In [5]:

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

Load Arguments¶

In [6]:

import pandas as pd
# import libraries
import subprocess
subprocess.call('pip install folium'.split())
import folium
ready_table = pd.read_csv(ready_table_path)

In [7]:

ready_table.head()

Out[7]:

	Unnamed: 0	Unnamed: 0.1	Company Name	BIN	Industry	Business	Program	Effective Date	Address	Postcode	Borough	Latitude	Longitude	Month Count	Periodic Savings	Total Tree Count within 0.5 Mile	Periodic Savings within 0.5 Mile
0	0	0	139 ACA Realty, Inc.	4003160	Commercial	Limousine Service	ICIP	2008-04-07	43-23 35th Street	11101	QUEENS	40.745706	-73.929565	116	1068.75	683	1423.931818
1	1	1	141 Lake Avenue Realty c/o JR Produce, Inc.	5146740	Wholesale/Warehouse/Distribution	Dist. of prepacked salads	ICIP	2009-12-08	141 Lake Avenue	10303	STATEN IS	40.633153	-74.150999	96	494.93	21	336.525000
2	2	2	14-10 123rd Street LLC	4098344	Commercial	Electrical Parts Mfg.	ICIP	2011-03-04	14-10 123rd Street	11356	QUEENS	40.785144	-73.844833	81	263.25	447	1079.380000
3	3	3	183 Lorriane Street LLC	3336622	Wholesale/Warehouse/Distribution	Commercial Storage facility	ICIP	2015-11-06	183 Lorraine Street	11231	BROOKLYN	40.673106	-74.002300	25	4200.66	224	2846.165714
4	4	4	21st Century Optics, Inc.	4003447	Manufacturing	Eye glasses	Tenant	2009-01-07	47-00 33rd Street	11101	QUEENS	40.742386	-73.932148	107	2016.42	658	1524.019111

In [8]:

len(ready_table)

Out[8]:

In [9]:

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

Out[9]:

	Company Name	BIN	Industry	Business	Program	Effective Date	Address	Postcode	Borough	Latitude	Longitude	Month Count	Periodic Savings	Total Tree Count within 0.5 Mile	Periodic Savings within 0.5 Mile
0	139 ACA Realty, Inc.	4003160	Commercial	Limousine Service	ICIP	2008-04-07	43-23 35th Street	11101	QUEENS	40.745706	-73.929565	116	1068.75	683	1423.931818
1	141 Lake Avenue Realty c/o JR Produce, Inc.	5146740	Wholesale/Warehouse/Distribution	Dist. of prepacked salads	ICIP	2009-12-08	141 Lake Avenue	10303	STATEN IS	40.633153	-74.150999	96	494.93	21	336.525000
2	14-10 123rd Street LLC	4098344	Commercial	Electrical Parts Mfg.	ICIP	2011-03-04	14-10 123rd Street	11356	QUEENS	40.785144	-73.844833	81	263.25	447	1079.380000
3	183 Lorriane Street LLC	3336622	Wholesale/Warehouse/Distribution	Commercial Storage facility	ICIP	2015-11-06	183 Lorraine Street	11231	BROOKLYN	40.673106	-74.002300	25	4200.66	224	2846.165714
4	21st Century Optics, Inc.	4003447	Manufacturing	Eye glasses	Tenant	2009-01-07	47-00 33rd Street	11101	QUEENS	40.742386	-73.932148	107	2016.42	658	1524.019111

In [10]:

#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)

Out[10]:

In [11]:

len(ready_table)

Out[11]:

In [12]:

# Make a data frame with dots to show on the map
# data = pd.DataFrame({
# 'lat':[-58, 2, 145, 30.32, -4.03, -73.57, 36.82, -38.5],
# 'lon':[-34, 49, -38, 59.93, 5.33, 45.52, -1.29, -12.97],
# 'name':['Buenos Aires', 'Paris', 'melbourne', 'St Petersbourg', 'Abidjan', 'Montreal', 'Nairobi', 'Salvador']
# })
# data

In [16]:

 # Make an empty map
m = folium.Map(location=[20, 0], tiles="Mapbox Bright", zoom_start=2)
 
# I can add marker one by one on the map
for i in range(0,len(ready_table)):
    folium.Marker([ready_table.iloc[i]['Longitude'], ready_table.iloc[i]['Latitude']], popup=ready_table.iloc[i]['Total Tree Count within 0.5 Mile']).add_to(m)
m.save('312_markers_on_folium_map1.html')

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-16-1f63ba64e252> in <module>
      4 # I can add marker one by one on the map
      5 for i in range(0,len(ready_table)):
----> 6    folium.Marker([ready_table.iloc[i]['Longitude'], ready_table.iloc[i]['Latitude']], popup=ready_table.iloc[i]['Total Tree Count within 0.5 Mile']).add_to(m)
      7 m.save('312_markers_on_folium_map1.html')

~/.virtualenvs/crosscompute/lib/python3.6/site-packages/folium/map.py in __init__(self, location, popup, tooltip, icon)
    256             self.add_child(Popup(popup))
    257         elif popup is not None:
--> 258             self.add_child(popup)
    259         if isinstance(tooltip, Tooltip):
    260             self.add_child(tooltip)

~/.virtualenvs/crosscompute/lib/python3.6/site-packages/branca/element.py in add_child(self, child, name, index)
    106         """Add a child."""
    107         if name is None:
--> 108             name = child.get_name()
    109         if index is None:
    110             self._children[name] = child

AttributeError: 'numpy.int64' object has no attribute 'get_name'

Render Map¶

In [ ]:

ready_geotable = ready_table.copy()  # Prevent SettingwithCopyWarning

In [ ]:

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

In [ ]:

# 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']

Hypothesis:<br>

Bigger circle tend to be darker<br>
Also, companies who saved the more money tend to be surrounded by more trees.<br> Discovery:
Biggest circles are found in Bronx and STATEN IS<br>
Bigger circles in Bronx are indeed surrounded by more trees<br> (ex.Albert Einstein College of Medicine of Yeshiva Uni, saved 102919.54, 419 trees<br> Montefiore Medical Center, saved 11400.63 in ICAP and 69506.82 in ICIP, 303 trees) <br>
While the biggest circle of all is found in Stanley IS but the color is near transparent(means very few trees around) (ex.VISY PAPER RECYCLING saved 274038.51, 41 trees)

In [ ]:

# See what we did
ready_geotable[:3]

In [ ]:

# 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

Render Plot¶

In [ ]:

# %matplotlib inline
# axes = address_table[[
#     'Tree Count Within 100 Meters',
#     'Predicted Graduation Rate',
# ]].plot(kind='bar')

In [ ]:

# # Save file to target folder to include it in the result download
# target_path = target_folder + '/c.png'
# figure = axes.get_figure()
# figure.savefig(target_path)
# print(f'c_image_path = {target_path}')

Predicted Metrics by Address¶

YOUR INTERPRETATION OF THE RESULTS

{a_table : YOUR TABLE NAME ? YOUR TABLE DESCRIPTION}

{b_geotable : YOUR MAP NAME ? YOUR MAP DESCRIPTION}

{c_image : YOUR PLOT NAME ? YOUR PLOT DESCRIPTION}

Pay Notebook Creator: Haige Cui	0
Set Container: Numerical CPU with TINY Memory for 10 Minutes	0
Total	0

ECSP

Load Arguments¶

Render Map¶

Render Plot¶

Predicted Metrics by Address¶