Chicago

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This notebooks displays some of the data available at Business Licenses - Current Active. We assume the data was downloaded.

from jyquickhelper import add_notebook_menu
add_notebook_menu()

%matplotlib inline

Data

from pyensae.datasource import download_data
file = download_data("rows.csv", url="https://data.cityofchicago.org/api/views/uupf-x98q/")

Businesses

import pandas
businesses = df = pandas.read_csv("rows.csv", low_memory=False)
df.head()

	ID	LICENSE ID	ACCOUNT NUMBER	SITE NUMBER	LEGAL NAME	DOING BUSINESS AS NAME	ADDRESS	CITY	STATE	ZIP CODE	...	LICENSE TERM START DATE	LICENSE TERM EXPIRATION DATE	LICENSE APPROVED FOR ISSUANCE	DATE ISSUED	LICENSE STATUS	LICENSE STATUS CHANGE DATE	SSA	LATITUDE	LONGITUDE	LOCATION
0	2488345-20160830	2488345	409564	1	BURKS HEATING AND COOLING SOLUTIONS, LLC	BURKS HEATING AND COOLING SOLUTIONS, LLC	5722 S WOOD ST 1ST	CHICAGO	IL	60636	...	08/30/2016	09/15/2018	08/30/2016	08/30/2016	AAI	NaN	NaN	41.789617	-87.669472	(41.78961679000226, -87.66947199630827)
1	2354707-20160916	2482568	393149	1	ANGELINE R. MC CARTHY	ANGELINE R. MC CARTHY	17707 S HARLEM ST 1	TINLEY PARK	IL	60477	...	09/16/2016	09/15/2018	NaN	08/30/2016	AAI	NaN	NaN	NaN	NaN	NaN
2	2374115-20160916	2481313	352159	3	Genesys Works Chicago	Genesys Works Chicago	180 N WABASH AVE 6 600	CHICAGO	IL	60601	...	09/16/2016	09/15/2018	08/30/2016	08/30/2016	AAI	NaN	1.0	41.885354	-87.626413	(41.885353703982936, -87.62641328540737)
3	2488347-20160830	2488347	409567	1	ROSA I. MONTOYA	SOUL MIND BODYWORK	3035 N MILWAUKEE AVE 1	CHICAGO	IL	60618	...	08/30/2016	09/15/2018	08/30/2016	08/30/2016	AAI	NaN	NaN	41.936432	-87.719244	(41.936432172755346, -87.71924410911548)
4	2488343-20160830	2488343	409562	1	TRANSITIONAL DEVELOPMENT REALTY INC.	TRANSITIONAL DEVELOPMENT REALTY INC.	29 W 159TH ST	HARVEY	IL	60426	...	08/30/2016	09/15/2018	08/30/2016	08/30/2016	AAI	NaN	NaN	NaN	NaN	NaN

5 rows × 34 columns

df.plot(x="LONGITUDE", y="LATITUDE", kind="scatter");

minlon, maxlon = df["LONGITUDE"].min(), df["LONGITUDE"].max()
minlat, maxlat = df["LATITUDE"].min(), df["LATITUDE"].max()
minlon, maxlon, minlat, maxlat

(-87.933976504, -87.525094136, 41.644670131999995, 42.022660268)

import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
ax.set_extent([minlon, maxlon, minlat, maxlat])
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.LAKES)
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.RIVERS)
ax.add_feature(cfeature.BORDERS, linestyle=':')

ax.plot(df["LONGITUDE"], df["LATITUDE"], '.', ms=0.9);