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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Store CSV to Store Segments
This Jupyter Notebook demonstrates how, by using ArcGIS, you can start with little more than a list of stores with sales volume and coordinates, and end with stores segmented by similar demographic characteristics.
Prepare Input Data
Acquire Demographic Analysis Factors Based on Drive Time Trade Areas Around Stores
Segment Stores Using KMeans
Note
Step1:  Load the data into a Pandas DataFrame from a CSV file.
Step2:  While the coordinates for each store are contained in an X (longitude) and Y (latitude) field, the data is not yet able to be recognized spatially. We need to create a point geometry for each location in a new field so the data will be recognized as spatial. Once this is done, we also can get rid of the explicity X and Y fields, since the location is now stored in the SHAPE field.
Step3:  Now, with the location data properly formatted to be recoginzed as point geometry, we can create a SpatialDataFrame with the store locations so the data will now be recognized as spatial data.
Step4:  Convert the SpatailDataFrame to a FeatureSet to use as input for subsequent analysis steps.
NOTE
Step5:  Acquire Demographic Analysis Factors Based on Drive Time Trade Areas Around Stores
Intantiate a Web GIS object instance.
Step6:  Get Variables for Enrichment from Server
Get the entire list of variables using the get method in the Python API, thus taking care of the access token for us.
Step7:  Convert the JSON response to a Pandas DataFrame filtered to just use the fields with names ending in _CY for current year variabes, which are the basic descriptive demographics. Using the DataFrame makes getting summarized values a little easier.
Step8:  Using the unique method to get a list of unique variable names, along with the enrichment categories.
Step9:  Perform Geoenrichment
Since the ArcGIS Python API requires a published layer to use the built in Geoenrichment method, we utilize the ArcGIS Python API's built in post method, which takes care of the token authetication, and also has the urllib.encode method built in for converting the payload from a dictionary for the post call.
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Python Code:
import pandas as pd
import arcgis
Explanation: Store CSV to Store Segments
This Jupyter Notebook demonstrates how, by using ArcGIS, you can start with little more than a list of stores with sales volume and coordinates, and end with stores segmented by similar demographic characteristics.
Prepare Input Data
Acquire Demographic Analysis Factors Based on Drive Time Trade Areas Around Stores
Segment Stores Using KMeans
Note: Increased IOPub
For visualization, if you did not start this notebook with an increased data rate limit, stop the notebook, go back to the command line, and start Jupyter Notebook using the following command.
jupyter notebook --NotebookApp.iopub_data_rate_limit=10000000000
Prepare Data
The data coming in from the CSV file, while the coordinate locations are present in the data, ArcGIS does not yet know how to recognize the data as spatial for subseqnet analysis steps. To accomplish this, we will load the data into a Pandas DataFrame, convert this into an ArcGIS SpatialDataFrame, and finally create an ArcGIS Feature Set, which we will then use for subsequent analysis.
End of explanation
df = pd.read_csv('./store_locations.csv', index_col='OBJECTID')
df.head()
Explanation: Load the data into a Pandas DataFrame from a CSV file.
End of explanation
df['SHAPE'] = df.apply(lambda row: arcgis.geometry.Point({'x': row.X, 'y': row.Y, 'spatialReference': {'wkid': 4326}}), axis=1)
df = df.drop(['X', 'Y'], axis=1)
df.head()
Explanation: While the coordinates for each store are contained in an X (longitude) and Y (latitude) field, the data is not yet able to be recognized spatially. We need to create a point geometry for each location in a new field so the data will be recognized as spatial. Once this is done, we also can get rid of the explicity X and Y fields, since the location is now stored in the SHAPE field.
End of explanation
sdf = arcgis.features.SpatialDataFrame(df)
sdf.set_geometry(col='SHAPE')  # assign the properly formatted shape field to be recognized by the SpatialDataFrame
sdf.reset_index(inplace=True, drop=True)
sdf.head()
# get a subset to test with, just the first five records
sdf = sdf[:5]
Explanation: Now, with the location data properly formatted to be recoginzed as point geometry, we can create a SpatialDataFrame with the store locations so the data will now be recognized as spatial data.
End of explanation
fs_store_locations = sdf.to_featureset()
fs_store_locations
Explanation: Convert the SpatailDataFrame to a FeatureSet to use as input for subsequent analysis steps.
NOTE: On 18Aug2017, this will not work unless you have access to the development reposititory, since there was a bug in the to_featureset method, which was recently fixed.
End of explanation
from getpass import getpass
gis_coldbrew = arcgis.gis.GIS(
    url='http://portal.coldbrew.esri.com/portal',
    username='headless', 
    password=getpass('Please enter the headless password: ')
)
Explanation: Acquire Demographic Analysis Factors Based on Drive Time Trade Areas Around Stores
Intantiate a Web GIS object instance.
End of explanation
resp_get_vars = gis_coldbrew._con.get(
    path='https://ba.coldbrew.esri.com/arcgis/rest/services/DefaultMap/MapServer/exts/BAServer/GetVariables/execute'
)
resp_get_vars
fs_service_area = arcgis.features.FeatureSet(result_service_area['saPolygons']['features'])
Explanation: Get Variables for Enrichment from Server
Get the entire list of variables using the get method in the Python API, thus taking care of the access token for us.
End of explanation
df = pd.DataFrame([field for field in resp_get_vars['results'][0]['value'] if field['name'].endswith('_CY')])
df.head()
Explanation: Convert the JSON response to a Pandas DataFrame filtered to just use the fields with names ending in _CY for current year variabes, which are the basic descriptive demographics. Using the DataFrame makes getting summarized values a little easier.
End of explanation
enrichment_variables = df.name.unique().tolist()
enrichment_variables
enrichment_categories = df.category.unique().tolist()
enrichment_categories
Explanation: Using the unique method to get a list of unique variable names, along with the enrichment categories.
End of explanation
trade_area_drive_time = 8  # in minutes
study_area_options = '{"areaType":"DriveTimeBuffer","bufferUnits":"esriDriveTimeUnitsMinutes",' + \
        '"bufferRadii":' + '[{drive_time}]'.format(drive_time=trade_area_drive_time) + '}"'
study_area_options = '{"areaType":"DriveTimeBuffer","bufferUnits":"esriDriveTimeUnitsMinutes","bufferRadii":[5]}'
url_geoenrich = gis_coldbrew.properties.helperServices.geoenrichment.url + "/Geoenrichment/Enrich"
payload = {
    'studyAreas': fs_store_locations.features,
#    'analysisVariables': enrichment_variables,
    'dataCollections': '["KeyUSFacts"]',
    'studyAreasOptions': study_area_options,
    'f': 'json'
}
headers = {
    'content-type': "application/x-www-form-urlencoded",
    'cache-control': "no-cache"
}
resp_enrich = gis_coldbrew._con.post(url_geoenrich, postdata=payload)
resp_enrich
response_feature_set = response['results'][0]['value']['FeatureSet'][0]
response_feature_set
fs_enrich = arcgis.features.FeatureSet(
    features=resp_enrich['results'][0]['value']['FeatureSet'][0]['features'], 
    fields=resp_enrich['results'][0]['value']['FeatureSet'][0]['fields']
)
fs_enrich
[field['name'] for field in response_feature_set.fields]
df_enrich = fs_enrich.df
df_enrich
Explanation: Perform Geoenrichment
Since the ArcGIS Python API requires a published layer to use the built in Geoenrichment method, we utilize the ArcGIS Python API's built in post method, which takes care of the token authetication, and also has the urllib.encode method built in for converting the payload from a dictionary for the post call.
End of explanation
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