWXe3xhEN8xYHz/3BEfxQAAAAAAKvBHyT5l0kmFr12JPlA5p+1eVhL3mleSplsv/yOJK+stT5m+V1rvT/JW9vX4vU/1L793BJ+9vokP5zkzEXrvtQeDzWz/OT2eKiZ5wAAAAAArECllKcleX77cSzJc0spE0kerLVuz0Gbtkspjya5r9b6pTyOJe00L6X8auYL817md5gfdrf4IRx4cOhHl7Dm2e1x/6Jzn02yJ8n3l1LWH5RzLMm/az/OPoGMAAAAAAAMr9OT3Nq+jsv8gz9vTfLOJ/vFR1yal1IuTPLbST6f+R3mDxzm3rG26T/4/BsyX5p/Osn/XnR+bSnlXx3iu16W5Nwk/czvOE+S1Fp3J/lI5melX3zQsrcm+Z4kN9Ra//4I/jwAAAAAAFaIWutcrbU8xuvnDnH/9xzJQ0CTpNRaH/+mUt6YZDrzxfUH80/zxBe7a9HTSZ+W5P4kf5Xk7zL/0M/vT7IxydYkZ9dadyz6/u9I8o0kX8j82Jd7kjwlyalJDoxz+ZVa6+8elOvEzBfwpyS5Kcmmds2rM7/9/t/UWv/ucf9AAAAAAADIkZfmFyf5rce57f/WWl/R3n9Mkj9MckaSA08ivTPJ/0oyVWt9+KDvPybJr2Z+ZvmpSZ6RpCa5N/Ol+O/XWv/2ENme3mb78cw/EfXrSf4iyUW11nse948DAAAAAIDWEZXmAAAAAAAwCpb0IFAAAAAAAFjNlOYAAAAAANBSmgMAAAAAQEtpDgAAAAAALaU5AAAAAAC0lOYAAAAAANBSmgMAAAAAQEtpDgAAAAAALaU5AAAAAAC0lOYAAAAAAND6//f3pJBC41lIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1800x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"msno.matrix(df[DISCRETE_VARIABLES]);"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1800x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"msno.matrix(df[NOMINAL_VARIABLES]);"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1800x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"msno.matrix(df[ORDINAL_VARIABLES]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cleansing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since only about 1% of the overall number of observations exhibit variables with missing data (disregarding the columns *Lot Frontage* and *Garage Yr Blt*), the decision is made to discard these rows entirely to not have to deal with interpolating meaningful replacements for the missing values."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"remaining_columns = sorted(set(ALL_VARIABLES) - set(missing_a_lot)) + TARGET_VARIABLES\n",
"mask = df[remaining_columns].isnull().any(axis=1)\n",
"assert (100 * mask.sum() / df.shape[0]) < 1.1 # percent\n",
"df = df[~mask]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The two columns with a lot of missing values regard the age of a house's optional garage and the length of the intersection with the street where the house is located. The first is assumed as not important for the house appraisal and the second is assumed to be captured in other variables (e.g. overall size of the house). Therefore, for sake of simplicity both columns are dropped from the DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Garage Yr Blt Year garage was built\n",
"Lot Frontage Linear feet of street connected to property\n"
]
}
],
"source": [
"print_column_list(missing_a_lot)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"df = df[remaining_columns]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# Remove the discarded columns from the helper dictionaries / lists.\n",
"update_column_descriptions(df.columns)\n",
"# Without any more missing data, cast all numeric\n",
"# columns as floats or integers respectively.\n",
"for column in CONTINUOUS_VARIABLES + TARGET_VARIABLES:\n",
" df[column] = df[column].astype(np.float64)\n",
"for column in DISCRETE_VARIABLES:\n",
" df[column] = df[column].astype(np.int64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clean Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The cleaned data comes as a 2898 rows x 78 columns matrix."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2898, 78)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
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" .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></th>\n",
" <th>1st Flr SF</th>\n",
" <th>2nd Flr SF</th>\n",
" <th>3Ssn Porch</th>\n",
" <th>Alley</th>\n",
" <th>Bedroom AbvGr</th>\n",
" <th>Bldg Type</th>\n",
" <th>Bsmt Cond</th>\n",
" <th>Bsmt Exposure</th>\n",
" <th>Bsmt Full Bath</th>\n",
" <th>Bsmt Half Bath</th>\n",
" <th>Bsmt Qual</th>\n",
" <th>Bsmt Unf SF</th>\n",
" <th>BsmtFin SF 1</th>\n",
" <th>BsmtFin SF 2</th>\n",
" <th>BsmtFin Type 1</th>\n",
" <th>BsmtFin Type 2</th>\n",
" <th>Central Air</th>\n",
" <th>Condition 1</th>\n",
" <th>Condition 2</th>\n",
" <th>Electrical</th>\n",
" <th>Enclosed Porch</th>\n",
" <th>Exter Cond</th>\n",
" <th>Exter Qual</th>\n",
" <th>Exterior 1st</th>\n",
" <th>Exterior 2nd</th>\n",
" <th>Fence</th>\n",
" <th>Fireplace Qu</th>\n",
" <th>Fireplaces</th>\n",
" <th>Foundation</th>\n",
" <th>Full Bath</th>\n",
" <th>Functional</th>\n",
" <th>Garage Area</th>\n",
" <th>Garage Cars</th>\n",
" <th>Garage Cond</th>\n",
" <th>Garage Finish</th>\n",
" <th>Garage Qual</th>\n",
" <th>Garage Type</th>\n",
" <th>Gr Liv Area</th>\n",
" <th>Half Bath</th>\n",
" <th>Heating</th>\n",
" <th>Heating QC</th>\n",
" <th>House Style</th>\n",
" <th>Kitchen AbvGr</th>\n",
" <th>Kitchen Qual</th>\n",
" <th>Land Contour</th>\n",
" <th>Land Slope</th>\n",
" <th>Lot Area</th>\n",
" <th>Lot Config</th>\n",
" <th>Lot Shape</th>\n",
" <th>Low Qual Fin SF</th>\n",
" <th>MS SubClass</th>\n",
" <th>MS Zoning</th>\n",
" <th>Mas Vnr Area</th>\n",
" <th>Mas Vnr Type</th>\n",
" <th>Misc Feature</th>\n",
" <th>Misc Val</th>\n",
" <th>Mo Sold</th>\n",
" <th>Neighborhood</th>\n",
" <th>Open Porch SF</th>\n",
" <th>Overall Cond</th>\n",
" <th>Overall Qual</th>\n",
" <th>Paved Drive</th>\n",
" <th>Pool Area</th>\n",
" <th>Pool QC</th>\n",
" <th>Roof Matl</th>\n",
" <th>Roof Style</th>\n",
" <th>Sale Condition</th>\n",
" <th>Sale Type</th>\n",
" <th>Screen Porch</th>\n",
" <th>Street</th>\n",
" <th>TotRms AbvGrd</th>\n",
" <th>Total Bsmt SF</th>\n",
" <th>Utilities</th>\n",
" <th>Wood Deck SF</th>\n",
" <th>Year Built</th>\n",
" <th>Year Remod/Add</th>\n",
" <th>Yr Sold</th>\n",
" <th>SalePrice</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Order</th>\n",
" <th>PID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <th>526301100</th>\n",
" <td>1656.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>Gd</td>\n",
" <td>Gd</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>TA</td>\n",
" <td>441.0</td>\n",
" <td>639.0</td>\n",
" <td>0.0</td>\n",
" <td>BLQ</td>\n",
" <td>Unf</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>SBrkr</td>\n",
" <td>0.0</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>BrkFace</td>\n",
" <td>Plywood</td>\n",
" <td>NA</td>\n",
" <td>Gd</td>\n",
" <td>2</td>\n",
" <td>CBlock</td>\n",
" <td>1</td>\n",
" <td>Typ</td>\n",
" <td>528.0</td>\n",
" <td>2</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1656.0</td>\n",
" <td>0</td>\n",
" <td>GasA</td>\n",
" <td>Fa</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>TA</td>\n",
" <td>Lvl</td>\n",
" <td>Gtl</td>\n",
" <td>31770.0</td>\n",
" <td>Corner</td>\n",
" <td>IR1</td>\n",
" <td>0.0</td>\n",
" <td>020</td>\n",
" <td>RL</td>\n",
" <td>112.0</td>\n",
" <td>Stone</td>\n",
" <td>NA</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>Names</td>\n",
" <td>62.0</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>P</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>CompShg</td>\n",
" <td>Hip</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>0.0</td>\n",
" <td>Pave</td>\n",
" <td>7</td>\n",
" <td>1080.0</td>\n",
" <td>AllPub</td>\n",
" <td>210.0</td>\n",
" <td>1960</td>\n",
" <td>1960</td>\n",
" <td>2010</td>\n",
" <td>215000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <th>526350040</th>\n",
" <td>896.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>2</td>\n",
" <td>1Fam</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>TA</td>\n",
" <td>270.0</td>\n",
" <td>468.0</td>\n",
" <td>144.0</td>\n",
" <td>Rec</td>\n",
" <td>LwQ</td>\n",
" <td>Y</td>\n",
" <td>Feedr</td>\n",
" <td>Norm</td>\n",
" <td>SBrkr</td>\n",
" <td>0.0</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>VinylSd</td>\n",
" <td>VinylSd</td>\n",
" <td>MnPrv</td>\n",
" <td>NA</td>\n",
" <td>0</td>\n",
" <td>CBlock</td>\n",
" <td>1</td>\n",
" <td>Typ</td>\n",
" <td>730.0</td>\n",
" <td>1</td>\n",
" <td>TA</td>\n",
" <td>Unf</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>896.0</td>\n",
" <td>0</td>\n",
" <td>GasA</td>\n",
" <td>TA</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>TA</td>\n",
" <td>Lvl</td>\n",
" <td>Gtl</td>\n",
" <td>11622.0</td>\n",
" <td>Inside</td>\n",
" <td>Reg</td>\n",
" <td>0.0</td>\n",
" <td>020</td>\n",
" <td>RH</td>\n",
" <td>0.0</td>\n",
" <td>None</td>\n",
" <td>NA</td>\n",
" <td>0.0</td>\n",
" <td>6</td>\n",
" <td>Names</td>\n",
" <td>0.0</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>CompShg</td>\n",
" <td>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>120.0</td>\n",
" <td>Pave</td>\n",
" <td>5</td>\n",
" <td>882.0</td>\n",
" <td>AllPub</td>\n",
" <td>140.0</td>\n",
" <td>1961</td>\n",
" <td>1961</td>\n",
" <td>2010</td>\n",
" <td>105000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>526351010</th>\n",
" <td>1329.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>TA</td>\n",
" <td>406.0</td>\n",
" <td>923.0</td>\n",
" <td>0.0</td>\n",
" <td>ALQ</td>\n",
" <td>Unf</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>SBrkr</td>\n",
" <td>0.0</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>Wd Sdng</td>\n",
" <td>Wd Sdng</td>\n",
" <td>NA</td>\n",
" <td>NA</td>\n",
" <td>0</td>\n",
" <td>CBlock</td>\n",
" <td>1</td>\n",
" <td>Typ</td>\n",
" <td>312.0</td>\n",
" <td>1</td>\n",
" <td>TA</td>\n",
" <td>Unf</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1329.0</td>\n",
" <td>1</td>\n",
" <td>GasA</td>\n",
" <td>TA</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>Gd</td>\n",
" <td>Lvl</td>\n",
" <td>Gtl</td>\n",
" <td>14267.0</td>\n",
" <td>Corner</td>\n",
" <td>IR1</td>\n",
" <td>0.0</td>\n",
" <td>020</td>\n",
" <td>RL</td>\n",
" <td>108.0</td>\n",
" <td>BrkFace</td>\n",
" <td>Gar2</td>\n",
" <td>12500.0</td>\n",
" <td>6</td>\n",
" <td>Names</td>\n",
" <td>36.0</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>CompShg</td>\n",
" <td>Hip</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>0.0</td>\n",
" <td>Pave</td>\n",
" <td>6</td>\n",
" <td>1329.0</td>\n",
" <td>AllPub</td>\n",
" <td>393.0</td>\n",
" <td>1958</td>\n",
" <td>1958</td>\n",
" <td>2010</td>\n",
" <td>172000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <th>526353030</th>\n",
" <td>2110.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>TA</td>\n",
" <td>1045.0</td>\n",
" <td>1065.0</td>\n",
" <td>0.0</td>\n",
" <td>ALQ</td>\n",
" <td>Unf</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>SBrkr</td>\n",
" <td>0.0</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>BrkFace</td>\n",
" <td>BrkFace</td>\n",
" <td>NA</td>\n",
" <td>TA</td>\n",
" <td>2</td>\n",
" <td>CBlock</td>\n",
" <td>2</td>\n",
" <td>Typ</td>\n",
" <td>522.0</td>\n",
" <td>2</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>2110.0</td>\n",
" <td>1</td>\n",
" <td>GasA</td>\n",
" <td>Ex</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>Ex</td>\n",
" <td>Lvl</td>\n",
" <td>Gtl</td>\n",
" <td>11160.0</td>\n",
" <td>Corner</td>\n",
" <td>Reg</td>\n",
" <td>0.0</td>\n",
" <td>020</td>\n",
" <td>RL</td>\n",
" <td>0.0</td>\n",
" <td>None</td>\n",
" <td>NA</td>\n",
" <td>0.0</td>\n",
" <td>4</td>\n",
" <td>Names</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>CompShg</td>\n",
" <td>Hip</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>0.0</td>\n",
" <td>Pave</td>\n",
" <td>8</td>\n",
" <td>2110.0</td>\n",
" <td>AllPub</td>\n",
" <td>0.0</td>\n",
" <td>1968</td>\n",
" <td>1968</td>\n",
" <td>2010</td>\n",
" <td>244000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <th>527105010</th>\n",
" <td>928.0</td>\n",
" <td>701.0</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Gd</td>\n",
" <td>137.0</td>\n",
" <td>791.0</td>\n",
" <td>0.0</td>\n",
" <td>GLQ</td>\n",
" <td>Unf</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>SBrkr</td>\n",
" <td>0.0</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>VinylSd</td>\n",
" <td>VinylSd</td>\n",
" <td>MnPrv</td>\n",
" <td>TA</td>\n",
" <td>1</td>\n",
" <td>PConc</td>\n",
" <td>2</td>\n",
" <td>Typ</td>\n",
" <td>482.0</td>\n",
" <td>2</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1629.0</td>\n",
" <td>1</td>\n",
" <td>GasA</td>\n",
" <td>Gd</td>\n",
" <td>2Story</td>\n",
" <td>1</td>\n",
" <td>TA</td>\n",
" <td>Lvl</td>\n",
" <td>Gtl</td>\n",
" <td>13830.0</td>\n",
" <td>Inside</td>\n",
" <td>IR1</td>\n",
" <td>0.0</td>\n",
" <td>060</td>\n",
" <td>RL</td>\n",
" <td>0.0</td>\n",
" <td>None</td>\n",
" <td>NA</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>Gilbert</td>\n",
" <td>34.0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>CompShg</td>\n",
" <td>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>0.0</td>\n",
" <td>Pave</td>\n",
" <td>6</td>\n",
" <td>928.0</td>\n",
" <td>AllPub</td>\n",
" <td>212.0</td>\n",
" <td>1997</td>\n",
" <td>1998</td>\n",
" <td>2010</td>\n",
" <td>189900.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1st Flr SF 2nd Flr SF 3Ssn Porch Alley Bedroom AbvGr \\\n",
"Order PID \n",
"1 526301100 1656.0 0.0 0.0 NA 3 \n",
"2 526350040 896.0 0.0 0.0 NA 2 \n",
"3 526351010 1329.0 0.0 0.0 NA 3 \n",
"4 526353030 2110.0 0.0 0.0 NA 3 \n",
"5 527105010 928.0 701.0 0.0 NA 3 \n",
"\n",
" Bldg Type Bsmt Cond Bsmt Exposure Bsmt Full Bath \\\n",
"Order PID \n",
"1 526301100 1Fam Gd Gd 1 \n",
"2 526350040 1Fam TA No 0 \n",
"3 526351010 1Fam TA No 0 \n",
"4 526353030 1Fam TA No 1 \n",
"5 527105010 1Fam TA No 0 \n",
"\n",
" Bsmt Half Bath Bsmt Qual Bsmt Unf SF BsmtFin SF 1 \\\n",
"Order PID \n",
"1 526301100 0 TA 441.0 639.0 \n",
"2 526350040 0 TA 270.0 468.0 \n",
"3 526351010 0 TA 406.0 923.0 \n",
"4 526353030 0 TA 1045.0 1065.0 \n",
"5 527105010 0 Gd 137.0 791.0 \n",
"\n",
" BsmtFin SF 2 BsmtFin Type 1 BsmtFin Type 2 Central Air \\\n",
"Order PID \n",
"1 526301100 0.0 BLQ Unf Y \n",
"2 526350040 144.0 Rec LwQ Y \n",
"3 526351010 0.0 ALQ Unf Y \n",
"4 526353030 0.0 ALQ Unf Y \n",
"5 527105010 0.0 GLQ Unf Y \n",
"\n",
" Condition 1 Condition 2 Electrical Enclosed Porch Exter Cond \\\n",
"Order PID \n",
"1 526301100 Norm Norm SBrkr 0.0 TA \n",
"2 526350040 Feedr Norm SBrkr 0.0 TA \n",
"3 526351010 Norm Norm SBrkr 0.0 TA \n",
"4 526353030 Norm Norm SBrkr 0.0 TA \n",
"5 527105010 Norm Norm SBrkr 0.0 TA \n",
"\n",
" Exter Qual Exterior 1st Exterior 2nd Fence Fireplace Qu \\\n",
"Order PID \n",
"1 526301100 TA BrkFace Plywood NA Gd \n",
"2 526350040 TA VinylSd VinylSd MnPrv NA \n",
"3 526351010 TA Wd Sdng Wd Sdng NA NA \n",
"4 526353030 Gd BrkFace BrkFace NA TA \n",
"5 527105010 TA VinylSd VinylSd MnPrv TA \n",
"\n",
" Fireplaces Foundation Full Bath Functional Garage Area \\\n",
"Order PID \n",
"1 526301100 2 CBlock 1 Typ 528.0 \n",
"2 526350040 0 CBlock 1 Typ 730.0 \n",
"3 526351010 0 CBlock 1 Typ 312.0 \n",
"4 526353030 2 CBlock 2 Typ 522.0 \n",
"5 527105010 1 PConc 2 Typ 482.0 \n",
"\n",
" Garage Cars Garage Cond Garage Finish Garage Qual \\\n",
"Order PID \n",
"1 526301100 2 TA Fin TA \n",
"2 526350040 1 TA Unf TA \n",
"3 526351010 1 TA Unf TA \n",
"4 526353030 2 TA Fin TA \n",
"5 527105010 2 TA Fin TA \n",
"\n",
" Garage Type Gr Liv Area Half Bath Heating Heating QC \\\n",
"Order PID \n",
"1 526301100 Attchd 1656.0 0 GasA Fa \n",
"2 526350040 Attchd 896.0 0 GasA TA \n",
"3 526351010 Attchd 1329.0 1 GasA TA \n",
"4 526353030 Attchd 2110.0 1 GasA Ex \n",
"5 527105010 Attchd 1629.0 1 GasA Gd \n",
"\n",
" House Style Kitchen AbvGr Kitchen Qual Land Contour \\\n",
"Order PID \n",
"1 526301100 1Story 1 TA Lvl \n",
"2 526350040 1Story 1 TA Lvl \n",
"3 526351010 1Story 1 Gd Lvl \n",
"4 526353030 1Story 1 Ex Lvl \n",
"5 527105010 2Story 1 TA Lvl \n",
"\n",
" Land Slope Lot Area Lot Config Lot Shape Low Qual Fin SF \\\n",
"Order PID \n",
"1 526301100 Gtl 31770.0 Corner IR1 0.0 \n",
"2 526350040 Gtl 11622.0 Inside Reg 0.0 \n",
"3 526351010 Gtl 14267.0 Corner IR1 0.0 \n",
"4 526353030 Gtl 11160.0 Corner Reg 0.0 \n",
"5 527105010 Gtl 13830.0 Inside IR1 0.0 \n",
"\n",
" MS SubClass MS Zoning Mas Vnr Area Mas Vnr Type Misc Feature \\\n",
"Order PID \n",
"1 526301100 020 RL 112.0 Stone NA \n",
"2 526350040 020 RH 0.0 None NA \n",
"3 526351010 020 RL 108.0 BrkFace Gar2 \n",
"4 526353030 020 RL 0.0 None NA \n",
"5 527105010 060 RL 0.0 None NA \n",
"\n",
" Misc Val Mo Sold Neighborhood Open Porch SF Overall Cond \\\n",
"Order PID \n",
"1 526301100 0.0 5 Names 62.0 5 \n",
"2 526350040 0.0 6 Names 0.0 6 \n",
"3 526351010 12500.0 6 Names 36.0 6 \n",
"4 526353030 0.0 4 Names 0.0 5 \n",
"5 527105010 0.0 3 Gilbert 34.0 5 \n",
"\n",
" Overall Qual Paved Drive Pool Area Pool QC Roof Matl \\\n",
"Order PID \n",
"1 526301100 6 P 0.0 NA CompShg \n",
"2 526350040 5 Y 0.0 NA CompShg \n",
"3 526351010 6 Y 0.0 NA CompShg \n",
"4 526353030 7 Y 0.0 NA CompShg \n",
"5 527105010 5 Y 0.0 NA CompShg \n",
"\n",
" Roof Style Sale Condition Sale Type Screen Porch Street \\\n",
"Order PID \n",
"1 526301100 Hip Normal WD 0.0 Pave \n",
"2 526350040 Gable Normal WD 120.0 Pave \n",
"3 526351010 Hip Normal WD 0.0 Pave \n",
"4 526353030 Hip Normal WD 0.0 Pave \n",
"5 527105010 Gable Normal WD 0.0 Pave \n",
"\n",
" TotRms AbvGrd Total Bsmt SF Utilities Wood Deck SF \\\n",
"Order PID \n",
"1 526301100 7 1080.0 AllPub 210.0 \n",
"2 526350040 5 882.0 AllPub 140.0 \n",
"3 526351010 6 1329.0 AllPub 393.0 \n",
"4 526353030 8 2110.0 AllPub 0.0 \n",
"5 527105010 6 928.0 AllPub 212.0 \n",
"\n",
" Year Built Year Remod/Add Yr Sold SalePrice \n",
"Order PID \n",
"1 526301100 1960 1960 2010 215000.0 \n",
"2 526350040 1961 1961 2010 105000.0 \n",
"3 526351010 1958 1958 2010 172000.0 \n",
"4 526353030 1968 1968 2010 244000.0 \n",
"5 527105010 1997 1998 2010 189900.0 "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"data/data_clean.csv\")"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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