Toronto Ambulance, where do you need us? - Python
Published:
A data visualization of Toronto Paramedic Services dispatches from 2010, compared to Toronto population size.
Section 1
1.0 Installing and importing dependencies
# !{sys.executable} -m pip install requests
# !{sys.executable} -m pip install pandas
# !{sys.executable} -m pip install xlrd
# !{sys.executable} -m pip install openpyxl
# !{sys.executable} -m pip install matplotlib
# !{sys.executable} -m pip install folium
import sys
import pandas as pd
import requests
import matplotlib.pyplot as plt
import folium
import re2.0 Loading and cleaning Toronto Paramedic data
# Read the dispatch dataset in with time-indexing
dispatches = pd.read_excel(r'/Users/erincameron/Desktop/datacamp/github/datacamp/datasets/tps_incident_data_2010-2019.xlsx', parse_dates=True, index_col="Dispatch_Time")
dispatches.sort_index()
dispatches.head()3.0 Subsetting data
# subset for only 2010 Dispatch_Times
dispatches_df = dispatches.loc["2010-01-01 00:01:36":"2010-12-31 23:57:27"]
# remove any missing data points
dispatches_df.isna()
dispatches_df.fillna(value=0, inplace=True)
dispatches_df.dropna()
# subset the data for the most recent year of calls (in 2019)
call_counts = dispatches_df.FSA.value_counts().rename_axis('FSA').reset_index(name='counts')
print(call_counts)
FSA counts
0 M6K 4481
1 M5A 4202
2 M4C 3846
3 M1E 3809
4 M1P 3802
.. ... ...
150 N2G 1
151 N4S 1
152 L2P 1
153 L5C 1
154 L6R 1
[155 rows x 2 columns]
# check if there are any missing values
print(call_counts["FSA"].isnull().sum())
0Section 2
1.0 Loading and cleaning Toronto Population dataset
population = pd.read_excel(r'/Users/erincameron/Desktop/datacamp/github/datacamp/datasets/canada_population_by_postal.xlsx')
population.sort_index()
population.head()
rename_pop_column = to_population.rename(columns = {'Geographic code': 'FSA'}, inplace = False)
# print(rename_pop_column.head())
call_counts = pd.merge(call_counts, rename_pop_column, on="FSA")
call_counts = call_counts.rename(columns = {'Population, 2016': 'population'}, inplace = False)
call_counts["call_per_capita"] = call_counts["counts"] / call_counts["population"]
print(call_counts.head())
FSA counts Geographic name Province or territory \
0 M6K 4481 M6K Ontario
1 M5A 4202 M5A Ontario
2 M4C 3846 M4C Ontario
3 M1E 3809 M1E Ontario
4 M1P 3802 M1P Ontario
Incompletely enumerated Indian reserves and Indian settlements, 2016 \
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
population Total private dwellings, 2016 \
0 40957.0 23344.0
1 41078.0 24186.0
2 46866.0 20178.0
3 46943.0 17637.0
4 45571.0 17129.0
Private dwellings occupied by usual residents, 2016 call_per_capita
0 22023.0 0.109407
1 22333.0 0.102293
2 19431.0 0.082064
3 17161.0 0.081141
4 16540.0 0.0834302.0 Subsetting data
# identify the outliers
print(call_counts.sort_values("call_per_capita").tail())
FSA counts Geographic name Province or territory \
23 M5B 2901 M5B Ontario
15 M5G 3143 M5G Ontario
81 M5H 1070 M5H Ontario
127 L4V 5 L4V Ontario
103 L5T 108 L5T Ontario
Incompletely enumerated Indian reserves and Indian settlements, 2016 \
23 NaN
15 NaN
81 NaN
127 NaN
103 NaN
population Total private dwellings, 2016 \
23 12785.0 8249.0
15 8423.0 5876.0
81 2005.0 1718.0
127 5.0 1.0
103 5.0 10.0
Private dwellings occupied by usual residents, 2016 call_per_capita
23 7058.0 0.226907
15 4929.0 0.373145
81 1243.0 0.533666
127 1.0 1.000000
103 4.0 21.600000
# Subset data to remove outlier datapoints at index 127 and 103 - data that is outside of Toronto boundaries
call_counts = call_counts[(call_counts["call_per_capita"] > 0.000000) & (call_counts["call_per_capita"] <= 0.533666)]
print(call_counts.sort_values("call_per_capita").tail())Visualizing the Data
1.0 Number of Dispatches per Postal Code
url = (
"https://raw.githubusercontent.com/BlizzWiz/IBM_Capstone_Project/master"
)
call_geo = f"{url}/toronto_m.geojson"
m = folium.Map(location=[43.7000, -79.3832], zoom_start=10)
folium.Choropleth(
geo_data=call_geo,
name="choropleth",
data=call_counts,
columns=["FSA", "counts"],
key_on="feature.properties.CFSAUID",
fill_color="BuPu",
fill_opacity=0.7,
line_opacity=0.5,
legend_name="Number of Dispatches per Postal Code",
).add_to(m)
folium.LayerControl().add_to(m)
m
2.0 Toronto Population, 2016
url = (
"https://raw.githubusercontent.com/BlizzWiz/IBM_Capstone_Project/master"
)
call_geo = f"{url}/toronto_m.geojson"
m = folium.Map(location=[43.7000, -79.3832], zoom_start=10)
folium.Choropleth(
geo_data=call_geo,
name="choropleth",
data=call_counts,
columns=["FSA", "population"],
key_on="feature.properties.CFSAUID",
fill_color="BuPu",
fill_opacity=0.7,
line_opacity=0.5,
legend_name="Population",
).add_to(m)
folium.LayerControl().add_to(m)
m
3.0 Number of Dispatches per Capita
url = (
"https://raw.githubusercontent.com/BlizzWiz/IBM_Capstone_Project/master"
)
call_geo = f"{url}/toronto_m.geojson"
m = folium.Map(location=[43.7000, -79.3832], zoom_start=10)
folium.Choropleth(
geo_data=call_geo,
name="choropleth",
data=call_counts,
columns=["FSA", "call_per_capita"],
key_on="feature.properties.CFSAUID",
fill_color="BuPu",
fill_opacity=0.7,
line_opacity=0.5,
legend_name="Number of Calls per Capita",
).add_to(m)
folium.LayerControl().add_to(m)
m