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 geopandasRequirement already satisfied: geopandas in /usr/local/lib/python3.6/dist-packages (0.7.0)
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import requests
import geopandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import jsonurl = "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...
2 POLYGON ((96.15778 29.38310, 96.16380 29.37668...
3 MULTIPOLYGON (((89.87145 25.53730, 89.87118 25...
4 MULTIPOLYGON (((88.10548 26.53904, 88.10505 26...
5 POLYGON ((76.80293 30.67548, 76.79437 30.66932...
6 POLYGON ((83.32760 24.09965, 83.34575 24.09707...
7 POLYGON ((73.02468 20.09630, 73.01955 20.10502...
8 MULTIPOLYGON (((72.86014 20.47096, 72.86340 20...
9 POLYGON ((77.32713 28.68516, 77.32539 28.68250...
10 MULTIPOLYGON (((73.78181 15.35569, 73.78181 15...
11 MULTIPOLYGON (((70.86097 20.75292, 70.86097 20...
12 POLYGON ((76.83715 30.87887, 76.85243 30.87069...
13 POLYGON ((76.80276 33.23666, 76.80630 33.23623...
14 POLYGON ((77.89957 35.42789, 77.90297 35.42759...
15 POLYGON ((87.59989 25.31466, 87.60688 25.31138...
16 MULTIPOLYGON (((74.67097 13.19986, 74.67097 13...
17 MULTIPOLYGON (((76.46736 9.54097, 76.46736 9.5...
18 MULTIPOLYGON (((73.01014 8.28042, 73.01014 8.2...
19 POLYGON ((78.36465 26.86884, 78.36688 26.86259...
20 MULTIPOLYGON (((73.45597 15.88986, 73.45597 15...
21 POLYGON ((94.57723 25.64833, 94.57609 25.64470...
22 POLYGON ((91.85384 26.10479, 91.86470 26.10035...
23 POLYGON ((92.80080 24.41905, 92.80370 24.41879...
24 POLYGON ((95.21445 26.93695, 95.21706 26.93420...
25 MULTIPOLYGON (((84.76986 19.10597, 84.76986 19...
26 MULTIPOLYGON (((79.84486 10.82653, 79.84486 10...
27 POLYGON ((75.86877 32.48868, 75.88712 32.47203...
28 POLYGON ((73.88944 29.97761, 73.89118 29.97007...
29 POLYGON ((88.64526 28.09912, 88.65411 28.08984...
30 MULTIPOLYGON (((77.55596 8.07903, 77.55596 8.0...
31 POLYGON ((78.33625 19.88319, 78.34669 19.88140...
32 POLYGON ((92.18520 24.52287, 92.18896 24.52019...
33 POLYGON ((77.58468 30.40878, 77.58639 30.40801...
34 POLYGON ((79.19478 31.35362, 79.19817 31.35196...
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>
