Sugar

Sugar and Education

Data Cleaning

CDC 2013 Source

TABLE 3. Crude prevalence* of sugar-sweetened beverage† consumption ≥1 time/day among adults, by employment status, education, and state — Behavioral Risk Factor Surveillance System, 23 states and District of Columbia, 2013

https://www.cdc.gov/mmwr/volumes/65/wr/mm6507a1.htm

import pandas as pd

cdc_2013 = pd.read_csv("https://raw.githubusercontent.com/noahgift/sugar/master/data/education_sugar_cdc_2003.csv")
cdc_2013.set_index("State", inplace=True)
cdc_2013.head()

	Employed	Not employed	Retired	<High school	High school	Some college	College graduate
State
Alaska	26.2 (23.6–28.9)	32.1 (27.8–36.8)	16.0 (12.6–20.2)	47.1 (37.8–56.5)	34.9 (31.1–38.9)	24.2 (21.0–27.8)	12.9 (10.5–15.7)
Arizona	33.0 (28.5–37.8)	28.7 (23.5–34.5)	13.8 (10.8–17.5)	40.4 (30.9–50.7)	36.5 (30.7–42.7)	24.4 (19.9–29.4)	14.6 (11.6–18.3)
California	22.9 (20.9–25.1)	30.2 (27.1–33.4)	15.0 (12.2–18.2)	38.5 (34.2–43.0)	29.9 (26.5–33.7)	21.4 (18.8–24.2)	11.5 (9.8–13.5)
Connecticut	18.9 (17.1–20.9)	24.3 (20.8–28.2)	15.0 (12.7–17.7)	27.8 (22.4–33.9)	26.9 (23.7–30.3)	19.9 (17.2–23.0)	10.2 (8.7–12.0)
District of Columbia	18.5 (15.7–21.7)	34.6 (29.5–40.1)	18.5 (15.3–22.1)	45.6 (36.4–55.2)	39.0 (33.1–45.2)	28.9 (23.4–35.0)	8.4 (7.0–10.1)

for column in cdc_2013.columns:
  cdc_2013[column]=cdc_2013[column].str.replace(r"\(.*\)","")
  cdc_2013[column]=pd.to_numeric(cdc_2013[column])
  
cdc_2013.reset_index(inplace=True) 
cdc_2013.head()
  

	State	Employed	Not employed	Retired	<High school	High school	Some college	College graduate
0	Alaska	26.2	32.1	16.0	47.1	34.9	24.2	12.9
1	Arizona	33.0	28.7	13.8	40.4	36.5	24.4	14.6
2	California	22.9	30.2	15.0	38.5	29.9	21.4	11.5
3	Connecticut	18.9	24.3	15.0	27.8	26.9	19.9	10.2
4	District of Columbia	18.5	34.6	18.5	45.6	39.0	28.9	8.4

cdc_2013.describe()

	Employed	Not employed	Retired	<High school	High school	Some college	College graduate
count	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000
mean	32.325000	35.408333	18.533333	44.662500	37.416667	30.262500	17.358333
std	9.917803	9.056485	5.975142	8.588658	8.243399	8.490138	6.730264
min	16.700000	21.500000	8.900000	27.800000	21.500000	16.900000	7.800000
25%	23.400000	29.750000	14.625000	39.625000	31.925000	24.200000	12.850000
50%	31.550000	32.600000	16.750000	46.350000	36.750000	28.200000	15.300000
75%	42.025000	46.025000	22.550000	51.200000	46.525000	39.250000	23.500000
max	49.700000	49.500000	29.700000	60.000000	50.800000	47.200000	34.900000

Education and Sugar

!wget https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/us-states.json
!ls -l

--2019-03-20 23:57:49--  https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/us-states.json
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 87688 (86K) [text/plain]
Saving to: ‘us-states.json.2’

us-states.json.2    100%[===================>]  85.63K  --.-KB/s    in 0.02s   

2019-03-20 23:57:50 (3.39 MB/s) - ‘us-states.json.2’ saved [87688/87688]

total 280
drwxr-xr-x 1 root root  4096 Mar  8 17:26 sample_data
-rw-r--r-- 1 root root 87688 Mar 20 22:43 us-states.json
-rw-r--r-- 1 root root 87688 Mar 20 22:43 us-states.json.1
-rw-r--r-- 1 root root 87688 Mar 20 23:57 us-states.json.2

Low Education == High Sugar

import folium
m = folium.Map(location=[36, -102], zoom_start=3)

folium.Choropleth(
    geo_data="us-states.json",
    name='choropleth',
    data=cdc_2013,
    columns=['State', '<High school'],
    key_on='feature.properties.name',
    fill_color='OrRd',
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name='<High school Education and Grams Sugar Intake Daily'
).add_to(m)

folium.LayerControl().add_to(m)

m

College Education Major Decrease in Sugar Intake

import folium
m = folium.Map(location=[36, -102], zoom_start=3)

folium.Choropleth(
    geo_data="us-states.json",
    name='choropleth',
    data=cdc_2003,
    columns=['State', 'College graduate'],
    key_on='feature.properties.name',
    fill_color='OrRd',
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name='College graduate and Grams Sugar Intake Daily'
).add_to(m)

folium.LayerControl().add_to(m)

m

Median Daily Sugar Intake by Category

cdc_2003.columns

Index(['State', 'Employed', 'Not employed', 'Retired', '<High school',
       'High school', 'Some college', 'College graduate'],
      dtype='object')

College Graduate

cdc_2003["College graduate"].median()

15.3

cdc_2003["<High school"].median()

46.35

cdc_2003[["State","College graduate", "<High school"]].plot.barh(
    stacked=True, figsize=(12, 12)).set_title("CDC 2013: Three Times Higher Sugar Intake College vs High School Grads")

Text(0.5, 1.0, 'CDC 2013: Three Times Higher Sugar Intake College vs High School Grads')

png