DATA201_projects/capstone_project.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "7530e3b7",
"metadata": {},
"source": [
"# NYC Building Energy Ratings"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "fe05b4a4",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "39a4ce3f",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('~/Downloads/DOB_Sustainability_Compliance_Map__Local_Law_33.csv')"
]
},
{
"cell_type": "markdown",
"id": "e0e97c85",
"metadata": {},
"source": [
"## Part 1: Data Exploration"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6b430c20",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(21681, 11)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "917a6779",
"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>Block</th>\n",
" <th>Lot</th>\n",
" <th>Building_Class</th>\n",
" <th>Tax_Class</th>\n",
" <th>Building_Count</th>\n",
" <th>DOF_Gross_Square_Footage</th>\n",
" <th>Address</th>\n",
" <th>BoroughName</th>\n",
" <th>BBL</th>\n",
" <th>ENERGY STAR Score</th>\n",
" <th>LetterScore</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>Y4</td>\n",
" <td>0</td>\n",
" <td>124</td>\n",
" <td>2598091</td>\n",
" <td>920 GRESHAM ROAD</td>\n",
" <td>MANHATTAN</td>\n",
" <td>1000010010</td>\n",
" <td>1</td>\n",
" <td>D</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>23</td>\n",
" <td>T2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>24346</td>\n",
" <td>20 SOUTH STREET</td>\n",
" <td>MANHATTAN</td>\n",
" <td>1000020023</td>\n",
" <td>0</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>7501</td>\n",
" <td>R0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2542563</td>\n",
" <td>1 WATER STREET</td>\n",
" <td>MANHATTAN</td>\n",
" <td>1000047501</td>\n",
" <td>61</td>\n",
" <td>C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Building_Class Tax_Class Building_Count \\\n",
"0 1 10 Y4 0 124 \n",
"1 2 23 T2 0 1 \n",
"2 4 7501 R0 2 1 \n",
"\n",
" DOF_Gross_Square_Footage Address BoroughName BBL \\\n",
"0 2598091 920 GRESHAM ROAD MANHATTAN 1000010010 \n",
"1 24346 20 SOUTH STREET MANHATTAN 1000020023 \n",
"2 2542563 1 WATER STREET MANHATTAN 1000047501 \n",
"\n",
" ENERGY STAR Score LetterScore \n",
"0 1 D \n",
"1 0 F \n",
"2 61 C "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "38d0ac47",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Block', 'Lot', 'Building_Class', 'Tax_Class', 'Building_Count',\n",
" 'DOF_Gross_Square_Footage', 'Address', 'BoroughName', 'BBL',\n",
" 'ENERGY STAR Score', 'LetterScore'],\n",
" dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "adf4092b",
"metadata": {},
"outputs": [],
"source": [
"# Columns seem to be self-explanatory, except BBL. According to NYC OpenData:\n",
"# \"Borough Block and Lot identifier as assigned by NYC Department of Finance\"."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "276d9619",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MANHATTAN 7858\n",
"BROOKLYN 5469\n",
"BRONX 4349\n",
"QUEENS 3659\n",
"STATEN ISLAND 346\n",
"Name: BoroughName, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Is this citywide or just Manhattan?\n",
"\n",
"df['BoroughName'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d3c8c305",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Block 0\n",
"Lot 0\n",
"Building_Class 2\n",
"Tax_Class 0\n",
"Building_Count 0\n",
"DOF_Gross_Square_Footage 0\n",
"Address 7\n",
"BoroughName 0\n",
"BBL 0\n",
"ENERGY STAR Score 0\n",
"LetterScore 0\n",
"dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Missing data?\n",
"\n",
"df.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "64eb852e",
"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>Block</th>\n",
" <th>Lot</th>\n",
" <th>Building_Class</th>\n",
" <th>Tax_Class</th>\n",
" <th>Building_Count</th>\n",
" <th>DOF_Gross_Square_Footage</th>\n",
" <th>Address</th>\n",
" <th>BoroughName</th>\n",
" <th>BBL</th>\n",
" <th>ENERGY STAR Score</th>\n",
" <th>LetterScore</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4254</th>\n",
" <td>1595</td>\n",
" <td>7501</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1330 5 AVENUE</td>\n",
" <td>MANHATTAN</td>\n",
" <td>1015950031</td>\n",
" <td>64</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8124</th>\n",
" <td>3016</td>\n",
" <td>7502</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1926 LONGFELLOW AVENUE</td>\n",
" <td>BRONX</td>\n",
" <td>2030160038</td>\n",
" <td>100</td>\n",
" <td>A</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Building_Class Tax_Class Building_Count \\\n",
"4254 1595 7501 NaN 0 0 \n",
"8124 3016 7502 NaN 0 0 \n",
"\n",
" DOF_Gross_Square_Footage Address BoroughName \\\n",
"4254 0 1330 5 AVENUE MANHATTAN \n",
"8124 0 1926 LONGFELLOW AVENUE BRONX \n",
"\n",
" BBL ENERGY STAR Score LetterScore \n",
"4254 1015950031 64 C \n",
"8124 2030160038 100 A "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Building_Class'].isna()]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cdf678d2",
"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>Block</th>\n",
" <th>Lot</th>\n",
" <th>Building_Class</th>\n",
" <th>Tax_Class</th>\n",
" <th>Building_Count</th>\n",
" <th>DOF_Gross_Square_Footage</th>\n",
" <th>Address</th>\n",
" <th>BoroughName</th>\n",
" <th>BBL</th>\n",
" <th>ENERGY STAR Score</th>\n",
" <th>LetterScore</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1228</th>\n",
" <td>506</td>\n",
" <td>12</td>\n",
" <td>W3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>49475</td>\n",
" <td>NaN</td>\n",
" <td>MANHATTAN</td>\n",
" <td>1005060012</td>\n",
" <td>10</td>\n",
" <td>D</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7145</th>\n",
" <td>1734</td>\n",
" <td>1</td>\n",
" <td>I1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1017118</td>\n",
" <td>NaN</td>\n",
" <td>MANHATTAN</td>\n",
" <td>1017340001</td>\n",
" <td>7</td>\n",
" <td>D</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9225</th>\n",
" <td>2758</td>\n",
" <td>6</td>\n",
" <td>N9</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>17200</td>\n",
" <td>NaN</td>\n",
" <td>BRONX</td>\n",
" <td>2027580006</td>\n",
" <td>89</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9226</th>\n",
" <td>2758</td>\n",
" <td>36</td>\n",
" <td>N9</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>37060</td>\n",
" <td>NaN</td>\n",
" <td>BRONX</td>\n",
" <td>2027580036</td>\n",
" <td>66</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13711</th>\n",
" <td>1769</td>\n",
" <td>72</td>\n",
" <td>C1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>30720</td>\n",
" <td>NaN</td>\n",
" <td>BROOKLYN</td>\n",
" <td>-2147483648</td>\n",
" <td>0</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15056</th>\n",
" <td>1602</td>\n",
" <td>13</td>\n",
" <td>C1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>14720</td>\n",
" <td>NaN</td>\n",
" <td>BROOKLYN</td>\n",
" <td>-2147483648</td>\n",
" <td>0</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16381</th>\n",
" <td>3755</td>\n",
" <td>22</td>\n",
" <td>C1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>25564</td>\n",
" <td>NaN</td>\n",
" <td>BROOKLYN</td>\n",
" <td>-2147483648</td>\n",
" <td>0</td>\n",
" <td>F</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Building_Class Tax_Class Building_Count \\\n",
"1228 506 12 W3 0 1 \n",
"7145 1734 1 I1 0 5 \n",
"9225 2758 6 N9 0 1 \n",
"9226 2758 36 N9 0 1 \n",
"13711 1769 72 C1 0 1 \n",
"15056 1602 13 C1 0 1 \n",
"16381 3755 22 C1 0 1 \n",
"\n",
" DOF_Gross_Square_Footage Address BoroughName BBL \\\n",
"1228 49475 NaN MANHATTAN 1005060012 \n",
"7145 1017118 NaN MANHATTAN 1017340001 \n",
"9225 17200 NaN BRONX 2027580006 \n",
"9226 37060 NaN BRONX 2027580036 \n",
"13711 30720 NaN BROOKLYN -2147483648 \n",
"15056 14720 NaN BROOKLYN -2147483648 \n",
"16381 25564 NaN BROOKLYN -2147483648 \n",
"\n",
" ENERGY STAR Score LetterScore \n",
"1228 10 D \n",
"7145 7 D \n",
"9225 89 A \n",
"9226 66 C \n",
"13711 0 F \n",
"15056 0 F \n",
"16381 0 F "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Address'].isna()]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e205df03",
"metadata": {},
"outputs": [],
"source": [
"# Missing Address is not a big deal because the rest of the values are complete.\n",
"# But missing Building Class could be a problem.\n",
"\n",
"# The two offending rows also have Building Count = 0.\n",
"# How is that possible, since they have Energy Star scores?\n",
"\n",
"# In the next secion we may decide to drop those two rows."
]
},
{
"cell_type": "markdown",
"id": "4d539a8c",
"metadata": {},
"source": [
"## Part 2: Data Cleaning"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "614dbd9f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Block int64\n",
"Lot int64\n",
"Building_Class object\n",
"Tax_Class int64\n",
"Building_Count int64\n",
"DOF_Gross_Square_Footage int64\n",
"Address object\n",
"BoroughName object\n",
"BBL int64\n",
"ENERGY STAR Score int64\n",
"LetterScore object\n",
"dtype: object"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Which columns are informative?\n",
"\n",
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6c58a084",
"metadata": {},
"outputs": [],
"source": [
"# Interesting for analysis:\n",
"\n",
"# DOF_Gross_Square_Footage\n",
"# ENERGY STAR Score\n",
"# LetterScore\n",
"\n",
"# Other columns are less interesting:\n",
"\n",
"# Building_Count is the number of buildings in one Block.\n",
"# Block can have more than one Lot, but Lot only has one Block.\n",
"# Block, Lot and BBL are identifiers assigned by the city.\n",
"\n",
"# A good visual reference is the Digital Tax Map put out by the NYC Department of Finance:\n",
"# http://gis.nyc.gov/taxmap/map.htm"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "14213bd2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Can any identifiers be used as an index?\n",
"\n",
"df['Block'].is_unique"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "1e1a5e9b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Lot'].is_unique"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "67b7f633",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['BBL'].is_unique"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "c4469ca8",
"metadata": {},
"outputs": [],
"source": [
"# Since their values are not unique, they cannot be used as an index."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "782190b5",
"metadata": {},
"outputs": [],
"source": [
"# Shall we rename or discard any columns from this dataset?\n",
"\n",
"# BBL and Tax Class could be eliminated. However, there are only 11 columns total, and since df.head() is easily readable on my monitor without scrolling horizontally (as you're doing now), I see no harm in keeping them."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "e085ba33",
"metadata": {},
"outputs": [],
"source": [
"# Rename columns containing whitespace or camelcase\n",
"\n",
"df.rename(columns = {\"BoroughName\": \"Borough_Name\",\n",
" \"ENERGY STAR Score\": \"Energy_Star_Score\",\n",
" \"LetterScore\": \"Letter_Score\"\n",
" }, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "c4a8ebb7",
"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>Block</th>\n",
" <th>Lot</th>\n",
" <th>Building_Class</th>\n",
" <th>Tax_Class</th>\n",
" <th>Building_Count</th>\n",
" <th>DOF_Gross_Square_Footage</th>\n",
" <th>Address</th>\n",
" <th>Borough_Name</th>\n",
" <th>BBL</th>\n",
" <th>Energy_Star_Score</th>\n",
" <th>Letter_Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>Y4</td>\n",
" <td>0</td>\n",
" <td>124</td>\n",
" <td>2598091</td>\n",
" <td>920 GRESHAM ROAD</td>\n",
" <td>MANHATTAN</td>\n",
" <td>1000010010</td>\n",
" <td>1</td>\n",
" <td>D</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Building_Class Tax_Class Building_Count \\\n",
"0 1 10 Y4 0 124 \n",
"\n",
" DOF_Gross_Square_Footage Address Borough_Name BBL \\\n",
"0 2598091 920 GRESHAM ROAD MANHATTAN 1000010010 \n",
"\n",
" Energy_Star_Score Letter_Score \n",
"0 1 D "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "38de98e9",
"metadata": {},
"outputs": [],
"source": [
"# Unforseen consequence of renaming: now I have to scroll horizontally."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "c0b5504f",
"metadata": {},
"outputs": [],
"source": [
"# Rename columns once more\n",
"\n",
"df.rename(columns = {\"DOF_Gross_Square_Footage\": \"Sq_Footage\",\n",
" \"Energy_Star_Score\": \"Energy_Score\",\n",
" \"Borough_Name\": \"Borough\",\n",
" \"Building_Class\": \"Bldg_Class\",\n",
" \"Building_Count\": \"Bldg_Count\"\n",
" }, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "0d3cf300",
"metadata": {
"scrolled": true
},
"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>Block</th>\n",
" <th>Lot</th>\n",
" <th>Bldg_Class</th>\n",
" <th>Tax_Class</th>\n",
" <th>Bldg_Count</th>\n",
" <th>Sq_Footage</th>\n",
" <th>Address</th>\n",
" <th>Borough</th>\n",
" <th>BBL</th>\n",
" <th>Energy_Score</th>\n",
" <th>Letter_Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>Y4</td>\n",
" <td>0</td>\n",
" <td>124</td>\n",
" <td>2598091</td>\n",
" <td>920 GRESHAM ROAD</td>\n",
" <td>MANHATTAN</td>\n",
" <td>1000010010</td>\n",
" <td>1</td>\n",
" <td>D</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Bldg_Class Tax_Class Bldg_Count Sq_Footage Address \\\n",
"0 1 10 Y4 0 124 2598091 920 GRESHAM ROAD \n",
"\n",
" Borough BBL Energy_Score Letter_Score \n",
"0 MANHATTAN 1000010010 1 D "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "c1c2e027",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Block 0\n",
"Lot 0\n",
"Bldg_Class 2\n",
"Tax_Class 0\n",
"Bldg_Count 0\n",
"Sq_Footage 0\n",
"Address 7\n",
"Borough 0\n",
"BBL 0\n",
"Energy_Score 0\n",
"Letter_Score 0\n",
"dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Repeat the search for missing data\n",
"\n",
"df.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "5debf1d6",
"metadata": {},
"outputs": [],
"source": [
"# Ignore the 7 missing addresses, but drop the 2 rows with missing Building Class.\n",
"# Building Class is a feature that will be used in the df.groupby() function.\n",
"\n",
"df.dropna(subset = ['Bldg_Class'], inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "5d2eb339",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Block 0\n",
"Lot 0\n",
"Bldg_Class 0\n",
"Tax_Class 0\n",
"Bldg_Count 0\n",
"Sq_Footage 0\n",
"Address 7\n",
"Borough 0\n",
"BBL 0\n",
"Energy_Score 0\n",
"Letter_Score 0\n",
"dtype: int64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "632701c5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Search for unexpected data\n",
"\n",
"# df['Energy_Score'].min() # looks good\n",
"# df['Energy_Score'].max() # looks good\n",
"# df['Sq_Footage'].max() # looks good\n",
"df['Sq_Footage'].min()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "c1f3edc4",
"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>Block</th>\n",
" <th>Lot</th>\n",
" <th>Bldg_Class</th>\n",
" <th>Tax_Class</th>\n",
" <th>Bldg_Count</th>\n",
" <th>Sq_Footage</th>\n",
" <th>Address</th>\n",
" <th>Borough</th>\n",
" <th>BBL</th>\n",
" <th>Energy_Score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Letter_Score</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>A</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F</th>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Bldg_Class Tax_Class Bldg_Count Sq_Footage \\\n",
"Letter_Score \n",
"A 3 3 3 3 3 3 \n",
"B 1 1 1 1 1 1 \n",
"C 5 5 5 5 5 5 \n",
"D 6 6 6 6 6 6 \n",
"F 14 14 14 14 14 14 \n",
"\n",
" Address Borough BBL Energy_Score \n",
"Letter_Score \n",
"A 3 3 3 3 \n",
"B 1 1 1 1 \n",
"C 5 5 5 5 \n",
"D 6 6 6 6 \n",
"F 14 14 14 14 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# A building cannot have zero square feet of floorspace.\n",
"# What's going on?\n",
"\n",
"df[df['Sq_Footage'] == 0].groupby(['Letter_Score']).count()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "e27467ce",
"metadata": {},
"outputs": [],
"source": [
"# The ones with F can be explained:\n",
"# An F grade means that the building owner \"didnt submit required benchmarking information\",\n",
"# according to Local Law 95 of 2019. So it's not that the building has no square footage,\n",
"# but that the data was not submitted. Thus the failing grade.\n",
"\n",
"# We'll leave 0 square feet with F grade untouched.\n",
"\n",
"# For more information, see https://www1.nyc.gov/site/buildings/codes/benchmarking.page"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "b73e15d9",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Block</th>\n",
" <th>Lot</th>\n",
" <th>Bldg_Class</th>\n",
" <th>Tax_Class</th>\n",
" <th>Bldg_Count</th>\n",
" <th>Sq_Footage</th>\n",
" <th>Address</th>\n",
" <th>Borough</th>\n",
" <th>BBL</th>\n",
" <th>Energy_Score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Letter_Score</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>A</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Bldg_Class Tax_Class Bldg_Count Sq_Footage \\\n",
"Letter_Score \n",
"A 3 3 3 3 3 3 \n",
"B 1 1 1 1 1 1 \n",
"C 5 5 5 5 5 5 \n",
"D 6 6 6 6 6 6 \n",
"\n",
" Address Borough BBL Energy_Score \n",
"Letter_Score \n",
"A 3 3 3 3 \n",
"B 1 1 1 1 \n",
"C 5 5 5 5 \n",
"D 6 6 6 6 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# What to do with the others?\n",
"\n",
"df[(df['Sq_Footage'] == 0) & (df['Letter_Score'] != 'F')].groupby(['Letter_Score']).count()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "47145374",
"metadata": {},
"outputs": [],
"source": [
"# 15 rows remain with 0 square feet of floorspace.\n",
"# Can we impute values from the mean square footage for each grade?\n",
"\n",
"# (There must be an elegant way to do this. What you see below is not.)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "2d643fd6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sq_Footage</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Letter_Score</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A</th>\n",
" <td>111197.291071</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>133270.963702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>128833.575964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>108170.778312</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sq_Footage\n",
"Letter_Score \n",
"A 111197.291071\n",
"B 133270.963702\n",
"C 128833.575964\n",
"D 108170.778312"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First, get averages\n",
"\n",
"subset0 = df[['Letter_Score', 'Sq_Footage']]\n",
"subset1 = subset0[(subset0['Letter_Score'] != 'F') & (subset0['Sq_Footage'] != 0)]\n",
"subset1.groupby(['Letter_Score']).mean()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "87c2ba5e",
"metadata": {},
"outputs": [],
"source": [
"# Assign variables, rounding to whole numbers\n",
"\n",
"mean_A = 111197\n",
"mean_B = 133271\n",
"mean_C = 128834\n",
"mean_D = 108171"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "27912675",
"metadata": {},
"outputs": [],
"source": [
"# Replace 0 values with mean_A, mean_B, etc.\n",
"\n",
"df.loc[(df['Letter_Score'] == 'A') & (df['Sq_Footage'] == 0), 'Sq_Footage'] = mean_A\n",
"df.loc[(df['Letter_Score'] == 'B') & (df['Sq_Footage'] == 0), 'Sq_Footage'] = mean_B\n",
"df.loc[(df['Letter_Score'] == 'C') & (df['Sq_Footage'] == 0), 'Sq_Footage'] = mean_C\n",
"df.loc[(df['Letter_Score'] == 'D') & (df['Sq_Footage'] == 0), 'Sq_Footage'] = mean_D"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "8124743f",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>F</th>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Bldg_Class Tax_Class Bldg_Count Sq_Footage \\\n",
"Letter_Score \n",
"F 14 14 14 14 14 14 \n",
"\n",
" Address Borough BBL Energy_Score \n",
"Letter_Score \n",
"F 14 14 14 14 "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now the only 0 values should be for F grades\n",
"\n",
"df[df['Sq_Footage'] == 0].groupby(['Letter_Score']).count()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "b83622b1",
"metadata": {},
"outputs": [
{
"data": {
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" <th>Lot</th>\n",
" <th>Bldg_Class</th>\n",
" <th>Tax_Class</th>\n",
" <th>Bldg_Count</th>\n",
" <th>Sq_Footage</th>\n",
" <th>Address</th>\n",
" <th>Borough</th>\n",
" <th>BBL</th>\n",
" <th>Energy_Score</th>\n",
" <th>Letter_Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11319</th>\n",
" <td>149</td>\n",
" <td>7502</td>\n",
" <td>U7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>138 WILLOUGHBY STREET</td>\n",
" <td>BROOKLYN</td>\n",
" <td>-2147483648</td>\n",
" <td>0</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11611</th>\n",
" <td>165</td>\n",
" <td>7504</td>\n",
" <td>U7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>133271</td>\n",
" <td>35 HOYT STREET</td>\n",
" <td>BROOKLYN</td>\n",
" <td>-2147483648</td>\n",
" <td>75</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13351</th>\n",
" <td>5804</td>\n",
" <td>2</td>\n",
" <td>U6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>COLONIAL ROAD</td>\n",
" <td>BROOKLYN</td>\n",
" <td>-2147483648</td>\n",
" <td>0</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14570</th>\n",
" <td>5322</td>\n",
" <td>4</td>\n",
" <td>V1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111197</td>\n",
" <td>23 OCEAN PARKWAY</td>\n",
" <td>BROOKLYN</td>\n",
" <td>-2147483648</td>\n",
" <td>100</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14668</th>\n",
" <td>5799</td>\n",
" <td>59</td>\n",
" <td>D9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>38315</td>\n",
" <td>3641 JOHNSON AVENUE</td>\n",
" <td>BRONX</td>\n",
" <td>2057990059</td>\n",
" <td>0</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15726</th>\n",
" <td>4282</td>\n",
" <td>100</td>\n",
" <td>V1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>25-70 REAR WHITESTONE EXPRESSWAY SR WEST</td>\n",
" <td>QUEENS</td>\n",
" <td>-2147483648</td>\n",
" <td>0</td>\n",
" <td>F</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Bldg_Class Tax_Class Bldg_Count Sq_Footage \\\n",
"11319 149 7502 U7 0 0 0 \n",
"11611 165 7504 U7 0 0 133271 \n",
"13351 5804 2 U6 0 0 0 \n",
"14570 5322 4 V1 0 0 111197 \n",
"14668 5799 59 D9 0 0 38315 \n",
"15726 4282 100 V1 0 0 0 \n",
"\n",
" Address Borough BBL \\\n",
"11319 138 WILLOUGHBY STREET BROOKLYN -2147483648 \n",
"11611 35 HOYT STREET BROOKLYN -2147483648 \n",
"13351 COLONIAL ROAD BROOKLYN -2147483648 \n",
"14570 23 OCEAN PARKWAY BROOKLYN -2147483648 \n",
"14668 3641 JOHNSON AVENUE BRONX 2057990059 \n",
"15726 25-70 REAR WHITESTONE EXPRESSWAY SR WEST QUEENS -2147483648 \n",
"\n",
" Energy_Score Letter_Score \n",
"11319 0 F \n",
"11611 75 B \n",
"13351 0 F \n",
"14570 100 A \n",
"14668 0 F \n",
"15726 0 F "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Unexpected values, continued\n",
"\n",
"df[df['Bldg_Count'] == 0]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "01c231f3",
"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>Block</th>\n",
" <th>Lot</th>\n",
" <th>Bldg_Class</th>\n",
" <th>Tax_Class</th>\n",
" <th>Bldg_Count</th>\n",
" <th>Sq_Footage</th>\n",
" <th>Address</th>\n",
" <th>Borough</th>\n",
" <th>BBL</th>\n",
" <th>Energy_Score</th>\n",
" <th>Letter_Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11611</th>\n",
" <td>165</td>\n",
" <td>7504</td>\n",
" <td>U7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>133271</td>\n",
" <td>35 HOYT STREET</td>\n",
" <td>BROOKLYN</td>\n",
" <td>-2147483648</td>\n",
" <td>75</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14570</th>\n",
" <td>5322</td>\n",
" <td>4</td>\n",
" <td>V1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111197</td>\n",
" <td>23 OCEAN PARKWAY</td>\n",
" <td>BROOKLYN</td>\n",
" <td>-2147483648</td>\n",
" <td>100</td>\n",
" <td>A</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Bldg_Class Tax_Class Bldg_Count Sq_Footage \\\n",
"11611 165 7504 U7 0 0 133271 \n",
"14570 5322 4 V1 0 0 111197 \n",
"\n",
" Address Borough BBL Energy_Score Letter_Score \n",
"11611 35 HOYT STREET BROOKLYN -2147483648 75 B \n",
"14570 23 OCEAN PARKWAY BROOKLYN -2147483648 100 A "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How can a block have zero buildings?\n",
"# Again, we'll leave the F grades as is.\n",
"\n",
"df[(df['Bldg_Count'] == 0) & (df['Letter_Score'] != 'F')]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "2bc61602",
"metadata": {},
"outputs": [],
"source": [
"# Have a peek at the Department of Finance Tax Map: http://gis.nyc.gov/taxmap/map.htm\n",
"\n",
"# Looks like Bldg_Count = 1 for both. However, rather than eyeballing it, let's just drop them."
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "396a0fdd",
"metadata": {},
"outputs": [],
"source": [
"# Drop two rows\n",
"\n",
"df.drop([11611, 14570], inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "4e874379",
"metadata": {},
"outputs": [],
"source": [
"# Finish cleaning\n",
"\n",
"# df['Tax_Class'].value_counts() # looks good\n",
"# df['Bldg_Class'].value_counts() # looks good"
]
},
{
"cell_type": "markdown",
"id": "d22ba85a",
"metadata": {},
"source": [
"## Part 3: Analysis"
]
},
{
"cell_type": "markdown",
"id": "1e5fdc73",
"metadata": {},
"source": [
"### What is the relationship between a building's size and its energy rating?"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "73f50d5c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Y4', 'T2', 'R0', 'O4', 'W1', 'O6', 'D5', 'D9', 'D0', 'O3', 'H3',\n",
" 'H2', 'D7', 'V1', 'D6', 'K6', 'D8', 'W8', 'O2', 'H1', 'D3', 'K4',\n",
" 'H9', 'K3', 'HB', 'RM', 'H5', 'O5', 'D4', 'HS', 'E9', 'D2', 'O7',\n",
" 'W5', 'U7', 'M1', 'I1', 'K2', 'Z1', 'W6', 'K9', 'Z3', 'H8', 'S4',\n",
" 'E1', 'C7', 'W2', 'C9', 'D1', 'C1', 'HR', 'O9', 'I9', 'K1', 'I6',\n",
" 'G1', 'N2', 'Y2', 'I7', 'M9', 'G2', 'I5', 'C4', 'E7', 'P9', 'W9',\n",
" 'P5', 'N9', 'S3', 'W3', 'J4', 'C6', 'M2', 'P7', 'W7', 'J3', 'H6',\n",
" 'P8', 'F9', 'G9', 'Y8', 'J8', 'F5', 'C5', 'N4', 'I3', 'P3', 'J6',\n",
" 'P2', 'W4', 'RC', 'I2', 'K5', 'J5', 'I4', 'M4', 'G8', 'J7', 'HH',\n",
" 'O8', 'M3', 'U0', 'O1', 'F1', 'F2', 'F4', 'H4', 'E2', 'Y1', 'Y6',\n",
" 'Z9', 'R2', 'Q6', 'K7', 'U6', 'RD', 'Y9', 'Q1', 'T9', 'V9', 'U9',\n",
" 'K8', 'U5', 'R4', 'G7', 'F8', 'J9', 'N3', 'P6', 'J2', 'GW', 'T1',\n",
" 'R3', 'C8', 'RS', 'Q2', 'V7', 'Q4', 'Y7'], dtype=object)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# There are many building types\n",
"\n",
"df['Bldg_Class'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "5449a9dd",
"metadata": {},
"outputs": [],
"source": [
"# It wouldn't make sense to compare, say, residential with commercial buildings.\n",
"# For an apple to apples comparison, let's look at office buildings.\n",
"\n",
"# O1\tOFFICE ONLY - 1 STORY\n",
"# O2\tOFFICE ONLY 2 - 6 STORIES\n",
"# O3\tOFFICE ONLY 7 - 19 STORIES\n",
"# O4\tOFFICE ONLY WITH OR WITHOUT COMM - 20 STORIES OR MORE\n",
"# O5\tOFFICE WITH COMM - 1 TO 6 STORIES\n",
"# O6\tOFFICE WITH COMM 7 - 19 STORIES\n",
"# O7\tPROFESSIONAL BUILDINGS/STAND ALONE FUNERAL HOMES\n",
"# O8\tOFFICE WITH APARTMENTS ONLY (NO COMM)\n",
"# O9\tMISCELLANEOUS AND OLD STYLE BANK BLDGS\n",
"\n",
"# Building glossary: https://www1.nyc.gov/assets/finance/jump/hlpbldgcode.html"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "bc229011",
"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>Block</th>\n",
" <th>Lot</th>\n",
" <th>Bldg_Class</th>\n",
" <th>Tax_Class</th>\n",
" <th>Bldg_Count</th>\n",
" <th>Sq_Footage</th>\n",
" <th>Address</th>\n",
" <th>Borough</th>\n",
" <th>BBL</th>\n",
" <th>Energy_Score</th>\n",
" <th>Letter_Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1473</th>\n",
" <td>702</td>\n",
" <td>10</td>\n",
" <td>O4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1835464</td>\n",
" <td>501 WEST 30 STREET</td>\n",
" <td>MANHATTAN</td>\n",
" <td>1007020010</td>\n",
" <td>58</td>\n",
" <td>C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block Lot Bldg_Class Tax_Class Bldg_Count Sq_Footage \\\n",
"1473 702 10 O4 4 1 1835464 \n",
"\n",
" Address Borough BBL Energy_Score Letter_Score \n",
"1473 501 WEST 30 STREET MANHATTAN 1007020010 58 C "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Context: 10 Hudson Yards. A new building with a poor energy rating.\n",
"\n",
"# It's one of the large glass and steel buildings that have recently cropped up in Manhattan (2016).\n",
"# Unfortunately, the dataset does not contain the newest behemoths to arise since then,\n",
"# like 30 Hudson Yards.\n",
"\n",
"df[(df['Block'] == 702) & (df['Lot'] == 10)]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "a344e1a3",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# That building belongs to class O4.\n",
"# What's the distribution of scores?\n",
"\n",
"office = df[df['Bldg_Class'] == 'O4']\n",
"office['Energy_Score'].plot(kind = 'hist')\n",
"plt.savefig('office.png')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "1dabd257",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Letter_Score\n",
"A 33\n",
"B 147\n",
"C 102\n",
"D 75\n",
"F 5\n",
"Name: Block, dtype: int64"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Distribution of grades\n",
"\n",
"office.groupby(['Letter_Score'])['Block'].count()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "9a9bcc23",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ul/ritbnFJTnZ2ad4DOWkd756i+55aCry4rZ2+bf9LAhM4ukfcR9fu+1/+oFv63e+8LgK+ZzKzg1scR5P/wAGW9oLz9E7H869l81fzOzdkn5P0pykv3XO/ZyZPe+cW1e3zEnnXNsUkEGdVLcr2mol7mKutBcEdhL38bXbvqRMjy0ABJH1v2fQmi/nvt2kOqn0j/WS3irppZJeImmNmd3Qxfo3m9mEmU2cOHEirjC9Viva6katmCsuzWKKe5/9FPfxtdt+1scWAILgu3BwpeHcJ1Wo+EZJ33LOnXDOLUr6K0mvlXS8mvah6p/PNlvZOXenc27cOTe+cePGvgXtk3ZFW630WswVNH8proI6X/Kn4i4YbLf9sfWrtVCmWDEuvlxjNb7FA/iCwu3BlYZzn9Sk+hlJP2VmLzIzk/QGSU9KOijppuoyN0k6kFB83mtWtFVvKCfdePmW0MV5ByantXPvId1w14PaufeQDk5OB4opioLAbvcftziOL+j2Dx99TuW6L5NC3ijOi4hP15iP8QA+ift7GP5Kw7lPMqf6dyS9TdKSpH+U9IuSRiTdI2mLlife1znnvtduO4OaU11z9Phpvfmjh7WwtHLC9Te/+tPaeuHa0I1jeslfiqqRiC/5U83iirNRSuP2m41Dccj09dve4NWXSRr5do35Fg/gqzQ0rEI8kj73XjZ/cc59QNIHGl4uafmuNQI6s1BWMZ9bMaleNbT8VAQpXFOPXpuORNVIxNemLXE3SmncfrNxGM7nEx+HLPDtGvMtHsBXaWhYhXj4fO4Ta/6CaMSZY5R0/lLS+/cF4xAf38bWt3gAAMExqY7RcjOWE7rvm8/2XHDUqWCp1xyjo8dP63MTxzTxrZkV22/c3y1XbVVxyJpuu7bs0eOnAx9nkAKs2jKSmh6bpNBFXJ3iaHy/m7ijLi7zOY8simMOu40w6/s2tr7FAwAILrGc6qj4mlN9YHJa77tnUrWsjELe9OHrtnf1kPJuHnLeTY7R+z//qO5+4Jmzv+dNKgzldP2rxs42i5lfKss5p9WFIS2UK7r1dVv1ztdsObvtWmySVuR/tjvOIMfTbJmdWzecPbbDR58L/eD3TnE0vn/9+JjumWjfRKcfD6RPOo+sURTHHHYbUY27b2PrWzwAgGVeNn+Jio+T6pnZkl77oXtVWlo5tsWhnL5+W7CCo7gKlo4eP603/uF9Xa9Xv+9OjWeaHWeQ4+m0TBRj0ss+2o1F0GPLmn6ci37EAABAN7xr/pJ1UyfnlLfzhzafs8APKY/rIeeT1bSKbtXvu1PjmWbHGeR4Oi0TxZj0so9G3cadRf04F/2IAQCAqDCpjsHY+tUqu/PvdJYrTmuG84EbqSyUyyteKy2VtWY433T5+rzSdvnAOzav6+mY6oulOjWeWSpXzouz2fE0FmB1KtJq9v5CuaxTcwuB82l72UejbuPOoiiOuZdt1F/LvcZAY5XwGMPBxvkHmiP9IyYHJ6f13oac6ne8enPH3NyaA5PT+vW/fESL5XPnZ1Vh+f+B2uUA1+dCL1YqK/Kka/uc+Pb3dPc3muRU1+UON26ncZ8HJ6e1u0lOtbTceGYon1uxTuPxDOWkP7h+x3nHX9tuqzGqf39ucUlmplVD+a7yabvZR9Cc6k7bzKIojrmbbTTLn5bUVQz9yH3POsZwsHH+MejIqU7IzGxJj3/nBUlOL7lgta7+2OFA+Z+d8nq7zQFutu7JMwuaPPa8Lh59kQpD+RWNRmoFUpLaFkstH98p/eKnJrRQPv86qu1LUpPmJa3zyzsVadXG9ZfunlBpqfd83E77aGzA0qlwbBCLy6I45qBj2yp/Wmp/nQbZxqCcr7AYw8HG+Qc8bf4yCEZHirryZRslLT8CLmhTh2YNIOrVr9dp2Vbrbt+8TlsvXNs05vp4OjV5uWD1sIpDeS2Ul1ruq/bzyuYlrRtadHqw+/J+CxrO51ZMqrtpkhFkH43j0Gm7Pj+QPi5RHHOQbbRrirJ987pAMdBYJTzGcLBx/oH2mFT3STf5n53yervNAQ6yz16123/9vqLOOR7EPOZBllQON1ZiDAcb5x9oj0LFPmnX1KGx6KNx2ULeNJRT02YQnZa98fItPTWSCFqIUr//Yt4kLf9zYP2+GmMsDuX0f7xik06eWYhlPLMi7mKgNBQbdWoE1M35bnbN3P6zl2rq5JzXY+CTQfjcoTXOP9AeOdV91pg/2q7oo9v85lbLdpv32kshSm0fa4bzOrNQbrqvmdmSbv/8o/rSY8fPvnbj5Vv0wbf+RMeYOu03a3nMcRcDpaHYqFMjoLA53I9Nn9Lv/vUTXo+Br7L6uUMwnH8MMgoVPeVj0UecMbVqPPN3v3Zl0/zuQRX3deHjddeIMQAA+IjmL57ysXlFnDG1ajzTa0OarIr7uvDxumvEGAAA0oZCxQT5UvRR/095nWJq989+nf5JsFXjmcbXe3mcXbei3mb94xMve8kFoVITTs0tdGyUE4Yv112jbq7DsNYM51Vaim+MASSH9BQkhUl1gmpFH43NK/r5JdAsb/X68bEVzWGuHx/rmP8dJEd364VrdePlW1Zs+8bLt6xI/WjcTpDGK1Ecc5htHpic1vsaGv18+LrtXW+zPq6KW26QU998J6rrwofrrlGzcxJXjLV95XImlZ2KeZPlLPExABBeGupFkF3kVHsgqf+rbpZXWhwySXZeU5Uv3npFy+Y10vnNXdrlpx49flqTx57XjoZnZQdpZBM27zXqXNqZ2ZJe+6F7VVpa+Tlq19wmaFzFoZw+ceO4LnvJD8RyXfhyNyeKxi5h9jU8lNOX3nUFef1AylErgX6g+Yvnkmoa0uxB/nnLSbZyuUIup8k2zWtqPwdtCLD1wrVNJzBBGtmEbTQQdfOCqZNzy2OmlakE+Zx1tc1mcQ3nc7pgdSG2a8OXZjVRNHYJs69iPqczC+U2awFIA5rTIGlMqgdYs7zVsqtIbuWserFS0Y7N69rmuEaR/xqkkY1vTWPG1q9eHrMG5YqjMUlA/Tz2QR5nIOv4fCNpPP0jA3pt4tHsQf53XLtdd1x7/sP9t164tuVD/6NqCNBsO9ePb9Jw3rRmOB+q0UCUTUTqtzl1ck7vv/oyDdV9koZy0q2v29rVtga5qUI/j32QxzkuaWgihMHA5xtJI6c65aIoymiWW9sq3zbM0z+6jafWnGMoZ1ooO33gLZfq517zw11vL44mIo3bvP3qS7V5/Yv0jf/1nD55/79oON/b+fAlzzkJ/Tz2QR7nKFEUBh/x+UacaP6SUVkuyojq2OIYo1bbbFfMmfbzATTK8vcPALRC85eMynIDi6iOLY4xarXNWjFnlPsCfJXl7x8A6AWT6j6LMv8wrqIMH3Ikozq2ZttZKK/cTpDjrV+mVWydijmBLKEoDABWYlLdRwcmp7Vz7yHdcNeD2rn3kA5OTofaXhxFGVHH2Kuoix8L+XNPNClXKrr/6HOSgh1v4zL3H32uaWztijmBrKEoDABWIqe6T+LMP4yyQNC3HMkojq1Zg5agOdC9NCahSAaDhOsdwCCh+YsH4nwofVRNPHx8cH4UxzZ1ck7D+bxKS0tnX+vU0Ka2z14ak/jSVAXoB653AFhG+keE2uXmBsk/DJvLHHb9NcN5lZZWdpbrJkfSh1zsRjOzJZ2aW9BC+fzjCpIDHeS8HT1+Wp+bOKajx093jMW38ZH8jQuDjesSQNpwpzoinZ7XOjpS1PWvGtPdDzxz9rXrx8fO3uEJ+7zXqNbP5UwqOxXzJstZ4BxJH59XWx9TxS03ZVldGDobXy0HendD3PXHW8sbbbXM+z//6IpzeuPlW/TBt/5E21h8GR+f48Jg47oEkEbkVEcgSC5yp9zcMLnMYXOhm60/PJTTl951hbZeuDb2/cehWUzFoZw+ceO4LnvJD3SdA91smaPHT+uNf3jfecv+3a9duWLcfBwfn+PCYOO6BOAznlMdsyDPa223TNjnvcaxfjGf05mFcos1ot1/HJrFNJzP6YLVhfP+Yh4dKbbMj263zGS17Xmjxtd9HB/J37gw2LguAaQV6R8RCJJ322mZMM97Dfu82KTXj0M/YtqxeV2g130cH8nfuDDYuC4BpBV3qiMQ5Hmt7ZbptH6ngp1m6//ylT+iP/nq/9JnH/x2x0Kf2vrFoZxeNJxXcaj982Yb40nqebXtxqUfMW29cK1uvHzLitduvHzLeSkzvj7Pt5e4KB5D3Hz9vABAJ+RUR6jX3Nx273VTsFNb//e/9IQe/NbJs6/nTPqjt+1oW+hzYHJauz/3iPKWU9lVdMe125su3y6efj6vNui49COmo8dPa/LY89qxeV3bHHRfn+cbNC6Kx9BPvn5eAAy2djnVTKo91kvBzsS3ZnTtnz5w3uvD+Zy+8RvN1wu6H18KiHyJY5Aw5gAAUKiYWr0U7Nz39HMt3nEt1wu6H18KiHyJY5Aw5gAAtEehosc6Few0++fRKy/ZoI8cOtpka9ay0CdoYVCvBUS1ONcM53VmoRz6n3N7iaNxrDr903Iv//Rcf5zfOTUvyemyl1zg3Z3cXo6t2ZgvlCs6NbeomdmSF8dYf1xS8xbyAADEhUm1x9o1HmmV3zr+0lH99NZRfe3ozNnt5Ez6T9e1LvTp1OCk2+Xq1eJ0FadS2WlVYfluZ5h83G7jaByr68fHdM/EVMvc4F5yh2vrSFqRIlHImz58XfP89CT0mhc9OlLU9eNjuvsb5xrdLJYruuXTD3uRX11/XPNLZTnnVjT68WX8AQDZRU51CjS7y9opv3XiWzP68uPHtXXjGv2ry34ocBOYIHf3ulmuMc5W8fYiaGFoqxiaxdJL7nCnfRSHcvr6bcnnHofJi+50jEnmV/scGwAgW9rlVHOnOgVqj92rqeW3zqvujmg1v7W23PhLRzX+0tFQ+wm7XLM4W8XbiyBxtIuhWSxBxrbbfeRzFvpYo9DLsbVbt14U57NXPscGABgcFCqmUFqaIzSLs6Zf8baLoVksvYxtp32UK86LcxPmuul0jElefz7HBgAYHEyq+6BTw4xuG2rUN0dYU8xreCin26++tOlzr5Ns1FEfZzFvkpb/KT7uZg71x92skcSNl29p2liilk5y+89e2rbxRLvmN7Wc8ZpC3nTHtX40rgjTVKNx3aHc8rH50JyjMbZC3jSUkxexxSXpzzYA4HzkVMesU2FYmIYan37g2/qdLzyuQj6nsnMr1vWpUUfUT/9op9Vxd3r6R+N6t199qX78JRecF2uQ5jdZfPpHs3Ulv56w4XNsUfLpsw0Ag4bmLwnpVBgWdeFYbV1JA9moo9fxTFvzGwwurkEASBbNXxLSqWFGmIYa7dYd1EYdvR532prfYHBxDQKAv5hUR6gxz7FZAVVpqaw1w/mW74cpHCuVK1oznNea4bxKS+WO2407L7PfeZ+9jmfczW/ilJbc2l7iDLpOWsYgCklcg2kf37THj+zgWsw+0j8i0irP8eDktHa3aX5Se7+X/MjautJyw5Fi3lR2TmamfM7OvmY5izSXO8x4xK3X8Qy6XpjzFbW05NaGaabTaZ20jEGU+nkNpn180x4/soNrMTvIqY5ZpzzHo8dP680fPayFpda51b0WVR09flpv/sjXtFBufh6Hh3L60ruu0NYL1waON6yk8z57Hc+om9/EKekxDiqqZjrkuK/Uj2sw7eOb9viRHVyL2UJOdcw65TmeWSirmG/9/uhIUds3r+vpw3VmoaziUL7l+8V8TmcWVqaCxJ2XmXTeZ6/jGXS9MOcrKkmPcVC9xEmOe2f9uAbTPr5pjx/ZwbU4OJhUR6BTnmOceZC9NL6IOy/Tx9zjrEnLGEfVTCctOe5ZkvbxTXv8yA6uxcHBpDoCnZpqBGm60WsBQ5CmHJJaNiuJo0FG3NvvpN1YZqVQJOkxDqqXOIOuk5YxSKu0j2/a40d2cC0ODnKqI9Qpz7HV+1EUMLRqfHH46HMdm5XElZeZRO5xu7HMYqGID/ndQfQSZ5py3LMs7eOb9viRHVyL2UChosfiLGAYtOIIGuIAAIA4UajosTgLGAatOIKGOAAAIClMqhPW7yLGhXJZp+YWes4p7iVfuV95zO3GkkIRf2Qlrx0AgHpDSQcw6GoFDI3NHKJISWjc9tzikipOuuXT/9hTTnEv+cr9zGPuNJZxjTOCy2JeOwAAEjnV3oizgGFmtqTHv/OCfunuCZVaNKAJso1u85W/eOsVuvpjh/uex9xuLCkUSc6g5fgDALKnXU41d6o9MTpSjG1iMTpS1AWrCxrO51ZMqms5xUH2W8tJntf569d+bnxv8tjzLdeJcxLVbizjHGe01+4a4pwAANKOSfWACJtT3Gn9Zu/t2LyOPGacRV47ACDLKFT0VNTFXJ0ePt9pf+3Wb/Xe1gvXZuqB9xTYheNTAwTOJQAgauRUeyjOYq5mOcXd7K+XfOUs5DFTYBedpK8HziUAoFc0f0mRfhdzUTzWGWOUHZxLAEAYNH9JkX43KaEpSmeMUXZwLgEAcWFS7ZlWDVviKuZqvr/eisd6aQwTZpv9EnWBnQ/HNKgolgQAxIVJtWdqxVxDdWem4qT7jz4X6/4KeTv7WrlS6Xp/ByantXPvId1w14PaufeQDk5OB3qv1232U5QFdr4c06DyqVgSAJAt5FR7aGa2pNd+6FDPjVp629+9Ki2duxbibgzTads+5r6GLbDz8ZgGVdLFkgCAdCKnOmWmTs5pON/fvOrhfL7n/bXLU+01h9XH3NfRkaK2b17X8yTMx2MaVGHPJQAAjZhUJ6BTTm2Uec5BxNEYZqFc0ZrhvE7NLWqhvPK9UvW9eo1jksXc1yweEwAAWMakus+C5NRGleccVNg802bxLixV9DMf+Zpu+fTDKlcqKuRNqwrLl5s5p6s/dvjssTcbkyzmvmbxmAAAwDJyqvuom5zasHnOvcbXa55ps3jrDeclJ9NieeXxfPHWK3T1xw63HJMs5r5m8ZgAABgE7XKqh/odzCCr5dTO69wEspZT2zi5quU5l5aWOi4blVrL8V40i7feUC4vmbRYLp99rZDLafLY823HJExMvsriMQEAMOiYVPdRNzm1acu/bRZvvbKrSM5WvLZYqWjH5nWpOk4AAIBmyKnug1oRnqTAObVpy79tjHcoJxXydjb2O67drjuuPf94tl64tuvjpHlKeqXl3KUlTvSG8wtEi8/UMnKqY3Zgclp79h9RIZfTYqWifbu2aefWDYFzatOWf1sfr6TzYm91PEGPs9l4XrNjU7wHhUik5dylJU70hvMLRGvQPlPtcqqZVMeIZh/RYjzTKy3nLi1xojecXyBag/iZovlLQmj2ES3GM73Scu7SEid6w/kFosVnaiUm1TFKQ7Fh0DyoXpfrNc+q2XrNm8yUdWpuIZY8rlax+5475mN8rRoEnZpb9D5O3z6z6B3nF4gWn6mVSP+I2fs//6jufuCZs7/fePkWffCtP5FgROcEzYPqdbnrXzWmex6a6jrPqt3+Dk5Oa3f1vbnFJZmZVg3lI8/jahWD77ljPsdXf+7ml8pyzml1YcjrOH2LDeFxfoFoDdpnipzqhPicaxQ0tjDLNQpy7EH2NzNb0uPfeUG/dPeESkvRj22rGDo1qkmaz9dbzfK5O1U9d/1rbNSttBUIozucXyBag/SZIqc6IT7nGgWNLcxyjYIce5D9jY4UdcHqgobz8YxtqxhqjWri2GcUfL7eapbP3bCG8/kVr/sY5/bN6zL/l8Og4vwC0eIztSyxSbWZrTOzz5nZP5nZk2Z2uZm92My+YmZPV/9cn1R8UfA51yhobGGWaxTk2KOOqxettu17oxqfr7d6aYkTAIBuJHmn+j9L+rJz7sckbZf0pKTbJN3rnLtE0r3V31OrWQOX26++VFMn5wIVZ0VdKFe/XtDmMrXlikOmFxXyKg6Zbv/Z849hdKSo26++VMNDOa0p5rWqkNONl285u/3iUE63XLVVJ88stI2927iaLRe2UK/VtntpVNNPYRoG9bO4MW2NjQAACCKRnGoz+wFJj0j6EVcXgJk9Jekq59x3zewiSV91zr283bZ8zqmuqeUaPTZ9Sr/7108ESuaPulCu1XpB8qAOTE5r9+eOKJ8zlRbLyuXOLw6sbT9vpsVyRR94y2X6uZ/6Yc3MlvTpB5/Rx//+aclJpbLTqsLy/8u1iz1oflbjclEW6oVtVJOUbuNLqrjR93EEAKCRd4WKZrZD0p2SntDyXeqHJL1b0rRzbl3dciedc21TQNIwqZa6KyKLulAuTAFbpwLETnFJarl+1MVpaSjU8w1jBgBAcD4WKg5JeqWkP3HOvULSGXWR6mFmN5vZhJlNnDhxIq4YI9VNEVnUhXJhCtg6FSB2iqvd+lEXp6WhUM83jBkAANFIalI9JWnKOfdg9ffPaXmSfbya9qHqn882W9k5d6dzbtw5N75x48a+BBxWN8VZ3RTKlZbKWjO8/CSFVnmxYQrDOhUgdirga7d+qxh6ze+NowAu7lzjpBu1pKVosN/jlPR5SXr/AMD3UPcSmVQ75/4/ScfMrJYv/QYtp4IclHRT9bWbJB1IILxYdFOcFaRQrpg3SVIuZ7r6Y4f1/gOPaufeQ7rhrge1c+8hHZyc7mnfnWIZykmFvAUu4KtfvxbzqkKuZQwHJqdbHkeUYxxEmFh82H4QaSga7Pc4JX1ekt4/APA91JvEmr9U86rvkjQs6Z8l/YKWJ/n3SNoi6RlJ1znnvtduO2nJqa7ppjir1bJHj5/Wmz96WAtLre8gt2rQ0mthWP26krou4Ku9t2Y4rzML5ZbLRJHfG0UBXNy5xr7lMvtaNNjvcUr6vCS9fwDge6i9djnVQ/0OpsY5NympWVBv6HMofVW7extm2TMLZRXzubaT6lpebP363ey7Uyyt7rC32n6Qfdfye+d17riaHUe3sfYiqliS2n63ohizOPR7nJI+L0nvHwD4HuodHRVTKKpGK77xKb837lh8Olaf9Xuckj4vSe8fAPge6h2T6hRqlgdb32glrrzYTkULcTVdSerObZyx+HSsvrvlqq0qDlnHcYqiqCbp85L0/hEdiryQVnwP9S6xnOqopC2nOkqNebBx5sV2ahDSj6YrSYg7Fp+O1Tf119RCuaJbX7dV73zNlqbjFHUDm6TPS9L7RzhJNVQCosT3UHPeNX+J0iBPqvulU9ECRQ2IWhTNkrj+kASuRyDbfGz+ghTp1CCEBiKIWhTNkrj+kASuR2BwMalOgbC5eWHXb1a0sFA+V7QQtqiB3EM0iqJZEkU1SALXIzC4mFR7LuwD2KN4gHutaKFQbd4iSeVKRfcffW7F+70UNfCAeTQTRbMk/qkdSeB6BAYXOdUeC5ubF2Vu38xsSa/90L0qLZ27Xhq31W1RA7mH6CSKZklAErgegWyKrPmLma2WtMU591QkkaGtsA9gj/IB7lMn5zScz6u0tNRyW902EOEB8+gkimZJQBK4HoHBEzj9w8zeImlS0perv+8ws4MxxQWFz82LMrcvjjxBcg8BAEBWdJNT/duSXi3peelsm/GLow4I54TNzet2/XYFg3HkCTbb5u1XX6qpk3OxFi1SGAkAAKIWOKfazB50zr3GzP7ROfeK6mtHnHPbYo2wgyznVNeEzc0Lsn7QZgVx5AnWtvnY9Cn97l8/EWvDBJoyAACAXkWVU/2Ymb1TUt7MLpH0q5K+HkWAaC9sbl6n9WdmS9qz/4jmFytn85t37z+inVs3nLdeHHmCte297c5vBIqhV90cJwAAQDe6Sf94l6TLJJUk/XdJpyS9J4aY0Gc+NCvoRww+HCcAAMimQHeqzSwv6aBz7o2SfivekAZLr+kU9etJCpWS4UPBYJwx1MZqzXA+8ePsJx7pBd9xjZ6PMQHSK9Ck2jlXNrPvm9kFzrlTcQc1KHrN761fb36pLOecVheGes4RrhUM7m6IpZ9f6HHF0DjG14+P6Z6JqcSOs1/IHYfvuEbPx5gA6dZNoeI9kn5K0lcknam97pz71XhCCyathYq9Nj5ptl69MM1TfLhDEmUMrcb4i7deoTML5czeCaKpDnzHNXo+xgRIh6gKFf+6+h8i0Gvjk2br1QvTPMWHZgVRxtBqjM8slLV987pI9uEjmurAd1yj52NMgPQLPKl2zn3KzIYlvaz60lPOucV4wsq+bnOI2+UFB91GL9J899qHXPEkDOpx1/hwzaK9Qb9Gm2FMgPTrpqPiVZKelvRxSX8s6ZtmdmU8YWVfN81UDkxOa+feQ7rhrgd19ccO6/rxsbPrFfKmoZwia8jSar879x7SwcnpSLbbrxjiaFiTBoN63JIf1yw6G+RrtBXGBEi/bnKqH5L0TufcU9XfXybpM865V8UYX0dpzamu6XRXLUhesBTu6R+t4ko6vy+qGAb1zuWgHbcP1yy6M2jXaBCMCeC3qHKqC7UJtSQ5575pZoXQ0Q24TjnEQfOCo/7y9SG/L6oYfMgVT8KgHbcP1yy6M2jXaBCMCZBe3UyqJ8zszyT939Xff07SQ9GHhHpJ5dn5kN/nQwxID64XAECSuumo+O8lPa7l9uTvlvSEpF+OI6hBMzNb0iPHntfMbOm895LKs4trv+2OtV8xIJu4XgAASeomp3qNpHnnXLn6e15S0Tn3/Rjj6yjtOdVBH/afVJ5dlPvttbEBOYboBtcLACAu7XKqu5lUPyDpjc652ervI5L+1jn32sgi7UGaJ9WDVFg1SMcKAACyqd2kupv0j1W1CbUkVX9+UdjgBlmtsKperbAqawbpWAEAwODpZlJ9xsxeWfvFzF4liRlRB+1yiAepsCrIsXaTbx1E1NsbRIwhAADBdPP0j/dI+ksz+07194skvS3yiDKkUw5xrbBqd8MyWUyH6HSsveZbtxL19gYRYwgAQHCBc6olqfpc6pdLMkn/5EObcl9zqrvJIR6kwqpmxxp1vjX52+ExhgAAnC9UTrWZ/aSZ/ZAkVSfRr5T0f0n6sJm9ONJIM6SbHOLRkaK2b143EJOVZscadb41+dvhMYYAAHQnSE71n0pakCQzu1LShyTdLemUpDvjCy3dBilfOqyox4qxD48xBACgO0Em1Xnn3PeqP79N0p3Ouf3OudslbY0vtHRLohFF2KKyJIvSbrlqq4pD0YwVTUDCS/sYUmCJZrguAMSpY061mT0maYdzbsnM/knSzc65+2rvOed+vA9xtuRrTnVNv/KlwxaVJVWUVr/fhXJZt77uEr3zNVsiGatBylWPSxrHkAJLNMN1ASAKoZq/mNlvSXqzpOckbZH0SuecM7Otkj7lnNsZdcDd8H1S3Q9hi8qSKkqjGA5R45pCM1wXAKISqlDROfd7kt4n6c8lXeHOzcJzkt5Vt5P14UNFL8IWlSVVlEYxHKLGNYVmuC4A9EOg51Q75x5o8to3G166V8tPBkFI3f6Tey9FZfX7aLd+nP/8TzEcosY1lV1hvou4LgD0QzcdFTuxCLc1sA5MTmvn3kO64a4HtXPvIR2cnO64zuhIUde/amzFa9ePj7X8i6dxH/cffa5pUdrho891HUs30l4MB/9wTWVTL9+L9bguAPRDV81f2m7I7GHnXN/vVGcpp7rXvL9uG820WlbS2TtBkvqWg5jGYjj4jWsqO6LMh+a6ABBWu5zqbtqUI2a1vL95nfvLo5b31+4vgG7Wa7dsfVOWR44931MsvRgdKfIXHCLFNZUdvX4vNsN1ASBOpH94pNe8v27WC7osOYgAfMB3EYC0CDypNrP/ZGaXtVnkDRHEM9B6zfvrZr2gy6YpB5GGDkB2pem7CMBgC5xTbWa/KOkXtJwy8l8lfcY5dyrG2ALJUk51Ta95f92sF3RZ33MQaegADAbfv4sADIZQzV+abOzlWp5cv0PS/ZI+4Zz7+9BR9iiLk2oEQ0MHAADQT6GavzRsKC/px6r/PSfpEUnvNbPPho4S6BINHQAAgC+6yan+A0lPabll+e87517lnNvrnHuLpFfEFeAg6DYnuJvls5hvXDumNcP5tk1rWh13FsekF4wDAADR6eaReo9J+o/Oue83ee/VEcUzcLrNCe5m+SzmGzce0/XjY7pnYmrFMR4++lzL487imPSCcQAAIFrdFCo2a+xyStK3nXNLkUbVhTTnVHebExxVk5e05hu3OqYv3nqFziyUOzatafdeWsekF1m8NgAA6Ieocqr/WNIDku6U9AlJ35D0WUnfNLN/HTrKAdRtTnA3y2cx37jVMZ1ZKJ9tXNPuuLM4Jr1gHAAAiF43k+p/kfQK59y4c+5VWs6jfkzSGyXtiyG2zOu2qUEcTV7SJMgxtVsmi2PSC8YBAIDodTOp/jHn3OO1X5xzT2h5kv3P0Yc1GLptahBHkxepdcFaVIVsUW0nyDG1WybOJhJpKvqjmQYAANHrJqf6HkkzWk75kKS3Sdog6eclHXbO/WQsEXaQ5pzqmm6bGkTZ5KVVwVpUhWxxFMQFOf52y0TdRCKtRX800wAAoDuRNH8xs9WSfkXSFZJM0mEt51nPS3qRc242mnC7k4VJdVLaFf5d/bHDoQvZBqEgbhCOEQAALGs3qQ70SL1q05cvOOfeKOnDTRZJZEKNcGoFa/M6NyEs5HKaPPZ809enTs51NVFstf1ut+OzQThGAADQWaCcaudcWdL3zeyCmONBH7UqWNuxeV0khWy+F8RFkQfd7hjTlGcNpAWfKwC+6qb5y7ykR83sK5LO1F50zv1q5FGhL2oFa7sb8oG3Xri26evd3nlttX0f7uBGlQfd6hjbNaAB0Ju01i8AGAzd5FTf1Ox159ynIo2oS+RUh9eqYC2qQjbfCuLiyIOuP0aJJjNA1KhfAOCD0DnV0vLkuVqsuMU591Rk0SFxtcfNBX09qu0nJY486PpjfCSinHQA51C/AMB3gZ9TbWZvkTQp6cvV33eY2cGY4gJiE3eut++55EAa8bkC4Ltumr/8tqRXS3pekpxzk5JeGnlEiIyvBT2NcSUR5y1XbVVxKJ7mJ742V/HhevAhBqSTr58rAKjpplBxyTl3yszqXwuWkI2+87WgpzGu68fHdM/EVN/irN+/5HTzlT+id75mS+R/MV+zY5N2bt3gTS65D9eDDzEg3Xz7XAFAvW7uVD9mZu+UlDezS8zso5K+HlNcCGFmtqQ9+49ofrGi06UlzS9WtHv/kcTvDjaL6+5vPNO3OBv3X1py+vhXj8ayL2n5ztr2zesS/4vfh+vBhxiQDb58rgCgUTeT6ndJukxSSdJnJL0g6T0xxISQagU99WoFPUlqFlejOOP0dVzi5sNx+xADAABx6ubpH9+X9FvV/9BBko+R87Wgp1lcjaKMs/Ec9GNc4n48YS/6eT00O86Z2ZJOzS1qoezfNQkAQFQCT6rN7GWSfl3SxfXrOedeH31Y6ZZ07qivTVeaxdUspzqKOFudgzjHpdU+B+V6aHacTjr7WrlSUSFvWjWU9+aaBAAgKt00f3lE0n+R9JCkcu1159xD8YQWjG/NX3xqUOBb05WaxriijrPTOYhjXFrt84u3XqGrP3Y489dDs+MvDi0Xg5aWXN1rpk/cOK7LXnKBV9ckAABBRNL8RctP//iTiGLKLJ8aFPjWdKWmMa6o4+x0DuIYl1b7nPSoEUyc10Oz48/nTHKmuv8H13A+rwtWD3t5XQIAEEY3hYpfMLNfMbOLzOzFtf9ii8xzrZ6362s+c1r18lzjJM5Bq33u2LxuIK6HZsdfrjiVXfaPHdHjeeYA0qibSfVNkv6Dlh+j91D1P3/yLvrowOS0du49pBvuelA79x7Swcnps+/RoCA67ca5nSTOQat9br1w7UBcD82O/45rt+mOa7dn/tgRrV4/9wCQtMA51b7qd0510JxpX/OZ0yKK3PQkzoGPT//op1ZP/xiEY0d4PtWkAEAzoXKqzWy3c25f9efrnHN/Wffe7zvnfjO6UP0XNGfa13zmtIgiNz2Jc9Bqn4NyPTQ7zkE5doTnU00KAHQrSPrH2+t+/o2G994UYSypQM50fzDOwODhcw8gzYJMqq3Fz81+zzxypvuDcQYGD597AGkW5JF6rsXPzX4fCNfs2KSdWzeQJxozxhkYPHzuAaRVkEn1djN7Qct3pVdXf1b191WxReY58kT7g3EGBg+fewBp1HFS7ZzL9yMQAAAAIK26eU41MiyqZgs0bQAAAIOomzblyKgDk9Pas/+ICrmcFisV7du1Tdfs2JTYdgAAANKGO9UDbma2pD37j2h+saLTpSXNL1a0e/+Rru80R7UdAACANGJSPeBqzRbq1ZotJLEdAACANEp0Um1meTP7RzP7YvX3F5vZV8zs6eqf65OMbxBE1WyBpg0AAGCQJX2n+t2Snqz7/TZJ9zrnLpF0b/V3xCiqZguttiOJwsU+o1gUAID+M+eS6d9iZmOSPiXp9yS91zl3tZk9Jekq59x3zewiSV91zr283XbGx8fdxMREHyLOtpnZUiTNFuq3c/jocxQu9hnFogAAxMfMHnLOjTd7L8k71X8kabek+pyBC51z35Wk6p8/mEBcA2l0pKjtm9eFbrhQ244kChf7jGJRAACSk8ik2syulvSsc+6hHte/2cwmzGzixIkTEUeHKFC42H+MOQAAyUnqTvVOSdeY2b9I+qyk15vZf5N0vJr2oeqfzzZb2Tl3p3Nu3Dk3vnHjxn7FjC5QuNh/QcacfGsgXnzGgMGVyKTaOfcbzrkx59zFkt4u6ZBz7gZJByXdVF3sJkkHkogP4UVVAIngOo35gclp7dx7SDfc9aB27j2kg5PTCUcMZAufMWCwJVaoeDYAs6sk/Xq1UHFU0j2Stkh6RtJ1zrnvtVufQkW/RVUAieCajfnMbEk79x7S/OK5O9mrCjndv+f1nBcgAnzGgMHQrlAx8TblzrmvSvpq9ecZSW9IMh5Ea3SkyF8ofdZszGv51vN1dcG1fGvODxAenzEAST+nGkAfkOMOxIvPGAAm1cAAIMcdiBefMQCJ51SHRU41EBw57kC8+IwB2eZ1TjWA/iHHHYgXnzFgcJH+AQAAAITEpDpCPPQfAABgMJH+EZEDk9Pas/+ICrmcFisV7du1Tdfs2JR0WAAAAOgD7lRHYGa2pD37j2h+saLTpSXNL1a0e/8R7lgDAAAMCCbVEag99L9e7aH/AAAAyD4m1RHgof8AAACDjUl1BPr10P8whZBxFFFSmAkAALCMQsWIXLNjk3Zu3RDbQ//DFELGUURJYSYAAMA53KmO0OhIUds3r4vlDnWvhZBxFFFSmAkAALASk+oUCFMIGUcRJYWZAAAAKzGp7oOwucdhCiHjKKIMu82s52Jn/fgAAMD5yKmOWRS5x7VCyN0N2wmSZjI6UtT142O6+xvPnH3t+vGxUCkqYeLJei521o8PAAA0Z865pGMIZXx83E1MTCQdRlMzsyXt3HtI84vn7uquKuR0/57X9zSpnZktdV0IGXUMYeKJMxYfZP34AAAYdGb2kHNuvNl7pH/EKOrc414KIePMf+42nqznYmf9+AAAQGtMqmMUVT5zmBxdnxrT+BRLHLJ+fECcqEUAkHZMqmMURVOYA5PT2rn3kG6460Ht3HtIByen+x5DVHyKJQ5ZPz4gLmG/5wDAB+RU90EvudC19aLK0e01hjj4FEscsn58QJSoRQCQJu1yqnn6Rx+MjhR7+suhlqM7r3N/2dRydLvdXq8xxMGnWOKQ9eMDohTl9xwAJIn0D4+Rowsg6/ieA5AVTKo9Ro4ugKzjew5AVpBTnQLk6ALIOr7nAKQBOdUpR44ugKzjew5A2pH+AQAAAITEpDrl0tQwIU2x+oIxAwAgHUj/SLEDk9Pas/+ICrmcFisV7du1Tdfs2JR0WE2lKVZfMGYAAKQHd6pTama2pD37j2h+saLTpSXNL1a0e/8RL+9opilWXzBmAACkC5PqlKo1TKhXa5jgmzTF6gvGDACAdGFSnVJpapiQplh9wZgBAJAuTKpTKk0NE9IUqy8YMwAA0oXmLymXpoYJaYrVF4wZAAD+oPlLhqWpYUKaYvUFYwYAQDqQ/gEAAACExKQaAAAACIlJNQAAABASk2oAAAAgJCbVAAAAQEhMqgEAAICQmFQDAAAAITGpBgAAAEJiUg0AAACExKQaAAAACIlJNVJrZrakR449r5nZUtKhAACAATeUdABALw5MTmvP/iMq5HJarFS0b9c2XbNjU9JhAQCAAcWdaqTOzGxJe/Yf0fxiRadLS5pfrGj3/iPcsQYAAIlhUo3UmTo5p0Ju5aVbyOU0dXIuoYgAAMCgY1KN1Blbv1qLlcqK1xYrFY2tX51QRAAAYNAxqUbqjI4UtW/XNq0q5LS2OKRVhZz27dqm0ZFi0qEBAIABRaEiUumaHZu0c+sGTZ2c09j61UyoAQBAophUI7VGR4pMpgEAgBdI/wAAAABCYlINAAAAhMSkGgAAAAiJSTUAAAAQEpNqAAAAICQm1QAAAEBITKoBAACAkJhUAwAAACExqQYAAABCYlINAAAAhMSkGgAAAAiJSTUAAAAQEpNqAAAAICQm1QAAAEBITKrhtZnZkh459rxmZktJhwIAANDSUNIBAK0cmJzWnv1HVMjltFipaN+ubbpmx6akwwIAADgPd6rhpZnZkvbsP6L5xYpOl5Y0v1jR7v1HuGMNAAC8xKQaXpo6OadCbuXlWcjlNHVyLqGIAAAAWmNSDS+NrV+txUplxWuLlYrG1q9OKCIAAIDWmFQPoDQU/42OFLVv1zatKuS0tjikVYWc9u3aptGRYtKhAQAAnIdCxQGTpuK/a3Zs0s6tGzR1ck5j61czoQYAAN5iUj1A6ov/5rWcWrF7/xHt3LrB2wnr6EjR29gAAABqSP8YIBT/AQAAxINJ9QCh+A8AACAeTKoHCMV/AAAA8SCnesBQ/AcAABA9JtUDiOI/AACAaCWS/mFmm83s783sSTN73MzeXX39xWb2FTN7uvrn+iTiAwAAALqRVE71kqT3Oef+N0k/JekWM7tU0m2S7nXOXSLp3urvAAAAgNcSmVQ7577rnHu4+vNpSU9K2iTprZI+VV3sU5L+TRLxAQAAAN1I/OkfZnaxpFdIelDShc6570rLE29JP5hgaAAAAEAgiU6qzWxE0n5J73HOvdDFejeb2YSZTZw4cSK+AAEAAIAAEptUm1lByxPqTzvn/qr68nEzu6j6/kWSnm22rnPuTufcuHNufOPGjf0JGAAAAGghqad/mKQ/k/Skc+4P6t46KOmm6s83STrQ79gAAACAbiX1nOqdkn5e0qNmNll97TclfUjSPWb27yQ9I+m6ZMIDAAAAgktkUu2cOyzJWrz9hn7GAgAAAISV+NM/AAAAgLRjUo1YzMyW9Mix5zUzW0o6lJ6kPX4AANBfSeVUI8MOTE5rz/4jKuRyWqxUtG/XNl2zY1PSYQWW9vgBAED/cacakZqZLWnP/iOaX6zodGlJ84sV7d5/JDV3fNMePwAASAaTakRq6uScCrmVl1Uhl9PUybmEIupO2uMHAADJYFKNSI2tX63FSmXFa4uVisbWr04oou6kPX4AAJAMJtWI1OhIUft2bdOqQk5ri0NaVchp365tGh0pJh1aIGmPHwAAJMOcc0nHEMr4+LibmJhIOgw0mJktaerknMbWr07lhDTt8QMAgOiZ2UPOufFm7/H0D8RidKSY6slo2uMHAAD9RfoHAAAAEBKTagAAACAkJtUAAABASEyqAQAAgJCYVAMAAAAhMakGAAAAQmJSDQAAAITEpBoAAAAIiUk1AAAAEBKTagAAACAkJtUAAABASEyqAQAAgJCYVAMAAAAhMakGAAAAQmJSDQAAAITEpBoAAAAIiUk1AAAAEBKTagAAACAkJtUAAABASEyqAQAAgJCYVAMAAAAhMakGAAAAQmJSDQAAAITEpBoAAAAIiUk1AAAAEBKTagAAACAkJtUAAABASEyqAQAAgJCYVAMAAAAhMakGAAAAQmJSDQAAAITEpBoAAAAIiUk1AAAAEBKTagAAACAkJtUAAABASEyqAQAAgJCYVAMAAAAhMakGAAAAQmJSDQAAAITEpBoAAAAIiUk1AAAAEBKTagAAACAkJtUAAABASEyqAQAAgJCYVAMAAAAhMakGAAAAQmJSDQAAAITEpBoAAAAIiUk1AAAAEBKTagAAACAkJtUAAABASEyqAQAAgJCYVAMAAAAhMakGAAAAQmJSDQAAAITEpBoAAAAIiUk1AAAAEBKTagAAACAkJtUAAABASEyqAQAAgJCYVAMAAAAhMakGAAAAQmJSDQAAAITEpBoAAAAIiUk1AAAAEBKTagAAACAkJtUAAABASEyqM25mtqRHjj2vmdlS0qEAAABk1lDSASA+ByantWf/ERVyOS1WKtq3a5uu2bEp6bAAAAAyhzvVGTUzW9Ke/Uc0v1jR6dKS5hcr2r3/CHesAQAAYsCkOqOmTs6pkFt5egu5nKZOziUUEQAAQHZ5N6k2szeZ2VNmdtTMbks6nrQaW79ai5XKitcWKxWNrV+dUEQAAADZ5dWk2szykj4u6WckXSrpHWZ2abJRxatWSHj0+OlICwpHR4rat2ubVhVyWlsc0qpCTvt2bdPoSDF0rGlJIWkXb9qOBQAA+M23QsVXSzrqnPtnSTKzz0p6q6QnEo0qJrVCQkmaX6yomDdZziIrKLxmxybt3LpBUyfnNLZ+dagJddqKHtvFm7ZjAQAA/vPqTrWkTZKO1f0+VX0tc+oLCecXl9M0SmUXeUHh6EhR2zevC32HOk1Fj+3iTduxAACAdPBtUm1NXnPnLWR2s5lNmNnEiRMn+hBW9JoVEtb4VlCYtqLHdvGm7VgAAEA6+DapnpK0ue73MUnfaVzIOXenc27cOTe+cePGvgUXpWaFhDW+FRSmreixXbxpOxYAAJAOvk2q/0HSJWb2UjMblvR2SQcTjikW9YWEqwrLp6GYt0gKCqMWR9FjnNrFm7ZjAQAA6WDOnZddkSgze7OkP5KUl/RJ59zvtVt+fHzcTUxM9CO0WMzMljR1ck5rhvM6s1AOXVAYp1qsPsdYr128aTsWAACQPDN7yDk33vQ93ybV3Ur7pBoAAADp0G5S7Vv6BwAAAJA6TKoBAACAkJhUAwAAACExqQYAAABCYlINAAAAhMSkGgAAAAiJSTUAAAAQEpNqAAAAICQm1QAAAEBITKoBAACAkJhUAwAAACExqQYAAABCYlINAAAAhMSkGgAAAAiJSTUAAAAQEpNqAAAAICQm1QAAAEBI5pxLOoZQzOyEpG9HuMkNkp6LcHvoD85bOnHe0onzlk6ct/Ti3Pnjh51zG5u9kfpJddTMbMI5N550HOgO5y2dOG/pxHlLJ85benHu0oH0DwAAACAkJtUAAABASEyqz3dn0gGgJ5y3dOK8pRPnLZ04b+nFuUsBcqoBAACAkLhTDQAAAIQ0sJNqM3uTmT1lZkfN7LYm75uZfaT6/hEze2UScWKlAOftKjM7ZWaT1f/en0ScOMfMPmlmz5rZYy3e57PmoQDnjc+ah8xss5n9vZk9aWaPm9m7myzDZ84zAc8bnznPDSUdQBLMLC/p45L+laQpSf9gZgedc0/ULfYzki6p/vcaSX9S/RMJCXjeJOlrzrmr+x4gWvlzSR+TdHeL9/ms+enP1f68SXzWfLQk6X3OuYfNbK2kh8zsK/z95r0g503iM+e1Qb1T/WpJR51z/+ycW5D0WUlvbVjmrZLudssekLTOzC7qd6BYIch5g2ecc/dJ+l6bRfiseSjAeYOHnHPfdc49XP35tKQnJW1qWIzPnGcCnjd4blAn1ZskHav7fUrnX7xBlkF/BT0nl5vZI2b2N2Z2WX9CQwh81tKLz5rHzOxiSa+Q9GDDW3zmPNbmvEl85rw2kOkfkqzJa42PQQmyDPoryDl5WMstRGfN7M2SPq/lf+KEv/ispROfNY+Z2Yik/ZLe45x7ofHtJqvwmfNAh/PGZ85zg3qnekrS5rrfxyR9p4dl0F8dz4lz7gXn3Gz15y9JKpjZhv6FiB7wWUshPmv+MrOClidmn3bO/VWTRfjMeajTeeMz579BnVT/g6RLzOylZjYs6e2SDjYsc1DSjdUq6Z+SdMo5991+B4oVOp43M/shM7Pqz6/W8jU+0/dI0Q0+aynEZ81P1XPyZ5KedM79QYvF+Mx5Jsh54zPnv4FM/3DOLZnZrZL+h6S8pE865x43s1+uvv9fJH1J0pslHZX0fUm/kFS8WBbwvF0r6d+b2ZKkOUlvd3Q4SpSZfUbSVZI2mNmUpA9IKkh81nwW4LzxWfPTTkk/L+lRM5usvvabkrZIfOY8FuS88ZnzHB0VAQAAgJAGNf0DAAAAiAyTagAAACAkJtUAAABASEyqAQAAgJCYVAMAACDTzOyTZvasmT0WcPnrzewJM3vczP57oHV4+gcAAACyzMyulDQr6W7n3I93WPYSSfdIer1z7qSZ/aBz7tlO++BONQB4wsx+q3pX5IiZTZrZa7pc//80sxPVdSfN7O4eYlhnZr/S7XoA4DPn3H2Svlf/mpn9qJl92cweMrOvmdmPVd/6JUkfd86drK7bcUItDWjzFwDwjZldLulqSa90zpWq7YeHe9jUXzjnbg0RyjpJvyLpj0NsAwDS4E5Jv+yce7p6E+OPJb1e0sskyczu13Kzud92zn2508a4Uw0AfrhI0nPOuZIkOeeec859x8zeZGb/ZGaHzewjZvbFbjdsZu81s8eq/72nw+sfkvSj1Tvdd5jZiJnda2YPm9mjZvbWuvVvr8b2FTP7jJn9evX1Vnd/AMALZjYi6bWS/rLaxfJPtfw9LC3fdL5Ey11l3yHpLjNb12mb3KkGAD/8raT3m9k3Jf2dpL+Q9KCkT2j5zsnR6mudvM3Mrqj+/J8lHdFyG+rXSDJJD5rZ/6vlmyrNXr9N0o8753ZIkpkNSfq3zrkXqnfPHzCzg5JeJWmXpFdo+e+ShyU9VN1vq7s/AOCLnKTna991DaYkPeCcW5T0LTN7SsuT7H/otEEAQMKcc7NanqjeLOmElifQvyzpW865p91yVfl/C7Cpv3DO7aj+918lXSHp/3HOnanu468k/XSb1xuZpN83syNanuxvknRhdf0Dzrk559xpSV+QOt79AQAvOOde0PKE+TpJsmXbq29/XtLrqq9v0HI6yD932iZ3qgHAE865sqSvSvqqmT0q6SZJYR/RZF2+3ujnJG2U9Crn3KKZ/YukVW3Wb3f3BwASYWaf0XI6xwYzm5L0AS1/v/2Jmf1HSQVJn5X0iKT/Ielfm9kTksqS/oNzbqbTPrhTDQAeMLOXVx/jVLND0nFJLzWzH62+9o4eNn2fpH9jZi8yszWS/q2kr7V5/bSktXXrXyDp2eqE+nWSfrj6+mFJbzGzVdW70z8rdbz7AwCJcM69wzl3kXOu4Jwbc879mXPuW865NznntjvnLnXOfbC6rHPOvbf62k845z4bZB/cqQYAP4xI+mi1GGZJyznUN0v6nKS/NrPntDyRbft81UbOuYfN7M8l/c/qS3c55/5Rktq8fn+1QcLfSNor6QtmNiFpUtI/Vbf7D9Xc6kckfVvShKRT1W21uvsDAJlF8xcASAkzu0rSrzvnrk44FEnL+dPOuVkze5GW73zf7Jx7OOm4ACAJ3KkGAPTqTjO7VMs51p9iQg1gkHGnGgBSxsx+QdK7G16+3zl3SxLxAACYVAMAAACh8fQPAAAAICQm1QAAAEBITKoBAACAkJhUAwAAACExqQYAAABC+v8BkK0A26cRMjwAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 864x648 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Square Footage versus Energy Score\n",
"\n",
"office.plot(kind = 'scatter', x = 'Sq_Footage', y = 'Energy_Score', figsize = (12, 9))\n",
"plt.savefig('scatter.png')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "a5c7986c",
"metadata": {},
"outputs": [],
"source": [
"# I can't tell from the plot whether there's any relationship between those two variables."
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "3b33f87e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Frequency'>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# How about smaller office buildings?\n",
"\n",
"office_small = df[df['Bldg_Class'] == 'O2']\n",
"office_small['Energy_Score'].plot(kind = 'hist')"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "41db8282",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Letter_Score\n",
"A 21\n",
"B 31\n",
"C 17\n",
"D 38\n",
"F 23\n",
"Name: Block, dtype: int64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Distribution of grades\n",
"\n",
"office_small.groupby(['Letter_Score'])['Block'].count()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "490d052a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Sq_Footage', ylabel='Energy_Score'>"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Square Footage versus Energy Score\n",
"\n",
"office_small.plot(kind = 'scatter', x = 'Sq_Footage', y = 'Energy_Score', figsize = (20, 10))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "a75608c7",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Something different: hotels\n",
"\n",
"hotels = df[df['Bldg_Class'] == 'H2']\n",
"hotels['Energy_Score'].plot(kind = 'hist')\n",
"plt.savefig('hotels.png')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "a6c9fbd4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Letter_Score\n",
"A 6\n",
"B 3\n",
"C 9\n",
"D 64\n",
"F 18\n",
"Name: Block, dtype: int64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Distribution of grades\n",
"\n",
"hotels.groupby(['Letter_Score'])['Block'].count()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "5d0ea74d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Sq_Footage', ylabel='Energy_Score'>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Square Footage versus Energy Score\n",
"\n",
"hotels.plot(kind = 'scatter', x = 'Sq_Footage', y = 'Energy_Score', figsize = (20, 10))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "e12fed9d",
"metadata": {},
"outputs": [],
"source": [
"# Again, no apparent connection between score and building size.\n",
"# But it's obvious that hotels score worse than office buildings."
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "9719cc6e",
"metadata": {},
"outputs": [],
"source": [
"# Lastly, let's take a look at residential buildings.\n",
"# This time we'll zoom out and consider two building classes:\n",
"# Walk up apartments and elevator apartments."
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "ca8d4f3e",
"metadata": {},
"outputs": [],
"source": [
"walk_up = df[df['Bldg_Class'].str.contains('C')]\n",
"elevator = df[df['Bldg_Class'].str.contains('D')]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "182f44b0",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Frequency'>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"walk_up['Energy_Score'].plot(kind = 'hist')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "81203621",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Letter_Score\n",
"A 403\n",
"B 302\n",
"C 388\n",
"D 1909\n",
"F 285\n",
"Name: Block, dtype: int64"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"walk_up.groupby(['Letter_Score'])['Block'].count()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "5d84db66",
"metadata": {},
"outputs": [],
"source": [
"# Many Ds and Fs, and the rest are evenly distributed."
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "727698e7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Sq_Footage', ylabel='Energy_Score'>"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"walk_up.plot(kind = 'scatter', x = 'Sq_Footage', y = 'Energy_Score', figsize = (20, 10))"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "31162192",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Frequency'>"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"elevator['Energy_Score'].plot(kind = 'hist')"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "c95f189b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Letter_Score\n",
"A 1683\n",
"B 1863\n",
"C 1803\n",
"D 4136\n",
"F 756\n",
"Name: Block, dtype: int64"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"elevator.groupby(['Letter_Score'])['Block'].count()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "bc9de94c",
"metadata": {},
"outputs": [],
"source": [
"# Elevator apartments fared better overall than walk ups."
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "4bc3c3d4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Sq_Footage', ylabel='Energy_Score'>"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"elevator.plot(kind = 'scatter', x = 'Sq_Footage', y = 'Energy_Score', figsize = (20, 10))"
]
},
{
"cell_type": "markdown",
"id": "29a22a46",
"metadata": {},
"source": [
"## Findings"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "87a96d0f",
"metadata": {},
"outputs": [],
"source": [
"# Scatter plots suggest that the relationship between a building's size and its energy rating\n",
"# is not simple. I had hoped the plots would show either a positive or negative relationship between square footage\n",
"# and energy rating.\n",
"\n",
"# The analysis elaborated here cannot answer the research question. However, some trends were uncovered in the\n",
"# process. Hotels scored worse overall than office buildings. And elevator buildings performed better than walk up\n",
"# apartments."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}