USA County Health Rankings¶

Create CSV files for choropleth maps of the USA displaying data from County Health Rankings.

In [1]:

import pandas as pd

# FIPS as string so leading zeros are kept
converters = {'FIPS': str}

df = pd.read_excel('data/2014 County Health Rankings Data - v1.xls', 'Ranked Measure Data', skiprows=1, converters=converters)
df.head()

Out[1]:

	FIPS	State	County	Deaths	YPLL Rate	95% CI - Low	95% CI - High	Quartile	Sample Size	% Fair/Poor	...	# Households with Severe Problems	% Severe Housing Problems	Quartile.31	# Drive Alone	Workers	% Drive Alone	Quartile.32	Workers who Drive Alone	Long Commute - Drives Alone	Quartile.33
0	01001	Alabama	Autauga	729	8376.42	7522.076	9230.764	1	407	22.8	...	2300	11.663286	1	21393	24369	87.787763	4	21393	41.9	3
1	01003	Alabama	Baldwin	2325	7770.16	7291.646	8248.674	1	1664	12.7	...	9370	13.486866	2	66189	79588	83.164547	2	66189	35.2	3
2	01005	Alabama	Barbour	422	9458.14	8107.935	10808.345	2	423	23.4	...	1650	16.845329	4	7431	8990	82.658509	2	7431	31.5	2
3	01007	Alabama	Bibb	390	11543.90	9945.206	13142.594	4	379	17.9	...	710	9.543011	1	6888	8366	82.333254	2	6888	49.7	4
4	01009	Alabama	Blount	826	8505.64	7672.032	9339.248	1	367	24.2	...	2595	12.594031	2	18606	23117	80.486222	1	18606	60.3	4

5 rows × 147 columns

USA Alcohol-Impaired Driving Deaths¶

# Alcohol-Impaired Driving Deaths: The total number of alcohol-impaired motor vehicle deaths
# Driving Deaths: The total number of motor vehicle deaths
% Alcohol-Impaired: Percent of driving deaths with alcohol involvement

Select relevant columns and drop NA values so columns can be converted to integers, see Support for integer NA.

In [2]:

df_out = df.copy()[['FIPS', '# Alcohol-Impaired Driving Deaths', '# Driving Deaths', '% Alcohol-Impaired']]
df_out.dropna(inplace=True)
df_out['# Alcohol-Impaired Driving Deaths'] = df_out['# Alcohol-Impaired Driving Deaths'].astype(int, copy=False)
df_out['# Driving Deaths'] = df_out['# Driving Deaths'].astype(int, copy=False)

Adapt column names to map title and write CSV file.

In [3]:

df_out.columns = ['FIPS', 'Total Number with Alcohol Involvement', 'Total Number', 'Percent with Alcohol Involvement']
df_out.to_csv('../static/data/csv/usa-alcohol-driving-deaths.csv', encoding='utf-8', index=False)

Physically Inactive¶

Percent of adults that report no leisure time physical activity.

In [4]:

df_out = df.copy()[['FIPS', '% Physically Inactive']]
df_out.dropna(inplace=True)

In [5]:

df_out.columns = ['FIPS', '% Physically Inactive']
df_out.to_csv('../static/data/csv/usa-physically-inactive.csv', encoding='utf-8', index=False)

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USA County Health Rankings¶

USA Alcohol-Impaired Driving Deaths¶

Physically Inactive¶

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Physical Inactivity: Percent of US adults that report no leisure time physical activity

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