.. _businesschicagorst:

=======
Chicago
=======


.. only:: html

    **Links:** :download:`notebook <business_chicago.ipynb>`, :downloadlink:`html <business_chicago2html.html>`, :download:`PDF <business_chicago.pdf>`, :download:`python <business_chicago.py>`, :downloadlink:`slides <business_chicago.slides.html>`, :githublink:`GitHub|_doc/notebooks/challenges/city_bike/business_chicago.ipynb|*`


This notebooks displays some of the data available at `Business Licenses
- Current
Active <https://data.cityofchicago.org/Community-Economic-Development/Business-Licenses-Current-Active/uupf-x98q>`__.
We assume the data was downloaded.

.. code:: ipython3

    from jyquickhelper import add_notebook_menu
    add_notebook_menu()






.. contents::
    :local:





.. code:: ipython3

    %matplotlib inline

Data
----

.. code:: ipython3

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

Businesses
----------

.. code:: ipython3

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






.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>ID</th>
          <th>LICENSE ID</th>
          <th>ACCOUNT NUMBER</th>
          <th>SITE NUMBER</th>
          <th>LEGAL NAME</th>
          <th>DOING BUSINESS AS NAME</th>
          <th>ADDRESS</th>
          <th>CITY</th>
          <th>STATE</th>
          <th>ZIP CODE</th>
          <th>...</th>
          <th>LICENSE TERM START DATE</th>
          <th>LICENSE TERM EXPIRATION DATE</th>
          <th>LICENSE APPROVED FOR ISSUANCE</th>
          <th>DATE ISSUED</th>
          <th>LICENSE STATUS</th>
          <th>LICENSE STATUS CHANGE DATE</th>
          <th>SSA</th>
          <th>LATITUDE</th>
          <th>LONGITUDE</th>
          <th>LOCATION</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>2488345-20160830</td>
          <td>2488345</td>
          <td>409564</td>
          <td>1</td>
          <td>BURKS HEATING AND COOLING SOLUTIONS, LLC</td>
          <td>BURKS HEATING AND COOLING SOLUTIONS, LLC</td>
          <td>5722 S WOOD ST 1ST</td>
          <td>CHICAGO</td>
          <td>IL</td>
          <td>60636</td>
          <td>...</td>
          <td>08/30/2016</td>
          <td>09/15/2018</td>
          <td>08/30/2016</td>
          <td>08/30/2016</td>
          <td>AAI</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>41.789617</td>
          <td>-87.669472</td>
          <td>(41.78961679000226, -87.66947199630827)</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2354707-20160916</td>
          <td>2482568</td>
          <td>393149</td>
          <td>1</td>
          <td>ANGELINE R. MC CARTHY</td>
          <td>ANGELINE R. MC CARTHY</td>
          <td>17707 S HARLEM ST 1</td>
          <td>TINLEY PARK</td>
          <td>IL</td>
          <td>60477</td>
          <td>...</td>
          <td>09/16/2016</td>
          <td>09/15/2018</td>
          <td>NaN</td>
          <td>08/30/2016</td>
          <td>AAI</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2374115-20160916</td>
          <td>2481313</td>
          <td>352159</td>
          <td>3</td>
          <td>Genesys Works Chicago</td>
          <td>Genesys Works Chicago</td>
          <td>180 N WABASH AVE 6 600</td>
          <td>CHICAGO</td>
          <td>IL</td>
          <td>60601</td>
          <td>...</td>
          <td>09/16/2016</td>
          <td>09/15/2018</td>
          <td>08/30/2016</td>
          <td>08/30/2016</td>
          <td>AAI</td>
          <td>NaN</td>
          <td>1.0</td>
          <td>41.885354</td>
          <td>-87.626413</td>
          <td>(41.885353703982936, -87.62641328540737)</td>
        </tr>
        <tr>
          <th>3</th>
          <td>2488347-20160830</td>
          <td>2488347</td>
          <td>409567</td>
          <td>1</td>
          <td>ROSA I. MONTOYA</td>
          <td>SOUL MIND BODYWORK</td>
          <td>3035 N MILWAUKEE AVE 1</td>
          <td>CHICAGO</td>
          <td>IL</td>
          <td>60618</td>
          <td>...</td>
          <td>08/30/2016</td>
          <td>09/15/2018</td>
          <td>08/30/2016</td>
          <td>08/30/2016</td>
          <td>AAI</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>41.936432</td>
          <td>-87.719244</td>
          <td>(41.936432172755346, -87.71924410911548)</td>
        </tr>
        <tr>
          <th>4</th>
          <td>2488343-20160830</td>
          <td>2488343</td>
          <td>409562</td>
          <td>1</td>
          <td>TRANSITIONAL DEVELOPMENT REALTY INC.</td>
          <td>TRANSITIONAL DEVELOPMENT REALTY INC.</td>
          <td>29 W 159TH ST</td>
          <td>HARVEY</td>
          <td>IL</td>
          <td>60426</td>
          <td>...</td>
          <td>08/30/2016</td>
          <td>09/15/2018</td>
          <td>08/30/2016</td>
          <td>08/30/2016</td>
          <td>AAI</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
        </tr>
      </tbody>
    </table>
    <p>5 rows × 34 columns</p>
    </div>



.. code:: ipython3

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



.. image:: business_chicago_7_0.png


.. code:: ipython3

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




.. parsed-literal::
    (-87.933976504, -87.525094136, 41.644670131999995, 42.022660268)



.. code:: ipython3

    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);



.. image:: business_chicago_9_0.png