ECSP

# CrossCompute
ready_table_path = 'Table with periodic savings(within 0.5 Mile) and tree count(within 0.5 Mile).csv'
target_folder = '/tmp'

import pandas as pd
ready_table = pd.read_csv(ready_table_path)

ready_table.head()

len(ready_table)

ready_table.drop(ready_table.columns[[0,1]], axis=1, inplace=True)
ready_table.head()

#ready_table = ready_table[['Longitude','Latitude','Total Tree Count within 0.5 Mile','Periodic Savings within 0.5 Mile']]
#ready_table=ready_table
len(ready_table)

len(ready_table)

ready_geotable = ready_table.copy()  # Prevent SettingwithCopyWarning

# Set radius for each point
# The bigger the circle, the more the company saved
ready_geotable['RadiusInPixelsRange5-50'] = ready_geotable['Periodic Savings over Months']

# Set color for each point using a gradient
# The darker the color, the more trees the company is surrounded by
ready_geotable['FillRedsFromMean'] = ready_geotable['Total Tree Count within 0.5 Mile']

# See what we did
ready_geotable[:3]

# Save file to target folder to include it in the result download
target_path = target_folder + '/b.csv'
ready_geotable.to_csv(target_path, index=False)
print(f'b_geotable_path = {target_path}')  # Print geotable_path to render map