The Data is provided by Adikansh Mittal's API and the shapefiles are provided by Vijay Meena. It provides a statewise visualisation of deaths.
Geopandas on Windows using pip install geopandas might not work. You can follow this to sort that out. Since we are running on colab, it is not much of an issue, you can right away install it using pip install geopandas.
pip install geopandas
pip install requests
pip install pandas
pip install matplotlibimport requests
import geopandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import json
url = "https://covid19-india-adhikansh.herokuapp.com/states"
response = requests.get(url)
response = response.json()
response = dict(response)
response = response["state"]
confirmed = []
for i in range(len(response)):
del response[i]['_id']
del response[i]['total']
confirmed.append(response[i]['confirmed'])
#prettyPrintJSON = json.dumps(response, indent=2)
#print(prettyPrintJSON)
df = pd.DataFrame.from_dict(response)
print(df) name confirmed cured death
0 Andhra Pradesh 86 1 1
1 Andaman and Nicobar Islands 10 0 0
2 Assam 1 0 0
3 Bihar 23 0 1
4 Chandigarh 16 0 0
5 Chhattisgarh 9 2 0
6 Delhi 152 6 2
7 Goa 5 0 0
8 Gujarat 82 5 6
9 Haryana 43 21 0
10 Himachal Pradesh 3 1 1
11 Jammu and Kashmir 62 2 2
12 Jharkhand 1 0 0
13 Karnataka 110 9 3
14 Kerala 265 25 2
15 Ladakh 13 3 0
16 Madhya Pradesh 99 0 6
17 Maharashtra 335 42 13
18 Manipur 1 0 0
19 Mizoram 1 0 0
20 Odisha 4 0 0
21 Puducherry 3 1 0
22 Punjab 46 1 4
23 Rajasthan 108 3 0
24 Tamil Nadu 234 6 1
25 Telengana 96 1 3
26 Uttarakhand 7 2 0
27 Uttar Pradesh 113 14 2
28 West Bengal 37 6 3
Now that we have fetched the API and stored it in a variable, our next job is to fetch a SHX file and plot it via GeoPandas. To do this, you have to fetch your SHX file. You can download them for your country from DIVA-GIS (https://www.diva-gis.org/gdata).
If you are on colab, look to your left, there should be a hamburger icon. Click on it, and click on files (or file icon), and upload your .SHX there.
fp = "IND_adm1.shx"
map_df = gpd.read_file(fp)
map_df.plot()
print(map_df) geometry
0 MULTIPOLYGON (((93.78773 6.85264, 93.78849 6.8...
1 MULTIPOLYGON (((80.27458 13.45958, 80.27458 13...
....
....
....
35 MULTIPOLYGON (((88.01861 21.57278, 88.01889 21...
merged = map_df.join(df)
merged = merged.dropna()
merged = merged[['name', 'geometry', 'death', 'cured', 'confirmed']]
print(merged) name ... confirmed
0 Andhra Pradesh ... 86.0
1 Andaman and Nicobar Islands ... 10.0
2 Assam ... 1.0
3 Bihar ... 23.0
4 Chandigarh ... 16.0
5 Chhattisgarh ... 9.0
6 Delhi ... 152.0
7 Goa ... 5.0
8 Gujarat ... 82.0
9 Haryana ... 43.0
10 Himachal Pradesh ... 3.0
11 Jammu and Kashmir ... 62.0
12 Jharkhand ... 1.0
13 Karnataka ... 110.0
14 Kerala ... 265.0
15 Ladakh ... 13.0
16 Madhya Pradesh ... 99.0
17 Maharashtra ... 335.0
18 Manipur ... 1.0
19 Mizoram ... 1.0
20 Odisha ... 4.0
21 Puducherry ... 3.0
22 Punjab ... 46.0
23 Rajasthan ... 108.0
24 Tamil Nadu ... 234.0
25 Telengana ... 96.0
26 Uttarakhand ... 7.0
27 Uttar Pradesh ... 113.0
28 West Bengal ... 37.0
[29 rows x 5 columns]
# set the value column that will be visualised
variable = 'confirmed'
# set the range for the choropleth values
vmin = 0
vmax = max(confirmed)
# create figure and axes for Matplotlib
fig, ax = plt.subplots(1, figsize=(30, 10))
# remove the axis
ax.axis('off')
# add a title and annotation
ax.set_title('Confirmed Cases by State in India', fontdict={'fontsize': '25', 'fontweight' : '3'})
# Create colorbar legend
sm = plt.cm.ScalarMappable(cmap='OrRd_r', norm=plt.Normalize(vmin=vmin, vmax=vmax))
# empty array for the data range
sm.set_array([]) # or alternatively sm._A = []. Not sure why this step is necessary, but many recommends it
# add the colorbar to the figure
fig.colorbar(sm)
# create map
merged.plot(column=variable, cmap='OrRd_r', linewidth=0.8, ax=ax, edgecolor='0.8')<matplotlib.axes._subplots.AxesSubplot at 0x7fde51e92ef0>
