【データ視覚化】新型コロナウイルス感染症の致死率を都道府県別に比較

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

jp_prefecture = pd.read_csv('../input/covid19-dataset-in-japan/covid_jpn_prefecture.csv')
total = pd.read_csv('../input/covid19-dataset-in-japan/covid_jpn_total.csv')
metadata = pd.read_csv('../input/covid19-dataset-in-japan/covid_jpn_metadata.csv')

latest_data = jp_prefecture.tail(47)

per_hunthousand = []
for i,j in zip(latest_data["Positive"],metadata["Value"]):
    per_hunthousand.append((i/int(j))*100)
latest_data["Cases Per 100000 Population"] = per_hunthousand

death_rates = []
for i,j in zip(latest_data["Positive"],latest_data["Fatal"]):
    death_rates.append((j/i)*100)

latest_data["Mortality Rates"] = death_rates

latest_data = latest_data.sort_values(by='Positive',ascending=False)
latest_cols = ["Prefecture","Positive","Discharged","Fatal","Hosp_require","Hosp_severe","Cases Per 100000 Population","Mortality Rates"]
latest_data = latest_data[latest_cols]
latest_data = latest_data.rename(columns={"Prefecture":"Prefecture","Positive":"Positive","Discharged":"Cured","Fatal":"Death","Hosp_require":"Active","Hosp_severe":"Severe"
                                          ,"Cases Per 100000 Population":"Cases Per 100000 Population","Mortality Rates":"Mortality Rates"})
latest_data.style.background_gradient(cmap='plasma_r')