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cleanup.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: jokeke
"""
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
raw_data_2020 = pd.read_excel('data/2007-2020-PIT-Estimates-by-state.xlsx', sheet_name = "2020", na_values=['NA']).iloc[:57, :18].drop([3, 56])
types_changed = raw_data_2020[['Overall Homeless, 2020', 'Overall Homeless - Under 18, 2020',
'Overall Homeless - Age 18 to 24, 2020',
'Overall Homeless - Over 24, 2020', 'Overall Homeless - Female, 2020',
'Overall Homeless - Male, 2020', 'Overall Homeless - Transgender, 2020',
'Overall Homeless - Gender Non-Conforming, 2020',
'Overall Homeless - Non-Hispanic/Non-Latino, 2020',
'Overall Homeless - Hispanic/Latino, 2020',
'Overall Homeless - White, 2020',
'Overall Homeless - Black or African American, 2020',
'Overall Homeless - Asian, 2020',
'Overall Homeless - American Indian or Alaska Native, 2020',
'Overall Homeless - Native Hawaiian or Other Pacific Islander, 2020']].apply(pd.to_numeric)
raw_data_2020[['Overall Homeless, 2020', 'Overall Homeless - Under 18, 2020',
'Overall Homeless - Age 18 to 24, 2020',
'Overall Homeless - Over 24, 2020', 'Overall Homeless - Female, 2020',
'Overall Homeless - Male, 2020', 'Overall Homeless - Transgender, 2020',
'Overall Homeless - Gender Non-Conforming, 2020',
'Overall Homeless - Non-Hispanic/Non-Latino, 2020',
'Overall Homeless - Hispanic/Latino, 2020',
'Overall Homeless - White, 2020',
'Overall Homeless - Black or African American, 2020',
'Overall Homeless - Asian, 2020',
'Overall Homeless - American Indian or Alaska Native, 2020',
'Overall Homeless - Native Hawaiian or Other Pacific Islander, 2020']] = types_changed
# Renaming columns
raw_data_2020.columns = ["State", "Total.Population","CoC", "Total.Homeless", "Homeless.U18", "Homeless.18.to.24",
"Homeless.Over.24", "Homeless.Female", "Homeless.Male", "Homeless.Trans",
"Homeless.NC", "Homeless.Non.Lat", "Homeless.Lat", "Homeless.White",
"Homeless.Black", "Homeless.Asian", "Homeless.Indian", "Homeless.Native"]
raw_data_2020['Homeless.Under.24'] = np.add(raw_data_2020['Homeless.U18'],raw_data_2020['Homeless.18.to.24'])
raw_data_2020 = raw_data_2020.drop(['Homeless.U18', 'Homeless.18.to.24'], axis=1)
raw_data_2020['Homeless.Rate'] = np.multiply(np.divide(raw_data_2020['Total.Homeless'], raw_data_2020['Total.Population']), 10000)
# including regions
regions = pd.read_csv('data/Regions.csv').drop([55])
new_frame = pd.merge(raw_data_2020, regions)
plt.style.use('classic')
plt.hist(raw_data_2020['Homeless.Rate'], bins=40, color="#ADD8E6", edgecolor="black");
def homeless_rate(x):
data = np.multiply(np.divide(x, new_frame['Total.Population']), 10000)
return data
new_frame.columns
rates = new_frame[['Total.Homeless',
'Homeless.Over.24', 'Homeless.Female', 'Homeless.Male',
'Homeless.Trans', 'Homeless.NC', 'Homeless.Non.Lat', 'Homeless.Lat',
'Homeless.White', 'Homeless.Black', 'Homeless.Asian', 'Homeless.Indian',
'Homeless.Native', 'Homeless.Under.24', 'Homeless.Rate']].apply(homeless_rate)
new_frame[['Total.Homeless',
'Homeless.Over.24', 'Homeless.Female', 'Homeless.Male',
'Homeless.Trans', 'Homeless.NC', 'Homeless.Non.Lat', 'Homeless.Lat',
'Homeless.White', 'Homeless.Black', 'Homeless.Asian', 'Homeless.Indian',
'Homeless.Native', 'Homeless.Under.24', 'Homeless.Rate']] = rates
new_frame.to_csv('data/New_Data_2020.csv')