Class 3 in-class exercise solution#
Step 0#
import pandas as pd
requests_by_cd = pd.read_csv(
"https://storage.googleapis.com/python-public-policy2/data/311_community_districts.csv.zip"
)
requests_by_cd.head()
| boro_cd | Borough | CD Name | 2010 Population | num_311_requests | requests_per_capita | |
|---|---|---|---|---|---|---|
| 0 | 112 | Manhattan | Washington Heights, Inwood | 190020 | 14110 | 0.074255 |
| 1 | 405 | Queens | Ridgewood, Glendale, Maspeth | 169190 | 12487 | 0.073805 |
| 2 | 412 | Queens | Jamaica, St. Albans, Hollis | 225919 | 12228 | 0.054126 |
| 3 | 301 | Brooklyn | Williamsburg, Greenpoint | 173083 | 11863 | 0.068539 |
| 4 | 303 | Brooklyn | Bedford Stuyvesant | 152985 | 11615 | 0.075922 |
Step 1#
import plotly.express as px
fig = px.histogram(
requests_by_cd,
x="requests_per_capita",
title="Number of Community Districts with different volumes of requests per capita",
)
fig.show()