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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>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>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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" </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, (np.int64(1), np.int64(526301100)) to (np.int64(2930), np.int64(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, (np.int64(1), np.int64(526301100)) to (np.int64(2930), np.int64(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, (np.int64(1), np.int64(526301100)) to (np.int64(2930), np.int64(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, (np.int64(1), np.int64(526301100)) to (np.int64(2930), np.int64(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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q1bRkyRKKjo6mgQMHkru7uwG/BWPsa+Dn55dspgF1BsAXL16Qi4uLoYqZKWzevJkWL15MFy5coF9++YWaNGki+3Tq4IGffvqJHjx4QHny5KEePXpQrly5OBsjS3c8omCMMcYYY4x9VsePH6f4+HgaPHhwskEDCmUAbGlpSfv376fatWvTwoULSaPR8KQ+Y4yxb4ZykqiTkxOZmZmRqakpqU8XVWft2bZtGy1ZsoQKFChAjRs3JiLioIEkxMfH6/w9PDyc4uLiuH/BMqxz585RixYtyNHRkWbPnk1jx44looT2Q2kvlD9rNBqKjY2VwQPdu3ent2/f0sqVK2nJkiX06NEjQ34VxpiBDRgwgGbOnElFihShwYMHy6ABABQfHy/H6b179yYfHx96+fKlIYuboU2ZMoV69epFFy5coB49epCNjY0MGlAowQPe3t6UNWtWunv3Ls2dO5eePXtGxsbGpNVqDVR69i3iMBXGGGOMsS9ESSXLvrwXL17Qw4cP6fHjx5Q/f35yc3PjVPfpJHH0u1arpTt37pCJiQlZWFgQUdKLGUowwd9//01eXl5kZGREVlZWdODAgXQrO2OMMfa1UBaz1fdMIYTcSaw8vm3bNho/fjyFhITQxo0bqXDhwgYp79dO6Z+8e/eO5s2bR8ePH6eHDx+Ss7Mz+fv7U6lSpbivyDKUFy9e0KhRoyg8PJxmzZpF7dq1I6L3QUVKG6IOjDExMSEiImtra5o7dy4REf3888+0fPly0mg05O3tTR4eHun8TRhjXwMzMzMiIrpx4wYdPnyY8ubNS25ubgRAzmN16dKFNmzYQAMHDuQd7x9p+PDhNGfOHKpZsyaNGzeOqlevnuxrEx9bsGHDBoqJiaGFCxeSi4sLZ5di6Yb/tTPGGGOMfQGLFy8mNzc3atKkiRyQsS9jyZIltH79ejp9+jQREdnZ2VHt2rVp+PDhVL58eQOXLnNTdiKEh4fTrl27qFOnTiSEIGNjY4qNjaUzZ85Q+fLl9Xb2KQPeqKgo8vf3p/bt21O3bt0M8yUYY4yxr5QSNBAbG0sAaMqUKfTTTz9RVFQU7dixg+rWrctp95Og9E8iIiKoYcOGdPz4cbKzsyNLS0u6du0aXbp0ifr160ddu3bVScvM2NfsxYsXdOHCBWrQoAH16dOHiPQD1a9evUqXL1+mgwcPUp48eahgwYLUoUMHItINHli/fj0tWLCAwsPDacqUKZQlS5b0/0KMJSGlzRe8aPp5KP2GOXPmkIODA40bN47GjRtH7969o379+lH27NmJiOi7776jjRs3Uq9evWjEiBFkb29v4JJnPPPmzaM5c+ZQkyZN6McffyRPT0/5nPp6VjLGCCH0gge2bt1KQgiaP38+ubq68r8Dli44cIAxxhhj7DMbNWoUTZs2jcqUKUMWFhZUp06dJM+oZZ9uxIgRNHv2bLK2tqY6depQSEgI3bx5k7Zv306vXr2ipUuXUqFChQxdzExJ2YkQGRlJlSpVomvXrlGWLFmoWbNm1KRJE9q3bx8dOnSIunTpQg4ODvJ96oHumDFjaP/+/dS1a1de+GCMMcYSEUJQZGQkDRo0iE6ePElBQUFUsmRJmjNnDlWvXl3nKAOWQKvVyv5JvXr16MyZM9SzZ08aO3YsZcuWjaZPn05Tp06lxYsXU0hICA0ZMoTy589v6GIz9kFnzpyh4OBgypMnDxElHL2hLPiHhobS//73PxoxYgSFhYVRdHQ0ERHZ2trSkSNHKDAwkIjeBw/ExsbSunXrqEiRIhw0wAxOGR8qmWKioqJo4cKFdO3aNbK2tqYiRYrQgAEDSKPR8JjxMxBCUExMDJmamtKYMWNIq9VSQEAATZ06lUxNTWnEiBHUu3dv2rhxI/Xs2ZPGjRtHOXLk4LpPo0uXLtGCBQvI0dGRxo8fL4MGAMi+iiIqKoosLS3l3xMHD2zZsoWMjIxo9uzZ5Obmlr5fhH2TOHCAMcYYy+Q2bNhAxYsXJy8vL0MX5Zuwdu1amjZtGhERnT9/nsaNG0cAqG7duhw88JlNnjyZZs+eTY0aNaKJEydSqVKlKDo6mlasWEHz58+nc+fO0alTp6hQoUI8yP1CtFotjR49ml68eEHDhw+nWrVqERFRxYoVKUeOHLRnzx4aPXo0TZs2jaytrUmj0ciggWXLltHatWupcuXKVKtWLf59GGOMsSTExcWRpaUlhYaG0uTJk6l169ZUsGBBnd1p7D1l8WnIkCF06dIlGj58OI0dO5asrKzov//+o5s3b1JUVBQZGxvTihUriIh0znZm7GulpAn/888/KTg4mOzs7IiI6I8//qBt27bJ69nLy4ucnZ3p9evX9O+//9KyZcvI1dWVJkyYQADI2tqaFixYQF26dKE6deoY6uswRn///TcVL16cNBoNxcbGkomJCUVERFC9evXo1KlTOq89fPgwrVixQl73LO0ePHhAT548oQoVKpCpqakM1Bg3bhwREQUEBFBAQADt2LGDrly5Qr169aLx48dT9uzZeT7lI/z777/06NEjGj58OJUuXZoAEADSaDQyaGDVqlV09epV2r17N3l5eVGlSpXI19eXiHSDB1auXEkbN24kc3NzWrZsGR+Jyr48MMYYYyzTGjRoEIQQWLRoEWJiYgxdnEzvwoULKFeuHIQQmDJlCpo0aQIhBMqUKYM9e/YgOjra0EXMNPbv3w9HR0eUKVMGV65cAQDExsYCAKKiouDt7Q0hBOrVq2fIYmZKSlui1WoBAEWLFkWzZs3w7t07AO9/h/3790MIASEEWrdujVWrViEoKAi3bt3C0KFDYW9vDzc3N9y6dcswXyQDio+PN3QRGGOZBLcn6SMuLi7Jx9Na/1FRUXj69Cn351Pp6NGjcHJyQtOmTREWFgYA+Oeff9C5c2cIIdC7d2/s2LEDOXLkgI2NDQYNGsT9EfbVe/LkCby8vCCEQJUqVbBu3Tr06dMHHh4eEELAxMQEEyZMwKNHjwAkXPPjxo2DmZkZKleujFevXgHQb5f4fsAMQZmr2rhxo3wsMjISdevWhYmJCTp37ozNmzdj8eLFcHZ2hhACDRo0wNOnTw1Y6oxr06ZNqF27NnLlyoUFCxbIx9XtwcSJE+X4vXTp0rhw4YJ8Thn7s9QbMWIEhBAYOXIkgIS+nPL/a9euyT6JkZGRrHchBAYOHKjzOc+fP4efnx8KFSqE69evp/v3YN8mzjjAGGOMZVI+Pj60aNEiatWqFdWuXZtMTEwMXaRMLSYmhv744w86d+4cdevWjUaNGkVBQUH07t07OnToEAUEBBARceaBzyAyMpI2bdpEb9++pfXr11PRokWJKGEXjlarJXNzc+rbty+tW7eO3rx5Y+DSZj4mJiYUFhZGlStXpv79+5ONjQ0NGzaMzMzM5K4FIqJ69erRvn37qHv37rRjxw7asWMHOTg4UFRUFEVFRZGnpydt3ryZChYsaOBv9PVKfH4hn2XIGPtYH2pPwDvJPjv1PfHkyZP033//UZYsWahkyZJpTjNrbm7OqWnT4Pr16xQbG0uLFi2iLFmy0H///Ufz5s2jDRs2UM+ePWnZsmVERLRr1y5as2YNrVu3joQQ1K9fPz7iin217OzsqHv37rRo0SI6ceIEnTp1irRaLZmYmFCTJk2oa9eu1Lp1a/n6fPnyUYsWLWjRokV08eJFevr0KTk6OurtVOX+JTMEZX6qV69eBIA6duxI58+fl5lixo8fT2ZmZkREVLt2bWrbti3t37+funXrRmvWrCFXV1dDFj9DmTx5Ms2cOZMiIiJo+PDhVLZsWfmckZERxcfHk5GREY0dO5ZiY2Np8uTJdPHiRdq7dy9ly5aN3NzcuI/4Eezt7YmI6OjRo/Tu3TsyNzen0NBQWrFiBW3evJnOnTtHGo2GOnXqRPb29hQdHU3Lli2jxYsXk5ubG40ePZqIiFxcXGjYsGE0fPhwcnJyMuRXYt8QAfCBaIwxxlhm4+PjQ/Pnz6e2bdvSpEmTOPVmOpkyZQodOHCAxo4dS7Vr1yYioqCgIBo0aBAdPHiQSpcuTQEBARw88IkePXpEtWvXJnd3dzp06FCSr7lz5w55enqShYUF3bt3j2xtbXlS7DOaO3cuDRs2TE40LFiwgAYOHCifVy9AXbp0ifbv30+7d++mqKgocnNzo7p161K7du0oe/bshvoKXz1lAoeI6MiRI3TlyhU6ffo0ValShUqVKkUVKlQwcAkZYxmFuj05ceIE3bx5ky5cuEBFixal/PnzU926dQ1cwsxHXec9evSgzZs3U1RUFBERZc+enVasWEG1a9eWgQXs89u7dy/VqlWLzM3NaePGjdSjRw9q1qwZbd68Wb7mr7/+ooYNG5KHhwcFBQXRsGHDaOrUqRxwzb5ar1+/pv3799OyZcvo7du3ZGlpSb6+vlS6dGny8PAgooRAMQBkZGRE4eHhVLRoUdJqtXT27FlebGVfDQA0ceJEmjBhApmamtKOHTsoKiqKhgwZQjdu3CAbGxuKj48nIQRpNBq6c+cOtWnThq5cuUL16tXj4IFU8vX1pVmzZlGtWrUoICCAqlSpkuTr1AGmEydOlBtfRo0aRYMHDyYXF5f0KnKmcffuXapWrRo9efKESpYsSSVKlKCzZ8/SrVu3SKvVUrFixWjBggVUpUoVEkJQTEwMLV++nAYNGkRlypShQ4cOkZWVFc9jMcMwYLYDxhhjjH0BP/zwA4QQaNeunV7KTU5D+GVptVqcO3dO/l1J+xYUFIS6desme2xBcmlsWdLevn2LmTNnYt26dUk+Hx8fjzdv3iBbtmxwcHBAcHCwTK3HKfY+n6FDh8p0ej4+PtBqtTr1m7iu4+LiZHo+ljJ1Wz1x4kRYW1vrpC/08PDA9OnTDVhCxlhGoW5PJkyYAFtbW532RAiBfv36cV/kC/nuu+8ghECxYsXQrVs3VK9eHUII2NraYuXKlYiIiDB0ETOdxMc5xMTEoGzZssiePTvu3r0LALIffvLkSVhaWmL8+PGoWrUqbt68me7lZexjxcTEyKPCFIn74+vXr4cQAt7e3oiOjuaxEPuqaLVajBs3DkIIWFtbo3r16qhduzYA3f6L8ud//vkHxYsXhxAC9evXx7NnzwxS7oxi9uzZEEKgSZMmeinuk2oL1PfPCRMmyH7imDFj8Pz58y9e3sxEuWZ37NgBV1dXnX53kSJFMHLkSNy5cwfA+/lAZT7RwsICzs7OeP78ObfZzGA4cIAxxhjLRPz8/GTQwO3bt3WeU84dBxIWstnnlTgoI/FCdVBQEOrVqyfPi9u9e7dO8AAAXL16NX0KmwmEhobKPyc1mIqLi0OhQoWQNWtWWc/q34gHvh9P3Zb4+PjIAfCOHTsA6E9YKo+l9Hf2nrpuRo4cCSEEihcvjo0bN+Kvv/7CtGnTYGxsDBMTE/j5+RmwpIyxjGT06NGyD7J582bs3r0ba9askecGd+jQAeHh4YYuZoan7mv8+uuvcHJyQp8+feTZ4kDCmbfGxsawtrbG8uXLOXjgI30o2EX5Lf7++2+Ym5vD09MTL1++1HlN//79kTdvXkRGRvLvwDIMdTuTuE+tfu748eMoUKAAbGxssH///nQrH2NpoQ4eEEKgbNmyMtg8qeCBO3fuoESJEhBCoHz58jyuT8bVq1eRO3duuLq64uLFi/JxrVard/8MDw9Pcnw+ceJE+buMGzcOT548+eLlzoz+/vtvdOnSBf3794e/vz8eP36MyMhIAO/bcGWO5f79+7CwsEC9evUMVl7GAIDzXDDGGGOZxNChQ2nGjBmUO3du6tmzJ+XPn5+IElLAxcfHy1So3bp1o/79+9O///5ryOJmOonThylp2oUQBIAKFChACxcupLp169LFixcpICCAfv/9d/n6kSNHUrFixWjHjh3pWu6MytraWv458Xl7ACg6Opri4uIoMjKSIiMjKS4uTv5Ga9asoa5du9KpU6fStcwZEZI41czY2Jiio6OJiGjOnDk0bNgwIiJq3bo1HThwQP4e6vcm/o34jMTkKXXz008/0Zw5c6hJkya0evVq6tChA1WqVIlMTU3J2NiY4uPjacaMGTRmzBgDl5gx9rXbsmULzZgxg2rUqEGrVq2idu3aUZMmTej777+n1q1bk7m5OT1//pyePn1q6KJmaPHx8Tr9wXv37lFcXByNGjWKHB0d5b1zxowZFBAQQNHR0eTj40O//PILRUZGGqrYGVJcXBwZGRlRdHQ0bd26lfz8/Khv3740YcIEunLlCkVFRcnfwtXVlXLkyEGhoaE645+ffvqJfv31VypevDhpNBqytLQ01NdhLE3U7Uzifrfy3JEjR2js2LH0zz//0LRp06hevXrpX1DGkpB4fCmEoPHjx9PYsWOJiOj8+fP0888/E1HC9azVanX+nDdvXtq2bRt5eHjQ2bNn6d27d+n7BTKIGzdu0P3798nb25tKlixJ8fHxpNVqSQghj1Jas2YN+fj4UN26dalx48a0du1aevDggfyMsWPH0oQJE4iIaNKkSbRq1SqKj483yPfJqABQsWLFaM2aNbR48WL68ccfKXv27GRhYUFECde/VquV87UzZ86kd+/eUa1atQgJm74NWXz2LTNUxAJjjDHGPq/Vq1fLaOAePXrg9u3bejt/O3fuDCEE+vbtq7fjhn1ZSWUeKFu2LPbu3St3AZqZmXHWgc8gNjYWISEhyJ07NywsLPD06VP53OrVq+Hi4gIHBweZGo4lTdmJEBcXh8jISFy5cgX//PNPkq8dMWKEbH+UHU1JZR5gqXPr1i0UK1YMhQoVwunTpwEkpFWeOXMmLC0tkS9fPgQGBsLU1BRCCPj7+xu4xIyxr1mfPn1gZmaGI0eO6Dyu7PBr1qyZzlFLCm7DUydxPZUsWRJVqlTB8OHDMWzYMADv76nq3ZOTJk2CmZkZsmTJwpkH0kCpy/DwcNSpU0fv6I1cuXJhxIgReP36NYCELFXe3t4QQqBEiRLo1q0bWrVqBY1GAzc3N87ExjI8dRsUGhqKwMBAZM+eHebm5pgzZ458jo8tZIamHl/ev39f56gBrVars8N948aN8rmkMg/cvXsX//77bzqVPONQ6mfYsGEQQmDs2LEA3rcT7969w7Vr1+TcoPo/V1dX9OjRQx7ro/D394cQAteuXUvfL5OJJHekozr7w5IlS2BpaYnSpUvjv//+S9fyMZYYBw4wxhhjmci2bdtkp79Lly4653R26tQJQgj07t0bDx8+NGApv13KAOHGjRto0KCBPKtcCAEHBwfcuHHDwCXMPOLi4uDl5QV7e3s5GPv555+RNWtW2NnZcYDGByip8iIiIjBo0CCULl0aQggYGxujUqVKmD9/Ph49eqTzHnXwwB9//AGAF50+1p49e2Bqaoo1a9YASPg9Fi9eDBsbG+TNm1cGfg0ePBgajQYajQYjR440ZJEZY1+pt2/fwt3dHSVKlNCZnFTOrm3cuDEuX74sH798+TK2bt1qiKJmKJs2bcKBAwf0Hn/y5Im8F5qZmaFOnToy5bIiqeABe3t7LF68mIMHPkDpV0RERKBs2bIwMjJCly5dcP78eRw6dAjz5s1Dzpw5YWJigkGDBsnjN/755x80b94cjo6OEELAysoKFSpUwK1btwz5dRj7rObOnYsiRYpACIH8+fNjw4YN8jkOGmCGpowvIyMjMWTIEBQuXBi+vr46Rw1otVoEBATIe+iHggdY8pYuXQohBKpVqyYD0V+8eIE5c+agXLlyEELAxMQEbdu2xciRI1G7dm1YW1vD3t4eCxcuBADExMTIz+ONR59X4nmSmTNnwtXVFc7OzjrzuIwZCgcOMMYYY5mA+sxxdfBA9+7d8fTpUxk00KtXLxk0wAt6aaeu54+lDL7u3LmDAgUKyKCB69evf/JnZyafWtexsbEoXLgw3N3dERUVhZ9//hlubm6wtbXloIEPUO/kK1OmDIQQyJs3LypWrAg3Nzc54d66dWu9DARK8IBGo8GePXsMUfxM4erVq/jpp5/k2YdHjhxB3rx54eHhgQcPHsjXbdmyRU6sCSHg4+NjqCIzxr5SL1++RNasWZE7d2456Ztc0AAADBkyBGXKlOFzbFNw/Phx2ddWZy9S7p9BQUFwcXGBEAIlS5bE/fv3Aej2bdSLHlOnTpVtOQf3flhcXByGDBkCIQR8fX11AjNCQkJQoEABODs7Y+zYsTqBGI8fP8Zff/2FuXPn4siRIzoZqRjL6EJCQjB58mSUKlUKfn5+OllkeJGVGZpyfwwLC0OlSpVgYmKC4sWL49SpU3K8ozZ+/PgPBg+wlJ08eRL58+eHqakpihYtin79+qFw4cIwNTWFRqNBsWLF8Ndff8nfJjQ0FAMHDoQQAmXKlJH3VqXOef7w83v27BmuXr2KFi1awMTEBIUKFeJ5QfbV4MABxhhjLIO6ffs29u7dK9NwqgdR6uCB/Pnzy6CBx48fA+BO/8eYOXMmVq9erRN1nVbqep8+fTpnGkjGp9Z1bGwsQkNDkT9/flhZWWHChAlwdXXloIE0ePfuHerVqwdzc3OMHj0a0dHRABIGt5MmTULhwoUhhEDLli11FrKB96kMra2tERERwe1NKiWup9DQUPnnPn36wNTUFL///juAhN8HAP79918ULVpUHncyffr09CswY+yrkngyXf33Bg0awNHREXfv3pWL1EkFDezatQtGRkbw9vbW2yXP3ouLi0Pr1q3Ro0cPnce1Wq3su9y6dUsGDzRt2lS+JrnggYCAAPz2229fuOSZw8uXL1G0aFF4eXkhLCxMPh4XF4eKFStCo9Fg9OjRePv2LYCEe+bnCP5l7EtJaTE0LQulISEhePbsmey3AzzuZ4anXIORkZGoUqUKzM3N4evr+8EMO+rggc2bN6dHUTOdGTNmwN3dXec4gqJFi2LUqFFyA4BWq5XtzPXr1+Hg4IAcOXJwqvxUUPoWcXFxOu1uaoSEhKBFixZyU0abNm30johgzJA4cIAxxhjLgNatW4eKFSvCyckJY8eOlZO76okBdfBAoUKFcPToUfkcTyCkzQ8//CB3NakX8z7WyJEjIYSAk5MTRxQn8rnq+t27d/Dy8oKFhQWcnZ1hY2PDQQOpoLQN69atgxAC3333nUzzqyxWh4eHY+PGjShSpAjMzc0xbdo0xMXFyeeBhHOzL126lO7lz0g+NBGs/BZPnz6Fg4MDChQoIAPFFMuWLYMQAvfu3ZM7Whlj3x51v+7YsWM6ZwhrtVoZXKRkjWnZsqXOblQAOHXqFCpWrIhs2bLh0KFD6Vr+jGLWrFno2LEjAN0zaXv27In9+/cD0A0eUGceaNGihXy9ehFb/TnK+7mfritxfRw8eFAev6aIj49HxYoVIYTAmDFjZB8yLi4OBw8exPnz59O1zIx9iHJdq9uAo0eP4pdffsH69etx9uxZ+XjidoKxjEar1WLcuHEQQmDgwIFyfPmha1sJHhBCYPv27elR1ExBPc7csWMH/Pz80K1bN/j7++PBgwey/hO3Q9evX4eVlRVq1KjBfZEPUOonJCQETZo0wbVr19L0/tjYWGzbtg1t2rTB9u3b8ebNmy9RTMY+GgcOMMYYYxnMhAkTYG1tDQsLCwQEBODChQs6z6s7+Js2bZIDrR49eiAoKIgHAGmkLGS3a9fus2QGePToEZo1awZzc/M0Dy4yu89Z11qtFlWrVoUQAo6Ojhw0kArqtmHo0KEQQsjzEBNP6kRERGDWrFkQQqBcuXLyed7Rlzrq+rxx4wb++OMPLF68GMePH9cJANBqtXjy5AmyZMkCGxsbXLx4UT538uRJlClTBpUrV9aZaOAUnox9u5QJ9lmzZum0MxEREShVqhSEELC3t8eBAwd03rd//35UrlwZQggsX748vYudIQQGBkIIgSJFiuD27dvyceUM4axZs+LIkSMAdIMHbt++/cHgAZY8pZ5iYmJkMMCpU6dgbm6OXr16yddUqFBBL2gASLgn5siRAwMHDkz/wjOWBCUTBqDbDkyYMAGWlpZy7O7m5oYRI0Yk+VrGMpr4+HhUr14dOXLkwKtXr+RjqXmfchQen/ueNqkdE6pf5+3tDSEEZs6cCYA3HH1IfHw8GjVqBDs7u4/+DG7b2deKAwcYY4x9VkrHkhcuvgxfX18IIdCgQQOcOHEi2depO/hbt26VExBdunTRO5OcJU+9kB0UFPTZPvfs2bN49OjRZ/u8zOBz1nV8fDwiIyMxdOhQPicuGWfOnMGGDRswbtw4zJ49G2/evNFptxs2bAghBDZs2AAg6Tb99evX8PDw0AkwYB+mrsvp06cjd+7cso02NTVFgQIFsHv3bp339OnTB0II1KhRA1u2bMGKFStQokQJCCGwatWq9P4KGQr3R1hmpr6+4+PjMW7cOFhbW6NAgQKYOXOmTvDAnTt34OXlBSEEChcujEmTJmHOnDn44YcfYGVlBSEE5s6dK1/Pk8XvKcEBNWrUwPHjx3Wei4iIkG20m5sbBw98Rsr1HRYWhjp16qBHjx4ICQnB5cuXYWpqCjc3N5w9exZVqlSBEAKjR4/Wy1Y1dOhQaDQarFmzxhBfgTEdBw8ehJeXl8xQolCCvjw8PNCvXz+0bNkS5ubmEEKgW7du8nXcZrCMInH/5Pbt29BoNKhcubJOavykxMbG6mRZ02q1ePny5Rct77dK3U8MDAyEpaUlypYty8cUJJLc9RcTE4Ny5crBy8sLAPedWebCgQOMMcY+C6XDqT6/jH1eCxYsgBACDRs21FsITWrgpZ5YUB9bwMEDqaNeyL5165bOc4l3X6d2YYoHEkn7EnUNAE+ePOFJhiTMmjULOXLk0DnrsFy5cti+fTuCg4MBAMOGDYMQAiNHjpTvU1+/yrEEzZs3hxBCLpSwlKnr0M/PD0II5MuXD9OmTcPEiRPRtWtX+ZusXLlSvvbcuXMymEMdZLBgwYIkP5slUO9UPXfuHDZu3Ih169bh8ePHOkdrMJYRqe+P//vf/zBt2jTUrl1bnmVboEABzJ49W+d1z58/R4MGDWBtba3TnlSuXFnn/GAOuHlPCRqoWrWqTtCAuo4iIyPRq1evVAcP1KtXL32/RAb27t071KtXDxqNBr6+vrI+e/ToASEE7OzsYGRkhEmTJsk+jCIwMBAuLi6oV68eXrx4YYjiMya9e/cO3bp1gxACJUqUkEfCXLlyBbly5UKLFi1khrTY2FgcOnRIBnV17dpVfg4HD7CvnXKNKsdpAgkZ1oQQyJ8/f7IbKJT+yr179zB+/Hg8fvz4yxeWAQBmzpyJrFmzwtHRkTM7JDJkyBDUrFlTb3OLVqvF69evkSdPHlSoUAEAt88sc+HAAcYYY59M6RxFRkZi1KhRaN++PSpUqIDAwMDPktqdJewS8/T0hIODg84ZnVqtVm9h9dWrVwgLC9P7DHXwQPfu3fUWaNl7SmaHVq1a4e7duzrPqev7jz/+SO+iZTpc1+lLOYLA3t4eXbp0QdOmTeHk5AQhBIoVK4Z9+/YBSDgLUWkvtmzZIt8fHx+v87tUqlQJOXPmxPPnz9P9u2RkK1eulNljLl26pPNc7dq1IYRA06ZNZb1qtVrcvXsXkydPRtOmTTF+/HidVOO8yKdP6ZuEh4ejQ4cOsLGxkdd0gQIFMHr0aJkqlbGMRh0o5O/vjyxZssDd3R1t2rTBd999Bzs7OxgbGyN79uyYM2eOTrsdHR2NS5cuYcOGDVi7di0uXryIp0+fyue5PXlvyZIlEEKgSpUqOkEDSe2WjI6OTlXwgJKK/Ny5c+n3RTIY9fV64sQJ2NnZwd/fHxEREfLxkydPyuMJihQpopeJbfr06XB2doaHh4fO0RKMGdLdu3fRs2dPCCHg5eWF8+fP48yZMzA3N9dpY5R/AxcvXuTgAZahKPfG8PBw5MiRAyVKlACQMFdYuXJl2NrayvGmuq1X31ObN2+OrFmz8gJ2KnxKn+3169e4fPkyWrVqBWNjY87UmIRHjx4hb968EEKgTZs2ev2Jly9fws3NDa1btzZQCRn7cjhwgDHGWJqdPHlSTrYrnf3w8HCUK1cOQggYGxvLyfm6detiz549hixuprBv3z4IITBo0CAACZMFiQcJixcvRvfu3VGgQAEULVoU06ZN0wkyAHSDB/r27SsnMtl7c+bMkTuY5s2bJx/XarU6kzTfffcdnJyccPjwYQOUMnPguk5fSmaHli1byrYhOjoaFy9eRNasWSGEQIUKFRAdHQ0A8PHxgRACOXPmxNatW/U+T1lQadmypV5qYJa86OhoNGjQAHZ2djh79qzOc5MmTZJ1qhz/8KFMArzIp0/dNylZsqTcLbxo0SL4+fkhZ86csLKyQqdOnTjohWVoc+fOlcF3V65ckY9fuHABPj4+yJIlC7JmzaoXPJAczlzynpJpoHr16rh48aJ8PHHQwN69e+VEe1RUFHr37p1i8EBQUBAHQ6ZAuQYjIiLw22+/Yfz48XBzc5P1p1zH0dHRWLt2LUqVKiV3sQYEBMDf3x/Vq1eHEALu7u64du2awb4LY0l58OCBzDBVrFgxdOjQAZUqVZLPJz768cKFCxw8wDKU6OhotGzZEnZ2dmjfvr3MRqqMRXPkyIF79+4B0L+nLliwANbW1mjXrh2PLxNR11NsbKxsA969e5fmTGrPnj1Dx44dZaBp27Zt9TZxsARnz55FxYoV5RhdHTzw6NEj2NjYyCNluB/NMhMOHGCMZXicdjB9KefvLVq0CG/evAGQMDBo1KgRLCws0LNnT1y9ehU//fQTGjVqJFPx7dixw8Alz5iUjuf06dMhhECPHj10no+MjMT58+fRoUMHnZSzQghYWVmhSZMmegtTGzZsgKmpKf7+++90+x4ZydWrV+Hp6QkhBEqVKoVNmzYhPDxc5zWdO3eGEAK9evXCkydPDFTSjI/rOv0omR0SHwehTMAfPHgQ9vb2EEJg586dAIBr166hY8eOsk2ZMGEC9u7di/v372P06NFwc3ODq6srH32SRnfv3oWRkRE6dOig8/iECRMghEDjxo1x+fJl+fj58+d1svdotVqelEiBUjdRUVFo0qQJzM3Ndc69vn79OurUqQMhBIyMjNCxY0fuS7IMR6vV4vHjxyhZsiQsLCzk7nV1cMDDhw8xZswYWFpaIleuXJg1a5Z8PjVBBN8yJaWykhlGER0drTNxP2bMGAghEBgYKCftIyMjPxg8oPyd2/KkxcTEoGLFinBxcUHDhg1RrVo1AO+PSVLqLTo6Gvv370e7du0ghIBGo5HnxHft2hX//vuvwb4DYym5f/8+vv/+exlAXbRoUdk+qNuFpIIHOnXqZJAyM5YSdSDLzZs34eDggGHDhukcVxAfHy8zq+XIkQNHjhzROVZQSZefK1cu3LlzJ13L/7VT2oKbN2/qHAsbFhaG7Nmzo1+/fmnu240fPx6dO3fGli1b5NwuS9rZs2flRrmWLVsiKCgIcXFxuHz5MoQQGDp0KAAO6GeZCwcOMMYytBEjRmDAgAG8mJROYmNj4e/vD1tbW2TNmhWLFi1CcHAw7t27Bzc3NwwbNkwnheTly5fh7e0NIQSKFy/OwQOfYNeuXRBCwNPTE7t27QKQEDQzdepUudPG1NQU3t7emDlzJnr27Ak3NzeYm5tj1KhRAHQniTl6O2lKHQUFBaFo0aLy2v3ll1/kazp16gQhBHr37o2HDx8aqqgZHtd1+lF2dxQpUkSenZp4weLJkyfw8vKCEEK2MUBCcMegQYN0gpKUrDKczvDDkprAUSYYmjVrJh9LLmgAAHr16oVOnTrp3F9ZyrRaLebMmQNLS0t4e3vL43tu3bqFLl26yLpW2p4OHTpw5gGW4dy8eRO2traoXr26fCzxQvTt27dRr149CCFQuHBhzJkzR05q8qJ18kJCQrBw4ULY2dnJNiIxpd2uW7euXrutDh7IkSMHDh48mF5FzxSePXuGfv36yYDGbNmyybGLct0mvn7//PNP7Nu3Dxs3bsTjx4/5nsm+evfu3UO3bt1k/3rz5s3yuaSCBy5evCj74Pv370/38jL2IaGhoVi8eDF+/fVXuLu7yyyl6qPunjx5glq1asmj88qVK4eWLVuidOnS8p7J48ukXbt2DUIIFCxYUD5WrFgxGBsbY8yYManOQqJe3FYyDbIPSxw8cO/ePZw+fRpCCEyaNAlAwvEPb9++xfPnz/H48WM8fPgQ9+7dw9GjR3H//n0DfwPG0oYDBxhjGZZyTnPnzp15p1g6ioyMxJQpU2Bvbw8XFxcsWbJEphMLDg4GAJ3dNLdv30bfvn05eOAT3bx5E+XLl5e7aJo2bYpcuXLJgIFy5crh5MmTOu9RshS4ubnpnJOt/j9LoAxk1YPaW7duyUWlkiVLYvfu3Wjfvr3c/a4sZHNdpg3XdfobO3asnJQcN26cThutTBw8f/4c+fLlg7m5ud4CCABs2bIF3bp1Q6VKldC2bVvMmjULjx8/TrfvkBGpJ2WGDh2KNWvWAACePn0KZ2dnmZY2paCBzZs3y2wPfP2n3uvXr1GrVi3kzZsXISEhABIyPXTv3l0e1QMkZOCxsbGBlZUV2rdvz/1JlqFcvnwZpqamyJMnD+7du5dsG7F+/XqZYcPd3R2rV6/m9iQVwsLC8NNPPyFLliwQQqB9+/byuXHjxiXbbisiIyPlGMjR0RGvX7/mek8kpfp4+PAh/P395VFKI0eOlLss1e/jOmVfO3V/MCIiQiel+L1792SQkbu7Ow4dOiSfSyp44MyZM1i8eHE6lJqxtImPj0elSpVkxlEPDw+9frVyTcfGxqJPnz5y/C+EQJ48edC1a1dOl5+CN2/eIEeOHBBCoEyZMihSpAjMzMwwefJkmdkhtfdEvnd+WFJ1pA4eaNeuHZYvXw5jY2O4uLggb968yJEjB9zd3eHq6gpHR0c4ODjIox8fPXpkgG/B2MfjwAHGWIak7J7s0KEDbt68aejifDOUAWtUVBQmTZoEe3t7ZM2aFR06dEDVqlUBJH3WXlBQkE7wgJIGm6XNpk2b5I5g5b+SJUti4sSJMhVnfHy8/J1evnyJPHnywMrKCkFBQYYs+lft8uXLWLRokTzzHXh/rat3wyud/m7duuHZs2cAeMCVVlzXhjN79mzZbvj5+cm2Wqn/lStXQqPRYMiQITIbQVJ1zr9D2k2aNElOGDx//hwhISGoX78+hBCoUqUKhBBo2rSpzr8LADhx4gRKlCiBPHny4PTp0wYqfcYUEhKCoUOHyh15wcHB8qgl5QxKAHj8+LEMwjMzM0Pjxo3l7ijGvnbPnj1DiRIlYG5uLq919QKVEpx35coV5MmTBz/88AOsrKxQvXp1DvxKpdDQUAQGBsrggW7dumHKlCkQQqBRo0bJBg0oIiMj0aNHD2zbti2dSpwxqDOfpZTW98GDB/D394ednR08PDywfPlyuTuS+yMsI1Bnntq2bRt69uyJJUuW4PXr1/Lx+/fvy8BGLy+vZIMHEmex4iNn2Ndm69atsLW1lZlirly5AkB3jlB93b58+RJnz57FqVOn8PbtW51jDZgudR0WKVJEBoQGBATIxzlN/uej3uyiPk4D0A0ecHBwgL29PfLmzYtixYqhRIkSKFWqFMqVK4datWqhbt26qFOnDh8TyzIkDhxgjGU4StBA4nOaWfpIHDzg7OwMIQQsLCxSTCmmBA+YmZnBw8MDe/bsSa8iZ3jqCYPjx49jyZIlGDFiBGbMmIGXL1/qRRcrv9Hjx4/h4uKCsmXL6uwwZu8tX75cBmO0bdtWJyo+qQVtJycnnV0ePGmZelzXhjdr1iwZPODr6yt3PO3ZswdCCJQtWxbHjx9P9v3x8fGctSQV1JM258+fR86cOdGuXTtcvHhRPn7gwAH5WxQuXFgvuOvQoUOoUaMGjI2NsXr16nQre2agXJshISEy28CdO3eQPXt21K5dW94PlTTWbdq0QZMmTZAjRw6Ym5vzgirLUHx8fCCEgIuLC65duwYgYbJT3UavXbsWFhYWOHjwIJo1awYhBBYuXGioImc4oaGhOpkHlGCv27dvp+r96qMh+N4JDBkyBA0bNsT06dN1zmlOrm4ePnwIPz8/ZMmSBQUKFMDPP//MwQMsQ1D3BwMCAmQbMn36dL0gxQcPHqBr164fDB5g7Gu3e/duea23bdtWPq4OGOAF7k+TL18+2R8pW7asfDy1RxWwlCn1GBUVhYCAAJQvXx7Lly/Xec3Zs2dRoUIFaDQaVK1aFadPn0ZYWBji4+MRFRWVZIZHxjIaDhxgjGUoyuRY+/bt9TINJI645pvz55VUfUZGRmLChAnIkycPNBoNfH19U0zze/v2bXTu3BkODg5yhzzTldx1m9L1nFQaQwAYPHiwTE2uXvBjCcaMGQNLS0s4OjpiyZIlSU4AK/V58+ZNuaBdqlQpbNq0CeHh4eld5AyL6/rroQ4emDx5Mnbu3AkhBDw9PTmg6zNQt7MhISE4ffo0jI2N8ddff8nnlWt9xYoV8rcYNGgQdu7cif3792PatGkyKG/OnDlJfjZLkNo6GTNmDIQQCAwMBACdNMG5c+dG//79cfLkSe6bsK9KSn0/ZdwTGRmJhg0bQgiBvHnz6u2AP3XqFMqXLy+PRlm3bp08BoilXmhoKJYuXQpLS0sIIdCgQQP5HJ8PnHqBgYE6mdOKFi2KCRMmyF2pisTjeg4eYBnZ6NGjIYRArVq19I4WVEscPHD48OH0KyRjaZTUHJTy2G+//QZra2sIITB48GD5Os6S8ekOHTqE+vXrY+TIkfD09IQQAuXKlZMB0Rw88GmUazQsLAw1atSAkZERPD09sXHjRkRFRelc90ofWwmSUW8E4COVWGbAgQOMfYJdu3bhv//+M3Qxvhl+fn4yLeSdO3cAvL8Bqzug8+bN49/lM1PqNzY2Vm9CMjIyEhMnToSzs7PcIfzmzZtkP+vOnTt4+vTpFy1vRjRs2DCcOHHikz5D/e9g2bJlsLa2RokSJXj3ZBKU9qRZs2Y4c+aMznOJO/ZKvd66dUsuaBcvXhybN2+WAzSWPK7rr486eMDExARFihTRCRrgwe2n8/b2hqWlJdq3by+P8gH063bz5s1yYk39X+HChbFmzRr5Og6G1Jc4heSlS5dw69YtnUAj5TVK4On06dN1PmP+/PnIkiUL/ve//6VbuRlLDXWf7uTJk1i5ciUCAgKwZMkShIWF6Tx/+fJl1KpVC0IIWFpaYujQoVi2bBkWLlyIwoULQwiBJUuWAEjYCSiEQM+ePdP9O2V0SvCAlZWVPDJPwRP1H6bVajFhwgQIIWBnZwd3d3dZl1myZMGYMWOwe/dunfeor/PEwQNr1qzRCQJj7Gu0adMmGBkZoVatWrh69eoHX68OHihQoIA8hoaxr0VSi/9JjR337NkjMw/4+Pik+H6WNsomuujoaJnRsXz58jrBA+qxI48jU0e5jiMjI1GuXDmYmprihx9+SHEeSn1sQcuWLVOdjYqxjIADBxj7SAMGDICNjQ3Wr1/PHZ90oKRRVnbIPH/+XD6nTgHUsWNHCCGwdOlS7hx9JspEWGRkJPr16wdLS0vUr18fgG7HatKkSbC3t4eLi8sHgweYruHDh0MIgU6dOulFsX6MqVOnImvWrHB0dMSNGzc+Uykzj+XLl8tdH+rjNdQ7gdXUqWXVqfR5QfvDuK6/XjNnzpT3VXUaSb53frro6Gg0bdoUQgjY2NggZ86cuHfvHoCkJ9YuX76MDRs2wMfHB8OHD8fu3bt12m7+TfQpfZOIiAj069cPRYoUgUajgZmZGerWrYsFCxbovP5///sfjIyMUL58eaxfvx5hYWGYPHkysmXLBk9PTw6wY18V9b/5KVOmwMHBQSewqEKFCvjll1/kOfHx8fG4f/8+2rdvrxeEZG5ujrlz58rP69q1KzQaDdatW5feXytTUI4t4OCBjxMZGQlPT084OzsjMDAQGzZsQLt27XSu2Y4dO2Lbtm16qdyB98ED9vb2cHJywoYNGwzwLRhLve7du8PExETn6IEPuX//Pr777jsIIbitZl8VdQr3RYsWoXv37qhYsSIaNGiAX375BQ8ePNB5vfrYAg4eSLuU6kl57s2bN3LORB08oFi+fDl8fX3l8aYsZXFxcRg6dCiEEPDz85P1ltJvoQ4eqF27ttzoyFhGx4EDjH0EZddSixYt+IaQjpTFVRMTEwwfPhz//POPzvPK4Kpnz5549OiRgUqZuajTNFWoUAEmJiaoU6cOdu/eLXf0KZObUVFRHDzwEZTFu06dOullc0iL58+f46+//kKTJk1kekMOGtD39u1b1KxZE5aWljh16pR8PPFCdnBwMG7duoWgoCAEBwfrfIZ6QbtMmTJYu3atzhmtLAHX9ddv+vTpcqJ+5MiRvHPvMwoLC0OXLl1k/W7duhVA2tMWcvYHfUrfJDw8HKVLl4YQAsWKFcN3332H6tWrw8zMDEII9O7dW77nwYMH6NKlC4yNjSGEkAuxOXLk0AlqYszQ1P/mR44cKTOQ/PTTTzhw4IBc+C9SpAgWLFgggwcUW7duxYwZM9CpUyfMnz8fBw8elM/Nnz8fGo0GpUuX/uazs12/fh0vX778qPeGhoYiMDBQLoZw8EDqxMXFQavVYuzYsRBCoG/fvvK5bdu2wdfXFxYWFjLgpVixYtiyZYveMQb379/HwIEDkT17dp6LYV+VxH22p0+fws3NDYULF5abXVLq16nHOHfv3sUff/zxZQrK2EdQzw3WrFlTzs2amprK8U6LFi30MseogweGDx9uiKJnSOogjc2bN2PKlCnYuXOnzpyhMnZPHDwQEhICAFi7di2yZcsGIcQ33+9LrZcvX6JEiRLIly+fbJOTC+JXt+fnzp1DgQIFYGpqyusRLNPgwAHG0uiHH36AEALt27fHrVu3DF2cb4I6o4C/v7/soA4dOlR2fpSggd69e+Phw4cAeLL9c4mKikL16tVhamqKkSNHJrlol1zwwNKlS5PcLcIS3L17F15eXnBzc8PZs2cBJAzEfvrppzRNZr558wadOnWCtbU1LCws0LlzZ7m7lem6fPkyhBAya0ZMTIzeQGDy5MmoV68erK2tYW1tjRYtWmD16tU6rwkKCkKpUqUghEDVqlX1Ju4Z13V6UnYWfMx9T31sgZ+fHy96pIJSR4nP80wsLCxMpps1NzeXx9Fw/+TTvXv3Do0aNYKRkRH8/f0RFhYmn/vf//4HIQRMTU1lwAYAXL16FVOmTIG7uzuqVq0Kb29v3L171xDFZ+yDFi1aBBMTEzRp0gQXL16Uj0+cOFEGx3h4eGDhwoWpui9OnToVrq6ucHJy+uYDS9etWwcTExPMnj0br1+//qjPSBw80Lx5889byEzs/PnzMDExgRACO3fu1Hnu6tWrmDp1KvLlywchBCwsLJAzZ05MnToVV65ckffPR48e6WQgZMzQktqNevv2bdja2iJXrlzJZjZSrumQkBDs3LkzyTaJM0+lDvevvxx1ptEKFSrA3NwcPXr0wOXLl3Hy5EkEBASgYMGC0Gg0KFeuHHbt2qXz/j179sDe3h5CCIwaNcoQXyFDUf7Nh4eHo0qVKnKsbmpqipw5c+pk21EHDxQrVgxCCBQpUgT16tWDhYUFnJ2d9QLwWPJ+//13nX5ddHR0sq9NPG9y8eJFnodlmQoHDjCWBkrQQLt27fSCBtK6g4x9mHqBWn2zVs7LNjExwejRo9GsWTMZNKCkxuLf4PPQarVyUalv377yN0lqYJw4eMDFxQVGRkZYsWIFD3aT8eTJE+TIkQPFihUDkLDIlDt3buTNmxcnT55M02f9/PPPGDBgAPbs2YO3b99+gdJmDvv375eR2Op2JTw8HIcPH0bLli0hhICRkZEcoGk0GuTIkQMrV64E8L59uX79OipXrswDsWRwXaePUaNGoWfPnnjx4gWATw8eGDVqlE7AHtN18uRJ9OzZE3///Xeq6iksLAzdunWDEALu7u4y+wb3Uz7N9u3bZUprdX8xNjYWVatWhaWlJfz9/ZOcgFcym6Q0EcSYIV27dg1FixaFl5eXDCyNjo7G9OnTYWlpidy5c2P48OGwtrZG/vz5dTIPKH30uLg4xMfH4/r162jYsKE8L/vatWsG+15fg/j4eAQEBMDKygpubm6YP3/+Rwc5h4aGyiOZhBA6AR4sZf7+/tBoNHIBKSYmRt4X//e//8Hd3V32IZX69fLyQr169TjLAPvqqOc6RowYgXbt2gFIGPOULVsW9vb2OHfuHIDk011PmTIFLi4un5SB8FuRuA553JI+4uPj5UYuX19fmYFUsX//fjRs2BAajQaNGzeWGWKVtn379u3IkSMHj+dT6d27d6hbty6EEGjatCl8fX3RqlUreU9cvny5zmuBhOCBhg0bwsrKCtbW1qhUqRJu3rxpqK+QIR07dgxGRkZo06ZNsq9R2qAjR45gzZo16VU0xtIdBw4wlkrDhg2T6cQT33iT6vzzhPCnOXbsGHx9fTFlyhT5mLqeleABZcGpXbt2ePr0KQCOyP7cmjdvDhsbG7kolVL9Ktd9VFQURo4cidy5cyMoKChdypkRPX/+HPXr14cQAv369YOnpyfMzMwQEBCQ6pTh6raGz4r7sLt378LR0RHGxsYYM2YMQkJC8ODBA4wYMQJFihSRu5smTZqE7du3Y/ny5ahbty6MjY1Rp04dPHv2DMD7uuaJiuRxXX95ly9fhq2tLUxNTTF06NDPFjwQEBDwuYuaKRw5ckTWUZYsWdC1a1ds3LgRwPs6T6odVh9bwMEDn4fSL1cfMxAfH48KFSpACCHbHCCh/tWvU+qd6599jbRarVyM3rFjB4CEgJhFixbB2toaefPmRUREBKKiomSqYE9PTyxatCjJzAMXL15EvXr1MHDgQN4F9f/evXuH6dOnw9HREY6Ojp8UPBASEoIlS5Zg3759n7mUmZsS/GVjY6OTAWPfvn3IkycPhBCYO3cuAGDLli2oWbOmPGom8XGFjH0tpk6dKo9PUuZAevToASEESpYsKfvp6kAZADh8+DBy586NkiVLciakD1Cnb1+6dCm+//57NGzYEH379sWtW7f4SLtPlFLfOD4+HjVr1oSbm5vMdhcfH68zP3jgwAEUL14cQgi5EUAtcbABS96JEyeQPXt2jBkzRqeO1WP2pIIHIiIicPz4cZw6dUq2OSz1Ll26BI1GA2traxw9elTvefVv0ahRI+TNm5ePgWCZFgcOMJYKY8aMgRAChQoVkjcOrVYLrVarM0HcoUMHdO/e3VDFzDQWLFgADw8PCCHQuHFjnahrdX2PGDFCBg94e3vLRSb28RIHBdy5cwdZsmSBm5sbQkNDU1y4S/xcVFTUR58d+i05f/68TNtmZGSESZMmyV2QqQ2C4cWP1AsPD8eAAQPkGar58+eHo6MjhBCwtrZGnTp1cOHCBZ33/Pnnn8iePTuEEDh9+rTOc1z3yeO6/vLCw8Pxyy+/oGDBgjA3N8cPP/zwScEDkyZNgpGREf7+++/PXdRMYeXKlRBCwNLSEi4uLnLSpk2bNpg/f75OunzgfV8R0A8eUK5vvq4/LHEayPDwcDRv3hxCCNmGxMfHo2LFijJoQL2AevnyZbRr106vvWHsa6C+vpU/37lzB/7+/nIS+ODBg8iVKxc8PDx0zk3ds2ePDMJzcnLCzJkzk+yrv379mhdT/p/St46OjsaPP/74WYIHlPGpus1nH9amTRsIITB+/HgAwN69e/WCBhTPnj3DqVOnONsA+6qo/+3fv38f+fPnR5MmTXSCFV+8eIGSJUtCCIHq1avLzS6KY8eOoWbNmjA1NcX69evTtfwZjVLf4eHhqFWrFoQQMDY2hqmpKYQQKFiwIBYvXsxzUJ/g7du3uH79OoKCgnD//n35uFarxa1bt2BkZIScOXMiODhYp/+ivvfNmDEDQgiULVsW7969480tqZS4/7BgwQI4OTnJTGnqely8eHGSwQO80eLjKX246OhofP/999BoNOjXrx/+/fdf+Rr15q5Zs2bB1NRUJzMvY5kNBw4w9gGRkZFo2rQpTE1N4ezsjIkTJ8oJG/VN+bvvvoMQAj169OA04Z/A398fZmZmyJs3LzZt2iR3i6klFTxgYmKCYcOG8Q6ET6B0/N+9eyfTooaHhyNv3rwycADQX8xWfo9Lly6hbdu2PGGWSup6cnd3h0ajgRACAwYMMGCpvg1Xr17F999/DycnJzngqlixIhYtWiSPO0kcPa/s6tu7d6+hip0hcV1/eUrwQL58+WBmZvbJwQMfe97zt+DixYvIlSsXhBAYO3Ysli9fDltbW7kL0tPTEzNnzpTpaBXK/VUdPODq6opjx44Z4mtkKOpJYvVCUp8+fXQyDijprBMHDQBAgwYN4ObmxgtOzODU97rEk7uzZ8/G5s2b5eSjenKyR48esLKywp9//gng/TEbx48fh4ODAwYMGACNRoMVK1Z86a+QKXyJ4AGWekq7vmnTJpiZmaF69erYtm0b8uXLByEE5s2bJ1+bOHCMsa/RhQsXcPnyZVhbW8t2Gnh//f71118oWrQohBDImTMn/Pz8MGfOHPj5+cmganUfh+dT9CntdkREhMwu1blzZ9y4cQMPHz5E//79IYRArly5MHXqVG7LP8KKFStQp04daDQa2NrawtnZGatXr5b9kuDgYOTPnx/Ozs4643iF8uc7d+7A0dERnp6e3IankjoIKTIyEjExMZg7dy5atmwpHwd06zu54AHOwJsydV3Hxsbi6dOnems4mzdvhru7O4yNjTFw4EC9Y2QXLlwIV1dX5M+fn7N5sUyNAwcYS4Fyc3779i26d+8OIQScnJwwcuRInZtDp06dIIRA7969dXaBsLRRMju0bNlS74zIxIMndQd05MiRMnhg+PDhHDzwEZTOZXh4OIoVK4YqVarg7Nmz8qxgZTJemahMquPaq1cvCCF4ISQN4uLi0Lt3b2TJkgXff/+9zDwwaNAgjhb+wp4+fYoLFy5g1apV2L59O6Kjo/XSVyvXd0xMDDw9PZE/f348f/7cYGXOqLiuvxzlXhgZGYktW7agcOHCyJIlCwYPHiyz8PDk4+elpMhXzj28dOkSli9fjtKlS8vJGwsLC4wbNy7J1NWhoaHo2LEjhBC8yPcByrUbERGBsmXL6kyMzZ07V55/rQQNjBw5Ui9oYMaMGbCwsED//v15Nwj7arRs2RJLliyR976hQ4fKM4MTZy65ceMGsmTJgkKFCukthCxbtgz29va4ePGiXsASSxkHDxjes2fPUKBAAXn8jxACc+bMkc/z4gfLCEaNGgUhBBo1aoQcOXLI3cFqWq0WN27ckMHR6v8KFy6Mn3/+Wb6Wr/vkxcTEoEuXLrCwsMDIkSNluvxnz56hQ4cOMgOBk5MTpk6dypkH0kA5ClaZ81ZnVps2bRri4uLw7t07eQ1///33MsBRuWaV/z979gw2NjaoXbu2wb5PRqIsZEdERKBv376oVKkSKlWqhDJlyqBAgQI6O94B3TZiyZIl8neaP39+upY7I1LPnYwcORLVqlWDm5sbcubMCV9fX/z+++/ytQsWLED27NlhZGSErFmzolevXujXr5/8N+Dm5qaTXYaxzIgDBxj7AGXx7u3bt+jatavsSI0fPx7BwcHysV69esmgAZ6kT7tNmzZBo9GgbNmyuHLlinxcq9UmO3hS17O/v79O8ADvKku72NhYtGvXDubm5hgxYoScuNy2bRvs7OxQoEABrFu3Tv6bUIIIgIRoV2trazRv3pwzbqTR7du35RnZR44ckcEDgwcP5uCBdJZUxDzwfkKoX79+iIqKMkTRMh2u60+nPuPz1KlTmDBhAtq1awchBLJnz44hQ4Z8UuYBpku5Ti9duoScOXPCw8NDJ4VnZGQkFi9ejL59+8oJHHt7ezRv3hx79+7VOfswIiJCZ2KC6VPSRWq1WgwePBiOjo4YP368DAwICQlBsWLFIISAmZkZfH199T5jyZIlyJYtG4oVK6bzWzFmSKtWrYIQAs7Ozvj1118xaNAgCCHQrl27JCcgHz16BGdnZ+TLl09nfHPixAmULFkSVatW1emT86JT6qmDB6ZNmwYnJyc4Ojpi3rx5HDzwhSl1v379enmk1dKlS/WeZ+xrFh0djcmTJ0Oj0cDMzAxOTk64desWACSZnj0+Ph579+7FkiVLMHXqVBw4cECnXefrPmV79+6FnZ0d2rRpI+eqbt++jc6dO0MIgW7dumHBggWwtraGh4cHpk6dyme8p8IPP/wAIQTq1KmDI0eO4PXr1zh8+LDMkiaEwObNmwEAR48eRdasWWFlZYXx48fL8bp63mrixIly4xHA49CUKHUTHh4uA6Ht7OxgbW0tj8dLKtBc3VYEBgbK3+nt27dc38lQZ7ErV64chBDw8PBA+fLlkTVrVggh4OLiggULFsj3bNy4UR6rpPyXPXt2tGzZkjcssm8CBw4wloQHDx7oTNwoNxh18ICzszO8vLzkAoeSqolv0h+nW7duMDExwf79++Vjic+JDAoKwoEDB7BmzRpcvXpV7xgDJXjA0tISffv2xd27d9Ot/BmVOnPD8+fPkTNnTgwYMADh4eHy8YcPH6J79+4wNjZGoUKFMGnSJERGRsp/F1OmTIGLiwvc3d05YOMD1B189bWt/h327dvHwQMGpp7oWbZsGezs7FCwYEFeePoCuK4/jnrgW7t2bdja2sLW1haVKlWCiYkJhBCwsbHh4IEvQDnCSsk0lbiv8ubNG5QpU0b2R4QQcHBwQOnSpbFhwwZcunRJ5/N4klifck9U6qZixYpo2rSp3Fmm7HA6ceIE8ubNCyEEKleujEePHuGff/7BvXv3MHDgQJibm8PZ2Rk3btwwzBdhLAmxsbEYP348jI2N5YJpq1atcPv2bQD6bfWbN2/QsmVLCCHQp08fbN26FevXr0fx4sUhhMCaNWsM8TUylJTaWXVANAcPpL8LFy7Aw8ND54gqvi+mjFN/f11CQ0Mxf/58mJmZQQiB/v37y+fU1/KHznnnfnrKtFotRo0aBTs7O5lV7eHDhzJgt1evXgASAkurVasmjy2YNm0aBw+kwMfHB0IItG3bVga9KF68eIFWrVpBCIGyZcvizZs3ePHiBcaPHw9bW1tYW1ujZ8+eePr0qQwgWLhwIbJmzYq8efPKOXKWMq1Wiz59+sDW1hbDhg1DUFAQLl26JDcE2NraJpnFTt2+rFy5En///Xd6FjtDioqKQs2aNWFsbIxhw4bh9evXiI6OxosXL2QG5Hz58uGvv/6S7wkNDcXx48exefNmrFixAkFBQUlmlmEsM+LAAcYS2bp1K+rXrw9zc3Ps2rVLPq7clJXgAXNzcwghUKJECZw5c0a+hjv8aff06VM4OzsjV65cMkJSvVgaHR2N4cOHo3DhwjLKL3fu3OjZsyceP36s81mjR4+WgR2c5jp1wsPDMWPGDCxYsADGxsbyGA51R/TatWv4/vvvYWNjI+u/WLFicsI+V65cuHbtmoG+QcagTPLExMTg4cOHOH78OEJDQ5OM0ubgAcNI3H6PHTsWbm5ucHR05DRknxnX9aeLiopC5cqVYWZmBn9/f5mG/e7du/D394e7uzssLCw4eOAzUu6LZ8+ehZ2dHYoXL447d+7Ien38+DFat24NIQSaNm2KO3fuoF+/fjKQQAih07dkyQsPD0eVKlUwdOhQuLu749ChQwB0J95jY2Px559/yv6hhYUF7OzsYGpqKic5b968aaivkGFwu5B+1NdvyZIlYWpqCjMzM0yfPj3F9/3666/Ily8fjIyMZFtiZmamsyuKf8ekqev8xo0b2LlzJ/bv369ztANnHvg8Ei9op3aBe+zYsRBCoF69enjy5MmXKFqmMX78ePj5+fHRO18BdZsbHByMBQsWyOCBmTNnyudSkz2Tpc7Lly+xceNG2a4vX74cJiYm6Nixo87r1q1bJ9OLK0egcECSPiXjUYsWLXSCK9TX5vLly2FkZITixYvLeauHDx/KsbsQAtmyZUOJEiVQtGhRCCGQNWtWnhv8AOX+qNVqERUVhdKlS+P777/Xa9sHDBiQ6uAB9mFKdoZevXrp1HV8fDzKly8PGxsb+Pn5cWAAY/+PAwcYU5kyZQqsra1hZmaGQYMGyah3hTp4oEuXLjA3N4eDgwMmTJjA0ZSf4N69e7C1tYWZmRkOHz4sHw8JCcHOnTvRsGFDeV6ZnZ0d3NzcoNFoYGRkhO+//x5v3rzR+bwJEyZwRzWV4uLiZOqlunXrIn/+/HKCLPFg9t69e1izZg3KlCkDR0dHCCFQpkwZDBo0SAYbsKSpdwe3bdsWuXPnhhACpUuXxg8//CAHahw8YHiPHj3C2rVrUaNGDQghUKpUKV54+kK4rj+O0jYvWrQIQgh06dJF7sRW0lW/efMGy5Ytg4eHB6ytrTFkyBAZTMcTlZ/u2bNncjfT7NmzASQEQSq7guvVqydfGx4ejhcvXsDX11cnDTNL2fLlyyGEQM6cOWFhYSHP/01qx97Lly/h7++PFi1aoHLlyujYsSPWrl2Lp0+fpnOpMxaebDQMrVaLI0eOQAiBPHnywMjICI6Ojli7dq1sy9WvVezfvx+jRo1C2bJlMXz4cOzevVs+x79l0tTtxfDhw+Hu7i4DL4yMjDBp0iR5/IkicfDAwoUL+ZzsVFDqOjQ0VGfHdUqU6/bixYvInTs3smXLJjdkMH2nTp2SC0iTJ0/m4IF0lJo2Njg4GPPnz4epqSmsra0xd+7cNL2fpUy90AokZADz8vJCoUKF5HFgSkaqLVu2wMHBAX5+fihcuDCCgoIMU+iv2K1bt2BrawshBAoVKiTnmpT/K/W8e/duCCFQsGBBvHz5Uj7+8uVL7N+/H5UqVZIBBF5eXujSpQtnIU2lsLAwdOjQAevXr0fOnDlx8uRJAAn3U3X/RQnwSC54gKXed999B0tLS50gxbi4OFSoUEEer6FkNg4PD+exJPvmceAAY/9v+PDhEEKgevXqOHLkSLKvS5x5QAgBJycnjBo1Co8ePUqv4mY6Sgqs2rVrY/fu3bhy5Qq6du2KXLlyyXOeVq9ejfPnz+PBgwfw8/ODq6srsmbNihMnTgAAL6x+pHXr1qF06dLQaDQQQmDVqlUpvj4qKgp3797FxYsX8e7dOzlAY0lTBlcREREoXbq0HJzlz59fBmDUqVNHdkqTCx7o0aMHX+NfWGRkJHx9fWFtbQ0HBwcMGDAADx8+NHSxMiWu60+nTCKcOnUKgP7OvuDgYEyYMEHuBBk8eDBnHviM1q5dKwPojh49KjMN1K9fX74muTabJ5A/7PXr15g8ebJc5FNS0AK69Ze4LpUzb1nK1FmQjhw5gmXLlmH27Nm4du2a3lFg7PN79eoVdu3ahRs3bmDixIkwMjKCg4MD1q1bJ3f0AUlns1MCxNSvYfrU9aIESRcoUACjRo3C+PHj5c7gvn376m0AUIIHsmbNClNTU8yZM4f74KkQGRmJSpUqQQiBiRMnpvp90dHR8jdq1aoVAO6nJOXVq1dYtGgRsmXLBnt7e3l8IPuy1At4Z86cwaZNmzB8+HAsXrwY58+f13ltcHAw5s6dC1NTU2TJkoWDBz7Ch+pJ+T0OHz4MIQRq1aqlc98EgO+//x6lSpVCaGgo3r59+6WKmqFFR0fjt99+g6enJ4QQKFKkiAySi46Olm2wMpZcuHAhAP3fJyoqCvfv38fp06cRGhqqFwDJkufr6yuzuZqZmeHgwYM6zycXPKA+3peljlarxcuXL1G4cGFkzZpVHncSHx+PihUryqABdTDp0aNHMXr0aA4eYN80DhxgDMCsWbMghEDz5s31dqqrB63KjVuZbOPggc9n9erVMu29EAJWVlYQQsDFxQVt2rTB1atXAbz/PV69eiUzEagHZCz11Nf25s2bZZRlo0aNkj0fSz1QUN7PEzspU86/Hjx4MOzs7DBq1CjExMTgzZs3+PPPP2UwQZUqVZIMHti/f7/8d6F0cFnS1Ndi4nPHU+vixYtYtGgRTp06xQtQKeC6NhylHW7UqBGEEFi2bBmApNviR48eyd0kjo6O6NWrF++c/ETqfki1atVgZWWFkiVL6mUa4DOIUyelCeLXr19j2rRp8h44b968VL0P4L5JStRZkFq1aiUXUJXxjLe3N27fvm3gUmYeyV2LykKHcm5zcsEDAHD9+nWcOHGCJ+Q/gpLmt0ePHjoBiu3bt5fXfc+ePfWCF6OjozFhwgSepP8AdR8wICAAzs7OSWZySI7Slh86dAiFCxfGlStXvlhZM4M3b95g8eLFcHFx4eCBdKDua/z4449wc3PTOTLGyMgI48ePx+XLl+Xr1MED1tbWmD9/viGKniEpfed3797h0KFDmDVrFqZOnYqNGzfqzbNev34dWbJkQZkyZXR2uC9btgxubm7o3r07B2t8QExMDPbt24dChQpBCAFPT0+dcaKSbaBmzZpJZnRNam6Qpd5///2HDh06wNraGkII+Pn56c2JJBU8IITQCzL41qXm+tNqtahZsyYcHR3lY+XLl08yaAAAKlSogEKFCukc48HYt4YDB9g37+zZs8iRIwdcXV1x4cIF+bhWq9VLh5rUzurEwQNjx47lYws+QmxsLBYuXIi6detCCAEbGxvUrl0bW7dulYupyq4b5XdQIjR5MPbx1AsbW7ZsQYkSJaDRaNCvXz/8888/BixZxqfUrTKgqlatGpo3b643GLhz546Mck0ueODgwYO4detWOpU8Y1Laa3VWGDWeOPh8uK4NSxkYT5kyBUII+Pj46D0HvN+V2rRpU9SvXx+urq5wdXXlAKTPyM/PT07g1KlTRz7OQQMp27FjBw4cOKDzWHLtxuvXr/Hjjz9CCAF7e3sZKJPSe1jy1EEDSsBLnTp1sHnzZsyePRvFixeHiYkJ6tWrh+vXrxu4tBmfeiz59u1bPHnyRGcCUt1WJA4eUIIELly4gOrVq6Nw4cIc0JFGW7duhYODA5o0aSIDA54+fSozDZYqVUoGrieXeUBZkOJFEX3qzCVAQgbBSpUqyWs3qaNlkvPixQuenE8lDh5IH+p/8/7+/jJd+4wZMzB+/Hi0a9dOHqfZoUMHnWM2QkJCMHfuXFhaWsLU1BQ//vijIb5ChqLunzRu3Bimpqayjy2EQPny5bFo0SL5+idPnqBx48YQQqBJkyYYP348unbtCnNzc2TLlo3nslIpcfBAkSJFAADHjh2Txw9wevxPl9yY5cmTJzLjTv78+XHw4EG916rvpd27d4cQgucGk/D69Wvcu3cPN27c0DtKNyYmBtHR0ejcubPs8ykZkkaNGqUXNDBp0iSYmppi9OjRepm+GPuWcOAA++atWbMGQgiMHz8eQNI7J3/55Rf4+/ujdOnS8Pb21jujVgkeUHbsTJo0KU0D5W+d0jHSarWIjIzEuXPnku0IqTtRNWrUgLOzc7K749l7ia/HV69eAdCfBNu6dSuKFi0KjUaDgQMH8oArjRLXZ3h4uBzIZs+eXR6Dkvj3uHv3rsz4oA4e4E5q6igTlxEREejbty+qV68OFxcXDBw4ELt27ZKv40WmT8d1nf7U2V3Ubcxvv/0mJ9M2b96s83p1G5MvXz706NEDO3bs4DMnPxPld3j9+jUKFy4MMzMz7Ny5EwD0dgozXX379oUQAiYmJujfvz927typd69LfC998+aNPLbA0dGRgwc+0bt379C8eXOYm5tjzJgxMqAxKChITsIrx4fduHHDwKXNuNTX5uzZs1G5cmXY2dnBy8sLAQEBMhBafb0rwQP29vaYNWsWli1bhurVq8vxJUu9yMhItGjRAmZmZnI88+bNG4wcOVKmtwaA33//XV7zffr0SfbYJA4cSFp4eDg8PDzQtm1blC5dGmvWrAGQtqABlnYcPJB+1q1bJxenE2fECAwMRMGCBaHRaNC7d288f/5cPhcaGiozmy5evDi9i52hJHW0Y+vWrfHHH39g9erV8PHxgaWlJezt7TFjxgz5vkOHDqF06dIwNzeXQRylSpXCzZs3DfVVMqSYmBjs3btXBg/kzJkTQggUL15cjm8Avg9+LOV+GBMTg9OnT+sF5j59+lQGIhUvXhx//fVXisEDvAlA14ULFzBnzhxkzZoVlpaWMDY2hrGxMfr27YsNGzbovPby5cvyKFgzMzOMHTtWr78SGBiIrFmzomzZspxRmn3zOHCAffOGDBkCIYSMXlUGXO/evUNQUBC+//57nUhX5QbTv39/nc8JDg5Gq1at4ODggKCgoHT/Hhldcp1Q9U1c3XmaOnUqhBBo06YNn8X6AcpCX1RUFObOnYt27drBy8sLDRo0wLhx4/R2L3HwQNoFBgbi4sWLAHSv5blz58pIbTs7O5nqNKmFjnv37snMAzVq1MDjx4/Tp/AZnFKX4eHhKFOmDIQQcHBwkO21g4ODTrAXLzJ9PK7r9JWaSfdRo0bJfsmWLVv0nl+0aBGsra3xv//97wuU8NsWHx+PqKgoeHt7QwiBLl26GLpIX71///0XTk5OEELA3NwcGo0GxsbGqFGjBvbs2aMX2JJ4tzYHD3w6rVaLJUuWwNLSEr169ZJBA7du3UKXLl3kZH2dOnVk8ABnHvg0ykK1lZUV3N3d5T2zc+fOSQZzKene1amw1cey8cR96i1ZsgQTJ04EkDC2X7p0KczNzVG1alWddqNTp06yvtu3b8/ZA9Pg6NGjcsHOyMgIQ4cOBcCBA+mBgwc+j8THryX+c8eOHWFsbIzTp0/Lx9XZYtatWwcXFxcIIbBt2zadzw4JCcGlS5e+YOkzj9jYWPTu3RtCCIwcOVInqPTp06fw8PCAm5sbJk6cqHOdX7x4ERs2bMDw4cOxefNmnkP5SErmgYIFC8o+yy+//CKf577Hx1FvuujWrRuyZMmCbt26yX6G0hd59uxZmoIHWIKffvoJhQsXhhAClpaWKFy4MPLly6czP6XOzhgVFYX58+fDxsYGQggMGjQIQEJbHRISAj8/P1hbW8PV1ZUDkBgDBw4wJtPMli1bFq9fvwaQsBt7wYIFchHP2NgYTZo0QZ8+fVCzZk1YWVlBCCEncZQbeHBwMP777z9DfZVMTd1p+umnn+Dk5AQPDw/8+++/BizV10+5NsPCwlC1alUIIWBqaqqT+s3Z2RlHjx7Ved+2bdtk8MCQIUM4FVYKZs6cCSEEqlatiqtXrwJ4P7C6c+cO/Pz8ZBS80jEFkg8eUH6nhg0b8uAglWJjY9G+fXtkyZIFgwYNwpMnT3DgwAE5WS+EwIIFC+TreZHp43Fdpw91wNfSpUvRp08f1KpVC3379sXevXvl696+fYtevXrJuh8zZgy2bt2Ke/fuYcyYMXBzc0OBAgU4Wv4LOnPmDIyNjSGEwK+//mro4nzVlCBbIQRKliyJgIAA5M6dG0IIWFtbI0+ePFi2bFmS56gCupkH3NzcdNoaljqhoaFo3rw5cubMKY+ZuXfvHnr06AEhBLy9vQEA+/btg6urK8zMzFCrVi3OPPCRNm7cCEtLSzRq1AinT5/G8+fPsWfPHuTKlUsGaSR1/MCvv/6K4cOHw8fHB3v27JGP8z01aYmPh1H3n5X0s2/evEGpUqVQuHBhPHnyBMD7DDG9e/eGu7u7XDDhbHZp8+uvv8r5EfWxPTyO+TLU7QAHD3w+sbGxOplg4uPj8fLlS9jb2yNr1qwIDg5OdlPL2LFjIYRApUqVEBYWluS1z+13yv7991/kzJkT5cqV0znaUavVomLFijAyMsKoUaNk3yWpY2TZp1EyD+TPn19ufgkODgbAx7B9DPVcbOXKlWFiYoKaNWvi0qVLSbYlqQkeYO+NHj0apqamcHNzw8qVK3Hr1i3Exsbi1atXWLJkCapWrSrnvbt27Srf9+DBA8ycORN2dnYQQiBv3rwoWrQoXF1dIYSAp6cnj3sY+38cOMC+eX///TcKFCggzzmcMGECSpUqBQsLC3nG05EjR+QA7M6dO/D19ZW73RV8Q/+w5KK5U/ueuLg4/PDDD3BycoKbm1uyE8tMV2RkJCpVqgRTU1MMHDgQt2/fxrVr17B8+XLUr18fQgjY2Njg8OHDOu/btm2bPP92xIgR8vxK9l5MTAxmzZol25Dq1avrpTC8e/cuRo0aBXNzc9jY2GDVqlXyuaTajTt37qBevXo8afkB6sFreHg4cuTIAW9vb52JhujoaCxYsIAXtD8R13X6Up/xWaNGDbk7O0uWLLJ+hw4dKqPgX758KYMglf9MTEwghIC7uzvvFk4D9bWamn6K8ppBgwbJ34XvlSk7dOgQhBAoWrQo/v77bzx9+hT+/v6oVauWvH4LFiyIgQMH4s6dO3LCUvHq1Sv8+OOPcqJHmUBmqRMTE4MpU6bgt99+A5CwqKoEY6gn1cLDw2W6YCMjI5QuXZozqqVC4on1fv36IX/+/LJPp7QZJ06cQLly5WTwQHLZvZJbpGJJW7t2rTyOLXF9LV68GEIIDB48GIDub9WmTRt89913+PPPP/Hnn3+mX4EzkV27dsl+ihKABHDwwKdI/O//7du3SR6HxMEDH+/69evw9/dHpUqVUKJECTRr1gx//fWXfD4yMhL58+eHm5ubPEpQ3bYov5GSUalo0aK8wJpKifvZ27ZtgxBCZokBEupaOc5xzJgxMhAsOjoax44d4yPYvgAleEDZxV2kSBF5X+VrO/XUx2+ULVsWpqamGDFihGzDk+vTqY8tKFOmDA4dOsT9vyT4+PhACIGmTZvi7NmzAPT7G+fOncOQIUPkJq6+ffvK50JDQ3Hs2DHUrVsXRYsWRbZs2VC7dm1Mnz6dN1wwpsKBA+ybFxwcjGnTpiFPnjwQQkCj0UAIgUKFCmH48OFyF4i6k6SchZg7d248f/6c0zalglJ/6npMbb3du3cPEydORLFixWQkN09ept7EiRMhhMAPP/ygN4kQFRWFtm3byjROiTMLbNiwAZUrV+aIyxSEh4dj6dKl8ky45IIH/Pz8YGJigoIFC2LdunXyuaQGAjwoS1riNiM8PBxjx47F0qVL4ebmhhcvXgDQr79FixYluaDNk5nJ47o2DKXeIyMjUbFiRRgbG6Nnz564ffs2bt68iY0bNyJv3rwQQmDYsGE6Zxxu27YNPj4+qFChAlq1aoVRo0bh/v37hvoqGcKHUkCmtp+yYsUKCCEwf/78z1a2zEir1SIiIgJNmzaFEAIrVqwAkDBJGRUVhYULF6JRo0awtLSUAQRNmzbF8ePHdQIEXr9+jRkzZnBQTBop13tMTIycfH/8+DE8PDxQtWpVOZkZHh4OAOjTpw8aNWqEMmXKwMTEhNO3p4Gfnx/27NmDrl27YtKkSQAS6l/d5pw5cwZly5ZNMniAx5Zp17dvXwgh4Ovrizdv3gDQrUelnQ4ICNB537Zt22Bvb6+zWJX4vSzlwBWlrnbv3i0zDwwbNkw+z33AtFP615GRkZgwYQLq1q2LfPnyoVy5ctixY4dcxFZw8EDaHTlyBB4eHnrHkgohsHnzZgAJC9Tly5eX17TyuyjXvPL/sLAwODo6Ik+ePPIeypKX1Nzgli1bIISQ98yYmBiZAVYdNAAkLPrlyJED06dPT9+CfyOU4AFlfsvT01Nm5+X2PPXi4+MxdOhQ2TdJqk2+fv263hzss2fP0LFjR5nVlNtyXUrQQNu2bXXmqRO3ywBw+/ZtDB06FGZmZjAyMsLixYt1PisyMhJhYWE8xmEsGRw4wBgSdi/99ttvaNasGdq3b48BAwbg9u3bstOv3HiUXWRXrlyBsbExmjdvbqgiZyjqFE0tW7bUm5j5kGXLliF37tzIkycPxo0bx8dBpIFWq0X9+vXh4OCAhw8fAng/8aOeQG7cuLHchRMbG6sziFMP0pgupW0IDw/HkiVLZIrTpIIH7t27hxEjRsDExAQFChT4YPAAey+pnXjx8fHyTNp69eohZ86cePr0abJBF+oF7UWLFn3pImdYXNeGFx8fLzMbDRkyRCezAwAUKlQIdnZ2GDFiBCIiIvTer/wu3K6kTD3xtWvXLowdOxYVK1ZE7969MWvWrDRPjO3fv/9zFzHTUrKT5M6dO8kjp6ZMmaIzgW9mZoZmzZphxYoVCA4O5gW9VEhtHc2ePRtCCDn5rk796+XlhU6dOuHGjRs8oZYGu3fvlteuk5MTxo0bl+xrT58+LYMH2rVrx4HRHykqKgqBgYHIkycPrK2tMWLECBk8oLTlmzdvhhACtra2OHjwIB49eoRVq1ahUKFCcHZ2xrlz5wz5Fb5qSr/i3bt3OH78OFavXo3z58/LXXlarVa2Obt27eLggU+knjupUqWKPFowX7580Gg0sLKywg8//KA31lQHD7i4uMDf3z/JDAUMOHDgAIyMjODs7IyRI0fiyJEjmDRpkk72I6Vft3XrVtja2iJPnjz4+eef5e+jzjD122+/QQiBnj17ymMOWNISzw0qwVx//PEHhBCoXLkynjx5Iq/9xEEDADBgwAAIIbBjx450L/+3InHwQNasWeV9laXOmzdvULJkSeTOnVtvPL9y5Uq0bNlSjnPatWun06Y8efIE3bt35yy7iSiBGE2bNsXjx48BfHjMc/nyZTRv3hxCCDRr1kxvnUeNx5iM6eLAAcZSSd35V84TVqLV+ObyYVFRUahWrRqEEBg5ciSio6NT/d6YmBgcOHAAd+7c4cFvEh4/foyzZ89ix44dePjwoU5H6OnTp8iRIwc8PDzw8uVLvfcq1/XWrVthbGyMSpUqyd+Gr+vUSRw8kFLmAQ4eSLvRo0cjX758Oue6AwmTmDt27JCpfoUQOHHiBIDkr131gvZPP/30xcue0XBdfx1iY2NRrlw5FCxYUOeel3jnTVJnfMbFxSUZbc90qdvbMWPGwMTEBBqNBubm5jAyMpJBMgcOHPhgfyVx281tefKUa/Ldu3eoWbMmrKys8OuvvwJ4PwG/b98+GYQ3c+ZMDBw4EDlz5pTtSZs2bXg33wcok/JxcXF49OgRjh07hlOnTsndYsD7hUAlSGPo0KE6n7Fo0SJYW1vj559/TrdyZybKooYQAgMGDEhxIen06dOyba9Xr57e8RwsdcLCwrBu3TrkypULlpaWOsEDim7dukEIAUtLSzg5OUEIgSxZsmDPnj0GKvXXT318UoMGDWBtbQ0hBOzt7VG2bFmcPHkSQMK9j4MHPo5y9FRcXJxsJyIiIlC5cmUYGxujT58+coFk9uzZsLGxgaWlJXr06IGrV6/qfNabN28QGBgIIyMj5M6dO8nx/7fuwIED0Gg0KFiwILZt2yYfj4uLw5kzZ1C7dm0IIVCtWjUEBwfjwYMH6NatG4yNjeHp6Yl58+bpXMsnTpxAjRo1YGxszG1JKiWeGwQS6r9SpUoQQsDNzU0eW5C4HV+yZAmcnJzQtGlTnX4N+/xiYmLw+++/y7Pfkwr2Zcn7559/4ODggAoVKsjHXr16hdatW8v+R8mSJeV9tWfPngDej5f4nqnr119/lX3rTp06ISQkBEDqxt4bN26U7z127NiXLipjmQYHDjD2/xJPsKv/rr5hL168GCYmJqhQoQKePHmSbuXLiNT1tmPHDtja2mLMmDEy1VJqFjV4Aj5lixcvRsWKFeVCR4ECBTBgwADcvXsXQMLkQf78+SGEwMaNG5P9nH///ReOjo7ImjUr7yr7gJQiU9OaecDT0xPLly9Pl3JnRG/fvpVnvG/ZskXv+ZiYGOzZs0dOPNSoUSPZc4IVs2bNgqWlJae4ToTr+utx9epVCCHQoUMH+Vh8fHyy6TpXr17Nu90/UkBAgJwcPnDgAG7evInTp0/LVPo1atTgXaifmVarRWxsLMaOHQshBOrWrSuf27t3L3Lnzg0hBGbPni0fP336NGbOnAl3d3e9RRKmSwkIiIiIQPfu3ZErVy45UVaqVCn4+/vrvP7UqVOwtbVFwYIFMWfOHDx+/Bjjxo2Dm5sbChYsKLNVsdRRZ+JRBw8oi0nJjX1OnTqFAgUKYMaMGelSzswqLCwMa9euTTF4wMfHB2XLlkXevHnRoUMHeZ45B9slLzIyUqZrr1SpElq1aiX/bmZmhsOHDwNIPnjA29vbgKX/unXr1g1Zs2bV2Y0aGxsLb29vWFhYwNfXVz738OFDdOnSRR4xaGxsjB49euiNNV+/fo0VK1ZwBpMkHDhwACYmJsifPz/++OMP+bjSdsfExMhMPAUKFJDtx+XLl9G2bVtYWFhACIGKFStixIgRGDJkiFxUnTt3riG+UobxoblB5XGlH1ixYkW9heo5c+bAxcUFuXPn/uA49FuX1Dzqx9znoqOj5SYupuvatWvYuXMnFi5ciN9//10vG2NwcDC8vLwghECXLl0wZMgQeX03bNgQt2/fRnR0NE6ePAkLCwuUKFGCg2FS8OTJEwwcOBBOTk6wsbFB3759dTIfJUX9eP369XWOomGMfRgHDjCWBnPnzoWrqyucnZ31ziFiupSOalRUFC5cuIDhw4cjX758csDAAQGfTkllbWlpCU9PTzloNTc3R/PmzWXwwLBhwyCEQO/evWVUpkL5HUJDQ+Hm5qaTcYDpOn78uNwFlprgASXzQJ06deROEsW9e/fg7+8PIQTKlCmj97uwhKjgt2/f4ubNm/jtt98AJLQnys4mRXR0NPbs2SMnMLt16/bBaHhOs6eL6/rrEhQUJBetFRUqVEg2XWfhwoVRvXp1zsiTRnv37kWWLFlQtmxZXLp0See5QYMGwcjICPXq1ePAly/k8ePHcHNzg0ajweHDh7Fv3z7kyZMHQgjMmzcPgP69ls/4TJnSpwsPD0epUqUghEC5cuUwdOhQtGrVSu7gUwdrvHz5Ej4+PsiSJQuEEHJRJGfOnHztf0Byk5TqLDCDBw+WwQPK4mpy73v27NkHP/tbl9wRSWphYWFYs2aNTvBA4on4169fIzQ0VGY6UafaZwnUY/Vp06bB0dFR59iNkJAQeHt7613f6uAB9bEdz58/T9fyZwRhYWFy/L548WJ5HZ44cQIuLi5o1KiRDBr4559/ZNBAnz598PvvvyNv3rywsLBA79698ffff+t8Ns+16FP6HUoAjFJHiec+jh8/DhMTE7i4uOgsTt+4cQMTJ07UyYJkbm6OAgUKYOXKlfJ1XPf6Ujs3+Pr1a0ybNg3u7u4wNjZG/fr1sWbNGixcuBBNmjSRKfO5f5Iy5V4ZHR2NM2fO4LfffsPVq1d1UuHzPe/TzJ49W45blP8GDx4sr03lmr5w4QKyZs0qX1OpUiUEBgbqZJd68uQJbGxsULNmTf5dkqHUy/PnzzF48GDY2trC2toaffv2/eCRBUo706ZNGwghsH79+vQpNGOZAAcOsG9C4hQ/aenMv379Gjdv3kS7du1kenE+Z0ifepJM/VipUqVQsmRJVK9eHa1bt072tSxtfvjhBwghUL9+fZw5cwaRkZH4999/0alTJ9ja2sLGxkZO7uzZs0dOBAcEBOidrwUA06dPhxACI0aM4JRYSRg+fDhsbGwwd+5cucifmuCB/Pnzw8jICD179sSLFy90Xvvvv/9i3LhxuHHjxpf/AhnMoEGDoNFoEBgYKB9T2hNPT0+93dUxMTH47bffULp0aWg0GnTt2pVT6aUS17XhJO6LKO1HVFQUChYsiJw5c+LgwYMyUGP06NF6QQO+vr4wMTHBsmXL0q3cmcX48eNhbGysdzSHkoWgadOmOH/+vN77+B756ZRr38/PD0IINGrUSE6+qXfsKa/j4zdSLzo6Gs2aNYNGo8GoUaN0+nxnz56FsbExTE1NddqM+/fvY9myZShdujTq1asHHx8f3L9/3xDFzzASt99v3rxBREREkq8dMmRIisEDKWW9Y0mbN29eku2zIiwsDKtXr0a2bNng4OCA4cOHy+ABrt+kqQOzEp/h3qNHD1SoUEEeE6O+DyZ1fauDB/bt26cXQM3eX4eTJ0+GkZGRTpapVatWwc7OTm4CePz4MQYOHKiTyhpI6MMrmwh69eqlFwTJdL148QIzZsyAu7s7hBBo0aKF7FfHxcXJxdZffvkFQgj06NFD7zNiYmLw6NEj/Pzzz5g7dy4OHTqkM5bnoIHkpXZu8NWrV1i9ejVKliypsyjr6OiI1q1b8873D1AfL9O8eXPY2dlBCAErKysMHjxYJ0073w8/jrIxy9nZGR06dECrVq3kderr6ytfp7QHT548wfbt27Ft2zZER0frjSXHjx8PjUaDadOmAeDfJTkfGzyg1HetWrUghMC+ffvSr9CMZXAcOMAyPeUmERkZqZOOLDWeP3+Onj17wtTUFGZmZmjRooUcwLH3Jk+ejAULFuidyXnr1i00aNAAZmZmEELonO3EnaGPp3RUO3bsKINYlIHuo0eP0KhRIwghUKJECfl4YGCg7Mz2798fu3btQkREBCIjIzFnzhy4urrCw8MD9+7dM9TX+mq9fPkSZcqUkSkL582bl+rggR9//BG2trZwd3fH8ePHAehOtqVm59S3xsfHB0IItG7dGrdv35aP37p1Cw0bNoSZmRmqV6+O33//Xed9vKCddlzXhqO0A7GxsToZjOLi4hAXF4fhw4fLM4SNjIwwevRovXPdly1bBldXV1SvXl1npypLmVarRUxMDEqXLg1nZ2edVOwTJkyAEAKNGzfG5cuX5eMXL17EnDlzDFHcTG3//v06k8ILFiyQz/Hk+8fZv38/TExM0Lp1a50sJDExMahatSpMTU0xatSoJFOhKu0SB8ekTF0/69evx/fffw9nZ2fkzZsXTZo0waZNm/Ta5A8trrLUGz16NIQQ+O677/R2Wau9fPkSffr0gRACOXLkgJ+fH6cATsa0adOwY8cOAO+v74iICBQpUgTt2rVDiRIlZICp0janJXiAJe/UqVMwNTXVS5984MABGYy0ZcsWWFhYoG3btjrvPXv2LKytrVG0aFEIITBgwADOHPgBL1++xPz58+UO4GbNmukEfQUHB+sE7wK64/2U+iZ8vacsrXODkZGRWLduHZYvX45Fixbhxo0begHULGkREREoW7asnBNs0KABHBwcYGRkhKpVq+oETfN1mzbKBq7mzZvr9EHmzp0r74PqrI1JzfepMz8sW7YMLi4uKFGihEy7z5KX1uABpa8SHByM7Nmzo2rVqulfaMYyMA4cYJmaegGvVKlScHBwkIPZ1Hjx4gUmTpyIdu3aYcOGDTzZkARl0jdr1qxYuXKlXsr1ixcvomvXrnLHu3pXK3dS007ZnVepUiW5e0OZmFEGsmfPnkWWLFlgb2+vsyCydOlSGBsbQwgBY2NjFC1aFPnz55cTapxJI3nKQFcIgbx586Y6eOD169cysrV79+7pWuaMSBmItW/fPsnjYC5fvoxOnTpBo9GkakG7R48efP5hMriuDUeZQIiMjMTAgQNhbW2NgIAAnddcvnwZRYoUgRAChQoV0uu7TJw4EU5OTsiWLZtO0AdLvcqVK8PCwgJXr14FkHzQAJCwE0QIwffJL0BZ2FMWRNT9GZZ2SsaMs2fPysfi4+NRsWJFveNOwsPDceHCBfk6zuzwYeprUzlyyszMDK6urrCxsZG7Ijt37qwXbK4srpqYmODQoUPpXfQMI6V+NZCQSa1KlSoQQqBTp04pBg8cO3ZMBuEJITBs2DAO2k1k0aJFMvOL2uHDh2FkZARra2tYWFhgypQpAHQDBpIKHjAxMUnzZo1v3ciRI+Wxgokzl8TGxqJixYo6afOV4IBLly7B3NwcEyZMQN26dTmLXSolDh5o2rSpfM7LywtmZmaYNWsWB2F8AamdG+R+YNqp62/06NGwtbXFuHHjZOD5gQMH0LZtWwghULZsWXk8YeL3suQpmy7atm0r50/UfYqOHTtCCIFTp07pvTepoFx/f384OzvDzc2N2+80SG3wgLrOe/fuDSEEFi5cyIGNjKUBBw6wTC82NhZdunSBjY0NRo4cmWwayeRERETwoCEFISEhGD16NOzt7ZE9e3asWLFCL3jg/Pnz6Nq1K4yNjVGmTBnupH6kmzdvynP5ypQpg7179+pM8ip/Pnz4sJy4fPnypU6HadeuXejduzfc3NxgZWUFT09PeHt7cyaNFCj1GhQUhLp166Y6eEAZ8P76668wNTXVSUHJ9CkL2e3atdNbyFYPyM6dO5eqBW3lTPj+/fvzJHEiXNeGo7THYWFhqFatGoyMjFC2bFns2rVLr+6OHDmCfPnyQQgBLy8vtGvXDv3795dHF+TOnZsXsj8g8SSNuo6Vs8d/+uknOWnfuHFjvVS/e/fuha2tLdq3b6/Xv2EfT7lvrl+/HkII5MqVizNnpFHiNuPdu3fo0aMHhBByYTouLi7JoAEg4bzmRo0apSmomiWYMmWKXGw9efIkXrx4gRs3bmDgwIHImzcvhBBo2bKlTgAv8H7SWQiBx48f8zgoGe/evcObN2/w6tWrJPsVBw8eROXKlZMMHlBPFl+7dg05c+bExIkTkSNHDr2jaVhCHTk7O8PZ2VmnLYiOjsavv/4qz3MvX768nEdRL+qp77NDhw6VgRoRERF8fafSnj17YGJiAgcHB7kxQKm7mzdvwsbGBvny5cPTp0913jd48GDkzJkTEREROhlm2IclDh5o3LgxPD09YWpqiokTJ8r65Gs49dRHDiR3HBvAc4NfgnKfVI7caNy4MRo0aKB3DMTVq1fRvXt3Dh74CEr/rUWLFnjy5InOc3FxcYiPj0fnzp1RqlQpeZTJ3Llz8b///U/nddHR0Vi7dq08oq1cuXJ8nM9HSE3wgGLx4sWwsbFB3bp18fz5c0MUl7EMiwMHWKaknmCIjY1Fnjx50L17dxltyRGsn1doaCjGjBkDa2vrZIMHLl68iO+++w5GRkZ6C1DcSU29PXv2yHSElSpVwtatW+VzynU/b948GBkZyTOyAN1rXqvV4sWLF7hz5w7Cw8N1UmWxpH1M8IAykbZr1y4IIdC1a9d0LXNGoqRl/+677/SirZOKzk7Ngvb//vc/1KxZk6O3E+G6NhylHY6IiEDp0qVhamoKHx8fvcledR/mzJkz6NatGzw8PORiU6FCheDt7c1Hy6TBpk2b5PWtBIPu2rULtra2sLW1lQuAiTMNnD59GlWrVoWzszMvOH2Act2mtY8dFxcnFwCHDh3KfZJUUgch+fv7y7+PHTtWJ3BACTRKHDQAAC1atICdnR0HIKXR6dOn4ezsjKJFi8qMJepUqGvXrkXhwoVhYmKCMWPGIDIyUue67tWrF8aNG2eQsn/tzp07h4ULF6JChQooWLAgChUqhGrVqmHGjBl6QV0HDhxINnhAMXXqVLi6uuL+/ftyop/Hne/Fx8cjODhY7kIdM2aMzvNK8EDu3LnljvjIyEgAyWce8Pf31/ut2Ic1b94cQgj06tVL1jGQkL3Oy8sLrq6uOHLkiKzrwMBA5MiRA82aNdN5PUu9xMEDJiYmGD9+vHye5wxTz9/fH/369cOmTZt0Hle3Deq2l+cGP7/w8HDUq1cP06ZNQ/78+bFu3ToACf1zdZ1ev36dgwfSaP/+/dBoNDJwQBnzxMbGyj9HRUWhatWqOkewKf99//338rOioqKwZ88elC5dGqNGjcJ///1nkO+UGSQXPODt7Y2XL18CAFauXImsWbPCw8ODs2My9hE4cIBlGok7OWFhYejbty+mTZsGW1tb3LlzBwAPAD4Hb29vNG/eXOex1AYPJLcAxZ3UlKnrZ9++fShcuLBO8IAyKNu5cyeEEKhatSouXrz4wc9iqZeW4AF1O9OzZ0+YmJjIgTTXv65JkybJ3dN//vkngPfHb6gnGxo1aoT27dvLv1+6dOmDC9qJz4T/1nFdG15sbCx69uwpJ+iTmux9+PCh3DECJCxEvX79GseOHcNff/2FsLAw3lmWBj/++COEEOjQoYNO2xwVFYWuXbtCCAErKyvMmjVL5/k9e/bIRdelS5caougZTnh4OH744Qfcv38/Va9X2p01a9bA1tYWZcuWRXBw8JcsYqYSEREhr9Hp06cDANatWwchBNzd3VG6dGkIITBy5Ei9PvmcOXNgZWWFnj17cvudRkodL1q0CIBu1i8g4d/BokWLYG5ujpIlS+Lt27cAkj7nlsel723YsEEGyZmZmcHOzk4GdhkZGcHGxgYbNmzQ2UGpDh5o06aNzo757du3I0+ePKhbty7fMz9gy5YtEELA2toap0+f1nlOCR5wd3eHEAIDBw6U9anuO3LGqZQldxSMUod79uyBg4MDSpUqJY/HjI+PR1RUlMwUVqhQIbRv3x5NmzaFEAKurq4ICgpK3y+SySjBA0pWxyZNmsjnkgqoZvpmz56ts0jasmVLrFixQt77FInvlTw3+HktXrxYHiFramqKFStWJPtadfBApUqVdHbFM30PHz7E6NGjkS1bNggh0LlzZ7kwrejQoYMMxti2bRuWL1+OLl26wMrKCkIIDBkyROf1r1694r5JIkePHk3zexIHD9jZ2cHa2hr9+vXDnDlzkD17dg6SZuwTcOAAy/AuXLggd3GoB2TKOXuVK1eGk5MTLl++rNdZZWmnpGhq0qSJ3LmhTHp9TPAAn4GYeikFDxw+fBi7d++WKa337dtnwJJmXurggXr16snggdmzZ+PNmzcAdNP0LVu2DNbW1qhWrRqnxUpCREQEevbsCUdHR2TJkgX9+/eXC07qI2K6dOkiJ4XVgzRuT1KP6/rr8ODBA+TJkwclS5bU649s374dvXv3hpWVFWrWrImAgACeUPgIiSd69+/fD0dHxySDB0JDQ9GiRQsIIeDs7Iz69etjwIABaN26NYyMjGBiYoJ58+bJ1/MiX/KUFJ1CCJw7d04+lhp37tyBqakphBB8XMEHqMcyfn5+cHR0xIQJE3SyCTRs2FAuvg4aNEjvMxYvXoxs2bLB09OTM5d8QFILR97e3hBC4Mcff0z2Na9evULBggUhhMAvv/wiH1e3+zwmfW/58uXyyJL58+cjKCgIt2/fxuXLlzFo0CB53IYQArNmzdJZkDp48CCqVasGjUaDfPnyoWvXrvD29oaNjQ1MTExw8OBBw32xr5z6GmzdujVMTU1lQIz6uo6OjsbOnTuRI0cOveABvi+mbOHChbh06ZLevS3xv////vsPxYoVgxACAQEBOs89fvwYHTp0gKurqwzwqFSpkt5xY+zjvHjxQifzQNOmTeWxHBw8kLKoqCi5AG1vbw87Ozu5M7tIkSJYsWIFLly4oPMedZARjy8/n5CQEIwdO1YG4DVu3DjF3ezXr19Hr169IIRA7dq1OYj0A/777z+MHTsWLi4uMtORch/s1KkThBDo1q2bzhGwQUFBGDhwIIyNjVG6dGkZFMb0+fr6QgiBGTNmpPm9SWUeUAI27O3tZXYwxljaceAAy9BGjx4NJycnrFmzRi944NixYzLlmxACy5Ytk+/jiZqPo0S7t2/fXi+6PS3BA8ruVXNzcxQvXlymU2UfllzwgPJ/T09Pjtb+wpIKHsiePTv69++vc97Zjz/+CFdXVzg5OfHETgpevnyJ4cOHw97eHlZWVujVqxf+/fdf+bwyEOvTpw8ePXoEQHeSktuT1OO6Nrz9+/fLBWxFeHg4unXrBgsLC5iYmMDKygoWFhbIkiUL5s+fb8DSZjzqCd7Vq1eje/fuqFGjhpzkUY7pUF/X4eHhGDt2LCpUqCBfY21tjWbNmmHnzp3ydbw4kjKtVgt/f385WalMuqfWqlWreGLnA9SpUYGEAIE6derIyV4lcPHmzZvyes6TJw/OnTuHy5cv49q1a/D29oapqSmcnZ1x/fp1w3yRDEL9b37FihXyKBNlV1+/fv3k8+r+tjImVc57//nnn9OnwBnU3r17YW5ujnLlyslsSGrv3r3DpUuX8N1338k2euHChTqvOXbsGHr27Alzc3P5muzZs8sjZng89GHTpk2TAdGJd1ICCdc1Bw+kjZLxyMXFBbVr18aWLVt0gryUelP+v3XrVmg0GlSpUgWPHz/WyQj28uVLnD17FoGBgTh58iQH2X1miY8taNasmezHcDaNlN28eRN2dnbImzcvFi1ahBkzZqBUqVLy+Ac7OztMnDgRx44dS/L9SvAAjy8/ntJOhISEYNSoUXBzc4ODgwOWLFmiNxer9vfff2PAgAHcH0ylxMED3bt3R5s2bSCEQM+ePfHw4UMAuuNRZXOXsbExbt26xf2RZMyfP1/232bOnJnm96uDB3x8fKDRaJAlSxa+thn7RBw4wDKst2/fok+fPjA3N4eXlxfWrl2rdy7qqVOn0K5dO7mDQZ2+kG/YaaMEDbRr1y7ZRdC0BA9cvnwZTZo0gaOjY6pT2rIEiYMHihQpAiMjI1hYWMDf318+x4PcL0f5DW7fvo02bdrAxsZG7litWLEiChUqJCffOC1W8pR6fPXqFYYOHSoXtPv164c3b96gW7du8rxPZSCWVKpPbk8+jOv66/DkyRPkyZMH+fLlw9y5czFr1iwUKVIEQghUrFgRFy5cwKVLlzB+/HgIIdClSxdDFznDUF+n/v7+MDU1RdGiRTFmzBhMmDABDRo0gIODg0wxqV7oiIuLQ1RUFI4dO4ZTp07h/v37SU7us6QpdR8cHIwiRYrAzc0N58+fB8B197mFh4ejVKlS6NGjB3LlyoUtW7YA0D9H+O+//0bt2rXlJJyxsTGEENBoNLxTNY2UgJj27dsjJiZGBoAJIbB161b5OuXoH4W3tzeMjY2TXSxhCTt9mzZtCmNj4yQDtdT1+ebNG3m8jBBCL5NAcHAwTp8+jenTp2Pbtm1yspgzDqZOVFQUypQpAyEElixZAkC//VYHD2g0GnTr1k0n0xp7LyYmBr///jsaN26M3Llzy+u2YcOGCAgIQGRkpJy7Uur51q1bcjOAOnU4X7/pI3HwQKNGjXgX9gfEx8cjJiYGPXr0gBBCBjzHxMRgzpw5aN++vbz2nZ2d0apVK5w5c0ZvJ/z58+d5fJlKH+pXh4aGYvTo0bCxsUH27NmxfPnyFIMH1JkH2YclDh5QxuuJjy5V2vdnz57BxsYGVapUMViZM4qVK1fqZJdKK6Xunzx5Aj8/Pz7Kh7HPgAMHWIZ2//59+Pr6wtLSEoUKFdLJPKA4ffo0WrVqJVMwnTp1Sj7Hg7DUUYIG2rZtqzfRmNzidFhYGMaOHQsbGxtky5YNy5cv1zu39sqVKymmz2LJU1+7v/32G7y8vCCEQJUqVbBjxw45gczXeNqoB2Ifqjvl+UePHmHu3P9j777Do6jeNo7PSQ8tJJBQpUpHBBXBgoKIImABpdnAgigiKAgqShMrxUJTASn+QAERpIqgghS7Ik1FkSogvSWQhOze7x+8M+5mN5CQsiz5fq4rF7g7O559Mkw55znPedNZY9UYo0svvdSrFDz88xzo2Lt3rzOgXahQIV188cUyxqhbt27avn27JN/fied/cz45M2IdeC6XS8ePH9crr7yi2NhY53xxySWXaNiwYV6ll7/99lsZY3TnnXcGrsFB6p133nE6fD0Tt/bu3avZs2c761Pefffdzr+LM609znU0c+zO46efftrvWp7IGXPmzHE64KOiopxZOf7KKaelpenNN99U165ddeutt+qRRx7RnDlzmKmajv1v3N+/+Xnz5ikuLk5t2rTRL7/84rz+zDPPyBij8uXLew1421avXq34+HhVr15df/75Zy5/g+C1YcMGJ762M51zd+zYodtuu03GGF133XU6cOAA5+hMOHTo0BkHm+zzx+uvv+6sUZ6R1NRUzZs3TwUKFFCRIkW0Z8+eHG/vheann37SgAEDVKpUKacqxhVXXKG+fftq7dq1Xtu+8MILMsaoQYMGXpXskDf279+v0aNHO0tczZs3L9BNCgqzZs2SMUZFixbVDz/84Lzucrn06aefqlu3bk5MS5curSuvvFKzZ892njul07Pf//nnn0A0P2jYzyupqan68ccfNXnyZE2fPl3Lly/32s6eyHWmvlicu927d6t///5OklHr1q2dcYi0tDSv6619v9i7d2+lpKRwz+KHZ7w8kwdee+21LO/Lji/LzAA5g8QBBB3PMuzS6fWCn3766TMmD3z//fdOJ8NNN92kb775xnmPC/eZ9ezZ01nvOv1AkefFeOrUqc77dkz9JQ+cKdsVWeOv8oA9a9VzBhTHuH/p42JnW588eTLTg6LpZ2T/+uuv+uGHH5SUlMTa5OmcKd72DPd9+/apd+/eTgZ3tWrVnPN1Rjf/HN++iHVgpY9f+kHpffv2admyZRo0aJBGjhyp/fv3+2zTq1cvhYSEaPLkyZKIfWYlJSWpZcuWCgsL04oVKyT5zsz5/PPPVbJkSWfJCPv3RQdD5pztWPzpp58UEhKiuLg4fffdd3nUqvwjMTFR77//vooWLSpjjJo3b+6853mspz/umVGWMXtpHsn3PDB69GjFx8c7azR7zgy2Z1JGRUVp5MiR2rBhg9LS0rRw4UI1adJExhhNnDgx775IEBo5cqSMMXr88cclnb1aWlpamv73v/+pcOHCKlGihDZv3uyzDddLb/fdd59Kliypp59+2msCheR7nli3bp2io6NljNEHH3yQ4T5TUlK0aNEikmLOIv355JdfftHkyZNVpUoVhYeHyxijyMhI9e3b16kes23bNl155ZUqVqyY8/vi/iRz/FVKO5fzwb59+zR06FCNHz8+x9p2ofKMb/v27RUeHq533nlHkryqkUyfPt1J3K1SpYozMNikSRN1796dyg6ZYF8fExMT1bZtWycRw/65/fbb9ffffzvni2PHjtEXm4vsygMlSpRwlsJLv8zP+PHjFRMTo6pVq3olycBb+mvckCFDVKhQoXNetgBAziFxAEHliSeekDFGs2bN8no9q8kDzZo1I3kgE77++mvnRrRly5bO66mpqV4Xd3s5iBkzZvis1eeZPFC+fHmNGjWKG9YclFHywNVXX61PPvmEYzsDdlw2bdqk1atXO68fPXpU9erV0x133JGl45RSzGeWmXgfOnRI0ulS+k899ZTi4+MVHR2tbt26aevWrV77QcaIdWDZnTrJycmaPn26evXqpQ4dOqhPnz6aO3duhh1jnoMl48ePV0JCgho0aMBMvizav3+/ypcvr8qVK+vEiROSfM/PycnJeumllxQZGSljjDp27Ojc03AuPzP7OD116pTXQHT6Kkd24svIkSO9XkfmnelYTExM1KRJk5zKJX369MnU5yR+F+ktWbJExhhNnTrV6/XnnntO999/v6666ip17NjRed0zfmvWrNFDDz3kPCvFxcWpTJkyztIQb775pt/P4T/2GvCPPPJIpj+TlJTkrJ89d+7cXGxd8NuyZYszqGH/9OzZU9OnT/faLi0tzTlGX3zxRYWEhKhr166SOHZzQvoY7tu3T9OnT1fbtm1ljFFoaKizjNKkSZPUsmVLZyAQmePZN3XgwAEdPHhQBw8ePOf9eU4A4N7wzOzlYOw1yuvUqeO13NfixYtVqVIlGWM0YsQIHTlyRIMHD1atWrWc85Kd2A7/7GMwMTFRl19+udOn/dZbb+nll1/WJZdcImOM6tWrpx9//NG5X6cvNnelX7bg7rvv1vHjxyWdnjlfqlQpxcbGOksnwZd9rCYlJalHjx5q0KCBc49n/4wYMSLArQTyLxIHEDTscvlt2rTxe+HNavLALbfc4sxGg3+7d+/WsGHDnJl5bdu29VnH8J577pExRp07d/bJovRMHrDXa65Ro4ZXOWZkX0bJAzVr1qRD7Qw2b96syMhIXX755U4569q1ayssLEyvv/66z/kD2ZOVeO/fv98ppV+wYEE99NBDGZbQhy9iHRh2p2ViYqKaNWvm9cBrjFHhwoXVqlUr5xro7xzz4osvqkSJEkpISGAN8nOwe/dulS1bVsYYLV26NMPtli9fLmOMIiIiZIzRE088QcewH7/++qvPa8eOHVPt2rXVoUMHn9l4dgw//vhjGWNUsmRJJxkJZzdt2jRNmzbN67WMjsvExERNnDhRRYoUUVhYmAYPHuy8x+zUzFm6dKnCw8NVuXJlzZ4923l93bp1io2NVeHChVWmTBl16NBBkv/Z8ElJSRo3bpyuvfZaVa5cWRdffLE6derkVd6ac0vGpk+frtDQUDVv3jxT9932Nt26dZMxxpmljYwdPnxYU6ZM0b333ut1T3Lbbbdp2rRpPgmK8+fPV0REhMLCwvTTTz8FqNUXrvTng1mzZqlPnz4KCwuTMUbFixdX8eLFFRkZqfj4eK1cuTJALQ0enjEdNWqUGjVqpCpVqqhmzZr64IMPvJYO5Nkm95w8eVJ169aVMUbjxo2TJC1atMhJGvBMppNOJ7nPmDFDv//+ewBaG3xSU1PVsWNHGWPUr18/J0FakrZu3aoiRYqoQIECGjVqlFwuF32xWeC5FFVW2csW2MkD9957r0aNGqUyZcooJiZG69evz8GWXljSJ8QULVpUzZs314IFCzRgwADdd999zj3L8OHDA9xaIH8icQBBwU4aaNeunTZt2pThdplNHmjdurWThEA58TPbu3evhg8frvj4eJ/1lu0b14cfftgp85n+Ycy+GTh69KhefvllBkJyiWfcP//8c5UqVUqFCxemw/4MfvnlFzVt2lShoaG6/vrrValSJUVGRurVV19VUlKSJDoXclJm423H/MCBA+rdu7czoP3www8zoJ1JxDrv2XFKSkpS/fr1FRYWpnvuuUdLly7VzJkz9dBDD6lChQoyxujyyy/36rQ5fvy4Fi9erIYNG8oYo7p16+q3334L0DcJfl26dFFoaKgGDBjgU57dvidJTU1VgwYNNHjwYJUsWVIxMTEk2qXz9NNPyxijKVOmeL0+YcIEp7yyPeNpzJgxPrPF7HvtCRMmSGLw9Gwee+wxp3Psjjvu0MSJE7Vv3z6vbdLHMCkpSRMmTFDhwoUVERHhlTxAvM9s6dKlCgkJUbVq1fTpp596vZeUlKTp06erTp06MsYoPj5ef//9t6SMr4lHjx51fjyfPfk9nNlnn33mHPefffbZWbe34/nwww/LGOMkfJDs61/642/27Nnq06ePihQp4iyzUbVqVU2ePNlr4OSBBx6QMUaPPvqoTpw4wb1gLkj/u/nll180YMAA1a5d2/k3ERERkenl8/DfWuLGGGft8cjISD388MNas2aNsx3Hc86zExbfeOMNp39w8eLFqly5sowxeuutt5xtWTrp3Pzyyy+Ki4vTjTfe6DWZKyUlRY0aNVJ4eLj69evnVBb0RF9sxuylef/3v/+d8z48kwdCQkJkjFHRokVJGsiEU6dOOUt/9e/f3+mrsr311ltUHgACiMQBnPc8kwbS3+j4u+nPTPLA6tWr1bFjR0oGZdK///7rlTzQsWNHdejQQcYYPfjgg05ncUYPYfaDMQ9pZ5ZR52JmOx094/vFF1/4XfcT/0lLS9OWLVt06623OmUiPUulnm2dVWRNVuLtOaDtORu+a9eu2rJlS563PdgQ68BwuVxO58Pzzz/v1amTmJioVatWOWU5mzdv7ixbsHfvXvXq1UvVq1dX9+7dKdeZBf7Ws33//fdljFGhQoW8ZhF7bjd79mwZY/T111/rnXfecco34z9PPfWUjDF64403fN777bff9OGHH6pGjRpO1YaLLrpI7777rlatWiVJmjNnjiIiInTNNddw/3cWu3fvVvXq1Z3KJHZSRtWqVTVp0iSfWb+eFQVIHsg6z6SBOXPmOK+7XC6vJLAZM2Y4syc7dOig3bt3S/K/frZnrIl71thL3l166aVeg3vpecb91ltvVVxcnFOK3LMsNnylPyZ/++039e7dWw0aNJAxRuHh4SpdurSeffZZbdy4UfPnz1eFChVUo0YNJ8acx3OP53nk+PHjGjx4sDp27MigUxZ88MEHioqKUsuWLbV8+XIdOXJEI0eOVN26dRUaGqoOHTro559/drbneM4dP//8s6Kjo2WMUbFixXwqDXB9PHf284rnUjMul0tXXXWVjDF64YUXnGthUlKSvvjiC6/Pc8z717dvX+c6mH7ZqqzYvXu3Bg0apIiICEVFRTHWkEm7d+9WhQoVVLt2ba8l8TyP15EjRzrJA8OGDQtUU4F8icQBnNfsi3iHDh18LryeAx/psyozkzxApYGsSZ88YCcN2GtkcSOaPZ7rYq9evVoffvihvvnmGx04cEDSuSUP4OySkpJUrlw5pzxk48aNvTroiWfOykq80w9o2+XfevToQVJHJhDr3OXv3HDo0CFdeumlqly5slNRIH38vvnmG1WuXFkRERFeMxv27Nmj33//3UkmgH/pr4UZlWTv0qWLMwg7ZcoUr6WUvvnmGzVu3Fg1a9bU3r17tWrVKhljVL9+fR0+fDjfn/c9v/93330n6XTiy8KFC3223b17t2bOnOmVpFS4cGENGDBA8+fPV8WKFWWM8VnSAN6Sk5OdY7ZWrVoaOnSorrnmGmfGaWxsrAYOHKhvv/3W7zHvmTxQsGBBPfPMMwH4FsHhq6++UkhIiKpXr65Zs2Y5r3smDdhOnjypGTNmqEaNGgoLC9MTTzyhf//9VxL3hznBjuH06dNVsWJFhYeH68477/QaLPWXmDFlyhTnWbR+/fqqUqWK6tevr4kTJ3INzQTPyjspKSl69dVX1apVKyem1atX1/XXX+/M2O7Vq1eAW5x/eB7n9FWdWfprYY8ePVSrVi2fZIuFCxeqcePGCgkJUceOHUkeyIJzjc/gwYOd88m7777rvE7SQPbY1RzsZSAyShqQTi9FWK1aNb333nuBau55z/P4fumll2SMUUhISLaSB3bu3KlXXnlFf/75Z040MV/45ptvnEkVkve1z/Oc0bVrV+e8MnTo0DxvJ5BfkTiA85ZdIrVWrVrau3evpP864D0fFB566CENGDDAZ62m9MkD//vf/yiLlU32sgXFixeXMUYtW7Z03mNw6dx5rot98803q1ChQs5sybp165KtmovWrVunKlWqqGvXrrr99ttljFGTJk281pP0N6MV5+Zc471//3498sgjqlixIusgZhKxzh3Lly93ErrS+/nnnxUSEqImTZpk+PmTJ0+qd+/eMsZ4VYHA2Xne+y1evFiDBg1S69at1bNnTy1btsxnxqm9nnN0dLQaNmyovn37qk+fPipbtqyMMXr77bclSZs3b1ZYWJhuv/32vPw65zXPWKempqpq1aoyxmjatGnO6+k7gD/55BM99dRTCg0NlTFGtWvXVvny5WWMUbdu3SglngH7/Pv7778rJiZGFSpU0KpVq5SSkqIRI0Y4s7GNMSpdurRuv/12/fDDDz7lq48fP66JEyfKGKOEhATt378/EF/nvPbVV1/JGKPY2FgtWbLEeT190sDHH3+s999/X263WydOnNCMGTNUtWpVRUZGkjyQC5KSkvTUU0+pYMGCioqKUvPmzfXNN9/43fbTTz9VjRo1nDLACQkJKlSokNq3b+8sm4fM8TyHHz16VPPmzVPz5s1VokQJJ2nJGKNbb72VhIyz8Dcgeq7nB84rWfPWW29p/vz5qlevngYMGCDpdAw972M+//xzJ3mAygNZcy59fIsWLVJsbKwiIyOdZTPpK8w+O2luwIABSkpKcpa4S580IEn333+/ChYs6PXcD1+e54kXX3wxR5IHMkpqh3+//vqrjDGqWLGijhw54vO+fX21qw7ExMTIGKORI0fmdVOBfInEAZyX7A4Eu6PMs+ymZwbafffdJ2OMunfv7rdEoZ08EBMToxIlSuijjz7Kk/ZfyNJXHrjrrruccszcJJ27EydOOOUir7nmGrVt21b16tWTMUYlS5bU119/HegmXnDsm9A9e/YoOTlZGzdudNZkvuGGG7wetDwfdu3OM7Lms+Zc421XNdm3b5+TRIYzI9a5Y8iQITLG6LXXXvO7fuQff/yhqKgolSlT5ozlZRcvXuwkcpw4cSI3m3zB8DzfDhgwwCnlbv8UL15cjz/+uM9x+/zzz+uSSy7x2XbUqFHONt27d5cxRq+//rrcbjedyH706dPHqSiQvjMtfWfwd999p2effVaVKlWSMUYFChTQhg0b8rK5Qcflcik1NVWdO3f2ee6RTi/7cO+99zpJL6VKldI111yjmTNnas+ePc52iYmJmjJlCmvY+pGUlKSHH37Y6RT27HD0TGr56KOPnCo9dnKGv+QB+1zD+SJ77HP7sWPH9NhjjykuLk7GGEVFRWn48OGaO3eu9uzZozVr1qh///4qUaKEQkNDtXjxYh06dEjHjh3T1q1bmZ19jtIfvwcPHtTvv/+ujh076uKLL1ZISAjn77Ow+z9SU1P15Zdfavny5QFuUf7x4YcfOsv6XHTRRXrnnXck+V8m0zN54J577vFZAgj/ue+++5zZv9K5Dfq3bdtWxhg98MADLCWTTfZxvHbtWpUpU0ZxcXGqUaOGQkJC9Pzzz/vEd+TIkYqJiVG7du38DsTCW24kDyDzXC6XrrjiCmcZgvR9I/b5Z82aNapevbqeeOIJxcTEsJQPkEdIHMB5a9++fc6F2xijfv36eb1/zz33ODP2PEvQprd9+3Y99thjKl26NGu+55D0yQNt27Z1qjmQPJB5ngMUr7/+uooVK+Zkykunb5I6dOjgzB6jIyJ7znZsut1u/fTTT2rTpo3fAVZJmjZtmjp16nTGcw5Oy8l4b9myJTebGvSIde47efKkXnzxRSUkJKhUqVJ6/fXXneQB+1x++PBhNW7cWGFhYZowYYLzns1+8N24caNCQkJ055135v0XCUKeMXzhhRdkjFGDBg306aefauvWrZowYYLXrNP0yQNbt27V3LlzNXLkSH366adeM81GjRqlqKgo1axZUzt27Miz7xQsPBM2ztSZ5m/w9MCBAxo0aBD33lnwySefyBijIkWKOIManrEdM2aMs/yM/dOoUSMNHjxY27dvp6rDWaxZs0aPPvqoE7vhw4d7vW8PQjVo0ECfffaZ13ueyQMFCxbUY4895pW0gXNnn2eOHz+uV199Vddee63XMW6vk21XMvn8888l8cyZm9LS0rRhwwbt3r070E05r9n3dUlJSbr33ntVqFAhlStXjopdeWTPnj1q166dU63xnnvucZKgbemTB5o2bSpjjFq1aqV9+/bldZPPa263W//++69zvr377rud9zKbPGCflxcvXqy4uDjVrVuXajCZlJlr2uOPPy5jjMLCwnT//ff7JAa8/fbbKlWqlKpWraq///47t5p6wSF5IHelf060/zslJUVut1sjRoxQdHS0LrnkEs2aNctJBrUnKErSo48+qvLly0sSVZCAPETiAM5r+/bt81oja+DAgZL+qzTw8MMPOx29Z5rxsXPnTmZP+vH555+f8wAoyQPZYz982Z289913n6688krnJsjzJsmeIUXywLmz433y5EmNHz9eQ4cO1ahRo/TPP//4PAh7DrDeeOONWrVqlSRp4sSJKl26tOLj47Vt27Y8/w7BhHjnHWKddw4fPqw33nhDJUuWVEJCglfygO3VV191OhvswY30nn/+eRlj9NZbb0lixmpmjR8/XgUKFFCLFi3066+/Oq8PHTpU0dHRCg8PlzFGHTp0yNQ93+DBg5WQkKD4+HiWBDqDc+lMoyLPuWvXrp3Cw8P1/vvvS/rvHL9o0SJnyYiRI0dq+PDhuuSSSxQdHe0khKUfMIGv9evXq0uXLs6z5YgRIyRJM2bMkDFGV1xxhb744gtne8/z84kTJ/Txxx+rQoUKMsZwT56D7HNGamqqtm/frsGDB6tFixYqWbKkLr30UjVt2lTjx4931g2mOkzu4fydOZ5LDV522WWKjIzUbbfdpk2bNikpKSnArbvw2fHfu3ev2rVrp7CwMJUrV04LFy706YvyPFd89tlnuuyyy5zlqvAfO07r1693libt0KGD835WKg/s2bPHqZ757LPP5nhbLzSez/PvvPOOHnzwQd1yyy1q3769li1b5rU81c033+xUUHvrrbc0e/ZsLVq0SB07dpQxRiVKlOC55hyQPJA77Lja55edO3c6S37ZNm/erNtuu03GGF166aUaNWqUjh8/7rz/7rvv6qKLLlKbNm104sQJ7v+APETiAM576ZMH6tSpI2OMunbt6qyZxYUj6+zBjRdeeEH//PPPOe0jffLAzTffzGynLEhMTFS5cuXUvn17NW3aVJMnT5b0382V583rQw89RPLAObI7wBITE9WoUSOvWUwNGzbU6NGjfUpi/fTTT7rzzjudh6+bbrpJERERiouL07p16wLxNYIG8c47xDrv2PcZR44c0YgRI86YPGCfr0NCQvTBBx94zbh+5513VLx4cVWrVo0Z7lmwdetWXXnllbr44ov13XffSTo9S2Ho0KEqUKCAKleurFmzZqlmzZoyxqh9+/ZOp4Rnh4Xb7daWLVvUokULZ/bqb7/9FrDvdT7IzAAcnWl55+2335YxRjVr1nQSARYtWuQs/fDGG284227cuFEffvih6taty/k7C9InD9gJ6fXr1/dK+PL37yIpKUn/+9//NGXKlLxsclDKanlrf/H27DhGxjyvc57J58hdycnJuuWWWxQeHq4BAwY4EwDom8pZdjwzmrW6d+9ep0rjJZdcoq+//vqMyQOeE2f4XXmz47Z+/XoVLVr0nJIH7JiOHz9eJUqUYLmTs7Bjfvz4cTVu3NhZ5suOf0xMjB5++GF9++23zmceeOABFSxY0OvZv1ChQrr55pudBDv4d6bkOHsinMTzTk6wzxcnTpzQM888o+uuu06RkZEqWrSo7r33Xk2fPt25z1u3bp1atWqliIgIGWNUvXp1derUSc2aNXP6wVmKDch7JA4gKKRPHrj88sudBzMGqs/N22+/rXLlyqlQoULq379/tpIH3nzzTYWEhMgYc877yY9WrlzpHNPh4eHOchyeD7r+kgfKlCmjpUuX5nl7g1lycrKaNWum0NBQ3X777Xrrrbd0ww03KCYmRgkJCRo8eLDP7JANGzboiSeekDFGcXFxatCgATermUS88w6xzjuZTR44ceKEHnzwQaeUZPXq1XXXXXepUaNGCgkJYSZIJqQf7P/qq69UrFgxjR8/XtLpjoh33nlHMTExqly5shP/fv36KSQkREWKFFHHjh39Vh7YsWOHBg8erMGDB+fr5I2nn35aCxYskETywPnk5MmTqlu3rowxmjVrlhYuXOgkDdhVStLjWSjr0icPlC9f3klKks48mOT5b4HZ2WfXs2dPrwGPs/EcJPSML7H2zz4ek5OT9d577+nFF1/Upk2bAtyqC5t9jE6ePNkZWLXvtTlOc5bn+fbw4cPatm2bfv/9dx0+fNhru/TJA8uXLz9j8oC//8ZpOZE8IJ2eRcxyJ2dmH4MnTpzQVVddpbCwMHXp0kW///67/vjjD33wwQdq0KCBQkJC9NBDD3ktKfjll19qxIgR6t27t1544QWtXLlSBw4cCNRXCQqeVV9Xr16t9957T9OnT9cPP/zgbGOXyZd43skOz4SYBg0ayBij0qVLq0aNGs7YQcmSJdWjRw9nyY0///xTL774oi699FLn/jwmJkYNGzakrwoIEBIHEDT27dun/v37OxeQwYMHO+9RGv/cTJgwQRUrVlRUVFS2kgf27Nmj0aNH00lxDhYsWKDChQvLGKPmzZs7r2eUPPDII4/IGKMqVar4zCRGxpYvX64SJUro+eefdzp2du7cqWHDhqlEiRKKiYnxO8AqSd99953Wr1+v/fv353WzgxbxzjvEOvf561j0lzxw8OBBr20GDRqkyy+/3LlvKVWqlNq0acNMkLPw7HS3j81du3Zp/PjxOnbsmKTTx2716tVVrlw5r+U11qxZ46wRb69h6+9aefLkyXw92PrBBx84x6Vdlj0zHegZJQ98+OGHudbW/MSO79ChQ2WM0TXXXKOKFSvKGKM333zT2S79wBSDH+dm3bp1znJgxhiNHj3aeY/Bv5xhV9B44YUXvDrjkTPsQZDExES1bNnSmaW3cuVK+kfyQJcuXRQaGqq1a9dKytx5g/N15nkew++++65uvPFGFStWTIUKFVLDhg31/PPPe22fmcoDyJycSh7A2blcLvXr10/GGD355JNez+ypqamqWbOmihQpohdeeIFlULLB83rZoUMH57i2qzD27t3b2Taj5AGed7Lm5MmTaty4saKjo9W3b18dPXpUJ06c0Pfff6/HHntMCQkJCg0NVffu3Z3KAykpKTpx4oSWLl2quXPnauPGjSTEAAFE4gCCyr59+zRo0CDnAm/P0JZIHsgKz4faCRMmqFy5coqMjFT//v3PefYd8T8zfx0JdsfBwoULnUGObt26Oe9nlDzwxBNPUJL2LNJ3yowcOVJlypRxynfa8Tx06JDeffddlSxZUkWLFtWgQYOcBzKO6cwj3nmHWOctz6Vj0g/4ZyZ5YPfu3frpp5+0ZMkSbdu2zamWBP88j+/u3bvLGOPE3XOgv2fPngoNDdXs2bMl/Vdacu/evapSpYpeffVVVahQQcOGDcvD1gePX3/9VXfccYdzP21XMTrX5AFjjGbOnJlr7c1vfv31Vyep1BjjtRYzA9o5a926dV6VB4YPH+68R6yz7/vvv9fFF1+sChUqOAnmxDVn2OfixMREXX755YqIiNCDDz6oPXv2BLhlFz632620tDTVq1dPBQoUcO5T/F1D3W63Tp065ZXkiLPzPE/07dvXmXnaokULNWrUyGvihWcC9L59+5zkgXr16mnZsmU895wjkgfyxsmTJ3X11VerUqVKXs+Jp06d0lVXXeUk39lVNlJSUkhAyiLP6+Vll10mY4yuvfZajRgxQq+//rpzPrnrrrucz/hLHuB5J3Ps43Ps2LEyxqhLly4+FaP37NmjN954QwkJCSpevLgmTZokt9udrxP7gfMRiQMIOumXLSB5IOvSx2nw4MG6+OKLVahQoXxfujc32PFOSUnR1KlT/S4zsGjRIueG9cknn/T5rMTDWWbZMbM7HE6dOqUZM2aoQYMGknxL+h4+fDjDAVY6N8+OeOcdYp23PNfl69mzp2JjY9W1a1evbc62bAHOzejRo1WgQAHVqVPHp8T14cOHVb16dZUoUcIp2Wl3UMycOVPGGH3//fdeyxTQweZr48aNatOmjd/kgcyWaZdOL3kQERGh3377LVfbm98MHDhQxhjdcsstzms85+SO9MsWvPHGG857XCszz/O8Yf89JSVFPXv2lDFGLVq08Fo/GNlnL1cVEhKiAQMGMBs1j7hcLp06dUr169d3lpXJaDtJOnr0qOrVq6fFixfnZTMvCG+88YZz/vAsJ757924VKlRIxhgNGjRIp06dcq6R+/bt0z333CNjjMqVK8dSmmeR0XXO5XKRPJAL0sf7xx9/dKqkeW7jmTRgV1yTpE8++USrVq3Ks/YGO/t+JDk5WbfeequioqL0wgsvOAPZ+/btU6tWrRQaGupzfHsmDzz77LM872TRQw89pNDQUP3111+SfJ9j/v33X/Xu3dsnaQPA+YPEAQTcuaxdmD55oH///rnVvAuOZ4mmrl276uqrr1ZsbKwKFCggY4wKFy6sgQMHaufOnQFu6YXBjndSUpLat28vY4yKFSumrVu3+mzruWxBRskDODM7VklJSXr00Ud1/fXXq2TJkqpfv77q1avnPDikj6nnAGt8fLz69u3LUhCZQLzzDrHOW57r8l1zzTUKDw/XNddcoxkzZjgd83bMz5Q8wIB15qQ/bq+77jpdc801fjtnjh07psqVK8sYo48//th5ffXq1br66qt1ySWX6N9//3Ve53eQsQ0bNmSYPOCP5+/p66+/dv7Ocidn5hnPzD7rLFq0SJGRkSpYsKB+/fXX3GraBcnfAPbZpE8eeOutt3KreRekMw0eHT58WNWqVVNISIgmTpzIOTkH/e9//5MxRvfdd5/PUhBbtmzR+PHj9eKLL2revHkBamHwO9M5+5VXXpExRm3bttXff/+d4ed69OhxxgQD+Pf333+ratWqqlChgs910E4oaN26tVavXu3z2b1796pVq1ZeVWTgy76vO3XqlLZv36558+Zp5cqV2rhxo8+2JA9knx2rlJQUpz9w+/btKlasmJo1a+Zs17BhQ79JA5JUrlw53XzzzSTiZYHb7dabb76pAgUK6KGHHnLK4m/atMlJMmrbtq1iY2NljFG7du2cz3peW3neyZy0tDSdPHlSl156qYwx+vLLLzPc9tdff1VERIRTZZB7ROD8QuIA8pTnRSAtLc3rxsnfNmeSPnlgyJAhOdvYC5D9AJuUlKTLLrtMRYsW1e233665c+dq/Pjxat26tYwxKlCggAYMGEDyQDZ5DjxdccUVCg8PV9u2bbVp06YMH7BIHjh39rkjMTHRmQFSrFgxZ02y9ElG6TuCDh8+rHHjxik8PFzly5fnweAsiHfeIdZ5y473yZMnde211yoiIkJ9+/b1uz5zRskDw4cPZz2+czBhwgStXbtW1atX19SpUyX5vy8cNmyYIiMjdfnll2vw4MF6++23VaVKFRljNH78+LxudlA7U/JA+vt224ABA3xmZuO09EkC9v3euZTetBNO77vvPmYSZyB9koC/++VzTR54+eWXc6ydFzLP+LZo0ULPPPOM1q9f77XN9OnTVbBgQbVo0cLpsKeaQ/bZ1RzSx3vw4MGqVauWcywbY/Tqq68GqJXBy7Nq4CeffKLVq1d7He/ffPONatasqejoaA0cONDvxID33ntPcXFxuuGGG7Rv3768avoF4YsvvpAxRkOHDvV63V66tGXLllqzZo3zur0cis0zUZrBKF+eE1wefPBBXXzxxTLGKDw8XJGRkerbt69Wrlzp9ZmMkgfoqzo7+5p3/PhxNWzYUB06dNCWLVu0a9culSlTRhEREfr444+dSgPPP/+8T9JA3759FRYWprFjx3JMZ8GRI0fUuHFjlStXzjkvbNmyRQ8++KBTSl+Sli5dqoIFC8oYozvvvNP5vL0kJLLmvvvu83o2T3/M2uega6+9VsYYqjkA5yESB5Bn7Bulv/76y2vtpuPHj6tEiRLq3bt3lve5f/9+9e3bl9k4WXDq1Cnde++9MsZowIABPustv/766ypZsqTzAMyyBdlz8uRJNWrUSBEREerfv3+m1rf2TB54+OGH86CVFwa32y2Xy6Vu3bqpaNGi6tWrlw4cOKDFixc7D1nR0dEaPXq085n0nZaHDh3SxIkTfToe4It45x1inffcbreGDBkiY4x69OjhdDKcaVDKTh646KKLFBoaqrfffpuBkSwYNWqUjDGqU6eOihQpogULFmS47fr169WpUydnhoIxRoUKFfL6N0CHWuadLXnA87i3S+jHxsbSwZOO/e99+/btXgMWx48f1yWXXKLXXnstU/ux4z1//nwlJCSoQoUKJPP64Xl+TT/zrlevXho4cGCW97l+/Xo98MADMsbozTffzGYL85fnnnvOOYdcffXVGjRokHMs79y5U82aNSPhKAelpaWpe/fuMsZo2rRpSklJ0XfffadGjRrJGKMyZcpowIAB6tWrl/N7+f777wPd7KDhOajaoUMHGWNUs2ZNbd++3ev+Yvjw4SpSpIiio6N1//33a8GCBTp8+LD279+v5557TsWKFVOJEiW4/z4HEyZMkDFGY8aMcV6zJw61bNnSq/8vJSVFDz74oCZOnOizH+4HfdnXT8813y+99FK1adNGN910k3POuOaaazRnzhyvz3omD7Ro0SIArQ9eKSkpuvXWW2WM8aoCOHToUIWEhKhgwYIKCwvzu/TMe++9pxIlSuj666/3qqwGX+knaR06dEjDhw93qu8cOXLEOZd07tzZ2e6ff/5RgwYNnIkZnsuFIetGjBghY4yKFi2qn376SZL/Z8vatWurWrVqmeorB5C3SBxAntqwYYOMMbruuuuc1+rUqaPw8HD169fvnMpcHThwgNmTWfDvv/+qWrVqqlKlihNvz/XLpNMPwCEhISpcuLAGDx5M8sA5sB9QX375ZRlj9Nhjj/ncCO3fv1+ffPKJ5s2b57Vmn3S6RK39wMaDwZnZx7Ed8zp16qh9+/Ze8f733381dOhQhYaGKiYmRqNGjXLeSz+wR+fCmRHvvEOsA8flcqlZs2YqXry4Dh8+7Lx2NkeOHNGQIUNUrVo1Oomz6I8//nBKGoaFhTkdxRndG27ZskUff/yx2rdvr5EjR2r58uXOeyRsZF1GyQOeM+XtpIG4uDi/ZWwhrV27VuHh4br33nud12rXrq2IiAi9+uqrWZqVd+DAAZUpU0bGGBIHzqBJkybq0KGDE9snn3xSxhi1b99eBw8ezPL+1qxZoy+++CKnm3lBc7vdev755xUSEqLy5cs7VZGuuOIKLV26VKdOndI333zjVExasWJFoJt8Qfj444+dc3b16tUVHR2tuLg4Pfroo16z2+1kmPnz5wewtcHDs2pg/fr1FR4errvuuksbNmxw7qU97zOGDh3qzNY2xqh06dIqUqSI83vhenlupk2bJmOMBg0aJOm/akfpkwYk6Z133pExRh999FEgmhqUUlJSdPvttyssLEyDBw/2uj/56KOP1KRJE4WEhOiqq67SsmXLJP33b8Pu1zXG6J9//glE84OGZ1x///13JSQk6Omnn/Z6nv/ll1908803KyQkRJUqVfJZ1mTIkCEqVqyYypQpw/NlOnZ87ecVuzrA8ePHvRJIDx8+7CRj/Pnnn7rooot0ww03OJ+z/2zfvr1uuOEGju9MONvzdlpamlPVuEGDBl7JA7aRI0fKGKN7772X5TSB8xCJA8hTGzZsUMmSJZ3svRo1aigyMlIvv/yy3xLAyHkrV66UMUZXXnml3G63V4ew54W/Y8eOzgy+QYMGafv27YFoblA4U+mq1q1bKzY21qvj0u1264033nBmhBhjVKNGDZ/yyp9//rn++OOPXGv3heTYsWO65ppr9MYbb+iKK67Q2rVrJXkPeBw9elTDhg3zO8BKeb2sId55h1jnPbfbrXXr1ik0NFTVqlVTamrqGdeRTD+wffTo0XMarIK0efNmXXHFFTLGqHz58k7inOdxfLYEGJIGzl365IHFixc779kd9rGxsQyCnMHChQu9qkbVrFlTkZGReumll7L0rGMfx4sWLaKywxl8++23TuWR3r17OzOw27ZtmyP30JxPzs6O0alTp9SgQQNVqlRJn376qXr16qWEhARFR0erffv2+uOPP/T666/LGKNnnnlGqampxDcT/F3zPK+J77//vmrXrq0SJUqoffv2+vzzz31mqt5www1KSEjwW0of/p04cULXXHONIiIi9MILL5x1JuSSJUv0/PPPq3z58qpQoYKaNWumIUOGkPSVDWvXrlVsbKwqVKjgnNtbtGjhkzSwfPlyVaxYUZdddhmDqplgn1MWLVqkqKgotWzZ0jlneD7vLFu2TDfeeKNCQkLUo0cPpwKeff757bff6Ks6CzvWSUlJmj59ul577TVFR0fryJEjkrzP5fPnz1fjxo0VGhqqYsWKqWPHjurUqZMuv/xyGWNUsWJF7r8z8P3336tdu3bavHmzpNOJ/LVr11ZoaKgWLlzos33fvn1ljNGwYcMkeffnVq9eXcOGDdPWrVs5vs/Acym2devW6aOPPtKECRP0448/ej2/r1ixwun3jo+P17Rp0/Tjjz/q2LFjGjJkiEqVKqVSpUrp77//DuTXAZABEgeQ53bt2qWqVavKGKPQ0FCvLEA6D3Lftm3bVKJECVWvXt15QPCMu33zOm/ePEVHR6tSpUoyxuiVV15hAMqPVatWqUePHvr444+9Xne73UpKSlLVqlVVoEABffPNNzp69Ki2bt2qm2++WcYYlShRQu3atVOzZs0UFhama6+9lsGmczR8+HAZY1SyZElFRkZq9uzZknw729IPsI4dOzYQzQ16xDvvEOvA2L17t0qVKqVatWo5r6W/R7GviatXr1avXr3ytH0XCs+Y2sf0X3/9pQYNGjglUu1Zkxndg1BJI2elTx748ssvNWzYMJIGMikpKUkrVqzQRRddpJCQEIWGhjqzJSWedXJaUlKSPvvsM+d5xRijNm3aMEMsF/mrAmMPNn311VeKiopS165dJZ0e+GvXrp2MMYqMjNRtt92mqKgoFS1aVD/++GOetjsY2dc9l8ulxMRE/fnnnz5rXkunyzB7VhjwNGrUKCd5gzLAmeO5XFX37t19EjGOHj2q+fPn6+uvv3ZmUNqOHTvmsz38O9v10OVy6a677nL6DZs1a+YzuLRixQo1bdpUhQoVotpAFtmVMe3lHezfh+d99axZs5z4e56zuZfJvNTUVDVu3FjGGLVu3VoNGzaU5FtZUDr9TPnss88qJiZGkZGRMsaoVq1aevzxx0n8ykBSUpKuvfZaGWN0zz336Pfff9ell16qyMhIvfLKK36Tdvv16ydjjEaOHOn1+siRIxUTE6NJkyblUeuDk33sJiYmqn379oqLi3PuwYsWLap69erp22+/lXT6+F++fLluu+02Z5vw8HDFxMTIGKPKlStrw4YNgfw6AM6AxAHkubS0NK/OnRYtWjg3SwxM574DBw6oevXqMsaoX79+zuv2zb/9548//qjChQvriSee0CWXXKLff/89IO09n40cOVKVK1d2Kjhs27bNec9zqYLw8HDVr19fjRs3dgb/OnTo4MxA+O2331SlShUZY+iQP0epqanq2bOnc16xs4eljAdYo6OjZYzxqfSAsyPeeYdYB8aBAwecsrOjR492XvdXovaee+6RMcZnBhR8ZaakoXQ6eaB+/foyxqhJkyZnTR6Af/46JTPDM3kgPDycpIEsOnTokMqVK+ectx955BHnPY7h3OHZIdmxY0fn9XNZBg+Z07dvX5/O9b179zrXxClTpjivT5gwQTfeeKNCQ0Od31PDhg2VnJxM8lcG7GP3xIkTevbZZ3XVVVcpNjZWNWvW1Msvv6x169Y527pcLr/9KaNHj1bp0qVVoUIFZvP5sWbNmgxLI99+++2KjY3V8ePHvV5/6623nEHAqKgoVaxYUZ988onzvr+ESPjyPE5//fVXffXVV/rwww/1/fffew30HTlyxKlEddFFF2nNmjXavn279u3bpw8++MDpi3nrrbeczxD3zLEHT+1ny4yqe915550yxngd58i81NRUPf/88ypbtqxz/fv++++9tkl/zP7111/68ccftWTJEh05coTqvGfgcrn01VdfObPaY2JiFBERoTfffNNJlkv//Dl58mQZY1S8eHHNmDFDe/bs0ZAhQ1SyZEnVqFGD5NMzsGOZmJioevXqyRij66+/Xi+99JJat26tOnXqOMmic+fOlXT6fiYpKUkvvfSSWrRooYoVK6p58+bq378/lY2B8xyJA8hzH3/8sS677DJ1795d1apVkzFGrVq1ch7K6FDLvow65u3Yzpw5UwUKFFDJkiX1zjvvOO97libr2bOnLr74YiUnJ/ud2ZDfPffcc05nwccff+yUY0pv48aN6tGjh7POYfPmzTVjxgyfmNasWVN169bloSATMpr1m5qaqqeeesp5IPNcx9PfAOvgwYNVvHhxSpCdBfHOO8Q6b2XUsWjHfcyYMYqIiFCDBg20aNEi533Pa+XIkSNVqFAhtWvXTkePHs3dBgc5z/u71atXa+LEierWrZtmzpzpNdPAM3nA7iwmeSDrPNdoHjJkiH744YcsfX7Dhg1OZ3FcXBzl8rPgnXfeUenSpXXvvffqoosukjFGjz76qDNAxUy9nLVkyRIVLFhQ11xzjUqVKiVjjB577DGnw5hzRs4bPXq0c0/StWtXrVq1yrk2rl69WlFRUWrUqJH+/PNP5zObN2/WpEmTnFmUCxYsCFTzz3ue52+7Ak/p0qV1+eWXO9VMGjdurK+//trns8eOHdOOHTv00EMPqVChQipfvjxJX348//zzMsZo8uTJXueItLQ0HThwQOXLl1eBAgX0yy+/KDU1Vdu2bdMtt9wiY4yKFSumli1bqmnTpjLGqH79+tq/f38Av01w8bwGvvLKKypdurSz5EyhQoXUoEEDrVy50innvnPnTmdQsFChQipRooRKlCghY4wSEhL07rvv+t03/LOff0aMGCFjjK666iq/lUjtc/rjjz8uY4xXnJExf8dgSkqKXnnlFWey0GOPPaZdu3b5/TyJL+fmiy++UHR0tEJDQ1WzZk399ddfkryXd7S5XC5n+RNjjLPE2EUXXcTzTiakpqaqbdu2MsZo0KBBTh92cnKytm7d6iSQRkZGauXKlV6fdblczrmd8zVw/iNxALnO38XALum2efNmZ/b7rbfe6pU84O9zdPycnT074dSpU/rrr7+0fv16n7JWO3bsULdu3RQREaHSpUtr0KBBXrF99913ddFFF6lly5Y6ceIEN6/p2B0NrVu31s8//+z1nr9YJSYmavPmzU65pvSGDh0qY4yeeOIJpaSkEO8z8BxI/eqrr3T48GGv91NTU9W7d29noMNzTbP0cT127BhLQ5wF8c47xDpv2dfKtLQ07d27Vzt27NCBAwe8tlm/fr1atWolY4yuvvpqjRs3zuv9YcOGqUSJEszkywTPe7qXX37Zq6ShMUaXXHKJ1+xUf8kDN954Y4almOHfiRMndNlllzkdvlntoFm7dq06dOig9evX51ILL1yrV6/WsWPHtGTJEqf6wGOPPeaVPODv90En2rn55JNP9Ntvv+mLL77IMN5nWysembd//35NmzZN5cuXlzFGlSpVUvfu3XXo0CFJ0htvvJHhQNO6dev03XffSWKAxB87JidOnND111+v8PBw9ezZ0+lonzt3ri666CKFh4frsssu8+mUX7lypQoUKOD0r9iDJ/D26KOPyhiT4dJe/fv3dypjtGzZUmXKlFFERITatm2rnTt3yu12648//lCNGjUUHR3tVXkQmWP3qZQsWVJdunRRixYtnGpf5cqV03vvvefcm6elpemll17SXXfdpdq1a6t+/fp65ZVXtGLFCmd/XD8zxz7H7Nmzx6kE27dvX+d6mb4v9tZbb1V8fLzWrl0bkPYGE/ue4tSpU/rll1+8qh7ZyQOlSpVS0aJFNWLECJ9nT2Sd2+2W2+1Ws2bNFB4erjJlysgYo7vuuss5L/s7Nxw8eFCvvvqqatSooaZNm+qJJ57Qli1b8rr557W+fftq2rRpkv6Ls3S6SkxsbKyaNm3qJGakT9C49957ZYxR9erVnUq79n78LYsC4PxE4gBylX3jlJKSoqlTp2r69Ok+26xbt041atTwSR6wvffeexo4cGBeNDfo2fFOTExU27ZtVaZMGUVGRiouLk6jRo3yympdu3atHn30Uadj4fLLL1fLli3VpEkTJ3ub2aq+PvzwQ2cQyXOGpOcN0Nl4PkC89957Kl26tKpXr06Hw1nYcUtKStIDDzzgzEJNX6UhNTVVTz75pLPG1pkGWJEx4p13iHXesq+VSUlJuueee1S1alXFxsaqVq1amjlzplcsly9frhYtWjgzoRo2bKjrr7/eKcN30UUXsS7fWXjG85lnnpExRjVq1NCECRM0e/Zs55guXLiwRo0a5WzrmTzQsGFD516Fyjxn5hnvV199VcWKFVO/fv3Oec1lzwob8HW2c+/Jkyc1b948v4PZtqlTp/qsswr/7HhnFPeUlBTNnTs3w+QBSfrll1/8PpPi7NIv/fDrr7+qf//+TmWNqlWraurUqVq8eLFuvPFGFSlSxEk88vesxL2L/xikpaWpR48eio6OVq9evZzz999//+10xtsDfvXq1fMaPJVOLw3x/vvvMyjlR/rqR9LpvpMFCxZ4nZv/+OMPde3a1UlwvPHGG/Xhhx/6VA2sWrWqLrnkEq6VmeD573/16tWKi4vTXXfd5Sz1dfLkSe3fv9+pdlSqVCl99NFHPv9Gjh8/7nMvyLnEV/rEOH+V7caOHau4uDjFxcXp2Wef9bk/GT9+vMLCwnTjjTeSlH4Wns/zXbt2VXR0tJo0aeKVuJiSkqLXXntNxYsXV7FixfT2229zns4hkyZN0qhRo7R8+XKnUs8dd9zhDFrbx3/6fxf//POP3G6338oE+dmsWbNkjFHZsmW1efNmr/emTJnitcyJ5/XPjvO///6rBg0aqECBAvriiy/yruEAchSJA8g1njdO7dq1U2hoqGJjY/12sK9fv95JHmjZsqVz4ZkyZYpKly4tY4z27NmTp+0PNvbNaFJSkjM7r3Llys5Nk10m1TP+W7Zs0QcffKDy5curUKFCTsZ3o0aNSBpIx46vXXbpq6++8nrP82H1r7/+0pdffqkFCxZ4VRmwb0bt8kzdu3dXXFycSpYsSQnJs/As2dmwYUOFhobqlltu0YoVK/x21HiWdo+JifEqh0rHwtkR77xDrPOWHaPExETnWlm2bFlnhpMxRi+99JJXydk1a9Zo2LBhKlOmjAoWLChjjGrXrq2HHnqImQlZYC/9cNttt+mXX36RdPpecfjw4U7p6vSz/zyTB6pWraohQ4YEpO3BwrPqVGJiopo3b65GjRo5cWQ2Xs6y45qWlqbdu3friy++0Lp163zuoZOTkzV//nxndvZjjz3mnIsmT56sMmXKKDY2looaZ+HZ2XvixAnt3r1b+/bt84lbSkqKT7ztgdcffvhBzZs3lzEmw0pg+E9mzhknT57U5s2b1aJFC4WFhalw4cK6+eabdfPNN6tIkSJ69NFHndny8C/9QMa6detUqlQp3XDDDc6SG5s3b9Z9990nY4weeeQR7d692ymdf/nll2vZsmVe++B8n7Hk5GTn76mpqU5lnqlTp/oMnK5du9ZnPXLb66+/LmOMnnzySaWmpnIffgaex+O+ffs0c+ZMFSpUyKt6o2f/4YMPPihjjC6++GLnHJ8+vsQ7Y3Ysk5OTNXv2bD333HN68MEHNWTIEC1ZssQ552zdulXPPfecYmNjnbXKJ06cqAULFqh79+4qVqyY4uPjtWnTpkB+nfNe+uf58PBwNWvWTAsXLnTO4fa/gZSUFL3++usqXry44uLi9NZbb5E8cA48kzFsdoxXr16t+vXryxij22+/XTt27JDkPSveThxLvz+cduTIEV1//fUKCQnRc8895xzH0ukl2Ywxuv/++zP8/KlTp5zz+Ouvv54XTQaQC0gcQK7wvHGqX7++IiIidNddd2n79u0+mXz2BXr9+vWqVauWjDGqW7eu2rZtq+joaMXHx1MiNZPcbrd69OihmJgY9evXz8kKnjRpkpOY0blzZ5947tmzRz///LNmzZqlP/74gxvXDOzevVvR0dEqW7asDh06pNTUVK+OnuTkZD3zzDPOcWxXbujevbuzTVJSkj755BNdcsklzqxiz/U/4cuzZGeDBg0UERGhZ599NsPZk54PEfYAa/HixTV79uw8a3MwI955h1gHhsvl0sMPP6yYmBj17dtXe/fu1aFDhzR8+HAnMaB///4+g1G7d+/W2rVr9cUXX+jQoUPnPIM7P1q3bp1q1qyp2rVrOx3wycnJGjZsmAoUKKCLL75YgwcPdq6dnpUH7M5Pz4EnOncylpiYqMsuu0y9e/dWw4YNNWbMGEneAyXIPs8Bjscee0w1a9Z0Erri4+P10ksv6Z9//nG2t5MHKlSoIGOMmjVrpocffliFCxdWXFwczzpn4Xm/PWXKFLVu3VolSpRQ2bJlVa9ePb355pteS/ykT9Zo06aNJkyY4AwQDh06NADfIrh4VhdYuHChhg4dqrZt22rIkCH69NNPfbZPTk7W5MmTncFs+6dcuXLO0gT4T/fu3dWjRw/nvz2P8SVLluiKK65wZmLv2bNHPXv2lDFGDz30kLPd8OHDZYxRRESErrzySmb1ncGZ4t2jRw+nSuMHH3yQ4f0dVQOz79lnn1Xjxo2dJQck76QC+++pqam65pprnDL66SdqIGOeVUibN2+u0NBQr3NyXFyc7r77bmfAdcuWLXrrrbec+xPPn3r16rHmeyadPHlSjRs3VkRERIaVvjJKHhg5ciTJo5mUPtHOXyW6tLQ0ffvtt17JA57L937wwQcqWbKknnjiidxubtA6deqURo8erejoaFWvXt3rPPDtt98qMjJSdevW1e+//+7zWXvMZ+LEiTLGaMSIEXnWbgA5i8QB5JqTJ0+qUaNGCg8PV//+/Z0MtTPd8G/ZssWZIV+wYEFdccUVfi9E+E/6kpF169ZV+/btfbLlFyxYoCuvvFLGGD3wwAOUVj4H//77r4oVK6bSpUvr33//dV4/evSo5s2bpxYtWsgY41TXSEhIUHh4uIwx6tatm7P9ypUr9cQTT+jtt9/22g8y5nK51KtXLxlj9PTTT/s8iLndbn333XfauXOnV0xTUlL09NNPyxij8uXLKzExkU6HTCDeeYdY5430SYuVKlVSp06dfOI9efJklSxZ0kke8Kw8gIylP/bS35tMnz5dxhinPLjdGVGkSBFVrlzZibNdgjkqKkqjR4/2u3+O8zPHe+bMmc5AkjFGPXv2zOPWXfjsjt/ExERdfvnlzhqeN910k9NJaYxRx44d9eOPPzqfS01N1VdffeUkGURFRalOnTp0yp+F56CSvdxJaGioqlSpolKlSjnxvv/++/XTTz8526akpOjzzz9X1apVnc9ERUV5LQvBrGz/PDvmH374Ya+qMPZPjx49fBJe3G63kpKS9Prrr6tixYrOtt98801ef4Xzmp38ad9r2Dzj/u233zrP8wsXLlThwoV11113ee1ny5YtKlasmOrWrStjjBo1auTTB4CM4+05U/WFF15wnuE/+OCDDAejEhMT1aNHD6dqIOfvzNu6dauuuuoqZ3JFjRo1/C4/Y9/T2PeOd999d0DaG4zSVyENDQ1V+/bttWDBAk2cOFF33323SpQo4Uxgsf8NpKSkaMeOHXr++ef12GOPqUuXLvrggw+o+ppJbrdbb7zxhowx6tq1q3MeTj/ILfkmD9j3Me+88w73JGdhnxtOnjypsWPH6qGHHlLdunV1//33a/jw4UpLS3P+DaRPHrjpppu0adMmvfnmmypTpoyKFi2qdevWBfLrnPcOHDigRo0aOcs+2LZu3erE1XM5Jcn7unr//fcrNDTUqYjEMzwQfEgcQK555ZVXnBun48ePe723b98+zZ49Wx9++KF+/fVXrwvIiRMnNHfuXH355ZfcqGbSsWPHdOutt+rTTz/VpZde6pRdOnXqlNfN58KFC50L/AMPPEB5/Cw6deqU6tWrJ2OMrr76av3000/69ttv1alTJ6dzLDY2Vu+9955++OEH/fDDD3r55ZcVFRWlqKgozZ8/39lXYmKiz8AKMpaSkqKGDRuqYsWKPg9UEydOVOvWrZ2S423atHFm6EinO+qff/55HgyygHjnHWKdd44ePap27dpp9erVqlevnnMNTEtL84r9lClTnE61/v37U4UnE1JTU3X48GF99NFHft9ftWqVRo4c6SRwLF26VBUqVFD58uWd8pHS6eUM7AE+Y4zeeOONPGl/sDl06JCWL1+uF1980e/7Y8aMcdZ4b9CgAcmiuSA5OVktW7ZUWFiYBg4c6HSaHT9+XFOnTlVCQoKMMWrfvr3XLCdJOnjwoN577z19+umn2rVrVwBaH5yGDh3qdF7aywz89ddfGj9+vIoUKSJjjO666y6fpPNdu3apV69eGjp0qBYtWuS8Tge9f57P5W3btpUxRk2bNtXcuXP1zjvvOEmLxhjdeeedztIz6TuEV6xYoX79+umzzz7L0/af73799VdFR0crJCREISEhMsZowIABzvv+lqlq0qSJChYsqB9++EHSf8mQ27ZtU0xMjCZOnKhHH32UyiV+nC3entV4nn/++QyTB06ePKn58+c7iUiNGzemfHs6mRkU+vrrr9WmTRvnHDJu3Difz9t//vDDD859TPp7dWQsLS1Njz/+uIwxeuGFF7zOKUeOHNHnn3/uVONp27YtfVI5pE2bNipUqJD27t0r6cz3GPYxnpqaqoEDB+riiy9mqVg/3njjDa1cuVKSd1Xjxo0bez0v2j+33367VqxY4Vwj09LS9N133znVS+yk6osuuohnowzYx63959q1a52lTDyT+mfNmuXE/emnn9bff//ttZ/x48erUKFCuuqqq5iIAQQxEgeQa+644w7FxsZ6lel0u90aPny4rr76auciU7duXY0bN05ut9tvRib+k9HDWJ8+fWSMcWZJzpkzJ8PPeSYPPPzww1q7dm1uNjmo+ct8nzt3ripVquQkCRQoUMDJmm/Tpo1Ph83+/fudh+P3338/T9sf7Ozzgdvt1qZNm2TM6XXF7XLV+/fv15133iljjAoXLqxatWo5CRxdunRRamoqHQxZQLzzDrHOXePGjfNZ61eSOnXqJGOMSpcuraioKK1YscLr/YySBwYNGsQD7xn8+eefGjBggKpUqSJjjDp16uR3O88y4p07d1Z0dLSWL18u6b8ykz///LNq1arldHiSOOBr1qxZuu222xQWFuZ0Cts8j+FRo0apZMmSCgkJUf/+/Z3lq5Az5syZo8jISLVp08YZeEp/v126dGmfQSqedc7N2rVrVb58eVWsWNFvZ+/ChQudMst9+vRxXs8o3lxDz85O1HjggQd8Elzmzp2rypUryxijBx980Omkd7vdXrG1B6woM/6fAwcOqGHDhipUqJBatWrl9IkMHDjQ2cbzuD106JAqVaqkUqVK+ZTE79evn4oVK6YdO3YQ3wxkJt6ZTR5Yvny5unfvruHDh1M10I+UlBQdO3ZMS5Ys0WeffabFixfr4MGDXmtjS9KyZcuc55wGDRp4LbHhWSFsxowZMsboySeflMRs1bOx47Nv3z7VrFlT1atX19GjRyX5Vl5bunSpSpUqpQIFCmjWrFmSTl8XqfCVMc9zgWdsXC6XduzYoZiYGCUkJOjw4cM+8faUmprq9fnU1FSS1P2wl+fxXPbBXt4xKipKXbp00Z9//qmlS5dq3LhxiouLkzFG1157rZYvX+51L/Lvv/+qU6dOat68uR588EGfQe78btq0afrss8+cPm/7HsTlciktLU0DBgxQeHi4br75Zq9k6EmTJjnX1KuuukrdunXT//73P3Xu3FmFChVSsWLFqCANBDkSB5ArUlNTVbduXRUtWlQ//vijDhw4oK1bt+qmm25yBrjvv/9+NWnSRBEREWrSpIlPVQKcnX0zdPLkSWdGSFhYmF588UWf2QrpOzPt5I3HH3/c78wG+O9UPHLkiKZNm6batWvLGKPo6Gg1bdpUM2fO1O7du53PeXacde3aVcYYDRs2LE/bH0x+/vlnzZkzR6+//rpmzpzpdOB4ZsDfcccdio6O1sMPP6yuXbs6HZY333yz/vrrLx06dEizZs1SwYIFVa1aNb8lJnEa8c47xDpv9ejRQ8YYvfbaaz5xOnHihFMitWDBgpo6daok7w769MkDZcuWlTFGr7zyCgNNfqxcuVKXXnqpjDG65pprNGTIEH322WdeHWbpOx7//vtvRUZGql69ejp+/LhX/N98801FRUXpn3/+YSaIH4MGDVJMTIwiIyPVp08fzZkzR9u3b/faxvM4HTNmjGJjYxUSEqLXXnvNSU5C9j377LMyxujDDz+U5N3JZrMHPaKjo51Z2Tg38+bNkzGn17qW/juveMZ7zpw5Tgfml19+GZB2XkhatGihuLg4p5M4/YzfBQsWOLP3PGcNI2P2cTt//nwZY/Tss8865dgzSh44deqUs9zg8OHDnT6TsWPHqmzZsmratKmOHTuW598lGGQl3mdKHvAsw3z8+HFmaPuxdu1aPfLII15LlNjL+Nx5550+93TLly/XrbfeKmOMbrjhBn366ade73/zzTfOEqYLFizIy68SFFJSUvTPP/9o8+bNPu999913MsaoVatWGX4+MTFR3bp1c2YL48xWrVqlnj17asyYMX7fP378uKpWrarixYs7ibrpnxvtc/ratWt1++23nzG5IL978sknnSpSdmUXl8vlLFfVp08fn4SkX375RQ0bNnSOffu8nf73wPnbmx3rGjVq6OGHH/b7rPj999/r4osvljFGb775ptd7M2fOVO3atb2WtAoLC1ODBg1IGgAuACQO4JycaZDffkAbPHiwwsLCdNlll+maa65RfHy8oqKi1LFjR6cKwaZNm5yHC881KeHt999/14IFCzR48GB98sknWrNmjfOe/ZCbnJzsVdLa31qSnp33c+bMUdOmTVmuwI+lS5dq2LBhuv7669WjRw+/pZePHTumVatW6eeff/a7D88b1Ouuu04lS5akukMGRowYofLlyzulxkJCQtS0aVNnbTj75v7TTz/1qlZy1VVX6b333vO6uT169Kji4uJ0/fXXB+KrBAXinXeIdd7y7GRIv+asHesTJ044HfBVqlRxEr48OxE8z9/vvfeeqlatyrXSjy+//FLR0dEqXbq0hgwZkuF2/hIHQkJCVK5cOe3cudN5/ZtvvlHdunXVrFkzr84gEjZOs8uDX3/99U6lBptnjNLHa+zYsSpatCjJAzmse/fuMsbogw8+kCS/s/TcbrduuukmRUdH+1Q4QdaMHj3aa6DPM+HIM/YPPPCAjDGaMmVKXjfxgrJz506Fh4erevXqOnnypFeSuWe83377baeTPiUlhfN1Ju3cuVOXXXaZIiMj9euvv2ry5Ml+B7PtgaUpU6YoISFBMTExatiwoa6//noZY1SiRAnKW2dCZuOdUfLA1KlTfQap8J9ly5apTJkyMsboyiuv1B133KH27durfPnyzmBSkSJFNGPGDK8kjBUrVjhVIOLi4vTII4/ovffe04svvqhq1aopLCzMZ5AK0uTJk9W+fXsVK1ZMRYsW1d13360ZM2Y4zzI//fSTjDGqVKmS/vzzzwz3Y5cab9mypdexD28jR450KhpdddVVfgdDXS6XbrjhBhlj9MwzzzjJ6/6SHLt06SJjjM+9PE6zn+fbtWvnc3277rrrVLp0aedZJn3S7i+//OIsFea5nBvVM/zbt2+fmjdv7pyDCxYsqEqVKumjjz7yekaXpHfeecdZ7iF9ZcdNmzZpyZIlGjx4sF599VV9+eWX2rdvXx5+EwC5hcQBZNlLL72kZ555Rnv27Dnjdps2bVK3bt100UUXOTeks2bNcspl2WrXrq3LLrvMGUiBtxEjRjjlf+2fKlWqeHWI2Z0KycnJuuuuu5wHBX/JGJ43TTwA+xoyZIizTqrnz9ixY51t/JU9zajT/pVXXpExp9e4TX/sQ+rVq5dzo9qlSxc1bdpUpUqVcs4Z6Wetbt++XZ9++qlmzZqllJQUn4eAgQMHOrOD7c/gP8Q77xDrvOXZyZDRmrOeyQN25YFatWo55SEzSh5goNXX999/r7i4OF188cWaMWOG87rL5fKK3Z9//qlvv/3W5x7P7ii+88479fXXX2vq1KmqX7++QkNDGfDzY9iwYTLGqHXr1j6z9jzvST777DMtXbrUq2NeInkgN/Tr10/GGDVp0sTvUib2v4N77rnHK8EA52bChAnOOdtfvO1/B/379/daroCB7HOzb98+Z1DKX0lfO67ffvutIiIiVKtWLaohZZG9FIR9X2cnx6QfzJakvXv3aujQoapRo4ZTvbFp06YZ3u/AV2bjnT55ICoqSsYYTZ8+Pa+bHBRWrlyp6OhoXXzxxRo5cqTXe7t379aoUaN07bXXOtW+Jk6c6PUMtGLFCt1xxx3O7+Kiiy7SRRddpHbt2ul///ufsx3n8tPse4/w8HBFR0c7catVq5befPNNnTp1SkePHtWVV16p6Oho5x7dM372884vv/wiY4zuueeegHyXYPDss88qMjJSFStW1MyZM3Xo0CGfbezYfvrpp86z0aRJk5xziWfi3ZgxY1SkSBHddtttXku44TR7Cd57773X63nH7XZr586dzv1GcnKyT+UAu2/kww8/dPpb0i+/AV/jx49XsWLFVLduXT311FOqV6+ewsLCdMstt+irr77y2vbee++VMUb3338/y/UA+QSJA8iSAQMGyBijUqVK6aWXXjrrxeLw4cP6559/tHr1ar/v2x2hjz/+uJKTk7mop9O7d29nNsFjjz2mu+++W3Xr1pUxRrGxsV5l2+wOs6wmD+A/9my+mjVraurUqZo+fbrzcFayZEmtW7furPvw7MB/7733VLx4cZUvX551tPywB/pat27tVG7Yu3evPvzwQ5UtW1YFCxbU999/LynjNWo9HxjGjx+v0qVL69JLL/XJkAXxzkvEOm+daWZC+k4Fu/Pm5MmTWUoewH927dql66+/XqGhoZo4caLzevp4rV+/Xq1atVKDBg00ffp0r+SBL774wqn8YP+EhITo7bffdrbhXuW0FStWqESJEqpYsaJXxSnJtyx+jRo1VKdOHc2YMcMnWcNOHoiKitLAgQNJZsymDRs2qGrVqkpISNC4ceOcamxut9vrvN6sWTOVLFnSpwoKsmbfvn2qW7euChUqpDFjxnhVv/P8dzB48GAZY3zKXiPr7Floffv29Uk2smN+/PhxxcTE6Oqrrw5EE8979gCpvwoZ+/fvV40aNVSlShXnfDx27NgMkweSkpK0Z88ezZgxQxs3bnTKYeM/ORVvz+SBp556SsWLF6eygx9btmxR3bp1FR8f75VY4Xa7nfvplJQU/f7772rZsqWTPLB48WKv/Sxfvtzpv7r99tu1cOFCr/e5Hz/Nft65+uqrtWjRIv3888/64IMPnGXVLrvsMi1dulTSfwOwhQsX1nfffefsw/Pe2i77/s477/i8B+mFF17weZ63+YvVP//8oy5duig8PFxVqlTRwIEDvZY2eemll5SQkKCyZcvqr7/+ypPvEEz69u0rY4wSEhKcqg72Od3tdmvPnj2KiYlRSEiIfvjhhwz388MPPyg0NFSVKlXSkSNHOH9kwPMYvuOOO1SgQAGNGTNG69evd6pihISEaODAgU4/+JIlS5SQkKDixYs755qM+rIAXBhIHECm2et02jegxYoV04svvpilTDPPDvl3331XpUqVUvXq1Z21E/EfOyv+jjvu8Cpx//XXX+u2225zEi6k/x6mspI8AG+e8fZMEDh58qRuueUWFSpUyGd9Wn9laW29evVSfHy8SpQoQYlrP5566imnEkP6cm9Hjx7VnXfeKWOMvv32W7+fT/8A8OyzzyohIUHx8fF0zvtBvPMOsc5bZ4q354PsrFmznL/b9yKZSR7AfzzXCg4LC9MjjzzivJf+uP3999911113Oetf165dWzNnznRmwqekpGjt2rXq1q2bmjRpol69eumzzz7LcH/5kR3vl1566ayzHWfPnq3q1as7y6LY8U6fPPDuu+/KGKNixYo5xzv+k5WBuKNHj6pnz54yxqhatWqaMGGCT0zHjRunsLAwtWrViioPfmRlhnpSUpL69eunsLAwVatWTZMmTXJm/tn/Vr755htVrFhRFSpUyFSyL/yzz7+TJk1SbGysKleurClTpjjJGp6zJ8eNGydjjHr37u1TcSa/e+utt9SjRw/t2rXLKy728Xrq1Ck9/vjjMsZo6NChzuv2eTqjZQvgX07H2zN5gJLL3uz42v/+n332Wee9jAafd+7cqZtvvlnGGJUpU0Zbtmzxev/rr79WixYtFBISoltvvVUrV6486z7zE8/nnfXr13u999lnnzkVM5944gnndbs/sHDhwpozZ4527NjhvPfuu++qWLFiqlmzJknpftiz1hs0aOAVb7fbfcbr3MaNG/Xggw+qaNGiTgWNWrVqOcvzVqhQwadyGP5LirGfG2+55RYnzp7P5ffdd5+MMerWrZuz3KDNfu4/evSoihQpoltuuSXvvkAQ8Tyf2jHbsWOHypcvr4YNG+rYsWOSTi+R1LhxY4WEhOjSSy/V6NGjJUmPPvqo86xpV83gHA1cuEgcQKZs3LhRTZo0kTFGw4cP1+DBgxUTE6P4+HgNGTIk08kDaWlpOnr0qLp166a4uDiVLl2aQVU/Vq5cqbJly6p69epO0oDnDZO9vlC5cuV8Zoz5Sx6oWrWqV6YxvC1cuFAJCQm6/PLLndl8LpfLiXnnzp0VHh6ujRs36vjx416znDxt3bpV/fr1c2ZRXn311ZSQ9MPOJm7RooU2b94s6fTNpucNZ9euXVWrVi2NHz9ew4cP19NPP61Vq1bpn3/+cbY5fvy45syZozp16sgYo4YNGzIbxA/inXeIdd7KKN6Sd9JAhw4dZIzR3Llzndf8JQ9ceumldA5ngp388vXXX0vynWmwYcMG3X777TLG6JFHHnHiX6tWLX388cc+yySl74Bj4Ok/Bw8eVMWKFRUdHa1t27b5Lbk5Y8YMlS9fXlFRUZo+fbruvvtur3inX7bg/fff53zix3PPPadHH33UOZdkxs6dO9WmTRsZY1S6dGndeuutmj9/vpYtW6bevXurWLFiSkhIOOMaw/nV8uXL1adPnyzF5p9//nHOPxUqVNBDDz2kn3/+WTt27NC8efN09dVXyxijcePG5WLLg9cPP/yQpXjv3bvXKUtbtWpVDR8+XHv37nXenz17tmrXrq24uDiS1NOx70/sc0PPnj21ZMkSn+22bNmiuLg43XTTTV6vZzSYzfXRv9yKt2eSDHzZiQB2YvPZjs/Vq1erTp06CgkJ0VtvvSXJu49rxYoVTmWCW2+9Vd98803uNT6I+HvecblcXvff9iSY+vXrO0l5hw8fdq6ZBQoUUN26ddW5c2c1bdpUYWFhSkhIoC82A/fdd5/CwsL0xRdfOK+lf6bftGmTvvjiC82YMUM//vijk9z1999/a9q0abrqqqsUHx+vkJAQXXnllerZs6e2bduW59/lfGcnDbRp00YjRoxQyZIlZYzRjTfe6GxjJ3LNmDFDJUuWVHx8vEaMGOGMQ3ieR+yE6/79+ystLY1BbQ8ZnaNTUlL06quvek1OlE4vOTh27FjFxMTIGKMOHTpo0aJFznLUffr04ToJXOBIHMBZuVwuTZ48WcacXos2JSVFR44c0bPPPquYmBgVL148U5UHUlNTNW3aNF1yySUy5vSaoHSk+UpLS3PWxp4zZ47Xe/ZNT2JioipWrKj4+PgzrvOZnJzsdNjXrVvXK3Mepx08eNDJXP3888+d1+0Y7t+/X5UqVVKZMmXUu3dvlStXTlWqVNFzzz3n1bmclpamTz75RPHx8SpTpoxeeOEF7dq1K8+/z/luwYIFTsdMly5dvDog7YetpKQkNWrUyNnOzjwuVaqUunTp4swqdrlcevfdd3XzzTerf//+xNsP4p13iHXeyky8pf/WF3/ggQd8Oms8kwfs38tVV13FeohnceWVVyo2Ntbvfd+pU6f03nvvyRijJ598UtLppQ3sSkm1atXShx9+qJSUFCfG6Tvi8J/Dhw+rdOnSql27tt/3k5KSnDXd7c7Nv//+20ncqFmzpmbMmKG0tDQGnM5g0aJFMsYoNDRUzzzzTKaSB+xjdseOHXrsscec2WT2ed2ejUOnvK+///7bidGzzz6bpXhv375dDzzwgEqUKCFjjAoVKqSiRYsqNDRUERERzmCU52fwX9L5k08+manl0zzj3bZtW0VFRSk0NFTly5dX586ddfvttysiIkLh4eGaN29ebjc/qHjen1SsWNFZZtAYo86dO3ut2y5JnTp1kjFGU6dO9XrdczC7T58+efkVggrxDgyXy6WaNWuqePHifvuj/Dl27JgT/5YtWzqve56r7eSByMhIXX/99fl+8suZnnek/wYCR48e7cyQT0lJ8UoqeOqpp1SrVi1nP8WLF9dtt91GX2wGdu/erUKFCql8+fI6dOiQTp065TUwnZKSor59+6pmzZpOTCtVqqQHH3zQq5JSSkqKtm3bprVr1yolJYW+WD/sfu927do5x+PUqVOVkJAgY4yaNWvmtf2JEyf01FNPKTQ0VPHx8erVq5fXMm4jR45UqVKlVKlSJW3fvj0vv8p5r3PnzmrQoIEWLVrkVEjzfDbcsmWLatWqpejoaJ/lvv744w+1a9dOkZGRKl26tEqUKKHw8HBVr16dxFHgAkfiADJl7ty56tChg9d6Y4cPH9Zzzz3nJA9kpvLATz/9pEceeUSjRo3K0hIH+cnx48fVtGlTVa1a1Wu9VFtaWpqSkpJ08cUXyxjjU5rZczvp9IBIp06dvJY7gLfXX39dr7zyivPf9g3UwYMHnZKGVapUUf369XXLLbc4pYDbtGnj1dl55MgRrVq1Sn/88QcPBmfQu3dvp3O9T58+Pmu8dezYUcacXqdv0qRJ+uSTT9S0aVMZYxQXF6eBAwc6A4Mul0t79uwh3mdAvPMOsc5bZ4u3nTTw8MMPO2U40w8k2R1BJ06c0E033eSzhiX+k5aWpiNHjqh48eIyxmj58uV+t/vzzz/1ySefeL22bds23XHHHU4FDcqinp3b7dZff/2lkJAQFSpUSL/99pvfwf/Nmzc71Y3s88e2bducZI1mzZo5Jd2RMXs94PDwcPXp0ydLg9mHDh3St99+q8cee0xt27ZVhw4d9O6773pVkoG3Hj16nHO89+/fr08//VR33nmn6tSpo8qVK+uJJ57wek4lUcabHe8CBQpkOd67du3Syy+/rAYNGjiDJDExMbr22mv11VdfOduSqPEfezAkPDxcw4cP19ixY9WsWTMVKlRIxhg1atRI//vf//Tvv/9qzZo1MuZ0CfJjx455Hbt2KXhjTKYHZ/Mj4p23XC6XDh065JRj//LLLzP92ZUrVyoyMlK1a9f2Wh7I8/yxatUqXXvttYqNjfUqr59fnel5x+7z69u3r0JDQzVp0iSf96TTSWArVqzQ/PnztXnzZqccOXzt2LFDMTExio2N9Vom8NixY5o7d65uueUWGWMUFhamuLg4FS9eXFFRUTLGqFOnTj7LyngmSuM/Dz30kIwx6tixo1efdmJioqZNm+YkiKZPHjh+/Lh69uzpPI+Gh4fruuuucyYolipViuUg0tmwYYNzbStfvrzuvvtuvzH67LPPnMoC9hJrdl/JwYMHNWfOHDVu3NjZV0hICM/0wAWOxAFkmufsBPviceTIEa/kgfSVB/ytE5yYmMj6wWfgcrm0bNkyTZs2ze/7duyuueYaGWO8ZjKl7yRjwOnMPG/e0y/5cOzYMWft2htuuEHr1q1zOt4///xzVa9eXcYYPf/883na5mDmeXw+88wzzo1+7969nRnV9kDfgw8+6NWpefz4cWcQpEqVKgyCZALxzjvEOm9lJd4PP/yw0+lon/MZUMqem2++WaGhoZo8ebKkM8fTc5bOX3/9pUKFCqlGjRoZLvmD/7jdbh06dEjVq1dXeHi4FixY4Lye0fZut9spGblixQrnnIOMeR6/djngrAxms+541mQ33umfIdPS0s66/AlOe+655845WSM1NVUpKSlasmSJ5s2bp02bNjnP/CQN/Mfz2LOTkaKiojR16lRt27ZNa9as0R133OHMpixVqpTefvttVatWTUWKFNEvv/zis89JkyYxCJIB4h1YzZo1U2hoqCZMmCApc+fejRs3qlChQqpVq5ZP/4vneeSbb77J9zOGM3reSZ88MHv2bBlj1Lx5c58qAlwPz42d2H/VVVfp66+/1k8//aTOnTs71aViY2M1YcIE/fjjj1q7dq0GDBig+Ph4xcfHa+nSpYFuflD44osvdPPNNzvLp3n++z9x4sQZkwdOnDihKVOmqG3bts4gdtWqVXXPPfdkqqpSfrRp0ybdfffdKlu2rIwxKlKkiN5//32vc0ZKSooeffRRGWM0ffp0Sad/L57nEbfbrZ49e6pBgwZUVQPyARIHcFbpOwLSd7yfLXlAOj3QSsda5nlmB2d0s9+8eXMZY5wbI8/P2DPPcHYZdXT98ccfuuGGG9SoUSO/28+bN0/GGJUtW1YHDhzgoSyTMuowfuGFF5zB0y5dujgdBW632+kk3rhxo4oXL66CBQtq3bp1AWl/sCHeeYdY560zxdsu054+3uk/9+mnn2rKlCle10+cnb0WZcmSJZ3OhjMNZtsmTJggY/5bf5zrZubcf//9ThlUu6M4o3h7xrRDhw4qXLiwVq9efcbP4NwHsz3PHbNmzaKzMpPONd6ex/Avv/zCTNRMyk6yhucxzjq2meNvsC8yMlIjR46UdHrAY+PGjerVq5czoG3/2AnpnK8zj3gHjj3JokSJEk7/09nuTzZs2KDQ0FA1bNhQJ06c8NmO34W3jM7fTz/9tJKSkpwZwpdcconXsps4N/bx9+GHH6patWpOZYECBQrIGKOEhATdeeedWr9+vdfndu/erVatWskYo7Fjxwai6UEpKSkpw/fOljxg+/PPP/Xbb7/p+PHjfs8p+M+hQ4c0b94851iNiIj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"text/plain": [
"<Figure size 2500x1000 with 2 Axes>"
]
},
"metadata": {},
"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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"text/plain": [
"<Figure size 2500x1000 with 2 Axes>"
]
},
"metadata": {},
"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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"text/plain": [
"<Figure size 2500x1000 with 2 Axes>"
]
},
"metadata": {},
"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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"text/plain": [
"<Figure size 2500x1000 with 2 Axes>"
]
},
"metadata": {},
"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": "ames-housing",
"language": "python",
"name": "ames-housing"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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