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ames-housing/00_data_cleaning.ipynb
Alexander Hess d25ed9661e
Rename notebook 1
2021-05-25 08:10:20 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Cleaning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## \"Housekeeping\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import all the third-party (scientific) libraries needed."
]
},
{
"cell_type": "code",
"execution_count": 1,
"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": 2,
"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": 3,
"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": 4,
"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": 5,
"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": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></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",
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" <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>Garage Yr Blt</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 Frontage</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",
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" </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>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>Gd</td>\n",
" <td>Gd</td>\n",
" <td>1.0</td>\n",
" <td>0.0</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>2.0</td>\n",
" <td>CBlock</td>\n",
" <td>1.0</td>\n",
" <td>Typ</td>\n",
" <td>528.0</td>\n",
" <td>2.0</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1960.0</td>\n",
" <td>1656.0</td>\n",
" <td>0.0</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>141.0</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>5.0</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>1960.0</td>\n",
" <td>1960.0</td>\n",
" <td>2010.0</td>\n",
" <td>215000</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>NA</td>\n",
" <td>2</td>\n",
" <td>1Fam</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>0.0</td>\n",
" <td>CBlock</td>\n",
" <td>1.0</td>\n",
" <td>Typ</td>\n",
" <td>730.0</td>\n",
" <td>1.0</td>\n",
" <td>TA</td>\n",
" <td>Unf</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1961.0</td>\n",
" <td>896.0</td>\n",
" <td>0.0</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>80.0</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>6.0</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>1961.0</td>\n",
" <td>1961.0</td>\n",
" <td>2010.0</td>\n",
" <td>105000</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>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>0.0</td>\n",
" <td>CBlock</td>\n",
" <td>1.0</td>\n",
" <td>Typ</td>\n",
" <td>312.0</td>\n",
" <td>1.0</td>\n",
" <td>TA</td>\n",
" <td>Unf</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1958.0</td>\n",
" <td>1329.0</td>\n",
" <td>1.0</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>81.0</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>6.0</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>1958.0</td>\n",
" <td>1958.0</td>\n",
" <td>2010.0</td>\n",
" <td>172000</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>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>1.0</td>\n",
" <td>0.0</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>2.0</td>\n",
" <td>CBlock</td>\n",
" <td>2.0</td>\n",
" <td>Typ</td>\n",
" <td>522.0</td>\n",
" <td>2.0</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1968.0</td>\n",
" <td>2110.0</td>\n",
" <td>1.0</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>93.0</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>4.0</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>1968.0</td>\n",
" <td>1968.0</td>\n",
" <td>2010.0</td>\n",
" <td>244000</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>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>1.0</td>\n",
" <td>PConc</td>\n",
" <td>2.0</td>\n",
" <td>Typ</td>\n",
" <td>482.0</td>\n",
" <td>2.0</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1997.0</td>\n",
" <td>1629.0</td>\n",
" <td>1.0</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>74.0</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>3.0</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>1997.0</td>\n",
" <td>1998.0</td>\n",
" <td>2010.0</td>\n",
" <td>189900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <th>527105030</th>\n",
" <td>926.0</td>\n",
" <td>678.0</td>\n",
" <td>0</td>\n",
" <td>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>TA</td>\n",
" <td>324.0</td>\n",
" <td>602.0</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>NA</td>\n",
" <td>Gd</td>\n",
" <td>1.0</td>\n",
" <td>PConc</td>\n",
" <td>2.0</td>\n",
" <td>Typ</td>\n",
" <td>470.0</td>\n",
" <td>2.0</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1998.0</td>\n",
" <td>1604.0</td>\n",
" <td>1.0</td>\n",
" <td>GasA</td>\n",
" <td>Ex</td>\n",
" <td>2Story</td>\n",
" <td>1</td>\n",
" <td>Gd</td>\n",
" <td>Lvl</td>\n",
" <td>Gtl</td>\n",
" <td>9978.0</td>\n",
" <td>Inside</td>\n",
" <td>78.0</td>\n",
" <td>IR1</td>\n",
" <td>0.0</td>\n",
" <td>060</td>\n",
" <td>RL</td>\n",
" <td>20.0</td>\n",
" <td>BrkFace</td>\n",
" <td>NA</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>Gilbert</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>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>0.0</td>\n",
" <td>Pave</td>\n",
" <td>7</td>\n",
" <td>926.0</td>\n",
" <td>AllPub</td>\n",
" <td>360.0</td>\n",
" <td>1998.0</td>\n",
" <td>1998.0</td>\n",
" <td>2010.0</td>\n",
" <td>195500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <th>527127150</th>\n",
" <td>1338.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>NA</td>\n",
" <td>2</td>\n",
" <td>TwnhsE</td>\n",
" <td>TA</td>\n",
" <td>Mn</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>Gd</td>\n",
" <td>722.0</td>\n",
" <td>616.0</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>170.0</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>CemntBd</td>\n",
" <td>CmentBd</td>\n",
" <td>NA</td>\n",
" <td>NA</td>\n",
" <td>0.0</td>\n",
" <td>PConc</td>\n",
" <td>2.0</td>\n",
" <td>Typ</td>\n",
" <td>582.0</td>\n",
" <td>2.0</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>2001.0</td>\n",
" <td>1338.0</td>\n",
" <td>0.0</td>\n",
" <td>GasA</td>\n",
" <td>Ex</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>Gd</td>\n",
" <td>Lvl</td>\n",
" <td>Gtl</td>\n",
" <td>4920.0</td>\n",
" <td>Inside</td>\n",
" <td>41.0</td>\n",
" <td>Reg</td>\n",
" <td>0.0</td>\n",
" <td>120</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>4.0</td>\n",
" <td>StoneBr</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>8</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>1338.0</td>\n",
" <td>AllPub</td>\n",
" <td>0.0</td>\n",
" <td>2001.0</td>\n",
" <td>2001.0</td>\n",
" <td>2010.0</td>\n",
" <td>213500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <th>527145080</th>\n",
" <td>1280.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>NA</td>\n",
" <td>2</td>\n",
" <td>TwnhsE</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Gd</td>\n",
" <td>1017.0</td>\n",
" <td>263.0</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>HdBoard</td>\n",
" <td>HdBoard</td>\n",
" <td>NA</td>\n",
" <td>NA</td>\n",
" <td>0.0</td>\n",
" <td>PConc</td>\n",
" <td>2.0</td>\n",
" <td>Typ</td>\n",
" <td>506.0</td>\n",
" <td>2.0</td>\n",
" <td>TA</td>\n",
" <td>RFn</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1992.0</td>\n",
" <td>1280.0</td>\n",
" <td>0.0</td>\n",
" <td>GasA</td>\n",
" <td>Ex</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>Gd</td>\n",
" <td>HLS</td>\n",
" <td>Gtl</td>\n",
" <td>5005.0</td>\n",
" <td>Inside</td>\n",
" <td>43.0</td>\n",
" <td>IR1</td>\n",
" <td>0.0</td>\n",
" <td>120</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>1.0</td>\n",
" <td>StoneBr</td>\n",
" <td>82.0</td>\n",
" <td>5</td>\n",
" <td>8</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>144.0</td>\n",
" <td>Pave</td>\n",
" <td>5</td>\n",
" <td>1280.0</td>\n",
" <td>AllPub</td>\n",
" <td>0.0</td>\n",
" <td>1992.0</td>\n",
" <td>1992.0</td>\n",
" <td>2010.0</td>\n",
" <td>191500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <th>527146030</th>\n",
" <td>1616.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>NA</td>\n",
" <td>2</td>\n",
" <td>TwnhsE</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>Gd</td>\n",
" <td>415.0</td>\n",
" <td>1180.0</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>Gd</td>\n",
" <td>CemntBd</td>\n",
" <td>CmentBd</td>\n",
" <td>NA</td>\n",
" <td>TA</td>\n",
" <td>1.0</td>\n",
" <td>PConc</td>\n",
" <td>2.0</td>\n",
" <td>Typ</td>\n",
" <td>608.0</td>\n",
" <td>2.0</td>\n",
" <td>TA</td>\n",
" <td>RFn</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1995.0</td>\n",
" <td>1616.0</td>\n",
" <td>0.0</td>\n",
" <td>GasA</td>\n",
" <td>Ex</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>Gd</td>\n",
" <td>Lvl</td>\n",
" <td>Gtl</td>\n",
" <td>5389.0</td>\n",
" <td>Inside</td>\n",
" <td>39.0</td>\n",
" <td>IR1</td>\n",
" <td>0.0</td>\n",
" <td>120</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>3.0</td>\n",
" <td>StoneBr</td>\n",
" <td>152.0</td>\n",
" <td>5</td>\n",
" <td>8</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>5</td>\n",
" <td>1595.0</td>\n",
" <td>AllPub</td>\n",
" <td>237.0</td>\n",
" <td>1995.0</td>\n",
" <td>1996.0</td>\n",
" <td>2010.0</td>\n",
" <td>236500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <th>527162130</th>\n",
" <td>1028.0</td>\n",
" <td>776.0</td>\n",
" <td>0</td>\n",
" <td>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>TA</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>TA</td>\n",
" <td>994.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Unf</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>NA</td>\n",
" <td>TA</td>\n",
" <td>1.0</td>\n",
" <td>PConc</td>\n",
" <td>2.0</td>\n",
" <td>Typ</td>\n",
" <td>442.0</td>\n",
" <td>2.0</td>\n",
" <td>TA</td>\n",
" <td>Fin</td>\n",
" <td>TA</td>\n",
" <td>Attchd</td>\n",
" <td>1999.0</td>\n",
" <td>1804.0</td>\n",
" <td>1.0</td>\n",
" <td>GasA</td>\n",
" <td>Gd</td>\n",
" <td>2Story</td>\n",
" <td>1</td>\n",
" <td>Gd</td>\n",
" <td>Lvl</td>\n",
" <td>Gtl</td>\n",
" <td>7500.0</td>\n",
" <td>Inside</td>\n",
" <td>60.0</td>\n",
" <td>Reg</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>6.0</td>\n",
" <td>Gilbert</td>\n",
" <td>60.0</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>Gable</td>\n",
" <td>Normal</td>\n",
" <td>WD</td>\n",
" <td>0.0</td>\n",
" <td>Pave</td>\n",
" <td>7</td>\n",
" <td>994.0</td>\n",
" <td>AllPub</td>\n",
" <td>140.0</td>\n",
" <td>1999.0</td>\n",
" <td>1999.0</td>\n",
" <td>2010.0</td>\n",
" <td>189000</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 NA 3 \n",
"2 526350040 896.0 0.0 0 NA 2 \n",
"3 526351010 1329.0 0.0 0 NA 3 \n",
"4 526353030 2110.0 0.0 0 NA 3 \n",
"5 527105010 928.0 701.0 0 NA 3 \n",
"6 527105030 926.0 678.0 0 NA 3 \n",
"7 527127150 1338.0 0.0 0 NA 2 \n",
"8 527145080 1280.0 0.0 0 NA 2 \n",
"9 527146030 1616.0 0.0 0 NA 2 \n",
"10 527162130 1028.0 776.0 0 NA 3 \n",
"\n",
" Bldg Type Bsmt Cond Bsmt Exposure Bsmt Full Bath \\\n",
"Order PID \n",
"1 526301100 1Fam Gd Gd 1.0 \n",
"2 526350040 1Fam TA No 0.0 \n",
"3 526351010 1Fam TA No 0.0 \n",
"4 526353030 1Fam TA No 1.0 \n",
"5 527105010 1Fam TA No 0.0 \n",
"6 527105030 1Fam TA No 0.0 \n",
"7 527127150 TwnhsE TA Mn 1.0 \n",
"8 527145080 TwnhsE TA No 0.0 \n",
"9 527146030 TwnhsE TA No 1.0 \n",
"10 527162130 1Fam TA No 0.0 \n",
"\n",
" Bsmt Half Bath Bsmt Qual Bsmt Unf SF BsmtFin SF 1 \\\n",
"Order PID \n",
"1 526301100 0.0 TA 441.0 639.0 \n",
"2 526350040 0.0 TA 270.0 468.0 \n",
"3 526351010 0.0 TA 406.0 923.0 \n",
"4 526353030 0.0 TA 1045.0 1065.0 \n",
"5 527105010 0.0 Gd 137.0 791.0 \n",
"6 527105030 0.0 TA 324.0 602.0 \n",
"7 527127150 0.0 Gd 722.0 616.0 \n",
"8 527145080 0.0 Gd 1017.0 263.0 \n",
"9 527146030 0.0 Gd 415.0 1180.0 \n",
"10 527162130 0.0 TA 994.0 0.0 \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",
"6 527105030 0.0 GLQ Unf Y \n",
"7 527127150 0.0 GLQ Unf Y \n",
"8 527145080 0.0 ALQ Unf Y \n",
"9 527146030 0.0 GLQ Unf Y \n",
"10 527162130 0.0 Unf 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",
"6 527105030 Norm Norm SBrkr 0.0 TA \n",
"7 527127150 Norm Norm SBrkr 170.0 TA \n",
"8 527145080 Norm Norm SBrkr 0.0 TA \n",
"9 527146030 Norm Norm SBrkr 0.0 TA \n",
"10 527162130 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",
"6 527105030 TA VinylSd VinylSd NA Gd \n",
"7 527127150 Gd CemntBd CmentBd NA NA \n",
"8 527145080 Gd HdBoard HdBoard NA NA \n",
"9 527146030 Gd CemntBd CmentBd NA TA \n",
"10 527162130 TA VinylSd VinylSd NA TA \n",
"\n",
" Fireplaces Foundation Full Bath Functional Garage Area \\\n",
"Order PID \n",
"1 526301100 2.0 CBlock 1.0 Typ 528.0 \n",
"2 526350040 0.0 CBlock 1.0 Typ 730.0 \n",
"3 526351010 0.0 CBlock 1.0 Typ 312.0 \n",
"4 526353030 2.0 CBlock 2.0 Typ 522.0 \n",
"5 527105010 1.0 PConc 2.0 Typ 482.0 \n",
"6 527105030 1.0 PConc 2.0 Typ 470.0 \n",
"7 527127150 0.0 PConc 2.0 Typ 582.0 \n",
"8 527145080 0.0 PConc 2.0 Typ 506.0 \n",
"9 527146030 1.0 PConc 2.0 Typ 608.0 \n",
"10 527162130 1.0 PConc 2.0 Typ 442.0 \n",
"\n",
" Garage Cars Garage Cond Garage Finish Garage Qual \\\n",
"Order PID \n",
"1 526301100 2.0 TA Fin TA \n",
"2 526350040 1.0 TA Unf TA \n",
"3 526351010 1.0 TA Unf TA \n",
"4 526353030 2.0 TA Fin TA \n",
"5 527105010 2.0 TA Fin TA \n",
"6 527105030 2.0 TA Fin TA \n",
"7 527127150 2.0 TA Fin TA \n",
"8 527145080 2.0 TA RFn TA \n",
"9 527146030 2.0 TA RFn TA \n",
"10 527162130 2.0 TA Fin TA \n",
"\n",
" Garage Type Garage Yr Blt Gr Liv Area Half Bath Heating \\\n",
"Order PID \n",
"1 526301100 Attchd 1960.0 1656.0 0.0 GasA \n",
"2 526350040 Attchd 1961.0 896.0 0.0 GasA \n",
"3 526351010 Attchd 1958.0 1329.0 1.0 GasA \n",
"4 526353030 Attchd 1968.0 2110.0 1.0 GasA \n",
"5 527105010 Attchd 1997.0 1629.0 1.0 GasA \n",
"6 527105030 Attchd 1998.0 1604.0 1.0 GasA \n",
"7 527127150 Attchd 2001.0 1338.0 0.0 GasA \n",
"8 527145080 Attchd 1992.0 1280.0 0.0 GasA \n",
"9 527146030 Attchd 1995.0 1616.0 0.0 GasA \n",
"10 527162130 Attchd 1999.0 1804.0 1.0 GasA \n",
"\n",
" Heating QC House Style Kitchen AbvGr Kitchen Qual \\\n",
"Order PID \n",
"1 526301100 Fa 1Story 1 TA \n",
"2 526350040 TA 1Story 1 TA \n",
"3 526351010 TA 1Story 1 Gd \n",
"4 526353030 Ex 1Story 1 Ex \n",
"5 527105010 Gd 2Story 1 TA \n",
"6 527105030 Ex 2Story 1 Gd \n",
"7 527127150 Ex 1Story 1 Gd \n",
"8 527145080 Ex 1Story 1 Gd \n",
"9 527146030 Ex 1Story 1 Gd \n",
"10 527162130 Gd 2Story 1 Gd \n",
"\n",
" Land Contour Land Slope Lot Area Lot Config Lot Frontage \\\n",
"Order PID \n",
"1 526301100 Lvl Gtl 31770.0 Corner 141.0 \n",
"2 526350040 Lvl Gtl 11622.0 Inside 80.0 \n",
"3 526351010 Lvl Gtl 14267.0 Corner 81.0 \n",
"4 526353030 Lvl Gtl 11160.0 Corner 93.0 \n",
"5 527105010 Lvl Gtl 13830.0 Inside 74.0 \n",
"6 527105030 Lvl Gtl 9978.0 Inside 78.0 \n",
"7 527127150 Lvl Gtl 4920.0 Inside 41.0 \n",
"8 527145080 HLS Gtl 5005.0 Inside 43.0 \n",
"9 527146030 Lvl Gtl 5389.0 Inside 39.0 \n",
"10 527162130 Lvl Gtl 7500.0 Inside 60.0 \n",
"\n",
" Lot Shape Low Qual Fin SF MS SubClass MS Zoning \\\n",
"Order PID \n",
"1 526301100 IR1 0.0 020 RL \n",
"2 526350040 Reg 0.0 020 RH \n",
"3 526351010 IR1 0.0 020 RL \n",
"4 526353030 Reg 0.0 020 RL \n",
"5 527105010 IR1 0.0 060 RL \n",
"6 527105030 IR1 0.0 060 RL \n",
"7 527127150 Reg 0.0 120 RL \n",
"8 527145080 IR1 0.0 120 RL \n",
"9 527146030 IR1 0.0 120 RL \n",
"10 527162130 Reg 0.0 060 RL \n",
"\n",
" Mas Vnr Area Mas Vnr Type Misc Feature Misc Val Mo Sold \\\n",
"Order PID \n",
"1 526301100 112.0 Stone NA 0.0 5.0 \n",
"2 526350040 0.0 None NA 0.0 6.0 \n",
"3 526351010 108.0 BrkFace Gar2 12500.0 6.0 \n",
"4 526353030 0.0 None NA 0.0 4.0 \n",
"5 527105010 0.0 None NA 0.0 3.0 \n",
"6 527105030 20.0 BrkFace NA 0.0 6.0 \n",
"7 527127150 0.0 None NA 0.0 4.0 \n",
"8 527145080 0.0 None NA 0.0 1.0 \n",
"9 527146030 0.0 None NA 0.0 3.0 \n",
"10 527162130 0.0 None NA 0.0 6.0 \n",
"\n",
" Neighborhood Open Porch SF Overall Cond Overall Qual \\\n",
"Order PID \n",
"1 526301100 NAmes 62.0 5 6 \n",
"2 526350040 NAmes 0.0 6 5 \n",
"3 526351010 NAmes 36.0 6 6 \n",
"4 526353030 NAmes 0.0 5 7 \n",
"5 527105010 Gilbert 34.0 5 5 \n",
"6 527105030 Gilbert 36.0 6 6 \n",
"7 527127150 StoneBr 0.0 5 8 \n",
"8 527145080 StoneBr 82.0 5 8 \n",
"9 527146030 StoneBr 152.0 5 8 \n",
"10 527162130 Gilbert 60.0 5 7 \n",
"\n",
" Paved Drive Pool Area Pool QC Roof Matl Roof Style \\\n",
"Order PID \n",
"1 526301100 P 0.0 NA CompShg Hip \n",
"2 526350040 Y 0.0 NA CompShg Gable \n",
"3 526351010 Y 0.0 NA CompShg Hip \n",
"4 526353030 Y 0.0 NA CompShg Hip \n",
"5 527105010 Y 0.0 NA CompShg Gable \n",
"6 527105030 Y 0.0 NA CompShg Gable \n",
"7 527127150 Y 0.0 NA CompShg Gable \n",
"8 527145080 Y 0.0 NA CompShg Gable \n",
"9 527146030 Y 0.0 NA CompShg Gable \n",
"10 527162130 Y 0.0 NA CompShg Gable \n",
"\n",
" Sale Condition Sale Type Screen Porch Street TotRms AbvGrd \\\n",
"Order PID \n",
"1 526301100 Normal WD 0.0 Pave 7 \n",
"2 526350040 Normal WD 120.0 Pave 5 \n",
"3 526351010 Normal WD 0.0 Pave 6 \n",
"4 526353030 Normal WD 0.0 Pave 8 \n",
"5 527105010 Normal WD 0.0 Pave 6 \n",
"6 527105030 Normal WD 0.0 Pave 7 \n",
"7 527127150 Normal WD 0.0 Pave 6 \n",
"8 527145080 Normal WD 144.0 Pave 5 \n",
"9 527146030 Normal WD 0.0 Pave 5 \n",
"10 527162130 Normal WD 0.0 Pave 7 \n",
"\n",
" Total Bsmt SF Utilities Wood Deck SF Year Built \\\n",
"Order PID \n",
"1 526301100 1080.0 AllPub 210.0 1960.0 \n",
"2 526350040 882.0 AllPub 140.0 1961.0 \n",
"3 526351010 1329.0 AllPub 393.0 1958.0 \n",
"4 526353030 2110.0 AllPub 0.0 1968.0 \n",
"5 527105010 928.0 AllPub 212.0 1997.0 \n",
"6 527105030 926.0 AllPub 360.0 1998.0 \n",
"7 527127150 1338.0 AllPub 0.0 2001.0 \n",
"8 527145080 1280.0 AllPub 0.0 1992.0 \n",
"9 527146030 1595.0 AllPub 237.0 1995.0 \n",
"10 527162130 994.0 AllPub 140.0 1999.0 \n",
"\n",
" Year Remod/Add Yr Sold SalePrice \n",
"Order PID \n",
"1 526301100 1960.0 2010.0 215000 \n",
"2 526350040 1961.0 2010.0 105000 \n",
"3 526351010 1958.0 2010.0 172000 \n",
"4 526353030 1968.0 2010.0 244000 \n",
"5 527105010 1998.0 2010.0 189900 \n",
"6 527105030 1998.0 2010.0 195500 \n",
"7 527127150 2001.0 2010.0 213500 \n",
"8 527145080 1992.0 2010.0 191500 \n",
"9 527146030 1996.0 2010.0 236500 \n",
"10 527162130 1999.0 2010.0 189000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"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": 7,
"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": 8,
"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": 9,
"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": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2930, 80)"
]
},
"execution_count": 10,
"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": 11,
"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": 12,
"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": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></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": 13,
"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": 14,
"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",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 1st Flr SF 2930 non-null float64\n",
" 1 2nd Flr SF 2930 non-null float64\n",
" 2 3Ssn Porch 2930 non-null int64 \n",
" 3 Bsmt Unf SF 2929 non-null float64\n",
" 4 BsmtFin SF 1 2929 non-null float64\n",
" 5 BsmtFin SF 2 2929 non-null float64\n",
" 6 Enclosed Porch 2930 non-null float64\n",
" 7 Garage Area 2929 non-null float64\n",
" 8 Gr Liv Area 2930 non-null float64\n",
" 9 Lot Area 2930 non-null float64\n",
" 10 Lot Frontage 2440 non-null float64\n",
" 11 Low Qual Fin SF 2930 non-null float64\n",
" 12 Mas Vnr Area 2907 non-null float64\n",
" 13 Misc Val 2930 non-null float64\n",
" 14 Open Porch SF 2930 non-null float64\n",
" 15 Pool Area 2930 non-null float64\n",
" 16 Screen Porch 2930 non-null float64\n",
" 17 Total Bsmt SF 2929 non-null float64\n",
" 18 Wood Deck SF 2930 non-null float64\n",
"dtypes: float64(18), int64(1)\n",
"memory usage: 621.3 KB\n"
]
}
],
"source": [
"df[CONTINUOUS_VARIABLES].info()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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": 16,
"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": 17,
"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": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></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": 18,
"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": 19,
"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",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Bedroom AbvGr 2930 non-null int64 \n",
" 1 Bsmt Full Bath 2928 non-null float64\n",
" 2 Bsmt Half Bath 2928 non-null float64\n",
" 3 Fireplaces 2930 non-null int64 \n",
" 4 Full Bath 2930 non-null int64 \n",
" 5 Garage Cars 2929 non-null float64\n",
" 6 Garage Yr Blt 2771 non-null float64\n",
" 7 Half Bath 2930 non-null int64 \n",
" 8 Kitchen AbvGr 2930 non-null int64 \n",
" 9 Mo Sold 2930 non-null int64 \n",
" 10 TotRms AbvGrd 2930 non-null int64 \n",
" 11 Year Built 2930 non-null int64 \n",
" 12 Year Remod/Add 2930 non-null int64 \n",
" 13 Yr Sold 2930 non-null int64 \n",
"dtypes: float64(4), int64(10)\n",
"memory usage: 506.9 KB\n"
]
}
],
"source": [
"df[DISCRETE_VARIABLES].info()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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": 21,
"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": 22,
"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": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></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": 23,
"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": 24,
"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",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Alley 2930 non-null object\n",
" 1 Bldg Type 2930 non-null object\n",
" 2 Central Air 2930 non-null object\n",
" 3 Condition 1 2930 non-null object\n",
" 4 Condition 2 2930 non-null object\n",
" 5 Exterior 1st 2930 non-null object\n",
" 6 Exterior 2nd 2930 non-null object\n",
" 7 Foundation 2930 non-null object\n",
" 8 Garage Type 2930 non-null object\n",
" 9 Heating 2930 non-null object\n",
" 10 House Style 2930 non-null object\n",
" 11 Land Contour 2930 non-null object\n",
" 12 Lot Config 2930 non-null object\n",
" 13 MS SubClass 2930 non-null object\n",
" 14 MS Zoning 2930 non-null object\n",
" 15 Mas Vnr Type 2907 non-null object\n",
" 16 Misc Feature 2930 non-null object\n",
" 17 Neighborhood 2930 non-null object\n",
" 18 Roof Matl 2930 non-null object\n",
" 19 Roof Style 2930 non-null object\n",
" 20 Sale Condition 2930 non-null object\n",
" 21 Sale Type 2930 non-null object\n",
" 22 Street 2930 non-null object\n",
"dtypes: object(23)\n",
"memory usage: 712.9+ KB\n"
]
}
],
"source": [
"df[NOMINAL_VARIABLES].info()"
]
},
{
"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": 25,
"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": 26,
"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": 27,
"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": 27,
"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": 28,
"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",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Bsmt Cond 2929 non-null object\n",
" 1 Bsmt Exposure 2926 non-null object\n",
" 2 Bsmt Qual 2929 non-null object\n",
" 3 BsmtFin Type 1 2929 non-null object\n",
" 4 BsmtFin Type 2 2928 non-null object\n",
" 5 Electrical 2929 non-null object\n",
" 6 Exter Cond 2930 non-null object\n",
" 7 Exter Qual 2930 non-null object\n",
" 8 Fence 2930 non-null object\n",
" 9 Fireplace Qu 2930 non-null object\n",
" 10 Functional 2930 non-null object\n",
" 11 Garage Cond 2929 non-null object\n",
" 12 Garage Finish 2928 non-null object\n",
" 13 Garage Qual 2929 non-null object\n",
" 14 Heating QC 2930 non-null object\n",
" 15 Kitchen Qual 2930 non-null object\n",
" 16 Land Slope 2930 non-null object\n",
" 17 Lot Shape 2930 non-null object\n",
" 18 Overall Cond 2930 non-null object\n",
" 19 Overall Qual 2930 non-null object\n",
" 20 Paved Drive 2930 non-null object\n",
" 21 Pool QC 2930 non-null object\n",
" 22 Utilities 2930 non-null object\n",
"dtypes: object(23)\n",
"memory usage: 712.9+ KB\n"
]
}
],
"source": [
"df[ORDINAL_VARIABLES].info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Missing Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizations"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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uBvq7+0olFlUaSDm3RES+hVJA7zzigX7PFDDvnQLF/YDbrAcuTFU0ZL22+MqXqV4+A/YkUrZsCPwpBQJmBI4BNk4Pv5WRHvzfS//OA+bFiPMBxWtmNtjM9kmj9CvBOsld7+5XEjlHRxOL76yd9o/JP1NMp+6WwjZZenjpTUwJfR3Y2t3vIxYs603k/v8JcHz6XYj0NL8iAsW7AX9093OJIOgQ4JUqBsyhlrYKwMz2IkYPrgf8N+uA/pzI330+cb/czsymLaXATVQ3qvwwM/snkZf4CDP7PkAaKfkw8DMzmw8YCuwI9AOur3pAqKfrJGDeK/1W+qfZbKell/ZKr1eqbdmTWbY2DoDH+g07ELMSVzKzVfLv292fJmZ83gIcbdXIWT078ew5wt2fy9vNZraImW1PXB/nTc9e1xHPX1MDt5vZbGUUutnSICzc/XJ33xZY1d13c/fHisEoKWA+D7Eo8D26NlSHguYiIt9OUwDXEznTjjezadP08v7EA10f4KYyC9hdJtfoSPVSBM5/SYwiLEYbnwHsDjza6gHSjuqho2NK+z4hHvKLRWSPI0ZTDmpyMbtFXfBjfTPbycx2SQ36Xu5+FZHC5zvAb7LA+fgKN2L7EJ0lV7j7m2Y2AlgRWInIzfoUsCtwQhp1K9KTDAXecfcnUwBsW2AEcLC7n2tmg9KowUows+vM7JK63f8iOg4WA1aom7k2nljY8FwidctPLfK/V0Z2z7iM6CQYCzwCDAPONrMd01tPAMYTC4BeT6Rf2N3dP+3uMkv50m/ku0Qn22lAG7BsCpD1bvW2pYS6duXPzWxDizUM7iUWvhxNzGhdsi5w/gxxDRnq1chZPSuxKPInMKHdPDC1KW8GziaerR4xszU9Fge9gUjv9Snp2aNqOuhQ+wRiFmuxw8xmAA4mUjudrmtDdVSqMSQiUgVp2tsnZnYe8YA7HDjLzH5ONFg+BLbwuvzmVZOmCJp3IddsatT1SSlZfgEMJEbWfgAs4e5PNbe0zfNV6iHzKTBtmjp5AhE0XTI17ltaFuDBzC4kco9ORSzu9xKxMNPh7n6ZmTnxkHOQRXqWq6rUiM2myPZKsy4OBsaa2Q+ANYjRP6+mUfZnA3MD2wIvAkeXVnCRJqqbXt+LeIgfSCwOjJltTeQgPcjdj07X2MOAj8zs2K94rf3WSaPE/w7cn+9395fNbCkihdP2wANmdmkeODezXYn6esIruBaGmf2SWKhta+Ahjzy9mxMLuM2Y3nYtkdpqHSLn+63u/mIJxZUmsK+Ysi5dQ35KjL69Fjgia3NW7jfSE9W1Ky8AliOuCfdAjDg3sx2ITsW/EZ2KD2azX58to9xNMhr4Etgi9Q1MSQSCv0sM1vo9sRDqnkR7e0l3f8XMrgCudfeWXyy6/hqRtykK2Xc/Jr3nx8SArbWBtSp2TvR4GmkuIvItkaZ+bg6snQJ8HxGLz+wNrEosSPQp8COv/mIrUwFXEoG/LkmjfnoR0+2nJALmK7n7I00pZDf4OvWQ9Cbu8UcCWwI/cPeHG1y8blWM7PHaIkSnAD8gps4uAXwPeJ4YQXh0GjX0D2J09fLAHlUZYV1Ml+0gXdFHaZro4sRv4LYUTDdgLmIWxm+ImQcilZN1JPWF+G2kkXB3AhuY2aFEwPxQdy86jhYkrhG9KhAwf4RIQXOhuz9jkZbrseJ1d3+TuG6OITrONk911laMtHT37d392irMyimm1GeWInKWP5AC5gsSHauXEAuKA0zj7i+4+6nufrYC5tVgZr1ToLvNzPqa2XfNbPo0g7PTWY3p/vobYrHHw7LZjQqYV0TWrjyduD7+FDjG3Udnbc97ge2ITsUziMB65XiknNmDGGzzD6ID9tO0bwt3v8PdTyPqYDpiwXncfUxFAuaWnQ9LQsezerP3T21mJxPpEWcDVnH3Ud1RVuk+GmkuIvItYLFS+WXESJZ/AyMB0ojzSwEnFpv5nGi8VFYKFD8KvELdSLkumIIYEbEhMcL8iQYXr9t8w3r4GFiBWKhmpVYPmEOt0ZoCxrMRaUdOB27ORgj9mAiIbQXcSOSgvcLMNgWeqcL0eqstNDQlERyblZgu/lfg5RQgfAmYlvgdnEd0KKwAnOfuZ6T/nXYL4rWajkb+SM+WpRvpQ+QTvc3d900vnwusCRwBnOzuv0+f+T5wCjCOGEHXsszsFqKz7KqsHnoB85nZre6+KoC7v2ZmyxL5zY8B2szsn/XXg1b/fdWlW5gqDUSYA3jf3b+wSMdzN5Fa4GceC7kNI2brnOHuY0srvDSMmc3l7i9l58JgYmHs+YhFsi82s1Pd/X8d3VfSvjHAa9nfLXvvlI6Z2WJEwPxI4M7iO06dsObhXjPbhlg/6XgzWy21uSrFI2XZ/4BFiDRWlwBfFveV1GE0lhjM9U6JRW2oullqfycGIG01qc+4+wdmdhORyus+d3+7+SWV7qaguYiU7qtOlayaFDC/D3gL2B+4IT3Q9Uqj5D6xWLDKiFQtF5nZ1u7+QWmFbpIUKH4EeBbY1t1H170+uXNlHHA7cIq3dkqWb1oP9xAB09Xd/b/NK2lzWSxa+X/AvETD/N/u/nwaMb4gEfwoHoT7uPt7FmmMHgdWIxqxuPu/SzmABkvf+7j00H8fkXe3N5GrfkciX/kZwAPA5cA5ZnYYEUh7i8jhDLRfILDV1Af8e/o9RNp1JvUjOog+A/Y2s3fc/Wh3f9HMjiM6oHdL75sdmIX4Hf0wG0Hacr8NMxtJdKBt7O5PpIf/cWZ2GvARcW243d1/CO0C53cSeZp7E4GRysjuDdcTz7yrA08TMw6WB64iUt7t7JHabQ7ivvE00d6SFmdmcxKLvd7i7kXw63bi+nAysdbB1sBCZvYrd3+8PnBeH0Rv9c4kCR1c62cm2pXP17Uv8kDqIHe/38zWIXW+dW+pu0c65nuBe7N9xQzHcWY2HbAWkcaqpRdHTgNQlkwj6D1rT85EDGLrtI2ZdaZc3r2llu6m9CwiUhoLfbJpUIMtFi3sMSymkF9MBLR2dvd/FaNh8xt0GiH1L2KxmZWIPHJVWKV9giwQ+DzwE3d/w9LU6qyxNsnAmLt/CVzW4gHzb1wPxJTKhVs8YD6YmHFxOHHenwjckUaGvkCk31nRIn9v0ZA3d3+dGGk9V/eXurnSKJ++wKXA28CPgeXdfU7gdWAXYE6PFAwHE6md7gbOIlu4rJzSN0bd6NEDLNZ+uNbMDqzaNVG6pq4z6VbgUGL2xefAUWb2ewB3v5LI5X0kEWD+gBiBvry7j03tkVYMmN8MTEPkU30y1UcR2PuOx+ySXwNDzez24nPu/hrwQ6KuqjRasHf2758SwY/j064TiU6Su4hZSj9294/NbBbiXrMIcJq3eJoemWAMkYd8TTM7IwX7ngJ+6u7Hu/u2xGyLBYATzWyRYmRxiWWWJrP2OcyPSZ0rY4lrQ5+0vw+0m+X4E2DdtO9Bd3+ujLI3UmfneQezLfL6mp9I7bUqMKyVB3ClZ6tTgdvMbL1sf39ijYsvoPNnLnWg9RwKmotItzOzfmY2JPXOjkv7TiYedp9KgZD5yy1lt/kuMCcxXfy5uoe9+czsR2b241RfHxDB0GHAssA1VQkSpYbbhcSDy0nu/g5MCBL2B14zs9278r/Vyo2YRtWDu3/o7u82t7TNY7XUNB8SUyNnIPIp9gIuSG/7K7ApsFHxO0gPu7On97Vsx0lHsoebBYg0Tqe5+1Pu/qGZbUakYDnZ3R9MDzhPuvtJ7r6Du/8mBRVbMihYqHtwu4hIT/MJ8B6wD3ChmU1TYhGlBOn62I+YMj8O2NfdVyGm2v8FONDMjkzvfcHdjwA2d/dt3P1Eb+EcxWa2IxG8uCddDzwbiHAVcGRqV5wDHMDEgfNXiI62W6sSKMyuERsS6VhuB65LL79I5J59lqiLrczsEOI82QTYtArBMJkwCvRN4EBicMr/Eal45gJeLQYjuPvxwEnEfXW4mS3cyu1ImbR81LBFDvOtiUXS/0O0O08ys2nz+4GZzUi0RddIbfGWVlzr8xH0k3p/uscOspitdTwxa2flVh6YAxOC4f8iOlFHmNn6aV8bka99wj3RYk2EYgBTJe6V0nUKmot0k3Sz2bHscpQtNTZuAP5UBDcs8oZtCDxIPNwcBfzezIaWVc5uNAcpzQTEw56ZDbCYUn0rkef8YuCR1JD/iMjFeGj6bCVG5qeG21HEaNmDzGxlmNAweYAYOXx1eSXsHqqHCVMlHyaC3lsAD7n7l+5+KrHw0HeJ9Au/J6ZOngIcYmYrmtlqRP3NSuTybnnFaCci9yrA9ERH26vp9a2IkecHu/uxqQPhiNR50E4rBgVz2YPuCcCSxKJUuxL3jqmJ8+IfZjZ1WWWU0sxGdChd7GkRLnf/DzEi7lTienpA9v52I8dauDPpHiLgu4uZ/a7YmQLm3wd+47G456fUAucLm9lD2f/GGGjtDud6ZrYucAUxevxLry2W/DmxsN1uwMvp9Z8QHbQtvXC4TKQIDL5JdJScT6TgGOjun2WDEXD3E4hZCHMRMznnKanM0kSpI6VoR0xLpOjaHbjL3T8krqUDgZFmtpiZzWBmSxOB4qWBEzxmtLYkM+tlZpsAq2T7zgY2tYkXTa63ArANMctxrVa/VmYdB5cTzxPPE4HzjTzWs/iQLPVMuo8qDWAPpZzmIt3AavmJHzSzi9z9s7LLVBZ3/9LMPgU2AD40szOJlci3IRotbWZ2DXA20M/MDvdqr0L9QfrvxqnBMhXwW2BhIjXFbsASwK7ApWa2bBpZei5wQWrkVYK7321mPyLyjB5lZocSo38+IoJjr5RawG6iepgw6ucRd38fwMwGpqDPHURDFo9F23Yjgh57A78C3iBGHq/h7s+WUPaGSg9449I95IY0IvJFIi/z3GY2gOgcOMTdj04fWwZYh7h+tHSuyY6Y2aJEx8n+HotyHQD8DtiBCJoeQow4/0krTxuWrksPv1MTHUqvp339U2fbq2Y2gsj5f1QaZXhUq3cgFdz9STM7nhgIdbCZfUHkaV4M2KAIbKRryadmdg7R5vqDmS3n7vdWIVieji8/jjuAXxCpN1Y2s3mLEeTpXnIzcLOZzUbMVGlr5WCYtJdmjoy3SGc2pUcO/z8Q987dzexcd98uPZMU14rhacTt4sR9ViomG1k9gkjF1Ad4PLsfnEMMUNib6Iz/kGh7G7C2t3Dqx2QAMeNiVTPbjrhG/gAYPrmAsLvfYGZLAh+ma2irM+J6gLtflwaoHAScmZ67egHbpAF8fdP7DZiWSBH5xzIKLeWwCrSTRL7VUrDjYWKE6Nbu/kbJRSpN3ZS484A1iMDOIsCq7v5O8eBjZlsTo4GuAA5v9R7tSUmBsN8S08r7ENMDzwL+XgR90giyXxGLlT1QUlEbKgX8tgH6uftfsv3LEAHjGYi62LDKgWLVQ3sWuWV3BI4ALvHa4l1YLHK5IbCYp9Q1af+yRP7Bj4Cn3P2t7i1142UP/X2I6aNTAvsC/yVG3G9NXC929chZjJktAJxJ5CfevIqjYiwWh92LqIMViMVNf+Xu51qkoXiQuKc8DqxWdLxI9aXR018Cq7j7mCIYll4rOtzWA7Z090otemlmcxN5y3cgHvbnSx0G+SJ2RftqIDCdu79cXom/uXQtWMLd78729fFa2r9BwM+I4MZfgcM8LaptLbrgq0ye1RYFHgScTixyO8zd3zSzmYiZmtsQ7Ytd0mfya0XxO9E5UkHp+rcfMSipH7Cuu9+Ttbl6ETMO1iFmJjwDjHT3SgxCMLPpgRuJwSljiWeLeyf9qWqx9mvjLJ51Lq9LrAc0P/HsdQXRAd1GtB/6EAH0Ldz98TLKLuXQSHORJrJYlOoRInfiDp4W9KtiIKOLJqxK7e7bmNmFRA7Jd4mAMUAfMxvn7uenmVPnAkPMbC93f6ycYjdORw+w7n6kmT1O3KQ/I0aOflIEzNID4CdEIGx0aYVvoPTb+BcxMvDhbCQxHivTr0/kb+9FpKJ4JX2ufjRZS1M9TCxdJ08njvk3Zoa7b2VmBwLbEqN93kkBUk/Xk/tKLXQTpN//AGAWojPgxGLWjZn9jUjPtGL8aQsTM1L2Itp2q6ZZOy19v+mo/GmGwXGpftYkguPXpdfGm9n7ROC8HzFzR0HzCukskJU6l4r0I6eb2c+zINjS6W1nEEGQjc3scmBsVa6j7v6Cmf2JCIL8AtiZSM2St7u8GHEOfAqtey+xyGH/ELCgpTVxPBZSnzCDwN0/MbO/EtfEY4A2i9mLoxUMrSarLQo8iLgPvEIEvj4GcPe3LC0MDGyb2he7pBHnfd19bPY70TlSAfXXOI8ZN6cSM32PBH5hZg+ljtaizfECkdarctx9tJm9Q8yoeAOYMetoasn7wVdRFzA/BVjGzP7m7qe4+7Wp02QfYsbWWe5+tZkNcPcv0mf6uRaK7nE00lwaLjVU5nD3J8suS5ks8vI+QIxwKFJq9HX3samxvyNwoVcovcak1N2kpvO0SKGZnQX8lBgdeaC7v5sefsenhuuOwAnAwq3cy5+Co4u5+11dfH8+Kn8GIl0NwFYeec1bVrpG3A+8SYwAuzUFwiaM8kgBv2Kk9YvEKPs7s4eZlr95qR4mLY2G2Y0Ycf40MSpmS4/8g5WXGu5XEiNj3wBW92xqsJmtAmwHbEmMrn2ZqKetvLboZ8umoKi7Z8wEDAJezPb1JYLlU7n70tn7ziDy3N/vSs9SKdmD/ZRAsfjtE8S6B09YzOw7FtiImN03nOh02gZ4393XNLPrgL7uvnopB9FkFrmYf0WkdDvS3Q9L+1u6A62exWLxVxP3hd7AF0Sb+zhglLu/lr13MLALETg/Bfi9u7/d7YWWbpGeIS4mRoru4O7Pp/29iI52t5jRdhCxuOMt7v7j0gosTVPXjjBgwoLPqb2wPZHP+mxg9+y1iQY4lXIATWJmmxJrWRxFrI21F3BtTwoGm9klwFJESr/7PFsA2szWS/vnBXZ0939nr1XufJDJU9BcGio9sDwF3AT8tCf30qeRTfcQI80Pd/er0v4piJFxbwNruvsn5ZWye9Q1Wg4lco9e5u73pH1/JwJD5xAPM/WB86laOVCcgqMvAKOA9VLHSac33br6mpto2G9GLFL1RDcVuynS93ohcQ7s7O4vTub9y1ILGO8N3F2FxorqoWuywPnPgVfdfbm0v3LTpuuDWmkk/UbE9708EQy/tK5DrTfRqJ+OyOX8crpmVilgfirwQ2K69OvAycCV7v6imW1L3DdOIK6vawGrA0t6D06FVkVZZ+JgIq1bPyJY2kbMVNvT3e9Mr29PdCgtRczQGkX8lvoTC2zfBezTSr8RM9sF+Jdnaakm8d4iVcuuwBHufkSzy9fdUgDsdKJtdACxzsGmwDzAY0SA/E5PKWhSR8uuRAf1H4EDqtSJIDVmNiNwLzFS+Pi6+2p+/5yNOE9mIFJ06HyokLp2xK+JkcPTAs8Rs3DetVg0/OfEiPN2gfOqqG8v13UIzA5cQy1wfnXqmJ6CuH8+7hVMcWdmuwIHEu2E24uBSERstLg+rEM8f68ArO/u15dWYCmdgubSMClgPoq4GW2nB1YwszWIm/BbwH7ufotFGo4PgJ94z8hPnN+cLwWWJBqyl+aBQjM7n8gfdy7wO3d/rwrTxdLIlnOAhYig9xdmNsvkfh8WOff2Ab6fto29AguiptE91xOBrzOJ+9D41FhZlsgdNz0xSugVj+mSyxIpSj4jri0tn3tP9dB1aTTQLsSCnxd7ynFelcB5Or738xE+2SyDvsTaD38k8iiu7+7PFNfEjq6NVRpRamYXECloTiCCn6sQM5P+QQTApgD2JHK9f0Is6Le5V3gNjJ4sBT5vIb7rXdz9eTO7l2hXvAZs7+63ZW2HxYC3PFIyDCSutxsBK7j702Udx1dlkabrKqLtdJinmXqT+czcRBtiD+Aodz+kuaXsPtn1cTZipsGZ7r5vOj8OBNYmzon/ELNRjiNS3rWZ2V7ADd7DZ8NWSfZ7H0h0pM1BdJyskZ676oOGUwMDPHKczwC86xVIZwYaBVuoe/b8B7ASsb7Y1MCixOy9n6X7xfTE7O/DgcuJ9nUlAud1nUQHA7MDnwPHeFr7x8zmIGbtTEXkev8PsD+wKrC8p3UgqiQNxliSGLz4Yba/fvDKj4hOlV+4+/+6v6TyreHu2rR94w0YTIykvQWYNe2zsstVQj30BRao27cWkVPvQWKU6J3AzGWXtYS6+QORPmBFoH+2v0/27/OJVBVnANOUXeYGHfdA4GbgUiKv5iXpOPtP4jNGLPL3PtGJMH/Zx9HA+liSGBm4WbZvCqJz6e30WhvwKrAFMZWSdN78D5i77GNQPTTk+AcCJwHzdPH90xOLd40nFsgt/RgaVA9DiYe3m4nR5LN28J6+RIfiM0SAaL6yy91NdbM+sR7IukQ6jeI8aCOCYP3Tvl7E6NIlgRnKLre2pp0PRgSBrwW+k/Zdlq6RvyYGbLwILJd9prhubgL8k5ipMLTsY/kaxz4F8EsiDdNfiEU8u/K5eYnR2BuUfQxNOh8GAH8j8rOvnr02A5Gq6t10vXgGuACYvexya2va+TCYmOW8JTAb8dx1PjAwvV5cCwwYRqwdNDj7fK+yj6EBddB7Un/3lI0s/kAEgV8hBi0V+1al9kw+T9o3IxE0fxuYpexjaEKdXEg8Xz9BPFu+TQTEi9fnIILlbcDz6dq5VNnlblJd9AJuI0aYT3TOpL83yv49sOwyayt/64XIN5RGdtxDjKZeD3gj9eq7mfU1sw3TNJ9KSyMcTgeuMbMd0j5z9xuAnxF5NWcERrj7m+n1HvEbTOlJViTy8z7kaXGuZMLoD3ffmmjIrE5MvW55HgtuPUBMG74XWBn4Q10d1H/G3f18YGlgD3d/plsK2z3eI1I47WZmP0m/lf8QOWcfIL77bYhG7lHEyAc8csEv5u4vlFHoJujp9bAsMUL4BDOba3Jv9hjpcioRON/azM5scvmaLqXo+REwE/EQdz1wqZltnVJMAODuY4mg+i+JAPoVZjZvCUVuqpRqJrcgESx81COl1ULEb+YfxGjbL81sKNDP3Z9394e8C6krpHWkmRZA3BeJB/2bPFLznETMwlrP3Y8jOhznBM5Ls/zw2ujSD4mZkKt4C87YcvfPicEEvyYW+PydmU3Xhc89BwzzWMjMmlzMbpXaSV8Q33s/4hpaOJlIN/BzosPxRWKWyoDuLaU0U3HPSOf2X4ApgfuI59GRxDPpT81skMdMvt7U0vh8QcxYAcBbf4R5norkIDM7D7jdzPbqShur1RXP02Y2MN0rCksSne8PZ/tGEjNw+hHrXuCxxsGJwEJegZny+fXezJYk0tttRAzO+DFRJ1eZ2coAHjPflyPWEPor0fn8YHeXu9Hq73spLtNGDGBczCJ3Ofk5Y2aLAgek9CzFc7z0dGVH7bW1/kZMkW4jpkDOke3vTzRULwSmKLucTa6DwcQD2e1EbsVpO3jPasSIqPuBDbP9lRuRz8SjHWYnRgLtO4nPLJz9e7ayj6FB9dAr+/dbxKIrfwSGlF22kuvlF8RU+vHp2nE/EQiYOnvPT9Jrm5RdXtVDU469H/Hg+j7ZyNEufG5GInC0UNnH0KB6WCFdG/9NdAi8lL7ve9LfA0izcdI9dR1qIygrcZ1Mx5ZfKzdL/z2YyNEOEeh4D7gIGJT2bU+kvpqp7PJra+i5MB3ZCDci+Pmb7O/+wKzEjJtdiU4TiJRWr6V21mUdnFd9yz62BtTNAKLzbCxfYcR51TdiRPFoYlHQc9O/16o/r8oup7amfPdTAJunc2DD4pkqXSceSPeNC9O986C07z/ZfbXln8HqrnMXEc/epxGj6d8hBixVZsZqB8dfpN66i5hxtE9xvMBDwI3Fd11XV2cSs1Aq1YZg4mfwxYArgCmzeliWCBy/C/yw7v0t/5uor4d0PehHbcbJSkRn+i3Aqtn7isXkH6tSG1vbN996xChXaS53Pw34LbAwcFrKbQ5xo3qdyOX9eVnla7Y0iv5G4sazB3Ccp3zc6fU+AO5+C7AT8bB3mJltlPZ7lUYApV7cYrTDhSkfGETQeDkzm6Z4X/aZNYE9zGxOAHd/rZuL3RReyyO3I3F+3EKMrt2+qIeepBgJ4u5/BrYlUtDsQCzyd5a7f5DNvhhMjCqs0ih7QPWQRkSNcfd/EVOpfwgcmXLwTpLHaKDjvSL5aN39bmAEMQPlL8SDzA5E4PAQ4uH+UDNb2GN2yvVE/u5biam2lZBdKy8Ezk65im8BZjezY4mHu5uIHKSfpDzwqxMPy1+UVGxpsNQuWIqYcbFrmsn4GLB+ykNM+h3Ml7YnPdZ7MCIwcA+Rm3aL9N4Jo0c9Zmy0jI7ahR4jq88krgFdHnHeA1xDLPD3ILEGxDZEu3zCaGTvQh54aS2pnXQmcBaRx/7J9Ew1RbpOFDNclyfOkZ8SqSeW8ciB3tvdWz7/d3b/PJ64fm7p7rsS983pgGWAkyo6Q20qoiPk+0Rb+TlicNIhZjaESMu1ipltUHzX2bX1TcCJTshKSDm5i2fwQ83sbCIVorv7ZzBhls59xH3kSeASM1u9+N+owm+ibubF74jz4A7gQjOb193vJBYBXQE408z+bGYnAH8nOuG2qUosQhqk7Ki9ttbciIVWpqrb91tihM+/iRFAtwNzlV3WbqiLXYlpX0M7eX2uos7Sf9dN9XQPsWBZ6cfQyPMi+/cBRKB4lfT3H4kFDLeifR7B6YiRQddSkRHY1OVGJEY/zUb0dP+dCPLsRTaiuKds9XXTybkzA5Gr9lY6mLVRha2n1kPd8R0C/JkYCdVG5P2fq+wyllAn6xIPbSdSy9M9gNosrg+Bj4EjqctNTIvnLKX9qK/liZFiq6Xj7010JHyR7rFFXvO5iY6GN4AFyz4GbQ0/J+YCTknn/ttER9Gsde+ZllhH59/Ad4iOt9uBU7P3tOxvo+53MYC6XP3AIDTivL7Ork7nzGpll0Vbt37v6xGpD9uAX2f78zUvhhCdatNRG4nep7vL2uR6WDS1FTdNfx8AjAM2I57P24iOg8qsiZKug8+me8S86bvuR6QYaSPS3y1KzAT/L9nsE2J9lGuIBZanLPtYGlQfeS73S1Lb8QFi5k0b0dHap+4zSxMd089RwawAxHPFa8RMhPPSufAetRmNq2XveZKYqVGJmazaGruVXgBtrbcROeNOJnrs5q9r3P+WGFH8PrBs2leJaT6TqI/TgAfq9hnRg3s9kZLjRiK3YtGIW5MIIN9CmmpepS3dhP9OpJ8YkPZNnRonHxGLuP0A+D/gYiK4vkjZ5W7QsedBwdmIXNT5b2TKdOPusYHzDuosb+jNT0yN+6Aq54TqocNjvSQ10g8k0myMIPKLXkXPDJxfT4yAmzH9PRWRv/v2dB09Oz30XFvFeypwDHB0+v77ZfuHEiMJ24B/peO/OT3gLF52uRt07PnvvuUXo2tQnXyPWtqqwzp4vQ+wC7VFk98jggNFx0rL/kbq2gt7E6kPP0htpe2y1/LA+Z+B6csuexPqYrIdH9QGpGyX6uLQssvdwOMfCJxARYJ6DaiP/LeRXzdXJjpW3yJGiBb7O0zJVNXrLJGWZCZgA2IwwvbZa7elffdQgcA50Zn4OtF5Ok/da3Ol9uQZ6e8tgceJ589jU3vjX1VqX9f9NmYk8rYvC0yT6uNZYsDe1vXXVSLv+3fKPoYm1MnuRLt6xaJ+0m+jjYhXFe2F/ula24+s/alNW771QeQrSIuT3UwE/J4B3nH3tjQdqM3dDzOzojfzEDPbzd1fLV4vs+xNNAaYx8z6Eb36CxLBrhWIB/vriCDyUsRowQvc/UYzWx941d0/6fh/tjWZ2YlEGprXgdPd/Yv0/X9gZj8Dfk886O1LNHBfI/KJPV5aoRukLjXNn4kb9dTEVPO/u/vj7v6Zme1MTAk8BhhvZhe4+3ulFbxk7u4pXc3eRCNvPmDlKpwTX0VPqYd07VuDCHJc67FA1+VE+o3TiMVB9/XWX+x0srIppMcT94qd0gKH9xK5zn/qsZhfcW19zN1bfupszsxmBtYnAqWjiIfhMQDuPsrMDiLq5sfEPfY+YJeiXlpZPoU4GUQ82PdIWVtxBqKzZBBwuJl96O4npveYR2qFc4lOpVWI4Mel6VrSx93HlXME30y2SBlmdiQx2OJs4G/ETJOlzWwGdz/eI03RCCIAcBLQz8z2aNVjr1fXnroJuNrdh9e/L/v93EgEhjY0s+Hu/nG3FbZ51iQGV8xpZtt4hVNdTk7xu7ZYHHgGYCYzexb4xN1vM7P9gD8Q6S9x9/M8FpCe6Pmz1Z9HO3umdvc/pddXAx4lFnos3utEJ2N/0v21xY0h7pUzA2ua2d88UvJADNz7gDhe3P0iM3uHGKi1c3r9KWClqrSvs/vGycQo+s+Bp9z9Q+B9M1uGWDfp2PS+i4prp7s/VE6pm24R4BUibVObmc1DzGy/ADg6XR/6eywqP6ZqbWtpsLKj9tpaZyNGyI4iguZLUVtEpW/+3/TvI4kezatJi4NS3Z799dKxvkmMvv+MCBgfTUpDQtzAXgcuLLu83VAfc1NbzO6Yjr53YprkKkQe/Kqknch7+c9N58QfgX8QI+lvov3iZlMQD8JtxINxy46Mm1RdfIXP/B8xRfAvwLxlH4Pqoan1siXwJbB03f6BwO/Sb2JEVY+/kzqZiRgp+xzRkfgQsBC1qeT5qLqWTTuRyt/RPWEhYqGqcUQKr4mmzrf6cU/qeIDDiFH0zwLDqeCor67WRX6OEFPuT0vXhGF175lqcv87rboRs29eBH6c/l4h1cHT6fowLHvvYKKjdd2yy92M84EIGr9EdKx1+P1m18l9iVkHlRh1T8w42pEIDl5OBdMnfJXzIZ3r1wEvp9/Dk0QQsHh9LSKv/dPAVmWXu5l1kf49O/HMNU3de64mAoXF3zMRKSiWpQKzW7PvuzcRCH6fGFVczGw+Kv1mvtPBZ6clOg4qN3uDGGhzL5GO5cZsf1Ev06Y2xovAz6pyv+ygHvoSM/5vA67L6uY9YsbWwLRvHyIVXCXrQVtjt9ILoK11NuA3RL7RBTp5fUDd30Xg/PKOblxV2oCfEItMPE6Mjvp+0bjNbu53AzeXXdYGH3d97u7iwWW29JDzFumhL+3vcKpkq29MnH/0XNqvxr1naqjcQRYoJDqiTqdCeXnrGvRzkDrN6s+RTj47R1UasqqHSdbNhkTqhTXS33mH69LpYaeNSOFSqbyjndRHcd38UTruUcBMZZerwcc4kJgOO0fd951fOxdMDzlvAuvU/YaMDjoQWnWjfSfIpcTMvRPTvaItPdgtU3Y5u6kuigEYA4hOw02BJbLX50/3yTZisfX+RLDoEeCgssvfhPoYRHQU/CH9vX66Xg4j8vO+lK6Rw7LPFO3Mlv9t1NXFEsTMzYO60n5M58rsZZe7CefDz4iZqpfTcwPnUwJPEHm7f5HaETcRAcJ/Zr+BNYjZSB+S5bCuwlZ3TzyVeOb8IF0T9iClKSEWwv2CmKWyNTGy9g1gtrKPoYF1Udw38sD5lsTAiy+BjTqos6pdHycK9hKpT/+d7pe7ZfuL9LDTEMHjJ+ig47mVtvTdzwBslO6TS9W9fiDRntwpXScuLo6ZWAvlQmKWVo+8pmr7alvpBdDWOhsxCuyfZEEM4kF2l3RDfo7osVsje/1wYorQRVQw+MHEQeMOF2NKDfn/AEeWXeYGHnveEBlSPKhkDZm5UiPtWdKCGx3VWatuRE7VQXX7TiFGCT8GzF332q7UAudLdVc5u7lO8kDQX4nRPmNSw33Vjt5XpXNC9TBRPXQ2KnAgcCcxWmy2utfWAq4k8g7OX/YxdHN9zUl0rj5FhTqa04PNNekh7hUiOLx6J+9dMF0j36AucF7FjZiR9jSwXPp771RPnxPBoaXLLF831sPg1EZ6ndqI6gOy1+ejNuL8VmJRt6eoQEd8R9d9Ili8DNE58ByR1m7K9NpPiUDZ68CBZZe/ifWyf3bNKEbcVyro9RXqoscGzqkFw4cRI2QXpTYTZRpiRufodC0tOlbXAc6q6v2DeOZ+mQiU/4xYJ6qNmL06LZGy5NhUL6OJEfmLl13uJtRDHjh/MNXBF6S89lW+XtD+GXwb4EfZ38sANxAdRztl+4vA+dS0eF57ohPtRGKGZhsxS/FjIlY1S3rP8sRz93jgluyzM6Xrw3P0sOcMbV9/64VIF5hZL+KBfoBHTrn+ZjY7MU3uFKJn/2MizcTxZrYigLsfTowuO9Qrkmcx5xPnlHsPIOU3J/17eqK3cwYiaNby8jysZvYn4iH2KTN7CNjZzOZ095eIG9ZA4Bgz2xRaP5cggJkNIGZd/MrMpkr7piJGyg1K2+i0vz+Au59GNO6nB0aY2RIlFL1pUt5ET/8+g8jFeSHRmN8M+L2ZbQwTcndb8dkqnBMF1UOou0asYmYbm9m6ZjaVu39K5B4FuMXMFjezqcxsLmALYmrlSHd/pqTil8LdXyYWwpyXCB5XRX9iJNyHxEJ9PwVuMLOzzWyzuvc+TeQcfY4YYbq+mfXuzsJ2FzObk/iuj3f3e81sXyKl2WbErINlify8y5ZYzKYxsz7pvwacQ6Qx+wnRcfYssG/K6427P0sEjvdNH7+bWMBtbPG/06q8lot22Wzfw+5+PxEg7Af8w90/Sy/PQOTqfZqYil9VfyUCxLMBG5nZtMW9tcrS81Y7HmsfXUoEjlcHLjSzKbq5aN3CzGYys2XMbHUzm8VrOesXAsa4+2Me+Yn7uvv7xHXhUWKk6QAAd7/O3X/mscZBpe4fZrYBMSNvF2LdqLOI9iXENfRzd3+TGLS2FJFC9Ifu/kgJxW2qFI/ok86RZYkO977AQDMbkNrYlYt11bWvzyNm4ixmZjMCpHvHb4hFX/9oZjul/V+aWT93/yDdU1tSWl/vdmBxYlT9CkQc6jJgVeAmM1vK3e8hUt+9BcxsZvub2eFE23IToqOhRz1nyDdQdtRe27d/o9Zz/3Oit+46YuT4m8R0qL8As6b3bE30+O1TdrmbUA9feaQ8cVE+l1ixfGjZx9CEOrmYGAV0FHAwETwfR9yQ5k7vmZMImLxPmi5XhY2Y/vkp8GtSPkFixfJixMfl2Xv7Z//ei8hX/J2yj6GBdZGnWJiD6Ehbi9pooNXTOfAAsElHn6vCpnrosB6KEVEfpvvHHdRGDW5BjC79khhB9jwxgnKxso+hQfXQ6Si3+u88O0cGER3QI8ouf4PrYtV0bziMeMA5Ph3neOLhZxOyFEbEyOL/EoHBgWWXv0l1MpCYVjw9sBoRCN2RGDU3JbXR+XeTpSup0pbqYEFilGg+S3HB1HZ6H/h93WemzP5diRmMREqaNrLR9Wl/kbJpqay+jiWCp8XI85YfTdnZPZAYSXwFaVHkql4LsuPNR49On34HvbL7wxBqOc6voGJp3IjA54PE82Vb+m+Rxu3o1D5YIHt/saZW8ftZtAq/h87OifT3MGKWSfGMtQAxYOsSamlBFym73N1cR8WI815EqpYPiNm9RX1U6pzIjvt8YoDBOnQw0x1YiVgr5R3gF2WXt0HHPBXwArG+3sK0f94YTKToeZ6YXVHMgN+ISJ1bPH+dSYXSomrrnq30AmhrnQ2YBdgvXYzeJhY4XI2JU1S8BZxZdnkbfOx5uoXfEulWJpWTeFqiM+FZIkfrwmUfQxPq5MfEglTr1u0/mQiCHUxtsY250g2spaeDpWPJH2quSce6H2lB0/Sg88fUaDs/e28eOJ+67ONoQD30pW5qfDrup1LDZP68voiFX4sGS5U6T1QPndfNGUTAfB3iYXbedH94hVoQaC4ij/MpxMiYSkyVpPYQNzAdV5E7cYfJfK4fsAMVCQbWHds5xMPbnOnv76a6eZRIR/I4sC21Tvg5qEjnIp0HBYvA51HEqLCps9fOIkZRPVuVeqivE6JTrY1Ix7NIXldEO+tcYvTkbzv4fGUCIcBQIv3h28B+2f5503nxDnACcB6x2PzaZZe5gceet6kWJFLSTJPtm5pINfAesUBqJQPndfVwEpGzf3xqK+QBwDxw/o+q1AeRi/lTYhHwzYgBJv9N94bFiOfNNiJ4PlP2ud5EWqvnqd5aIHlAcLv038OAV9O/506/i4tIz+LpHnp51eqiC3VVHzh/J50XA8osVwOPrz6d49pE8Hjt7NhnIGZcTFjkE1iR6Hh/MV1LW/a+SQwmeJ6YkTlT1lbI17zpT6yN8h7ZYqjptSImUbn2tbbmb6UXQFtrbalxMg2dBDaIKWPPA7uUXdYGHnPeaDkpPbAsw2RGhxLTjDdr9YYLkbt7zvQwM322/xAiADZ9+rtf9trlRGBw6vx/p+xjaWSdZP++lokD5zMQIynfBy7I3tuvO8vZxOMfQCxct2Hd/uNTI+4jagGQPlnDZhUiAPQMsF7Zx6F6aGrdLEA89O9YnPfEKJCPiUX9KvGg38mxF433wUTnyePELJxR6f5xDh08yDHxQ1FlrpnpeLYkgh5/oP1o4RvSveSx9PpoYP+yy9vA486DYfMQ+aq/U/eeU9P1YLb092xEwHyDqp0Hdce9YTrOscCWaV+f7Dc0H5Gntw3YuezyNuiY8wf9vH25CJGC4738/CdmYfyDGKRwP7UF7lo2+JEdWz4g5XTi+eEtYoHHxbLXpk7XifeJoOCg7i5rk45/ADC4bt9FxOjR3dP5/0q6NhxK+8D5Dul3cX53l7sJ9bACkYv6j3l9EAHAz4j0RAB/JjoSjiGNOE/X03uI546W/010Uj/nEc9U8wBLpuvlGUQn24VFnRG5zP+WrhdDyi53g459ou+0s++Z9oHz/5ECxWUfQ4PqoXfdf3cgBmcZEShelxik8ka6LjyYvXcp0mCFVt3Sce6Xju2P9fVS997BxECcL0nrSFHhBWG1dc9WegG0tdZWf6GhfaB0WmKEwDOtfnEujrWuQb8YkS/rRx1dpDuro1beiFQB/0oN+LbUMN02vXYoERTMH2yK4Njq6f3rZq9Vpl7S8XQUOM9TtRSB87eAq8oubyOPOzXcf100yuuuA/sTo4XuBObJPlMECtYkRpbO3V1lVj2UUj/LE4ufrpP+np/aFOJidO2PgLnKLmsTz49/EsHyfDr5ZenauHK2r1LXxsnUy81Eh1HxMHcJMSJsVSK91WbpelqJ2Vm0D4qOIEZOtqV76t+ppRfYmggaXUDkJz2fCJJW5vpA56Pt10lti0+BZdO+PHC+UGpvVKrzIGsr9M32LZp+E+8Dv872DyICYjOkv9u1T1ttY+IOg5OJgM/ewHBiZuL7ZIumE4Hza9Pv5yetfPzpePoQo8gvzPYdkNoFy6e/dyOCxI8RKc4OohY4n4ZYAHCB7i57g+th2nTMn1JbwC/vWHoI+Ff2/hPSOfBBupe8lN7TN/9sK29M3Jl2PzHYoh8xe+00YgDCE9n75ibWzXoDWKjsY2hQPeSBzqlp/9zV2ULzeeC8pe+fxMjq/Yn1Hf5M1l4mAuFvpXvnFcAnRMfjssBWRDq8n5R9DA2uj4WIYHgb8Jtsf/57KdoNSxCdS1uXXW5t1dhKL4C2b99G7WG2yw1SYlrd2amRu3jZx/ANj3+imzLRc38X8cA7R9rX8g2zydTDVEQHyC2pIX8A8VDzLpGn+XvpxnVsapzkHQybE4GQRcs+jgbUw5REoOu7kzlXriWmke5O7aFmutS4fYE0grCVN2J0063EiIaiYXI6MY02Hzl6CPAqkb6mCBj3pvYg1NJ5OFUPE9XHRNdC4IfECLEVien27xFrIBQjolYGRhILVJV+DE2ok2mIwM8wap2JmxMdCb9Kf1di2nAX66O4l25KPMztRwSI36Uu1QR16Y6qsBEB8peBnYj8mvsRD3S3U5syfEC6XrxDzEqoRG7/dGxFIKMPkXZnIVK+0bR/TSKA2GHgvP5/p9W2Do5jF6L99N30dx44X5xob7YBvyy77A2uh45GBQ4k2tibZvvWyc6HPHA+DdEZ2dKB4nQsUxNrRP2PCIxOQQSEfpte/yXRkbYBMZL0qXQfPSS7ZrR0x0E6hsHp9/BWqo8h2WtTEiPtT6j7zCbEQpcnEh0Lxf2lJa8Pk6ibk4nBN1fSvm25MDFjbSxwPZGu4laio3Vo2eVu0LHngdCjiQEoNwMHUusgmWTgvJW39Lt4hFj350miM+RVauntBhGzbm4kZqptmX12JaJjfoWyj6MJ9TIP8VzdBhxef75Qeyabi3gm/3nZZdZWja30Amj7dm11N6ltyRbm6uT90xF5N/+XGrgtvfhIapg+xsSLMR2QLtBtVCiX5CTqYRAxTfbfRGqW4ia0NhEIuzzV1VHFjYvatPLZU2NuFGlUVCtvRNDv49R4maeD1/PA+Y2p0Tpntm9aYMayj6MB9TBVaoTdS2202+DUmP+CyKGXN+oPIx52rqW2YNFX7pD7tm2qh4nqIx8JtAJZ2gli0c+XiBFh51LrTJo+3TfuoMXTV3VSJ0aMGG0D1kz7ikWyD0x/T0E8EK9Vdnm7uW5mSe2FNmKU4Fr1v4cq/C7qjvmHRMfphqSOEiKH9efEtPv8erEA8bA3fRllbdLxF9/vYCIo9kz2/R+cva8InH8CLJP2VXJwAtGB9jSx0GGx7kXf7DewHjGTbzxwUNnlbdAxDyRGRC6X7TuNSKEwirrZJcSMxSJwvmS2vzLXB2ANohNxz/T3XESbe6HUbtiT2sysXVNdvEOW974KWzo3tiMGXt1AraP56vQ7mdDpNon/jU5nALfils6D+6il2pi27vVZgY2JlE4XEbMeJ3pGacWN9oOwziMCxmcTAeR3iPQz/av4vadjyhe7/B4xw2BDomPp0Ox9vYj2Zt6GmJFI3fMwMHPZx9Kk+ukscJ4/j+yc2hoTDXjTpu3rbKUXQNu3Z6u7SV1C9GguMqmHFuIB+JdET38VRtLOT/TYt1G30jTw07T/Olq8c2AyddAnPdi0kUaK036q9HXEKChL3/9x6b1PEIuN3E2MHly87GNpUH30JUY9PUvMNOgocF4EBeZKx/679HclHvizBtxNpOmz2WszEqMovyBGUdYHjJ9P58RcZR+H6qHh9ZE3UP9KPNj9jNoouA2Jh5xPiamkA4l8nH9Pv5NKXEfpoBMknStPEoHxn6Vr5EHZ66sTD0SblF3+bqyn4h6yJREoOqLsMjXzfMj+3proOJo9/b1AOv8vpBYQ27Q7y1hCnUyZ2gh3EoHAnYhO+TbgL9n71iI6JNuA75Vd7gYc90rE7LMjiED50Oy19VKdjGbiBaO3JFJOXAasUfZxNKguNkzf60+zc2JXIrDxMSllFe0H76xOpB9ooyIjaLNjK3IRX0l0Ls+bvbYVESicJ9t3MDFr7VIqGAiiFjj/gAiWX0t0qBTrwlQuOFp3/B3N2FuaeB4fA2xVnDdll7W76iFdI24EVimOHTgy/TauooKB8/Q7+F86/2egNqp+CiIQvj8xKOM7xfFnn/0R0cnwHhV5Bp9EPXUWODdiTZiiM2lwWWXUVq2t9AJo+3ZsdTephYiRxGt05eZMlqe3VTfSCMj07wWJUZEdBc73SPsvIBsVU6VGDNFzfTgRHLwLmC7tL27cD6YGbTESpD8xAv3S1Ig5vmoNeiJwvi4xwvi/+cNN/XlEPACeWHaZG3jsg1MD7ibSiHlq0+CK/w4iZhd0FDA+mlgIsaXXOVA9TLJuLiU6lbYi6zwlFjnbIv1mPiUegB9Nfy9edrkbXAdTElPFf5DtO4OYPj22aNQT6XnmT9fWK1v93pkda++O/t3JexdK58A9VGhEdTq2vC21DZGH+kep3TAd0dFcpCoalN63DhFArkQe97r6KDpK9k/XiDy//xxEW2PCDIy0f33gTFp8in36jh8jgoAvUMvDnI+uXy/9FkZTCw4OAH5LdLZOnddjq2/EoIvnsuMaQsxqfYtIBVjkec9/R+sSHYwLll3+JtVJkapnu2zfpkSgdJP090zEOgd7lV3eJtdFETh/MdXJSmWXqRuO9zfUDSCg/QzW76ffzcekNWKy1yakxqzKNSI7tr8THQbX5O2EVGeHUwucF8+iLR84JwK+56Rz/1d1r81IDGZ8Lp0LHxHP28WCuFsRszTurGJbopP6ygPnv0n7piQG8bxKBdJ4afv2bKUXQFuJX37HK1IfR206XMunlOhiPUxFpBz5XbZvwXTD7ihw/ou0/0IqMBKqo3OCCOzsk24691ALnB9OBL++18FnJpljrtU3Ogmc5w1WYhrdKGD3/LVW3dIxnZnO9w1pP5K2PxEE+U36eyY6Dxi3dGBM9TDJutmTGEW/QnYNGAJ8F5gv/d2bmKnzC6KDbZayytvgY88fbP8vnR//IKWWSPuvJ0ZVn0qMOt2bGEk7imzBqrKPpYF18ifSyNhJHRexsGMbWSdDq29114V/EKOIFweWI9J2XUiMML+AWm7/GYmRYVeRAoZV2KiNmC46FP9GdBrWr38yJzGi7smOrgu0aOA8HdN/idzLsxPth42IhdXbiA61ItizATGq/EMiSFSk+lq3zGNoUr38nFg0fRdqbadB1FJzXEctoN5uxGnZZf+Gx92n/vdN+47Gu4AHs78XINKXvU4tX/W7VOyZo5O6GpzOh3fS72hI2WVq0nH2pjar99N079yok/d+nwiIThQ4r+JGdC5fQTybP0RtRlbRxiwC5y8T64L0K7vMDTz2H6Tf/nOkfNzpvvk4MWhtYyLl24np3Pl9Vmcr0eKxm/TdHkAXn5doHzg/kuhI+IyKzUzSVv5WegG0lfTFx4jYa4El0t+WGnV3E3k2X6I2xb4yD/Qd1EORn/hGskWp0mtdCZxfTcVGv1B7yO0N7EsEzkcSC35+Afy4o/OCio52qDvG+sD5YtlrsxE5916kIik40nVheSLFxtOkUT/p3Hg8XS/y/O0zEgHjj4lg6hRllFv10K11czJwe/b3skQe2leAt8mmTVZpo32e5nOAvxAPvm3EyKg8b++ZxLTa8cD96Tox2RytrbaluvgSOGsS7ynuL/MRD8MdztpptY32Ab7ZU9tgtWzf8HRuPA4slPZ9l8jt/1axrwpb9h0PIRY8HQScQAT9pk6v5QHD3dJvY/6yy96g498rfc/LdNBOmhv4QzoXjs72LwWcRCz2eD2wftpfqfYUkZv3SeCmuv15TusOA+etuhEzBx4knrk2qrtWFB0nexDt6x2z135IjJh8gOhI6RGjRzs4H64tzocqbekYz033zMeIZ+9xRMfZBtS1G4nUdjcSM5U6DK5XaQMWS+d/G9k6Y9QC51MSz6VPMZn111ptIzra7yYG5OxGrKl1F2ldpKIeiFme71GB9cOy4yrW/zmRujz+k/jMPMSzSBsxQ+f7ZR+HtuptpRdAW0lffNx8XyNGCC6a7e9H5IBqI4IAxfThlm+4dlAHRX7iW0gjnOofUIgVyjsLnP+aWLCq5XO5d1A3eeB8H2pTi3cu9pddxm6qh45mY/QlRsv+lxgJ86d0c78+NV6Gll3uRtcBMcrlibStnBr4d3fUUCUCxv8kFjibuuzyqx6aVh9F0Pf3xKjpI4A/Eg/+lxIjCi8j0hXNRwU71dJD23/TPWSzdE4UAbGraR84H0x0xE6R1UWVAubFMRUL305yBHm6jk5ddrmbUA/HESmcnmLidQ9OSfeI/wK3EYG0F1r9nkH7kePFeTCASGd1BfFA+2MiAJa3Ky1t+6Rr6qxlH0uD6uMSYnRwnvYvr6PZyWYu1X12MLUFk60K18vsnCg6GncigoPb1b2vCJS+TcxwHFJ22Rt0/NMRMy2ez+4Nu9W9Z9Z03by8bn//dK0cWPZxlFBvxfkwmug4GFR2mZpwjIsRne2/S+2k49P5Py7dHzYizdhL71+c6Gx+pSrnBJN4nkz1U6RLHZbtzwPnlZvBmY5teeL54tPUTpiqvr6A04mBPAPKLm8Dj3sgMIxIa/gX0kz3LnxuQSINplKyaGvKVnoBtHXzF1670fQiRjE8TExvqg+cX0EEBH9HBUecpxvtM8QIhmlSfRSB4j7Aqtl7JzXivDLTqTuoozxw/itiBHWe47wyAZ/smPt28X29gXnTefEEMarsHKo1WrBv3d9D03G2EcHiTnNzE4vXVCUAonrwzh9siIWqrkwPcTeRBQOIBfCeo8Wni06iTnYmOp+Xqdu/J7UR58tm+/MRhi0dDOvsWIgO+feo5XDvaHGzlj72SdTJ9ESw703g8Wz/gOzfWxAdC2en8+c7ZZe7Acdd5KEuOtKK++PV1PKtGpGW5h0iVdGQ1Nb6HhEcurzVz4t03IOImZpHFvs6ee8KxEyk0/O6q8KW6uBYYtBJkYYov0YskOroklRneV7mgUTqlhcndW9txY1YvG9nIgA2hggE70Ja7DP9+0uyRYFb/TdRd/xfebBNOh92JWYfVOYZNB1br7SNIAKjxQykWYg0Ew8RgxAeImY3D0mvz0N1ZrPmAeBfEB3Lp9M+QP49as/g+f7KXDMnUT/LEoHzl4Cd8nojni+uINY6qETQnFrHaj/gZ8QMtD/QxZH0dPEZXpu2r7OVXgBt3fhlx8jql4AN0t+9iJFxo5g4cN6fGCnzdrp5VypwTjy0tgFn1O3vT0wdPYmJFwctbtr7Zfsr06DtpJ46StVyN6ln/+s0gr9tWzq2tcgW4iHyo23Qxc/PSHTC9G9G+b4FdXE6KbcqMYX8LuKBdrkyyqh6KKc+sn9vTuTl/g21h7zB6XeQLwI6Q7pmXkcaIVO1jUg98Qm1oGG/7LVj0v3in8CSZZe1wcedB8yHdPD6MURAsBLpNiZTF8VDXhH0+w61vNUnZu9r+ftDJ8e/KvFgu2BxnESO2YeIxRv7Z+/tRzzgv0eMOn+CCCD+h2xAR9nH1IA6uZuYfTLJkaDE4q//qVo7kshH20ak4/k30UEwqO49v07vWSH9nQfVp+zoutKqW/05TaTy+2k6T74k8pbvmvY9TnSoTVGl84Jah1r/1H5aue71To+VmLVSXF9b/vrQwfFtln4Lh9e1tW4hOhkfTdfYt8kWEW71re43/w+is/mm9BsYTbSxZ0yv58/gB5ZR3ibWwyTPaWqpWp6nNtt7AJG65h0qkiK27tzfGziKWsrD4+niiHNt2pq1lV4Abd30RUfA/Jl04Z0l29+VwPmrRD7Kll6Ip64+5iGCYB8AJ2X7HyNyzs7ewWcWJEZDfUyMTq9Mg3YydVUfOH+BeNitxA0MmD99r/el7/gf6bewyGQ+V8XGe0d18UpxXSBGCy6ZGrX/Ix6GK/c7UD20q4s8QHpx+m2MTtfOT4kHvTnqPrMsMR39vcn9jlp5A7ZN94NNyEbIpP+uTYwSe4fIW93S18vUhji4bt/wdD/4MdmIaWIxqjeBg9JvpTLXSmIU7SFMvCCwZefAnMTsizfIcvpTsZFxxIJlnxIjx4uUIgsSa6C8DdyQvbd/UQfp9/InYlThsKzeKlE/xCKfH5ICwpN437XAA3X7Wv63QswimJUYfFCkJLmClI4l/VbmIWbpXEoajd4Ttvp2ArBDqoMxxLPHF8QidvOUXdZGHzPRuf4A0ZnSRixuuh61Z4xJtqEm93orb+n38RppxDDR1hoNrEh0xK5DBJQrtxAssSj4C+lYi3tB0fG2Z/a+71F7Bp+2lc+H1I7YNvv7qwTO9yBSgn5GWpeuShvxvPU/Imh+QLpPjgf+TBdznGvT1oyt9AJo64YvOR52nydG/czSweu9gVXoPHB+W7qAVWKhiawBNyfRU/sRsYDEI8CdTGLaG7FwV1XSLXQ0bb6zqcR54PwQIifrd8o+hgbWxS5EGol3iEBHJRana1ZdELm9i4Dxcq3ceFU9dLk+hhMdqGsDc6VrYZGX92hqC7f9lhhp+lh+L2nlrbMHGmBqYvbWSCJYmI+U2YKYsXQgERBZpjvK2sQ62Ct9r3mqkV8RI+nHEyNmD6I2hXxEancUnQgt/9sgBhncls75V1J9rNlRm4AIdFxFBEKOmNy51GobETAfQwyoqF+wbigRBGoD9s/295vE/14VZq3lsw1eJYKC02Wv59eH2YA7iODYssTglanLPoYm1Mm86TrxTLpO3ALsml77EzHK+jvp70r8NrpYL73r/l6byN1cBJQrETSn/bPDv4m1f7YgOlpfTPeI/6Nuxk5P2bL62YbIY34AsRbMe8DaHb23ahvRwXw2tVROc6bfwTnUDdYjUju1/DM4sQZQG7B3V79f4hnj9vS5T6jgYpfAT9K5v0Z2TZgZ2D/9Pk6gojnstX37t9ILoK3JX3Dkg3shPcDMnvbljZgil96kRpz3o3orU9cHzt8jgmNT53VU//6qbHUPb/OnhsjkRnnk500lentpP1Lw/nRTfoSUTqFq3/s3rItede/5PrEmwju0eEBQ9TDZOpkp3RsO7+C1k4nRcSumv1ckRsJUIh8ttWnl/dJ18oe0Dxz/kEg1cRsxzXoGYqT1vcQI3KmI0bgtPaWY6CAoRgv/pO61DYmZBZ8Qs5B+RwSB3gYOKbvsDa6H/YiH1juJFCRt6b9bATPVvXceIq/3i8BxZZe9gXWwdLouHk7dYvHECONBRCfSlcSghGHZZ1s+ON6F+umfroGfATemuuiTvT6I6GBqS9unRBu0Mu1sJm5DfxfYmkh/OIbobC5SWJ1ednkbfOxfq91IPK/NSXXyVRfPWf2JNE4XActnr89EBM1fouKB88kdU7omFGvlPA+sXn+trGi9DAHeAo5Of383XQsvppYW9lfAHmWXtcHH/T1qg072zfZPLnC+EtHxVLkZB+n49iRmaS1Qt39aYiBGW2pfVnKdJG3f7q0XUllm1ovIRz4XcJ67v2pmfd29zcz6EwtW7VDsI0a97EXcsC43s6EA7j7G3V8p5yiaw93dzMzdXybq6B9EUOTw9HqbmfXO319KQZsgHff49O8ziWDPg8A9ZrZwZ59LddLL3ce7+3vdVNymSudBbzMbRKTiOJaYQnqSmS2VXp/oOmlm1t1lbbYu1EUbceiW3v8fYjT288SokEroyfVgZlOa2QVmNnvdS/2JAGBx3bDs+ngoMapyTwB3vws4NV1bW5qZ9Xb3cWY2mMhT/W8iEPiEmW1sZlO5++3ARsR99hwiAPBPonNxz7T/Y2KkZcty9w/c/Usz2wg438z+lL12FbHo61LEg//WRF1NDyxhZlOUUeZGyq75lxMzrcYDmxLnf3/gPOBaMzvCzKYxsync/Xki/cjLwAZmNn23F7zBzGxaYrRXL+Amd/8ka1cuRXQWreHuTxHrHtwCHGFmewG4+/gq3j9z7v4lcCExK28osa7DH81sdTPbg8jPejARKFyIGEH4/Sq1s9N9csLvxt3/5+7nE2nNdiA6kvZKb1/ezKYqoZgNl+4Znv49vZn16epn3f1Td3/Z3V9qXgm7T2pL9SHSK/wVWIR41sDM+rv7W8AyxLX0WOBHef21OjObwsyWh9ozZyfv6+XunxCjjz8G/uXuNxfPaYWq1EvO3T8kcpcvZ2arEXGJG4nc3Z+a2YJE+sOBZta3xKI2lLs/QZzz5wDHmdm+aX9bR8+c2efuBDZOn6+iNiIWU1xD+wKkmMMVaf/BwH6TqieRpig7aq+tuRuRB+0WYrGZNdO+KYkH2weom+ZEPAj9gHjwf4xJTKetwkZtJMRcRKPuQ9ov3lWpUVG0z098HDEL4ZfUUq68DKxGBUc01NVDPtK+Psfk7kQA9G5g6Wx/f6o5He4r1QWRj3QKYPGiXso+BtVDw+pglXSvuI/2a19MnY7/yvr6SveMB4F/ll3+BtdFcW8YmK6NtxCjp+cjHmyfB3akNqV4CLAlsE/6bzHy9gLgWSoyipSYKvsbYrToH7P9xfH2I2YvHQ08RcVGRKXf/dnpHFgq7ZuPmGVwP7WR58cDc2d1VonvPx3PDkQ6nueBZdO+pVKdnE02pR5Yguhweo8KzTroShuJGD26IhEEei+dG6OJjrd1yj6GEuqsfuTsZkRu3iouZPdHYt2ktbtyrlR5A35BLe3Mutn+InXX9ESn8pfAamWXt0HHbMRspFdJz97F/kl8ZqF0Tb0jtbkqc97U//brXtsrffdfAFdk+6cl1oN5oriXVm0jRtafzVcccd7qW2fHB8ySfgN3UbfWQbpf/BP4GbBQ2cegredtpRdAW5O+2PbpA35A5MEakxpw/yF6czt8iCOCICvQ4jn1iGDHb5nM4lLZBblI1fIh8Keyy9/kupkPOB/YItu3aLpRvUaFA+d1DzY7EDmHNyCb7kVMrX4+NXqXAfoSi5Y9SoXyjn6DuvgL0ak2pOxjUD00tB76E6k2XiYC4XngfLfUsK9fDHLWdD85jnhQrMx1g1i08CzgBlIeRSLf6MvpPPiQCJxPlK4qnT8XE8Gyxcs+lgbXywxE4Hw8HQTOs78rtcBfXVvhHeDcutevIzpIHiJS1bQRM9kq8QBM+073LdO17xlgOyINyxmkKfV1n1uCCATdUKXrQzq2LnWGpHNmKBEgHFKcT61eH1/n3Kb980nfso+hQfWQH9OlxHoovwZmK7tsZZ0Pde2qbYhOtdvIFsilFjifMdVbZQYqEan73iOeudfq6Fzp4DO/SPeNH5Zd/gbWQ34e7EU8Z58N/Drbf3w67kuIwRs7EOl8PgAWK/sYGlQP+TUir5MF6EGB87pjXxhYnkhXU1wLdiZSHt4ETEM8l8yS6ugSsvSI2rR151Z6AbQ18cvtOHDeRixa12EgudUb8HXHslU63rMm1xCj/cPwaelzfyj7GJpUL2cQPfcPUbe4IZF7swicr1rVm3Y61otSI350+r4vIstJTQRJn0mNtvvSeyu3UrnqQvWQjq24BvYDNiYWOpwQOKc2G6ct/XctIjD8d2IU2fxlH0OD6iFv0A8hFqvbKv19PmnNj9SI/y+RZmAHss40Io/5fkSAYOGyj+krHv+UxCJMk3wwIYIcEwXO02vtRghVaSMCnQOJadWfAsul/ZcQD3orEp0taxNpTKo20r4+cP5IuiZcXv963ee+W7XzghjxdiuTWJiuo/qo0PHn18pNgaFll6nsjchV/xrxzFWJDoGvcOzF+h99iJR2c9e9vktqW91A+8B5/7r3tXzgnNpMvEWJDsWHmUTgPGt/DSWezb5b9jE0qB7yOMRl6bdxAzHQ4mNiEfX50uuHEzPgxxCz1K5utfbT5M6Hok6AaepeX4geEDivaz+cRQxEagM+J1KaLUcM3BwGvEmsi/Mw0UH/EbBI2cegredupRdAW5O/4PY3rNWIPKNjgVXTvpZvnEzi2KcnerW/IAI7XR1xPjcxZXSBZpexu8+B9PfmRNCvjWykefb6gkSw53MqNNqh7hjXTI2zlYiOkm1TfVxPWtAwve/H6Vw4l4pOB1NdqB7q7wNE4HwjInD+ELXA+XxEKqcP033krdSYXbzsY2hkPaR7x8/TvxcjZhVsTIweXINa8O90aov5bVD3v9WXFpyVQuTmHg/8iMmkHKJ94PzYssvezfW0SvreDyZGSL5LdCRVPlBG+wffrYiRlC9QS1czqVRXlQkCEKNCxwIb1R931be67/gcIrBxLB3MNOgpW7rmX008b1R+EceOzgciWH4VseDrOOAaYMfsfTsRgfPryRYFreKW1clidCFwnu2fuuyyN6EuDiYCpD+g1rlSjKrfPXvftMTI68FVuZbUXSuPJEZQj07tx2Wz1/LA+d5ll7vJdXIu8XyxI9Hhui+Rouch4AfpPYsRaa4uBv5MRWIy2lp3K70A2rrhS24fOP8hMU12DLB62leZh5jsOIub8uDUSBsDnMxkcrRTC5xPMsDeKlvdzbpf9u+1UiPuVjoYKUtMmbqOCo4eTX//HzGKuE+2b1NqQdIVJvX5Vt5UF6qHzo4jNWBXSf8uRpy/TATF8lQtcxFpXJajYqvYAwOI0bOP0D49z2+Ih77psn0npf2HVeieMSuRu/81ooO1K4HzQ9Pv5Hdll7+b6+qCdNxvpLZV5dpSkzj2PHD+E6Lz7Hlqaz5Uqi46u94ToydfBKZKf1c6ONrB8V9IdCauD8xUdnlKroshRHqm0zo7F4jc/5VcK4qYgfNEeq74FTHw4B6iQ/G32ft2JDrc/wMsWna5G1wH+fN2L2rPlIszmcB5la4d9cdCdCadAwxKf89NBI7PA6aY1Gdbeav7fi8hZqoeTaQrGkN0Kq2XvWchajM69yi7/E2qkxWJTvat6uqniEtcl7eztWn7tmxaebZCzKx3R/vda6t2u/vtxPTB+4BrzWwVn8xqza0mrb4+Lv25PTHd7X1iMb/hk1rN3t09/XdcZ+9pFakexqd//x44ysxmAnD3G4gb1rJp/9D8s+7+X2BDd3+me0vdeHX18AszO4wIcLzq7uPMrF9avf5fxEIjawKHmNkPiv8Nr1vFvlWpLoLqIaRjLOrhAmI00OZmNo27jwGuJUYDzQBcbWazALj7S+5+lbvf6+5vl1X+Rinunek+uRbxQL8F8VBXcCK34hJm1sfM5icCIK+7+2/TedPpvaVVuPvrxCjz14hOgQ3NrP8kPvIuMfPiICKA1vK+QnvoBuLh9l/AXe7e1rxSlWMS7coJ7UZ3vxD4PZGu5iIzWyq9bt1Y1Kaqu1+sm710DHEOHGVm/Yo2ZE9gZlsRI0d3BK5197fMbGozW8rMViy5eE2V/y6y68XnxL1jSTPrk5698vetDvwUmKNbC9t9itGiuwPD3f3vxECEqYGXije5+wjievEmkeKsElK70tO/pyQ63T3tf4RoY84LHGNma8FEz+eVuHbU1cNs6fi+D7zm7p+Y2YLEiOKbiRl9n5vZ4Wa2C1SnHqB2LOkZfFFgG3c/gFgkug+RBvXI4p7i7k8S7am/EKlrqmhaYjbvC8XvIz2L3ADsSbTBv59/oEptCWlhZUfttX2zjciNNahuX4cr0TNxjvNbicb+D8o+jibVzT+I/O3HEhfiW9Lxnk1FRolO4tjzUWCXAk8TC63MVve+DYHPiODY4mWXu8l1cimRVuKtdB58RkqvQTReipQLG6fX/0FFFxxRXage6urhTGKq5A9JIzyK+wXtc5zfW38NqcpGjDC/lgj8np/tL86BeYDXiREydxAP+w9RkRHmHdTHzMD96ZgnjDivu7d8lxg9tVrZ5W3A8Vr+m0/7ZujC525hEuvEtOIGTJF+972yvzvM283EOc5Hpetoh+3QVt6IFIdFOqbfU0tzeAwxwrjIb1+ZkZKTqY8Dgcezc2Rloq35eqqj4WWXsUnHnc/O+jmwSXGtIDocxwIn1n1mBuLZ4z7SotJV24jOw2uyv7cmUncdkP4eRErhlP4u2hgtPyul7pw4Nn3P7xOpLrcnzVojBnEVI87XLLvcTa6Tc4k1YfqkdsIDRBtzdPp7cHrf/MA/iRlrlUhvVndfnJ2YkbR7+ntfYpT5msQimJ8Ra86tn32mkrNR0rFtTKRu2ja7BhSpjOYiFlHfr+xyatNWv5VeAG3f8AuMRsnhwOzp7+uIxRU6zAUGE+U4v4aK5OWtO84fE8GwNamlapkd2D81aM+oys15MvVwMhHkWb44J4rjpn1A8ENiSn5lpkrWNWLXS43YVYhRL3sSI1+epBYk7ZvVyfpUaAE31YXqYRJ1shQxZXR76h5es2PvR62D7VYq2OlIjHx5iAj23Ep6oMvPG2K9h4uIqcanZfeWytVHOq4icF6kapkie21eotOpjRbvcCUC5lsDu2X3xxuI4EeH7YTst7FlqoO9yj6OBtXFFMQitr9Ofw8iFuPat/76UF8X6d870kFO51bc6CAfO9Fx+iLRXroJOIpIy/EScF5nn63ilp37JxCpij5P3/3GREC9DViy7HI2+Jjzc/08IiXRwdQ6m2dK1422dJ/YEfglcCURRK1MG7uuXvqmY7w6/f2TVAcHZK8fkepqYPa5Sv1OiGDwK+k38XsiBc0X6e+Z03sWJ2ZovUDqeKvCRvv29e6p3VAsjL1zuo98AVyZvW96YASR1mfu7i5zg4+/o3RMQ9J/1yDaUz9M9bATKTBOpK0ZT6Q4W6Ps42hgfXTWXrB0rA8Bc9S99v30+/m/ssuvTVv91vJTiYUxRD7V2c1sTmAR4GB3/7SjN7vHVDAPt5jZPe7+eXcWuJvMQDzgPO8p1Yq7v2pm5xD5uncCPjGz/d19bInlbIg0dWnK/HtP58PKwDHufk/aNwewt5lNAzxiZue5+xVmtiOx0MYH3V/65vDadOq9iUDAKODOdD6cbGafEZ0o/zSzTd39KTPrm34e/y6t4E2gugiqhw7NTOSXfNqz9BLpPtEG4O5jzOx6IlXNs16B1DT13P1lM9uaGDW6NrCumf3T3ce5+/g05fgpM9s2v2ekafgtn86rI+7+ppltRARDTgLazOxqIjD0R6JTegmPqeetrDfRcfZnYDozW46YSn1wZ+2D7LdyD/AoMWChCsYTD7VHm9n0xACEJ4ALvZP0M55Stbh7m0fqhRHQPg1Wqynayenfg939Y6JNeTUxSu5moq6K1F4PAFuZ2S3uPqL4bBUU320HL91CLGy3PTELaY/0/ZNSVb1ARdqU2fndlv7+O7Fo+E+Bx9z9XQCPFDVHE8H0g4g2+AfECPwfuPvjZZS/kTo6H9x9rJk9CexqZocTC4Yf4u5Hp7csAKwOXJ8/p1Tsd7I1cU7s4JFqAuBgMzuPmJHwkpmd5u6PmNkawBVEB1wlZO3rTYj29SnA3ek7/quZLQbsAPRL6Zu+R5wT6wA/dPcXyih3I6QUTZuY2ezuflLadxnwlpn9Arg1tSN3IYLC//RIgQgxGOU6In7zfAnFb7i6FJhLA7MQnQVvu/vzZrYv0el4iZkdTMy8mJFIBzmeaFeJfLuUHbXX9s03YorLWGLK16bZ/k578Cf1WhU2YpGNNmCx9Hef4piJkaWfptdPL7usDTjWKYjRG8eQjYQF5iOmye5ETKPfkRhR/igxsvRjYLvs/ZVYqbyubhYgRj+1ARd08PpPiYeZx6joCCDVhephMvWxcaqLFdPf9Quk/gxYu+xyNviYO1vUz9L5cQvRwF+f9gvDWn7vbPX7aGf10MH7ZiJmZbxOjCC7nJhCO7TsY2hgXfQjAj1jiRGhK3fhM8Vo80kultqKG9EpMo5Y5LFLqVZa/ffQyTH9kkg7UaRemYoYYX5h+rsvMVJwVLqO3g9MU3a5G3j8+ejRNYgFs7eue8/UZGkiidGjZxKj8Vt6QTc6SJOQ6uF/ZOk1iIDPz4jZKkukfUOIkZNzkBaKbfWN2uyqPul7ni17bSoi2NUGnJztXyKdC3d09Z7Tihsxkv5laqOL+2WvjSRmMeaztSqXgiP9BtqI3Pa/TPvyYz4YuCu9/jTRcbBw2eVuwHEPIJ7BRxMdiVcAr5IWxs7edxSRDnLu9Pcs6b2rVuX+SfvZOH+nlq6rDXgQ2CK9tiERixib6u3J9N6hZR+DNm0dbZVZ/LGnqVsUYSZipNQgoqdzXmi/wEg9d69E7/4kFuy6G3gcON3MZvH2IwGnJ27aOxP51lqWmQ0mAjz/BwwmerABcPdniRFixwA3Ar8lRtIt7+7zE9MDl8ve3+HshFbSwaJlLwDrEufCyma2XP6bcPeziUbMzMCZFgtAVmLBEdVFUD2ESVwrHyEaqyeY2cyejQw1sxmI0eUbmVm/bihm06WR4ePNbAozW8/MhpnZSmY2W7ov/g/Ylbh2ng2sXZxDnhT/W618H7X2i8BuMKn3uvtbwEZEAPVkouN5RXcf1eRidhuPUV9TEG2pqYA1zGyqyXymGG05ZlLva1EzESNkvwNsPbm6gNb+PUzCzERqvxvN7ECi42xHYC0z28/dx7r79sSo4rOBw939/fKK2zh114hziPbjCcCfzezfZrZgGpH/gbt/kt63NpGeZDNikb93yyr/N2VmUwAPmNk2dS/1Jc6Ldy0Whd6MuI8eARwHXGxmS7r7h+7+H3d/xd0/6t7SN146H8al545ridQjr5jZFWa2dTrGfYnA+fZmdraZXUXMPOlNrH0xvoM2WUvL2ocD0jYjTJihVyyifTzRIb9s9v4qzHKub1feS3QijiNm4OCx0Gf/9O/fE6lhFyc6lLZy95ZfDNbdvwCOJjpU9yFmmGzk7g/UPT/cT9TN6Wb2O2IG37LAK618/yyOMZ+hamZnEefAMGIU/XaAA39J14uriPPgQOB04v6yQpXalVIxZUfttX31jfYjPwYQoxlmBnYhevLOAeav+0wlejAnUQ8rAkvmx03cuF4mbmLzpH2zEjnfL6DFR1YDA4mG+i1E8LtYqK037Xt69yNmI6yS/u5F9G7fDexb9nE06XzYnNrCTL2JIM9LRINlotHDRD7becs+BtWF6qEb6qGja+VBxAiPW4gHuymJxuzfgDeABco+hkbWA9HBeA8x0ulNorPxH8Ai6XUjZufckuplMyqwWFkndXIpMfNqvi68d1Ziwa7Fyi53g469mH1m6ZqwYPptHElMET4SmLrscnZnXaR/9yGC5fOkOmhL/x1S95lK/SY6Ox4ipd/viCDX7USn2u5EipZls/cVOWor1d5O94GXiRHWg4g2dBuxyGE+u3H7dF19sKN7aqtt6R5wBHWjxImA2MNESp5bgPeIVBRzEQNYPiSCgaUfQwProrhW9idmVdxFpCbaJ91HXwEOSu8ZQgQQLyFGmv6SbIR62cfSgLro7DqxZvpdHJzXWfr3tsA7wHfLLn8D6yFvV65ObYT9vOma0QYcl72nb/bvql0ji7blCGIW3jvAsZ0c+7D0G3qTeP5o6fYU8bxwQdFWItpS3yUGWexJ+9maK6Z76NNkiwJr09YKW+kF0PYVv7D2N6mjUsPku9m+n1MLnBeB4n5EI3/5ssvfpDo5h5gG9QUxYnLH7LVD0sX543STeio1aFu6QU8Evk8lpjt2GOwgcpx3tP87RK/uq1QzKHg2MZp4eNZQ70UESV+pQiNFdaF6+Jp1Mqlr5W+J/JqfEQGSZ4hOhaFll7vBdTAlsQDRzaRpwcSsg4/SvvrA+WOkxc2qsNG+Q3UFYtHT1eniwthUZHp9/XHQfir9FET7qgicD077+xOp3+bornJ2U11MWNCWaC/OkL02APhDVheD0v5ZgAOAWcouf4PqIP9dLAwsTd2ibMQU+iuJjrTXibblr8sue5Pr5SdE23nl9Pev0v3jd6kObsuuowsRU+5nLbvcDTz+Ihj2Z+AP2f6diMDgibRPi/ldYrbSxmWXvYF1UKSh6k3M0rspHWcRSF8EuCy1G35SX3ed/d2KG+3bldOleuhPrcPslHSt3JvaII1Zic6DB4Hpyz6GJtTDiNROGp7dS+ZOv48PaR88bvlzYDL1si7xXHFZuj7mx563MaYhFp+ftuwyN+CY12HilEw/SPtWSX/ngfON0mv1ab4q1ZGirXpb6QXQ9hW+rPaN+kuIoMaR9Q1UIkA+HrgI2IoIkLaRguitvtXdrIsFd7Yhcglel471l9l7ViJGRFxKmiJX9jE0oA6mIEZ6/KGDOlmPeMg9j8gvl/dwH57OndeBxcs+jibUy4VE4G8tYM661yw1Zl4lRtkvUXZ5VReqhyYf+1e9Vi5PjBw7gQgKzNndZW5SPRQP972IYM+1xX0z3RdeJVJ1FYHz72Xvn4MKPuil8+FEYqRk5XJyT+bY89/FQcBVREfKX4gAUG8iBcNRxFTqY4lRtqel30wlfhd5XRAjiP9GjKB9ObUflq2ri7FEep6t0vXyJSow2pz2I0J/QwQ9v0zHO5J4yB+QXp+dWBz1/nQuvE8FZiOk7/9wsvz1xIyDjYAD0t8/JzpVtyAChfulOriS1NnY6ltWDwtl+2YjUhy+RxpFXJw3ddeSWYlc7k+S5fquwkZ0nt2R7p0js/3F9WNR4pn0n2WXtYl1kH/XJwP/Tef//4g201TELLZiJsaDRAfD7encWbzsY2hCnVyQ7gPrkTqTqbW3ihHnHwFHlV3WBh93LybRLkzXjH8Qz9r5aPtBRHqvGcs+hgbWxbREvOkNYP20b0Eivdtx1GbB54Hzt4A/lV12bdq+ylZ6AbR9jS8tbtYvEgGOYtRP3/TfYkTATumm/U66oVUqGJSOcToiX/d22U16UeJhr10wqGobkY7nBWB4tm9GYoG2z9LxjyHr/SXSuVxKTK9v+Y6DDupkh/S7WCH7HQwhOk2+T23q4MpEOoKbqeBCPKoL1QMTL1jZ466VRFBnIHUBLWJqbDGN/LR0HS1GShYdzDdSN3WUFg+ck9KRpXNjEBHYaSNyt09ZvFZ2ObuhHvLfxWVEh8m/iMW4PqLWsdSH6Jz+Taqnt4DXgO+XfQyNrot0PjxFLPb6l7S9QwTPtyEC51MSi7i1EdPK76Cu3dnqWzq+T4C9gB8R6UaeS9/7NrR/6O9HdLStV3a5G3DcfYlR821EkGue7LWZiLblNERHwcHURtUulM6FNqLzraXvnZOph0WIDvh2gfPs9U2IwNE7VCw4ml0n7kp18yppxDRZ8JCYeTIWmLnsMje5Pi4knqt/Q6Tx+3f63m8ipfIhOhb/nfb9mWo+c22W7hFr191XJ7Q/iRRfRSfC4WWXuQHHPNEAA+DX6XpxM9HhVrQn5yAC568RAxTmJ2aIvwt8p+xjaUBdGLXnqu8RswouIdpOfYlZSK8C69M+fexCxHPZzmUfgzZtX2UrvQDavuIXFjnzHgV2yfbNQSwmcW66YBc37UWJadctP1WSiYNAv0834ReJxWXy9y4EnJ9e363ssjepPvoTI0VfJ4JhhxPBn8+IaYDfAYYCf0z18KP0ub6kqeZV21KD/T/EaI9exFTqYjXutlQX/dNrK1GX979Km+qiZ9YDMcppubp9X+taSQsHUIn0CqcQI8HOYeJczP2J3O1FgHSKtH99YuToh8BJZR9HA+phCmB/YnTghWRTgYEZiBHWbUR+5g7TeVV1IzpPXkvXhOLBb16iE+FV4IfZe1ck0lRUZoR5dmy90m/lIdqn+ps91cVLZB1IxFoHq2R11vI5itNxzEOkGDiY9rPzpkv18AQwe9rXt+6zLXutTOWfCriaCIB/ma4Lc9e9Z14iYLxHtm994BpisbeWn8naST3UB84vTvVwYLa/mHlwE1mO91bcgOmJ4N7mxGCC2epevyjdM/5Qdz/pRcx8fg6YpuzjaEK9FEHgdYG3iTRERSqSfkQq0LeI4GnRnihmp7T0NZJ4/u4oWHwgWQdKfV1lfy+U7jEt3XGQrg8vkAV7iaD4m0SauweJjvc3Selw0330AqIz9t30Wkt3vBMd6BPNLCJmsLYBO6W/ZyHa2M8R6YqmJtKe/ZV4BvtO2ceiTdtX2UovgLav+IVFgPxFIq/gfMDORL7u/xDBoDfTxamlR8VNpg56ETm0bibS0Py02J+9ZyEiWNIG/KzsMjfgmIsGW54TbcZ0k/48HecNRM//4Ow9SxIjPyqRd5MIAm1PpFjYMm+cp9/El8Q08vOIkcMXENPqj0nnSks32lQXqodJ1MPgdP2/ktoIUOuB18oViQe5fxNpNRaiNso6P+5NUp3Ml9XV7sSoqP9r9XtoOh/uA+4lRlGvTV3+aSJwfjsRBNiC9LDfEzZikMGd1EbNFqMlZyamGVc2zUAHdXF7frzUgkFzEA/653TyuZb+jdQdy/xEru5dO6iH76f7yEFll7OJx38QERDanQj+XUMWOCem248mAh4LpnPjr0RgeWDZ5W9yPXQlcL48LZ6vmkjHdHu6/rWlbTQxonqJ7H1Xp3vn0dTydi9MPIdeRYt3IqXj6ZuOaRmy4F46Lz4mtTOptbX6Ee2ot4v3U+tYbNn6INrX+xCdAnPUvXZCOj8mpL+re/3n9fXUqhsRMH+eaEvPkvYdQAR/l6M2kvr/0u/gA9J6QERH1MZEvKalOxfTeV5cH35BGlWfXpuP6Dh8mdTRTgzg+0+6f7YRa0hVbq0kbT1jK70A2jr4UqIXr8OFKtMN7MrUqHuVCKAfmS5k/YBngb+UfQwNrIe9iByBFxIjQYvgR29ihNgDqR4WT/vz0eiLAmfkF/VW3IgUAycQ018fTzeq2dJrg4AlgBXqPlM01tZIN6hNyj6OBtTDYCIA9ExqmLal7zfvJDgj/SYup/0ih2sTvd1VCpD2+LpQPUw4lqJBfwMTr3HRG1ith1wrlyFG9JxE+4DPROkjiGD6e+m4pwSWIgIGR+d1V/Yxfc16KNJt3EyM7Ok0bQIROL+TCBBtSRodV9WNlE4g/VbuI9pNxf2yCIAcQQSL5+3o3KnKlupiMPAIcEXa16fuv39N18npKl4XixKdqoenv/P8xQPTPWZE2eVsxjmQnQvPEimrNiRm3LQbcU4EwYr0PM8RAbMOn1VabetCPXQUOH+LiuRqJoL+H6f74eZEoG9LonOxjZjZ+oPs/Vem/a8TaVtuJtpiLZ+yKd0/r0q/+XFE6pUt0mvbp+POZyIVxzxfem3T7i5zk+phMJGG67H0exhU9/o2xKCtfakbTU/Mih9JmsFIa3ccDCLa19eSZhul/RcQqfz6190vNiRmsZ1PBdtTwPXUZq9eBPwqe+0nROfz8dSyHvQnct7vQayRMXsZ5dam7ZtupRdAW90XEo3zF4jRwcvXvZbn5N0D2La4cROj5GYgckgV+Vpb+SY1mFiQ6gHige55IsCxRV4fxDThR4jA8OL1x03r51gcnBos96Sb0y2pEfd/nbw/n1Y8Tbppj6JuhGGrbdSmxd1MBLdmJRY5bQPWrXvv9GQjn9Lv4m/EwmUtP21UdaF66KAe6kfA9Kr7b58ecK2ckXjAO4ssjzntZ+ecRAryEFNFjyUCBZ8SAZCHaP2p1H2Ih7mRtA945fmYdyKbXkstcP4qERSo3INeB/W0b7pWrN5B/RyefidDyi5nN9XFEUS7YqX0dx4A+CvRudCSHUhfsR5GpOtB/XoGMxPt0cPT3y3btk7lrx8RWoyS/HW6bixEjJj8mAgezpu9d2NiEMeRtH46s69aD3ngfGEiiPYC0aHUsucEsWjhKCKFxlQdvP7LdK28mvYjSy9I+y8A1s72t+w9lGhPPUM8S/+cmLn9YDrO9Yg0Tl8So8rnys8lInXL6PrrRytuRCziMeKZ8wfUUs3k94apiDbTa8DPs/1zEIPdnqPFU3AQAfOHiU70+bP9fYnnhzuzfXkb4lwiqFylWTjF88RGxPpoJxBt6P8RgfS50+t/JDpTli67zNq0NXIrvQDasi8jRjz9jRgx+TzRu71i3Xs6fHBJN/Iz0s1r3maXtcn1MEVqsNxA5J0dRDzU35wuzlNm760PnC+W9rdsAzY7timJkRs3pe+3uGHdB1zZwfvz1APfJ4JHHxZ10qob0XHwbLopz5rtnyN95xsSjf6OGvsrEw/C71KBEVGqC9XD5Oohu070IxYdmrfYX9VrZTqOJdN9c4NsX/6AV+RinfC9E4H2dYHDgD2ppeho5Yf+2dN3vEd2PPm94depHj6hfeB8emIm0/86+t200ka0IWbsQj3dQYwkXS7bPxORp/QGKrD+BxH4m20y75mf6Jj/gPajSb9HBNNOL/s4GlAPx5EFuTp5z3JEEOh9YiHQmYC5iU6Fj6gbyNKKG7XZWb+hbhFTYh2c96mNDt0iHXe7gHEVtkbUQ1d+W62wpXbBs3W//foOhb3TfWNY3f4rU93sT4sHCKm1p27Iv1dilvMLwKPp7/1SXZxCSltDpKE4k0iTOlPZx/IN66E3EYu4rbPfPbUOpplSu+Ej4tn0CmKwW8sviEttQMo4YnbN72m/oPofiGfsTbPPFDO0jiTaUi3fhuigXoYQ7ekLgDmJeMMjxLofhxIpEkcSaVmK9HctO/NEm7ZiK70A2rIvI9IFfEissrwy8UA3mrrAeQefO5jo9Xut1W9S6Xh2TzffFWg/EvJHwBjSYna0n065CvGw8zEdLFDRalu6Ie9PjHBYsu54/5pu3isRi3LlQcPp0rkwMjVkWj1g3pvaQoVFPRSNkrmI2Qf/SefFQ8AB2Wd/RjSAH6TFg6OqC9VDF+uhCJL2J9JzjASmyz5TuWtldmx7pWMa1MFrdxIdBbsTQcB3O7tX0uIjaomRoG10kHaIGBU0hnjYK0ZPLZq9Ph2tPzKsN5FK4EUmHyxei1jA78t0X/0LMZryw1a/PqTjG0KMmnymi3VxN5Gr+Eoi+PEY8TBcXF9bsoONSNs0Nn3Xk5waDqxJBM2KNCTPpN/J5mUfRwPqoTe1dBsvEkGdfxILeQ5J7zks7Z8n/f1/6ffwL1p8ZLnqodP62C8d20QBPmr5qnsTKe5eJmax5h2xVwGfEZ1LQ8o+nm9wTtS3p/Lnz0uIoGARBDwovfeddJ18LP17aNnH0oC6GEIEvg8o6iZ7bdV07H8GNkr7pk7f/bVEm/NPZItKt+JGbcbBtURg+F/EgMY/FL8TYqDGaKLDeZ3ss9MSayFcTYvP2iNmHOxLXXuS6Ez+CDgi23cUka7mhXT8bxFBdAXMtVViK70A2rIvI0b1nE166CeConczicA5tdxrF9df1FpxI6Y8nZZuUBMWs0v/nZV4oNuqg8/1Ih767qhCg5aYYr85MfoxTy8wkFouyS+IKVDXURs1OjXxwHckkxlV1QobEfz7STrmB4pjAgYA/yWCn3sQuRcfTnXyy/SeGYBNqcBIINWF6uEr1EN/osPsbjoIEGXXyjurcK3MjmsPIs1KsfBU/rB/QnaN/CHRcfJBul5WqkFPdC5/UXy3tO9cHlHcP4k896OIgPHMZZe7gcffN/32X0i/i45+A3kwZBFiOvELREfTP2nx3P7ZsfUjgn3Pp+vi5OpiHiJY+BAxw+146nKct+KWrokbEx1nXamHwcBWxOj0YdQWNTNatOMgq4etiM6Dd4hO5HvTdeBuIg3Dj9O/N80+szkRILywlc8D1UOn9bFXundOl/7u8J6Y3jeWWhqGPB1FsaD0dM0sa5PPibw9VawbVdw/byRmO0+RfWY9Ioh6EbFQZiXaU8So+dHAIdm+aYgBWR9RWyS2DThsUudMK25Em/EjogNgjmzf5dQC50PS/jWJwRovEp3uhxExmQ+pQDsi3f/aiIFI+9S9tj2xpsHG2b6lifZ2cX7cSAcDWbRpa8Wt9AJoy76MeKjtW7dveToJnBc3b2IqcmWmABGjRSekFEj/7UP03n4IbFNfb+m/vclSt7T6RqRnyXOU9yWCYQ8QOcWmI0ajv0VMCyzOhz60eH7iunroD2xCrLp9L/Fg/2j6XcyZff/fI0aGXZc3bKu0qS5UD12sh3voODBUdERW6lqZjmkF4oH+sGxfPkKqCKL3I6aVXlp2mZtUDwsRo/6Ozfb16uTfJxMB0srcL7LvuPhddBYk7V3394D0e6ra76LLdUEnAWEqECDsSj1k752ODta66Kx+WmlL5/mmRAfCLcSIwQ2JgTejqY22vS7//lPdtfzAHNVDh3VR3DvzUaN5J1LRnlqObCR2USfZv+co+1i+YT3k7amHqAXODyMGKBVp3Vp6NloX6mFq4jnzv8Qz5j7EQIMv0u9jQWLGYjFbY6Wyy9yEOliciTtO+tI+cD512r8C0aHwBtHpci0VmcWZjnkl4rnicyIIvni6fk5LzMAYQTbjPX3uR+m9C5V9DNq0NWorvQDaOvli2jdY8sD58mnfvMRo4qFll7Ub66RXulntne0bmBq6s5ZVrm48/qHA3+sbpkTA/FUq3Jtb15htIx56J8obmBord1DX+VSlTXWheuhCPUyUz5kYPXkGFUgz0Ek9zECkkniBzvOa9wKWAG4F9i27zE2qh6mpLeq5cX7s2b+NyP9/ETEqqC8VCAjW1UOnQdK69tV8REq8Zcsuc5l1kX4bKxKpWYZ2VFetvtVdKyfUQ935MBOxYPATRPC8cgGyrB7eSNeKoh5+REyxf4kYZdnSC8irHrpcD9MTs/JerLt3tutsBQ4g0pAMqft8ZX4jddfKu4hZSF8U7SYqNKJ6MvWwaDofxhLtypHESPypsvesnl7buezydkN9FB3LeeD8aGojzqck0tpMQzUH6MxKpOV5kYhF/Z5oc6+U/t6kqJ/sMy2dmkabtvqtF/Kt5O5uZpb+fQ+xeNfTwFVm9mPgGCJ1xyfllbJ7uXsb4MTIesxsMDFS7kTixl1p7j4K2NHdXwEozg9iati7RC9wJbn7l0Twcy9itH1/YrTsBGY2BxEweogKnw+qi6B6CJ3UQ5/8PWY2iLhn7ESkaqgcdy+m2c8AHGFmG6f94wHMrA/R2XwKEQAYXk5Jm8vdPyDaBlMDh5nZhml/fv5PSyyAtxxwiruPdXfv5qI2lbuPIfJq/oIIhF5uZrOn1yJSajYvked9Z2IWWyWluriWTurCzHoByxKdavMQ15His5U5L9K1sv6cmCM7H2YCDiR+P79x93eL60eVZPWwG5GK4Sozm83d/0nkoF0W2Mnd3yivlM2negjuPpq4Bk5P3Ds3SfvbANL1YW4ixdEDROqK/POV+Y3U3TemI0ZZ7+3ul6XXK9mOrOfujxEB0dWBdd19FXe/0N0/MrOinT2QSM9RyTZlzt3Hm1lvdx9LLXXTjsABZjbY3T9z9w/d/X13r9yzuLu/TnQgrUZ0oOxDbT2YK4CTzWwGdx9bnB/u/kVJxRVpimKEiXxLmZllDfrliQDxUkSwfGV3f7jM8nWXFPAwYmGOU4mL96lEXsKV3f2hEotXGjObDTidWJznl8D4Kj3k1jOzAcA6xCI0bxG926+mDpQ/AusDq7r7MyUWs1uoLoLqIXShHrYFVkidb5VlZusAlxEP9hcCZxGjg1YDtiA6FZYpGvdVeuDPZfUwGjiH6GAeRyzktRmRj3XVHnA+9AfWpfa72NDd30gB8+OI/P4rVb0eAMysH/G9F3Wxgbu/mdqWZxJrxizZA34b9fWwIfHwfxjRjvo/d78sb39XUd1v421iZsqr5Zaq+6keQnbP+JhIw/FX4rlrKWAXYjTtUu4+rof8NtYnOlU/IM6J10otVMnMrI+7j0v/npbI4T0PcR95u9TCdZPivmhmfYmULEsTv5mD3b3HDGI0s52BnxKLbN9GXCMuBvaqYqeBCCho3lLSQ97fgIWJh7wnyi1R90k9l23EaPt/E4GPHYg87z2i4wBixEc2+mMuYjTM2sBqVQ8KFjoIgmxNjLL9KREU7Enng+oC1UNhEvWwAz3oWmlmixN1sCQx+6AfscjbI8RU4nH5A2BVmdmSROBjUWKatREjw14GfuHuj0/i45WR/S5OJtY42ItY4GpdekjAvJAFjE8m0lIcCfwuvVwEzHvCbyM/J94mrg/bA1u6+6XZTM9KPyR1cM/okcFB1UNI986TiCBYH6LD+b/A/4Atesq9Ezo8JzbpiZ0pMNEAvkWJe+jmwA/SqPQeoy5wfj2R6m6FNNux0upiEN8hRt0fTmQAeBhYPc12FKkcBc1bhJnNTCxetjQR/Hi05CKVwszuJqaUfwb80N3/U3KRSmFmuwNrEHWxrrs/UnKRulXWmD2emDb6GdF46xFBwZzqIqgeguohmNkQYGZi0aqxRFDsjZSOorKjaOuZ2fREWpoliKD53cAr7v5eqQXrZlmw+DiiPj4lfhejyixXGVJdrEvMQJmXWORr6Z4SMC+keliHSNk0G/Bjd/9HTwmYF7LfxgnEbIMfpun4PYrqIaR75yzAIkQ9/Bd4Jt07e8z1AdqdE38iBm71yHMCJowu/xMwO3G93KIHxyKKwHkfYOae1JlSP8vEzFYAdgWOcvenyiuZSHP1mfxb5NsgTaF9Adi/J96k0kNMfyL48SmxaFePGWmfS3k3VyGmE6/WE29S7v6lmV1DrOD9c2JKWI/7XYDqoqB6CKqH4O4fEnmqn873p5EyPSJgDhPy1Y4G7iu7LGVy9zFmdi2138Uve9oIuUKqi+uInLSrAbv2pBGkhVQPNxCzDt5097t6WsAcJtTDNcSMnMOItnaPo3oI2b2z3bNFunf2mOsD6JyoMzUxa+0WYsbeC+UWpzxey3E+jlh0vceoC5ibu99tZg945HsXqSyNNG8BVc8d91WkFDXm7s+WXZYymdlUxL3r47LLUqaUx7lvT68HUF0UVA9B9SAyMf0uasysn8fCd/S0gHmubsp5j21vp1G1/XpSbt6OqB6kns6JYGYDgXEei+iKiPQYCpqLiIiIiIiIiIiIiCS9yi6AiIiIiIiIiIiIiMi3hYLmIiIiIiIiIiIiIiJJywfNzWxzM/uzmd1hZh+ZmZvZeWWXS0RERERERERERERaT5+yC9AAhwCLA58QKxgvWG5xRERERERERERERKRVtfxIc2Bv4LvAVMBuJZdFRERERERERERERFpYy480d/dbi3+bWZlFEREREREREREREZEWV4WR5iIiIiIiIiIiIiIiDaGguYiIiIiIiIiIiIhI0vLpWRphlVVW8bLLULbhw4cDMGzYsFLL8W2gugiqh6B6CKqHoHoIqoca1UVQPQTVQ1A9BNVDjeoiqB6C6iGoHoLqoUZ1UTNy5Mgq5l+ufOzR3Rk/fjzjxo1j3LhxjB8/nvPPP5/LLruMW265pdH/d00/RxQ0FxEREREREREREZEuWXXVVcsuQtMpPYuIiIiIiIiIiIiISKKR5iIiIiIiDTJ06FBGjhxZdjFKN2rUqLKLICIiIiJNMu+88/Lcc8+VXYymUtBcRERERKRBRo0a1eNzkRY5WUVERESkmsaMGVN2EZpOQXMRERERERERERER6ZJzzz2Xtra2CQt+Flvxd/Hfyy67jKuuuqrs4n4tLR80N7NNgE3SnzOn/y5vZn9L/x7t7r/q5mKJiIiIiIiIiIiIVFKvXr3o16/fJN8zZMiQbipN47V80BwYCmxft2+etAG8BChoLiIiIiIiIiIiIiKT1avsAnxT7n64u9sktu+UXUYRERERERERERERaQ1VGGkuIiIiIiIiIiIiIl/Tc889x0477VR2Mb41Wn6kuYiIiIiIiIiIiIhIo2ikuYiIiIiIiIiIiEgPNu+883Lrrbd26b1PP/00jz/+eIevuTttbW2MHz+eBx54gFGjRjWwlN1HQXMRERERERERERER6ZIpp5yScePGTfI9vXr1Yvz48bh7N5WqsRQ0FxEREREREREREZEu2W677couQtMpp7mIiIiIiIiIiIiISKKR5iIiIiIiDTJ06FBGjhxZdjFK16q5K0VEREREQEFzEREREZGGGTVqFMOGDSu7GKUaPnx42UUQEREREflGFDQXEREREWkQjTQPGmkuIiIiUl277bYbN910U6evt7W1MW7cON58802+/PLLbixZ4yhoLiIiIiLSIBpprpHmIiIiIlV36qmnll2EplPQXERERESkQTTSPGikuYiIiIi0MgXNRUREREQaRCPNNdJcRERERFpfr7ILICIiIiIiIiIiIiLybaGguYiIiIiIiIiIiIhIovQsIiIiIiIiIiIiItIlxx9/PPfcc89k3/f444/z1FNPdUOJGk9BcxERERERERERERHpkssvv5w77rij7GI0ldKziIiIiIiIiIiIiEiXVD1gDgqai4iIiIiIiIiIiIhMoPQsIiIiIiIiIiIiItIlZ5xxBrfccstk33f//ffz/PPPd0OJGk9BcxERERERERERERHpkmeeeYYbbrhhsu977733uqE0zaGguYiIiIiIiIiIiIh0yXHHHVd2EZpOQXMRERERERERERER6ZLrr7+ejz76CAB3p62tjfHjxzNu3DjGjx8/Ybv88su5/vrrSy7t16OguYiIiIiIiIiIiIh0Sb9+/Zh++ukn+74ZZpihG0rTHAqai4iIiIg0yNChQxk5cmTZxSjdqFGjyi6CiIiIiDTJOeecw9/+9reyi9FUCpqLiIiIiDTIqFGjGDZsWNnFKNXw4cPLLoKIiIiINNGll15adhGaTkFzEREREREREREREemSI488kquuugroPKf5uHHjePrpp/nyyy9LLu3Xo6C5iIiIiIiIiIiIiHTJ3nvvXXYRmk5BcxERERERERERERHpkosvvphHH310su+77bbbuOuuu7qhRI2noLmIiIiIiIiIiIiIdMmLL77IjTfeONn3PfbYY7h7N5So8RQ0FxEREREREREREZEu2X///csuQtP1KrsAIiIiIiIiIiIiIiLfFgqai4iIiIiIiIiIiIgkCpqLiIiIiIiIiIiIiCQKmouIiIiIiIiIiIiIJAqai4iIiIiIiIiIiEiXrLTSSmUXoekUNBcRERERERERERGRLrnzzjvLLkLT9Sm7ACIiIiIiIiIiIiLSGn71q19x1llnTfZ977//fjeUpjkUNBcRERERERERERGRLllggQVYd911J/u+++67j+eee64bStR4CpqLiIiIiIiIiIiISJfsvPPOZReh6ZTTXEREREREREREREQkUdBcRERERERERERERCRRehYRERERERERERER6ZJDDz2UK6+8crLve/755/n444+7oUSNp6C5iIiIiIiIiIiIiHTJaqutxmqrrTbZ940YMYLzzjuvG0rUeAqai4iIiIiIiIiIiEiXPPLII10aaT5q1CjcvRtK1HgKmouIiIiIiIiIiIhIlwwbNqzsIjSdguYiIiIiIiIiIiIi0iUXX3wxjzzyyGTfd/vtt3PXXXd1Q4kaT0FzEREREREREREREemSGWeckTXXXHOy73vllVcUNBcRERERERERERGRahs3bhyff/4548aNY9y4cYwfP37Clu975513yi7q16aguYiIiIiIiIiIiIh0yVZbbdXSAfGu6FV2AURERERERERERESkNSy00EJlF6HpNNJcRERERKRBhg4dysiRI8suRulGjRpVdhFEREREpEnmnntubr/99rKL0VQKmouIiIiINMioUaMYNmxY2cUo1fDhw8sugoiIiIg00bXXXlt2EZpO6VlEREREREREREREpEvee++9sovQdAqai4iIiIiIiIiIiEiX7LDDDmUXoekUNBcRERERERERERGRLjnzzDPLLkLTKWguIiIiIiIiIiIiIpJoIVARERERkQYZOnQoI0eOLLsYpRs1alTZRRARERGRJll66aV54IEHyi5GU2mkuYiIiIiIiIiIiIh0SdUD5qCR5iIiIiIiDTNq1CiGDRtWdjFKNXz48LKLICIiIiLyjShoLiIiIiLSIErPEpSeRURERKS6Zp55Zt58882yi9FUCpqLiIiIiDSIRpprpLmIiIhI1VU9YA4KmouIiIiINIxGmgeNNBcRERGRVqaguYiIiIhIg2ikuUaai4iIiEjrU9BcRERERKRBNNI8aKS5iIiIiLQyBc1FRERERBpEI8010lxEREREWp+C5iIiIiIiIiIiIiLSJSNGjOCmm25i/PjxjBs3rsP/jh8/nqeeeoq33nqr7OJ+LQqai4iIiIiIiIiIiEiX7LjjjmUXoel6lV0AEREREREREREREWkNv/vd75hqqqk63IYMGTJha2UaaS4iIiIiIiIiIiIiXXLooYeWXYSmU9BcRERERERERERERLrk5JNP5pprrpns+x566CHlNBcRERERERERERGRalt44YVZeOGFJ/u+ESNGcN5553VDiRpPOc1FRERERERERERERBIFzUVEREREREREREREEgXNRUREREREREREREQSBc1FRERERERERERERBIFzUVEREREREREREREEgXNRUREREREREREREQSBc1FRERERERERERERJI+ZRdARERERERERERERFrDBhtswKefflp2MZpKQXMRERERERERERGRHu7pp5/mk08+6fT18ePHM378+MoHzEFBcxEREREREREREZEe7Y477uCwww4ruxjfGgqai4iIiIg0yNChQxk5cmTZxSjdqFGjyi6CiIiIiHwFSy+9NOuttx5vvPHGZN/78MMPd0OJyqWguYiIiIhIg4waNYphw4aVXYxSDR8+vOwiiIiIiMhXNGDAAH7961936b2rrrpqk0tTPgXNRUREREQaRCPNg0aai4iIiEgrU9BcRERERKRBNNJcI81FREREpPUpaC4iIiIiIiIiIiIiXXLGGWdwww03MH78+AnbuHHjGDduHGPHjuXLL79kzJgxPP7444wdO7bs4n4tCpqLiIiIiIiIiIiISJfssssuZReh6XqVXQARERERERERERERaQ177rknvXr1muzWyjTSXERERERERERERES6ZLPNNmOzzTab7PtGjBjBeeed1w0lajwFzUVERERERERERESkSz755BNeeumlDl9ra2ubkOf89ddf7+aSNY6C5iIiIiIiIiIiIiLSJRtuuGHZRWg6Bc1FREREREREREREpEt++9vfcuGFF3b4mrtPGGn+/PPPd3PJGkdBcxERERERERERERHpktNOO62lU690hYLmIiIiIiINMnToUEaOHFl2MUo3atSososgIiIiIk1S9YA5KGguIiIiItIwo0aNYtiwYWUXo1TDhw8vuwgiIiIiIt+IguYiIiIiIg2ikeZBI81FREREpJUpaC4iIiIi0iAaaa6R5iIiIiLS+nqVXQARERERERERERERkW8LjTQXEREREWkQpWcJSs8iIiIiIq1MI81FRERERERERERERBKNNBcRERERaRDlNFdOcxERERFpfRppLiIiIiIiIiIiIiKSKGguIiIiIiIiIiIiIpIoaC4iIiIiIiIiIiIikihoLiIiIiIiIiIiIiKSKGguIiIiIiIiIiIiIl2y5JJLll2EplPQXERERERERERERES65KGHHiq7CE3Xp+wCiIiIiIhUxdChQxk5cmTZxSjdqFGjyi6CiIiIiMjXpqC5iIiIiEiDjBo1imHDhpVdjFINHz687CKIiIiIiHwjCpqLiIiIiIiIiIiISJfceuutXXrfiBEjOO+885pcmuZQTnMRERERERERERERkURBcxERERERERERERGRREFzEREREREREREREZFEQXMRERERERERERERkUQLgYqIiIiIiIiIiIhIl4wZM4YPPvhgwt9tbW2MHz+ecePGtfvvO++8U14hvyEFzUVERERERERERESkS9Zee+2yi9B0CpqLiIiIiIiIiIiISJfsv//+nHXWWZN93+jRo7uhNM2hoLmIiIiIiIiIiIiIdMk666zDOuusM9n3jRgxgvPOO68bStR4CpqLiIiIiIiIiIiISJccdthh3HHHHWUXo6l6lV0AEREREREREREREWkNH374YdlFaDqNNBcRERERERERERGRLjnxxBO79L5WTs+ikeYiIiIiIiIiIiIiIomC5iIiIiIiIiIiIiIiidKziIiIiIiIiIiIiEiXPPzww1xxxRWTfd8jjzyCu3dDiRpPQXMRERERERERERER6ZJ99tmn7CI0nYLmIiIiIiINMnToUEaOHFl2MUo3atSososgIiIiIk2y0EIL8eSTT5ZdjKZS0FxEREREpEFGjRrFsGHDyi5GqYYPH152EURERESkiaoeMAcFzUVERERERERERESki4444gguvPDCTl8v8pg//fTT3VWkhlPQXERERERERERERES6ZMiQIcw222yTfd/o0aN59913u6FEjaeguYiIiIiIiIiIiIh0SU9IR6iguYiIiIhIg2gh0KCFQEVERESqa5llluH+++8vuxhNpaC5iIiIiEiDaCFQLQQqIiIiUnXPPvts2UVoul5lF0BEREREREREREREWsPYsWPLLkLTKWguIiIiIiIiIiIiIl0y99xzl12EplPQXERERERERERERES65NFHHy27CE2nnOYiIiIiIg2ihUCDFgIVERERkVamoLmIiIiISINoIVAtBCoiIiIirU9BcxERERGRBtFI86CR5iIiIiLV1atXL9ra2souRlMpaC4iIiIi0iAaaa6R5iIiIiJVN9100/HOO++UXYymUtBcRERERERERERERLrkkksu6XB/W1sbbW1tjBs3jvHjx/P3v/+90/d+2yloLiIiIiIiIiIiIiLfSK9evejVqxd9+kTIuV+/fiWX6OtT0FxEREREpEGU0zwop7mIiIhIdZ1yyilcdtllZRejqRQ0FxERERFpEOU0V05zERERkaq75ppryi5C0yloLiIiIiIiIiIiIiJdcv755zN69GjGjx/P+PHjJ+Qwr//vzTffzJ133ll2cb8WBc1FRERERBpE6VmC0rOIiIiIVNdtt93WpWD466+/3g2laQ4FzUVEREREGkTpWZSeRURERKTqTjnlFMaOHVt2MZpKQXMRERERERERERER6ZI///nPvPbaa52mZym2Bx54gMcee6zs4n4tCpqLiIiIiIiIiIiISJcssMACLLDAApN935gxYxQ0FxERERHp6ZTTPCinuYiIiEh1HXXUUdx4441lF6OpFDQXEREREWkQ5TRXTnMRERGRqqt6wBwUNBcRERERaRiNNA8aaS4iIiIirUxBcxERERGRBtFIc400FxEREZGaPn1aM/zcq+wCiIiIiIiIiIiIiEj1jBs3ruwifC0KmouIiIiIiIiIyP+3d+9Rlpb1nei/bzcYFIPE0UxGZ7HUJfGWxFpoiEiOBkaJE1ZIJuIYHR2RFT3qyplUjhhR8AKKC/CSmjPxQLyUQEoUImaQBWNjS5fQCiKu2Vyai9zUgIqgrSBgd+9dz/ljb/o0LU29NO9bD1V8PmvttWvv96m9v/Xvt3/9ewGYWJ7z8QAAAAAALLl3vetdOeussxY9993vfjebN29egkTdU5oDAAAAANDKQQcdlIMOOmjRc7Ozs5mbm1uCRN2zngUAAAAAACZMmgMAAAAA0Mq73vWuXHLJJbVj9MqkOQAAAAAArQyHw9oRemfSHAAAAACAVj784Q+3Orecd5orzQEAAAAAaOXWW29ttZ7lqquuSillCRJ1T2kOAAAAAEArr3vd62pH6J2d5gAAAAAAMGHSHAAAAACAVk488cScffbZGY1GGQ6HGY1GWx/3vR4Oh7n99ttz99131467U5TmAAAAAAC08p73vCebNm2qHaNXSnMAAAAAAFp57Wtfm3PPPXfRc3fccUcWFhaWIFH3lOYAAAAAALRy6KGHZv/99/+V9xcWFjIcDreuaDnvvPOydu3aCgkfPqU5AAAAAACtHHzwwbUj9G5V7QAAAAAAAPBIoTQHAAAAAIAJ61kAAKAjU1NTmZ+frx2jusFgUDsCAADsNKU5AAB0ZDAYZHp6unaMqmZmZmpHAACgR3vssUfuvPPO2jF6pTQHAICOmDQfM2kOALByvfzlL89ZZ51VO0avlOYAANARk+YmzQEAVrqVXpgnbgQKAAAAAABbKc0BAAAAAGBCaQ4AAAAAQCuveMUrakfondIcAAAAAIBW1q5dWztC79wIFAAAOjI1NZX5+fnaMaobDAa1IwAA0JPhcFg7Qu+U5gAA0JHBYJDp6enaMaqamZmpHQEAAB4W61kAAAAAAGBCaQ4AAAAAABNKcwAAAAAAmFCaAwAAAADAhBuBAgAAAADQyic+8Ymcf/75GY1GGQ6HGQ6HW38ejUZbf77uuuuycePG2nF3itIcAAAAAIBW3vzmN9eO0DulOQAAAAAArZx66qn5xje+sei5iy++OFdcccUSJOqe0hwAAAAAgFb22muv7LXXXoueu+eee3LllVcuQaLuuREoAAAAAABMKM0BAAAAAGDCehYAAAAAAFo599xzc8YZZ6SUklLK/a41TbP1vVtvvbVGvE4ozQEAAAAAaOXkk0/OL37xi9oxeqU0BwCAjkxNTWV+fr52jOoGg0HtCAAA9GSvvfbK1VdfXTtGr5TmAADQkcFgkOnp6doxqpqZmakdAQCAh+jmm2/O4YcfXjvGI4bSHAAAOmLSfMykOQDA8jIajWpHeERRmgMAQEdMmps0BwBYjp75zGdm3bp1rc4ecMABPaepT2kOAAAAAEArRx55ZE455ZQdXh+NRhmNRvnpT3+6dKE6pjQHAAAAAKCVpz3tadl///13eL2UkiS57LLL8v3vf3+pYnVKaQ4AANADO+7H7LgHgJXlLW95S+0IvVOaAwAA9MCO+zF77gGA5WZV7QAAAAAAACwPL33pS2tH6J1JcwAAAAAAWnn/+9/f6tzs7Gzm5ub6DdMTpTkAAAAAAK2ce+65+dSnPrXouZ/97Gf9h+mJ0hwAAAAAgFY+8pGP1I7QOzvNAQAAAABgQmkOAAAAAAATSnMAAAAAAFp5y1veUjtC75TmAAAAAAC08tnPfrZ2hN65ESgAAAAAAK2ccMIJueqqq3Z4fWFhIaPRKN/61rdy+eWXL2Gy7ijNAQCgI1NTU5mfn68do7rBYFA7AgAAD0EpJRdccEE2bty46NmPf/zjS5CoLqU5AAB0ZDAYZHp6unaMqmZmZmpHAADgIVq3bl0++MEP1o7xiKE0BwAAAAB4FHvpS1+a2267LXfccceiZ7/4xS8uQaK6lOYAANAR61nGrGcBAFheVq9ende85jWtzt511135yle+0nOiulbVDgAAAAAAwPKw0gvzxKQ5AAB0xk5zO80BAFj+lOYAAAAAALRyzDHHZHZ2NsPhMMPhMKPR6H6Pbd9frpTmAADQETvNx+w0BwBYuWZmZrJx48baMXqlNAcAgI5Yz2I9CwDASvf85z9/xQ+KuBEoAAAAAACtrPTCPFGaAwAAAADAVkpzAAAAAABaef3rX187Qu+U5gAAAAAAtPLYxz62doTeKc0BAAAAAGjlM5/5TO0IvduldgAAAAAAAJaH888/P0mysLCQ0WiU4XCY0Wi09XHf6zPPPDNnn3125bQ7R2kOAAAdmZqayvz8fO0Y1Q0Gg9oRAADoyb/8y7/klFNOSSnlQc/dddddS5Soe0pzAADoyGAwyPT0dO0YVc3MzNSOAABAj0455ZTceeedtWP0SmkOAAAAAEArp59+ejZu3Lh1DcuO1rOsWbNm2f4vTKU5AAAAAACt7L777tl9990XPbdhw4YlSNOPVbUDAAAAAADAI4XSHAAAAAAAJpTmAAAAAAAwoTQHAAAAAIAJpTkAAAAAAEwozQEAAAAAYGKX2gEAAAAAAB6pmqZ5SZIjkrwgyVOSvLGUcso2109J8obtfu2bpZQXLVXGpXTHHXdkw4YNi5773ve+twRp+qE0BwAAAADYsccnuSrJaZPHA1mb5PXbvN7cd6haXvWqV9WO0DvrWQAAAAAAdqCUcl4p5d2llC8kWdjBsU2llB9t8/jpUmZcSr/3e79XO0LvTJoDAAAAADw8f9g0zY+T/CzJ15IcVUr5cddfMhqNcumll+b666/P3nvvnX333TerV6/u+mse1BVXXLGk31eD0hwAAAAAYOd9OckXk9yc5GlJPpjkgqZpXlBK2dTVl4xGo/zd3/1drrnmmvzyl7/MYx7zmDzjGc/IO97xjqxatXMLRUopKaXc7+ftH9tfe+1rX5vTTz+9qz/rEUlpDgAAHZmamsr8/HztGNUNBoPaEQAAlkwp5fPbvLyyaZpvJ/lekoMzLtM7cemll+aaa67JvffemyTZtGlTrrnmmhx++OFdfQUTSnMAAOjIYDDI9PR07RhVzczM1I4AAFBVKeUHTdPckmTvLj/3+uuvzy9/+ctfeX+PPfbInnvu+UA5tv7cNM0Or21/fftr214vpeRf//VfH1Lu5UhpDgAAHTFpPmbSHAB4NGua5klJnprkh11+7t57753ddttt66R5kuy222458sgjs99++3X5VQ/qPe95T9avX79k31fDzi27AQAAAAB4FGia5vFN00w1TTOVcZ+61+T1XpNrH2maZr+maZ7WNM0fJTknyY+T/EuXOfbdd9885znPyW677ZamabLbbrvluc99bvbdd98uv2ZRK70wT0yaAwAAAAA8mBcmWbfN62Mmj1OTvDXJ7yb5r0n2zHi6fF2S/1xKuavLEKtXr86JJ56YSy+9NDfccEOe+cxnZt99983q1au7/BqiNAcAgM7YaW6nOQCw8pRS5pM0D3Lkj5coSlavXp399ttvSdexPBopzQEAoCN2mo/ZaQ4AsHK9+tWvzhlnnFE7Rq+U5gAA0BGT5ibNAQBWupVemCduBAoAAAAAAFuZNAcAgI5YzzJmPQsAAMuZSXMAAAAAAJgwaQ4AAB2x09xOcwAAlj+T5gAAAAAAtPIHf/AHtSP0zqQ5AAAAAACtHH/88a3Ozc7OZm5uruc0/VCaAwBAR9wIdMyNQAEAVq4jjzwy3/zmN2vH6JXSHAAAOmKnuZ3mAAAr3UovzBOlOQAAdMak+ZhJcwCAlWvXXXfNli1basfolRuBAgAAAADQykovzBOT5gAA0BnrWaxnAQBY6V74whfmsssuqx2jV0pzAADoiPUsY9azAACsXCu9ME+U5gAA0BmT5ibNAQBY/pTmAADQEZPmYybNAQBYztwIFAAAAAAAJkyaAwBAR6xnsZ4FAGCl23XXXbNly5baMXpl0hwAAAAAgFZWemGemDQHAAAAAKCl0047LevXr89wOMxoNPqV5/se1157bW666abacXeK0hwAADriRqBjbgQKALBy/c3f/E02btxYO0avlOYAANARO83tNAcAWOme+MQnKs0BAIB2TJqPmTQHAFi5nv70p+fGG2+sHaNXSnMAAOiISXOT5gAAK90tt9xSO0LvlOYAAAAAALRy0kkntTo3Ozububm5ntP0Y1XtAAAAAAAA8Ehh0hwAADpip/mYneYAACvXSSedlDPPPLN2jF4pzQEAoCN2mttpDgCw0j0ahkSU5gAAAAAAtPL5z38+CwsLGQ6HGY1G93vevHlzNm3alE2bNuXss8/O+eefXzvuTlGaAwAAAADQyoEHHlg7Qu+U5gAA0BE7zcfsNAcAYDlTmgMAQEfsNLfTHACA5W9V7QAAAAAAAPBIoTQHAAAAAKCVgw8+uHaE3lnPAgAAAABAK0cccUSOOOKIB7xWSsnCwkJGo1FOOeWUfP7zn1/idN1QmgMAQEfcCHTMjUABAFauI444It/+9rdrx+iV9SwAAAAAALSy0gvzxKQ5AAB0ZjAYZHp6unaMqmZmZmpHAACAh0VpDgAAAABAKx//+MezZs2aLCwsZDgcZjQaZcuWLdm8eXM2b96cTZs2ZdOmTfnOd75TO+pOU5oDAAAAANDK9PR0tmzZUjtGr+w0BwAAAACglf333792hN4pzQEAAAAAaGV+fr52hN4pzQEAAAAAYMJOcwAAAAAAWjnuuOPyhS98YYfXR6NRRqNRfvjDH2bjxo1LmKw7SnMAAAAAAFo56qijakfonfUsAAAAAAAwoTQHAAAAAIAJ61kAAKAjU1NTmZ+frx2jusFgUDsCAADsNKU5AAB0ZDAYZHp6unaMqmZmZmpHAACAh0VpDgAAAABAK8ccc0zOPffcjEajDIfDDIfDjEajrY8tW7Zk06ZN+fGPf1w76k5TmgMAAAAA0MpJJ52UH/3oR7Vj9EppDgAAHbHTfMxOcwCAlWulF+aJ0hwAADpjp7md5gAAK91ee+2V73//+7Vj9GpV7QAAAAAAACwPK70wT5TmAAAAAACwldIcAAAAAAAmlOYAAAAAALTylre8pXaE3inNAQAAAABo5eSTT64doXe71A4AAAAAAMDy8NGPfjRnn332Dq+XUpIkl19+ee68886litUppTkAAAAAAK3ss88+2WeffRY9Nzs7m7m5uSVI1D3rWQAAAAAAYEJpDgAAAAAAE9azAAAAAADQyrp16/JP//RPi567+eablyBNP5TmAAAAAAC0cuyxx9aO0DulOQAAAAAArZx77rn54Q9/eL/3SikppWQ4HGY4HGY0GuW8887L2rVrK6V8eJTmAAAAAAC0ctFFF2V2dnaH10spSZLbb799qSJ1TmkOAAAAAEArxx9/fO0IvVOaAwAAAADQype//OVs3Lhx6+tSSkaj0da1LPc9n3POOVmzZk3FpDtPaQ4AAAAAQCtr1qzJP/7jPy567p577lmCNP1QmgMAAAAAkCRZWFhIKSULCwtbH9u+/vu///vaEXunNAcAgI5MTU1lfn6+dozqBoNB7QgAADwE3/72t3PEEUfUjvGIoTQHAICODAaDTE9P145R1czMTO0IAAA8RE9/+tPzlKc8JT/4wQ9qR3lEUJoDAEBHTJqPmTQHAFhenvjEJ+azn/1sq7MHHHBAz2nqU5oDAEBHTJqbNAcAWOkOPPDAXHDBBbVj9GpV7QAAAAAAACwPK70wT5TmAAAAAAC0dOSRR2aPPfbY+th9992z2267ZZdddknTNLXjdcJ6FgAAAAAAWjn++ONrR+id0hwAAAAAgFaOOOKIfPrTn1703MaNG5cgTT+U5gAAAAAAtHLggQfmd37nd+733sLCQkaj0f0e55xzTtauXVsp5cOjNAcAAAAAoJU/+ZM/qR2hd0pzAAAAAABaOeqoo3Laaac96JmFhYXceuutS5Soe0pzAAAAAABaOe6442pH6N2q2gEAAAAAAOCRQmkOAAAAAAAT1rMAAEBHpqamMj8/XztGdYPBoHYEAADYaUpzAADoyGAwyPT0dO0YVc3MzNSOAAAAD4vSHAAAoAf+58GY/3kAACw3SnMAAOiIknRMSTrmfx6M+d8HAMByozQHAICOKEkVpAAALH9KcwAA6IhJ8zGT5gAALGdKcwAA6IhJc5PmAAAsf6tqBwAAAAAAgEcKpTkAAAAAAExYzwIAAB2x03zMTnMAAJYzpTkAAHTETnM7zQEAWP6U5gAA0BGT5mMmzQEAWM6U5gAA0BGT5ibNAQBWuj322CN33nln7Ri9ciNQAAAAAABaueeee2pH6J1JcwAAAAAAWjn22GNz4403bn09Go3u9xgOhxmNRtmwYUNuuOGGikl3ntIcAAAAAIBW9txzz9x+++2LnrvnnntSSlmCRN1TmgMAAAAA0Mrb3va22hF6Z6c5AAAAAABMmDQHAAAAAKCVmZmZfOlLX9r6+oH2mQ+Hw1x33XXZtGlTxaQ7T2kOAAAAAEAr09PTtSP0TmkOAAAAAEArZ5xxRi6//PJFz1144YX5+te/vgSJuqc0BwCAjkxNTWV+fr52jOoGg0HtCAAA9OR973tfrr322toxeqU0BwCAjgwGg0fFf1d9MDMzM7UjAADQo5VemCdKcwAAAAAAWvroRz96vxuB7shgMMjPf/7zJUjUPaU5AAAAAACt7LPPPtlnn30WPTc7O5u5ubklSNQ9pTkAAHTETvMxO80BAFauj33sYznnnHNqx+iV0hwAADpip7md5gAAK90VV1xRO0LvlOYAAAAAALRyyimnPOj1UkoWFhYyOzubz33uc0sTqmOragcAAAAAAGBlaJomq1evzurVq2tH2WlKcwAAAAAAmLCeBQAAAACAVt761rfm2muvrR2jVybNAQAAAABo5alPfWrtCL0zaQ4AAAAAQCtHH310jj766EXPzc7OZm5ubgkSdc+kOQAAAAAATJg0BwAAAACglUsvvTT//M//vOi5K6+8MqWUJUjUPaU5AAAAAACtvPOd76wdoXdKcwAAAAAAWvniF7+Y66+/fuvr0Wh0v8eWLVuyefPmXHTRRbnssssqJt15SnMAAAAAAFr5i7/4i9oReudGoAAAAAAAMKE0BwAAAACglcMPP7x2hN4pzQEAAAAAaGV2drZ2hN7ZaQ4AAAAAQCtnnXVWrr322q2vFxYWfuVmoJs3b8769evdCBQAAAAAgJXtla98Ze0IvbOeBQAAAAAAJpTmAAAAAAAwYT0LAAB0ZGpqKvPz87VjVDcYDGpHAACAnaY0BwCAjgwGg0xPT9eOUdXMzEztCAAA8LAozQEAoCMmzcdMmgMAsJwpzQEAoCMmzU2aAwCsdC984Qtz2WWX1Y7RK6U5AAAAAACtfPjDH251bnZ2NnNzcz2n6ceq2gEAAAAAAOCRQmkOAAAAAAATSnMAAAAAAJiw0xwAADoyNTWV+fn52jGqGwwGtSMAANCT4447Lhs2bEjTNEmShYWFjEajDIfDjEajrY/NmzdvPbPcKM0BAAAAAGhl7dq1tSP0TmkOAAAdGQwGmZ6erh2jqpmZmdoRAADgYbHTHAAAAACAVp70pCfVjtA7pTkAAAAAAK3ccccdtSP0znoWAAAAAABaOf/883PPPfckGd8EdPsbgG7ZsiWbN2/O2WefnfPPP79y2p2jNAcAgI5MTU1lfn6+dozqBoNB7QgAAPTkqKOOyre+9a3aMXqlNAcAgI64EagbgQIArHQrvTBP7DQHAAAAAKClJzzhCbUj9M6kOQAAAAAArZx++un52c9+dr9d5vf9PBwOt/68Zs2arFu3rnbcnaI0BwAAAACglcc97nF53OMet+i5q666agnS9ENpDgAAAABAK9dff32uueaaB7y2sLCQ4XCYhYUFpTkAAAAAACvf29/+9tx11121Y/RKaQ4AAAAAQCtHH3101q9fv+i5DRs25KabblqCRN1TmgMAAAAA0Mq+++6bfffdd9Fzs7Ozufnmm5cgUfdW1Q4AAAAAAACPFEpzAAAAAACYUJoDAAAAAMCE0hwAAAAAACbcCBQAADoyNTWV+fn52jGqGwwGtSMAANCTD37wg/nqV79aO0avlOYAANCRwWCQ6enp2jGqmpmZqR0BAIAerfTCPFGaAwAAAAA8qo1Go3zhC1/I7bffnoWFhZRSsrCwsPXn0Wi09b1HA6U5AAAAAMCj2Ne+9rWcfPLJtWM8YijNAQAAAAAexQ444IAMh8PceeedaZomq1at+pXn+34+4YQTasftndIcAAAAAOBRrGmaHHTQQa3O/vSnP80nP/nJnhPVpTQHAICOTE1NZX5+vnaM6gaDQe0IAAD05KKLLqodoXdKcwAA6MhgMMj09HTtGFXNzMzUjgAAQI+uvfba2hF6t6p2AAAAAAAAeKRQmgMAAAAAwIT1LAAA0BE7zcfsNAcAYDlTmgMAQEfsNLfTHACA5c96FgAAAAAAWnnDG95QO0LvTJoDAEBHrGcZs54FAGDlOvXUU2tH6J1JcwAAAAAAmDBpDgAAHbHT3E5zAACWP5PmAAAAAAAwYdIcAAA6Yqf5mJ3mAAAsZybNAQAAAABgwqQ5AAB0xE5zO80BAFa6l7zkJbnwwgtrx+iV0hwAADpiPcuY9SwAACtXKaV2hN4pzQEAoCMmzU2aAwCsdBdddFHtCL2z0xwAAAAAgFZe9KIX1Y7QO6U5AAAAAACtXHLJJbUj9M56FgAA6Iid5mN2mgMAsJwpzQEAoCN2mttpDgDA8mc9CwAAAAAATJg0BwCAjljPMmY9CwAAy5nSHAAAOmI9i/UsAAAr3WMe85hs3ry5doxeKc0BAAAAAGhlzZo1rc7Nzs5mbm6u5zT9UJoDAAAAAPCwlVKysLCQ0WiU4XBYO85OU5oDAAAAANDK6173utx66621Y/RqVe0AAAAAAAAsDy94wQtqR+id0hwAAAAAgFa+9KUv1Y7QO6U5AAAAAACtvO51r6sdoXdKcwAAAAAAWpmbm6sdoXdKcwAAAAAAmFCaAwAAAADQyiGHHFI7Qu92qR0AAABWiqmpqczPz9eOUd1gMKgdAQCAnvz85z/PnnvumaZptr5XSsloNMpoNMpwONz68+rVqysm3XlKcwAA6MhgMMj09HTtGFXNzMzUjgAAQI++9rWvtT47Go16TNIfpTkAAHTEpPmYSXMAAJYzO80BAAAAAGDCpDkAAHTEehbrWQAAWP5MmgMAAAAAwIRJcwAA6Iid5mN2mgMAsJwpzQEAoCPWs1jPAgDA8qc0BwAAAACglTPPPDNXXnnloufm5+ezfv36JUjUPaU5AAAAAACtPPnJT86BBx646Lnvfve7y7Y0dyNQAAAAAACYMGkOAAAAAEAr11xzTa644oodXi+lZDQaPeiZRzqlOQAAAAAArbztbW+rHaF31rMAAAAAAMCE0hwAAAAAACasZwEAgI5MTU1lfn6+dozqBoNB7QgAALDTlOYAANCRwWCQ6enp2jGqmpmZqR0BAIAePeMZz8hNN91UO0avlOYAANARk+ZjJs0BAFaulV6YJ3aaAwAAAADAVibNAQCgI9azWM8CALDS7brrrtmyZUvtGL0yaQ4AAAAAQCsrvTBPlOYAAAAAALT0spe9rHaE3inNAQAAAABoZe3atbUj9E5pDgAAAAAAE0pzAAAAAACYUJoDAAAAAMDELrUDAAAAAACwPJx55pm54oor7vdeKSWj0WjrYzgc5uKLL85ll11WKeXDozQHAICOTE1NZX5+vnaM6gaDQe0IAAD05Nhjj81VV11VO0avlOYAANCRwWCQ6enp2jGqmpmZqR0BAIAePeUpT1nxpbmd5gAAAAAAtHL11VfXjtA7pTkAAAAAAK3ccssttSP0TmkOAAAAAEArb3jDG2pH6J2d5gAAAAAAtHLYYYflsMMOW/Tc7Oxs5ubm+g/UA5PmAAAAAAAwoTQHAAAAAIAJpTkAAAAAAEzYaQ4AAAAAQCs33HBDLrjggkXPffOb30wpZQkSdU9pDgAAAABAK29605tqR+id0hwAADoyNTWV+fn52jGqGwwGtSMAANCT3/iN38jGjRtrx+iV0hwAADoyGAwyPT1dO0ZVMzMztSMAANCj5z3veVm/fn3tGL1SmgMAAAAA0MoHPvCBVudmZ2czNzfXc5p+rKodAAAAAAAAHimU5gAAAAAAMKE0BwAAAACACaU5AAAAAABMKM0BAAAAAGBil9oBAAAAAABYHr7yla/k1FNPXfTcrbfeugRp+qE0BwAAAACglQ996EO1I/TOehYAAAAAAJgwaQ4AAAAAQCuf+9znMhgMFj134YUX5uKLL+4/UA+U5gAAAAAAtPJbv/VbecUrXrHouR/84Ae55JJLliBR96xnAQAAAACACaU5AAAAAABMWM8CAAAAAEArF198cc4444wdXi+lJEk2bNiw9eflRmkOAAAAAEAr7373u2tH6J3SHAAAOjI1NZX5+fnaMaobDAa1IwAAwE6z0xwAAAAAACZMmgMAQEcGg0Gmp6drx6hqZmamdgQAAHhYTJoDAAAAAMCE0hwAAAAAACasZwEAAAAAoJVjjjkmH/vYx3Z4vZSS0WiUu+++ewlTdUtpDgAAAABAK+973/tqR+id0hwAAAAAgFbOOOOMXH755Yueu/DCC/P1r399CRJ1T2kOAAAAAEArv/mbv5mXvexlWVhYyGg0ynA4fMDnDRs21I6605TmAADQkampqczPz9eOUd1gMKgdAQCAnhx22GH53ve+VztGr1bVDgAAAAAAwPKw0gvzxKQ5AAB0ZjAYZHp6unaMqmZmZmpHAACgR8961rNy3XXX1Y7RK6U5AAAAAACtnHzyya3Ozc7OZm5uruc0/VCaAwAAAADQyj/8wz/krLPOqh2jV0pzAADoiBuBjrkRKADAyrXSC/NEaQ4AAJ2x09xOcwAAlr9VtQMAAAAAALA8PPvZz64doXcmzQEAAAAAaOWkk05qdW453wjUpDkAAAAAAEyYNAcAAAAAoJXhcJh77703pZQsLCxkOBxmNBptfb7v8ZOf/KR21J2mNAcAAAAAoJWXv/zltSP0znoWAAAAAABa+du//dvsvvvuiz6WM5PmAAAAAAC0csghh+SQQw5Z9NxyvhGo0hwAAAAAgIfsgfaa3/fz3XffXTveTlOaAwAAAADQyp/+6Z/mF7/4Re0YvbLTHAAAAACAVg499NDaEXpn0hwAAAAAgFae97zn5UUvetGi56688splu6JFaQ4AAAAAQCvveMc7akfondIcAAAAAIBWPvOZz+S88857wGtN02RhYSGbN2/O1VdfnRtvvHGJ03VDaQ4AAAAAQCtvfOMba0fondIcAAAAAIBWjjvuuJxxxhmLntuwYUNGo9ESJOqe0hwAAAAAgFZe/OIX58UvfvGi52ZnZzM3N7cEibqnNAcAAAAAoJXbbrstt912W4bDYYbDYUaj0dbHtu9df/31taPuNKU5AAB0ZGpqKvPz87VjVDcYDGpHAACgJ3/5l39ZO0LvWpXmTdP8myT/KcnBSX43yVOTbE5yZZLPJPlMKWVhu9/59SRHJnllkqcluTfJpUlOLKV89QG+48+TvCbJ85P82yS7JbklyWVJPlpKuWwH2Z6Y5L1J/jzJv0vykyRfTvLeUsotbf4+AADowmAwyPT0dO0YVc3MzNSOAAAAD0vbSfNXJTkpyQ+TrEvy/YyL7b9I8qkk/7FpmleVUkqSNE3zG0nWJ3lukg1JTk7y+CR/lmRt0zR/VUr59Hbf8WdJfj/Jt5L8IONS/pkZl/WvbprmzaWUT237C5My/xtJfjvJBUk+n+TZSd6Y5OCmafYrpdzU8m8EAAAAAOBBnHDCCVm/fv2i5zZs2JCbbuqvmm2a5iVJjkjygiRPSfLGUsop21z/t0lOSHJQkj2TXJjk/yqlLLo3pm1p/p0khyQ5d9uJ8qZp3p3x9PgrMy7Qz5pcen/GhfkXk7y6lDLc5vxlSf5H0zRrtpsEf2sp5Zfbf3HTNL+bcZH+kaZpTiulbN7m8ocyLsw/Vkp5+za/89+S/Pck/2+SV7T8GwEAAAAAeBA333xzzjnnnNoxkvGQ9lVJTps8tmqapknyP5MsZLyh5OdJ/u+MB7qfW0q5+8E+eFWbby+lXFBKOWf7FSyllB9lPEWeJH+0zaX/NHl+732F+eT8j5N8LMljkxy+3Wf9SmE+ef/KJNckeUKSJ9/3ftM0j0/y+iR3Z1zSb+sfknwvyR83TfOMxf9CAAAAAAAW88lPfvIhnb/44oszGo06z1FKOa+U8u5SyhcyLse3tXeSFyV5Wynl0lLKdUnemnEv/ZrFPruLG4FumTwPt3nvtybPDzR/f997/yHJsYt9eNM0v53kWUnuyHg9zH1elPEfeX4p5a5tf6eUstA0zZokb05ywA5yAAAAAADwEKxduzallIxGo4xGowyHwwyHw2zevDmbN2/Ovffem4985CO54YYbMhqN8oEPfCDPec5zcuKJJ2b16tVLFfPXJs9bB7UnnfGmJH+Y8crxHXpYpXnTNLsk+a+Tl1/e5tIdGd+U8+lJrt7u1+6b/H7WDj7zZRkHf8zk9/90cumvtpt0v+/3v7ODePftpvntB/kTAAAAAABo6dBDD81PfvKT1ufvvffeXH311bn00kuz33779Zjsfq7N+L6cH2qa5k1JfpHkb5P8+4x76wfVTO7duVOapvlIkrcnOa+UcvA2738yyV8l+UKSvyyljCbvPznjneZ7JdlcSvm1B/jM45O8c5u3fpTksFLKmu3OvTvJcUmOK6Uc/QCf86Ykn0jyiVLK/7nTfyQAAAAAAK0ccMAB78l4nfa2q8EXkrxv3bp1H+zjO5um+UWSv97uRqAvSPLpJM9PMkqydpKjKaX8xwf7vJ2eNJ/cbPPtGbf2r9/u8nuT/HGSQ5MMmqb5apLdk/xZklszLs233zOTJCmlHJnkyKZpds94SvyIJP+raZr3lFKO29m8AAAAAAD0a926dR9I8oHaOUop304y1TTNE5I8ppRye9M038x4qPtBtboR6PaapvnrJP8949UrB5RSfrpdoB8m+f0kH0/y60neluTgJGckedXk2I8f7DtKKXeXUv53KeW/JFmT5ANN0/z+Nkd+Pnl+wg4+4r73f9bmbwIAAAAAYGUppfx8UpjvneSFSc5e7Hce8qR50zTTSf4+yVVJ/kMp5QHL71LKbUn+evLY9vcPnPz4rYfwtV9O8ookL93m966bPO9oZ/nek+cd7TwHAAAAAGAZaprm8UmeOXm5KsleTdNMJflpKeX7TdO8KuN7b34vye9mPAT+P0sp5y/22Q9p0rxpmndmXJgPMp4wf9Bp8R2478ahpz+E33nq5Hm4zXuXJLk3yf5N0/z6djlXJTlo8nLdTmQEAAAAAOCR64VJ/vfk8dgkx0x+PnZy/d8lOS3j9eL/T5J/SvKaNh/c+kagTdO8Z/KF305y0PYrWbY7uyrJ40opv9ju/dcnOTXJxUn+j1LKwuT9X0vy7FLK5Q/wWb+f5KtJHpfkd0op125z7R+TvDnJx0opb9/m/f+W8b8crCmlvKLVHwgAAAAAwKNeq9K8aZo3JDkl47uM/o/8//vEt/Xd++5OOhmNvy3JV5LcmPFNP/dPsl+Sa5K8rJTyg20+f88kG5NckfHal1syLsmfk+S+dS7vKKV8dLtc/ybJNzJe0XJBkksnv/NnGe9Mf3Ep5cZF/0AAAAAAAEj70vz9Sd63yLGvlVL+aHJ+1yQnJ/nDJP9+cv36JGcmmSml3LPd5++a5J0Z7yx/TpInJSlJbs24FP94KeWbO8j2xEm2P8945P4nSf5XkveWUm5Z9I8DAAAAAICJ1utZAAAAAABgpXtINwIFAAAAAICVTGkOAAAAAAATSnMAAAAAAJhQmgMAAAAAwITSHAAAAAAAJpTmAAAAAAAwoTQHAAAAAIAJpTkAAAAAAEwozQEAAAAAYEJpDgAAAAAAE/8fQ6VHpMaz2RoAAAAASUVORK5CYII=\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": 30,
"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[DISCRETE_VARIABLES]);"
]
},
{
"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[NOMINAL_VARIABLES]);"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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": 33,
"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": 34,
"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": 35,
"metadata": {},
"outputs": [],
"source": [
"df = df[remaining_columns]"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2898, 78)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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>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": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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