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107_analysis_of_covid19_data_using_python_part1.py

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+#!/usr/bin/env python
+__author__ = "Sreenivas Bhattiprolu"
+__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"
+
+# https://youtu.be/29YnoZcrW3o
+
+#To download the file locally
+#import urllib
+#url = "https://covid.ourworldindata.org/data/ecdc/full_data.csv"
+#urllib.request.urlretrieve (url, "data/full_data.csv")
+
+import pandas as pd
+import datetime as dt
+from matplotlib import pyplot as plt
+import matplotlib
+
+#date	location	new_cases	new_deaths	total_cases	total_deaths
+CVD = pd.read_csv('https://covid.ourworldindata.org/data/ecdc/full_data.csv')
+print(CVD.head())
+print(CVD.dtypes)
+
+#dateFormat = '%Y-%m-%d'
+# Convert string values of date to datetime format
+CVD['date'] = [dt.datetime.strptime(x,'%Y-%m-%d') for x in CVD['date']] 
+#print(CVD.dtypes)
+
+#Let's look at multiple countries
+countries=['United States', 'Spain', 'Italy']
+CVD_country = CVD[CVD.location.isin(countries)]  #Create subset data frame for select countries
+
+
+CVD_country.set_index('date', inplace=True)  #Make date the index for easy plotting
+
+#To create subset range based on dates
+#CVD_country = CVD_country.loc['2020-02-15':'2020-03-22']
+
+#print(CVD_country.tail())  #Check the last date 
+
+#To calculate mortality rate
+CVD_country['mortality_rate'] = CVD_country['total_deaths']/CVD_country['total_cases']
+#print(CVD_country.tail())
+
+fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14,14))
+
+CVD_country.groupby('location')['new_cases'].plot(ax=axes[0,0], legend=True) #for log scale add logy=True
+CVD_country.groupby('location')['new_deaths'].plot(ax=axes[0,1], legend=True)
+CVD_country.groupby('location')['total_cases'].plot(ax=axes[1,0], legend=True)
+CVD_country.groupby('location')['total_deaths'].plot(ax=axes[1,1], legend=True)
+#CVD_country.groupby('location')['mortality_rate'].plot(ax=axes[1,1], legend=True)
+#CVD_country.to_csv('data/output.csv')
+
+axes[0, 0].set_title("New Cases")
+axes[0, 1].set_title("New Deaths")
+axes[1, 0].set_title("Total Cases")
+axes[1, 1].set_title("Total Deaths")
+
+fig.tight_layout()  # adjust subplot parameters to give specified padding.

108_analysis_of_covid19_data_using_python_part2.py

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+#!/usr/bin/env python
+__author__ = "Sreenivas Bhattiprolu"
+__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"
+
+# https://youtu.be/tnOlX6_t0n4
+
+#To download the file locally
+#import urllib
+#url = "https://covid.ourworldindata.org/data/ecdc/full_data.csv"
+#urllib.request.urlretrieve (url, "data/full_data.csv")
+
+import pandas as pd
+import datetime as dt
+from matplotlib import pyplot as plt
+import seaborn as sns
+
+#date	location	new_cases	new_deaths	total_cases	total_deaths
+CVD = pd.read_csv('https://covid.ourworldindata.org/data/ecdc/full_data.csv')
+print(CVD.head())
+print(CVD.dtypes)
+
+#dateFormat = '%Y-%m-%d'
+# Convert string values of date to datetime format
+CVD['date'] = [dt.datetime.strptime(x,'%Y-%m-%d') for x in CVD['date']] 
+print(CVD.dtypes)
+
+#Check for missing data
+print(CVD.isnull().sum()) #No missing data
+
+
+#Change column titles to something appropriate
+CVD.columns = ['Date', 'Country', 'New Cases', 'New deaths', 'Total Cases', 'Total Deaths' ]
+
+#Select all countries except for china and World
+CVD_no_china = CVD.loc[~(CVD['Country'].isin(["China", "World"]))]
+
+#Group them by location and date, select only total cases and deaths for closer observation
+#Reset index because groupby by default makes grouped columns indices
+CVD_no_china = pd.DataFrame(CVD_no_china.groupby(['Country', 'Date'])['Total Cases', 'Total Deaths'].sum()).reset_index()
+print(CVD_no_china)
+
+#Sort values by each country and by date - descending. Easy to interpret plots
+CVD_no_china = CVD_no_china.sort_values(by = ['Country','Date'], ascending=False)
+print(CVD_no_china)
+
+
+################################################
+#Plot cases and deaths as bar plot for top 10 countries
+#Function to plot bar plots using Seaborn.
+
+def plot_bar(feature, value, title, df, size):
+    f, ax = plt.subplots(1,1, figsize=(4*size,4))
+    df = df.sort_values([value], ascending=False).reset_index(drop=True)
+    g = sns.barplot(df[feature][0:10], df[value][0:10], palette='Set3')
+    g.set_title("Number of {} - highest 10 values".format(title))
+#    ax.set_xticklabels(ax.get_xticklabels(),rotation=90)
+    plt.show()    
+
+filtered_CVD_no_china = CVD_no_china.drop_duplicates(subset = ['Country'], keep='first')
+plot_bar('Country', 'Total Cases', 'Total cases in the World except China', filtered_CVD_no_china, size=4)
+plot_bar('Country', 'Total Deaths', 'Total deaths in the World except China', filtered_CVD_no_china, size=4)
+
+##########################################
+#Plot world aggregate numbers for total cases and deaths. 
+def plot_world_aggregate(df, title='Aggregate plot', size=1):
+    f, ax = plt.subplots(1,1, figsize=(4*size,2*size))
+    g = sns.lineplot(x="Date", y='Total Cases', data=df, color='blue', label='Total Cases')
+    g = sns.lineplot(x="Date", y='Total Deaths', data=df, color='red', label='Total Deaths')
+    plt.xlabel('Date')
+    plt.ylabel(f'Total {title} cases')
+    plt.xticks(rotation=90)
+    plt.title(f'Total {title} cases')
+    ax.grid(color='black', linestyle='dotted', linewidth=0.75)
+    plt.show()  
+
+#Group by dates. 
+#Reset index because groupby by default makes grouped columns indices
+#Sum values from all countries per given date
+CVD_no_china_aggregate = CVD_no_china.groupby(['Date']).sum().reset_index()
+print(CVD_no_china_aggregate)
+
+plot_world_aggregate(CVD_no_china_aggregate, 'Rest of the World except China', size=4)
+#################################################
+
+#Plot aggregate numbers for total cases and deaths for select countries. 
+#Starting from Feb 15th 
+
+def plot_aggregate_countries(df, countries, case_type='Total Cases', size=3, is_log=False):
+    f, ax = plt.subplots(1,1, figsize=(4*size, 3*size))
+    for country in countries:
+        df_ = df[(df['Country']==country) & (df['Date'] > '2020-02-15')] 
+        g = sns.lineplot(x="Date", y=case_type, data=df_,  label=country)  
+        ax.text(max(df_['Date']), max(df_[case_type]), str(country))
+    plt.xlabel('Date')
+    plt.ylabel(f' {case_type} ')
+    plt.title(f' {case_type} ')
+    plt.xticks(rotation=90)
+    if(is_log):
+        ax.set(yscale="log")
+    ax.grid(color='black', linestyle='dotted', linewidth=0.75)
+    plt.show()  
+
+CVD_country_aggregate = CVD_no_china.groupby(['Country', 'Date']).sum().reset_index()
+
+countries = ["United States", "Italy", "Spain", "South Korea", 
+                         "France", "Germany", "Switzerland", "India"]
+plot_aggregate_countries(CVD_country_aggregate, countries, case_type = 'Total Cases', size=4)    
+
+plot_aggregate_countries(CVD_country_aggregate, countries, case_type = 'Total Deaths', size=4)
+
+#log scale
+plot_aggregate_countries(CVD_country_aggregate, countries, case_type = 'Total Cases', size=4, is_log=True)
+
+##############################################################
+#Calculate MORTALITY and plot
+
+def plot_mortality(df, title='Mainland China', size=1):
+    f, ax = plt.subplots(1,1, figsize=(4*size,2*size))
+    g = sns.lineplot(x="Date", y='Mortality (Deaths/Cases)', data=df, color='blue', label='Mortality (Deaths / Total Cases)')
+    plt.xlabel('Date')
+    plt.ylabel(f'Mortality {title} [%]')
+    plt.xticks(rotation=90)
+    plt.title(f'Mortality percent {title}\nCalculated as Deaths/Confirmed cases')
+    ax.grid(color='black', linestyle='dashed', linewidth=1)
+    plt.show()  
+
+CVD_no_china_aggregate['Mortality (Deaths/Cases)'] = CVD_no_china_aggregate['Total Deaths'] / CVD_no_china_aggregate['Total Cases'] * 100
+plot_mortality(CVD_no_china_aggregate, title = ' - Rest of the World except China', size = 3)
+
+############################################################