import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

import geopandas as gpd

from shapely.geometry import Point,Polygon

we will need to install geopandas to plot world map. To install geopandas we can run the following line in your python notebook: pip install geopandas and similarly we can install shapely for plotting geometric points : pip install Shapely .

confirm_data= pd.read_csv('covid19_Confirmed_dataset.csv') #read csv file

lat=[i for i in confirm_data['Lat']] #for confirmed cases

lon=[i for i in confirm_data['Long']] #for confirmed cases longitude

death_data= pd.read_csv(covid data analysis/covid19_deaths_dataset.csv')

happiness_report= pd.read_csv('worldwide_happiness_report.csv')

confirm_data.drop(columns=['Province/State','Lat','Long'],axis=1,inplace=True)

confirm_agg= confirm_data.groupby('Country/Region').sum()

death_data.drop(columns=['Province/State','Lat','Long'],axis=1,inplace=True)

death_agg= death_data.groupby('Country/Region').sum()

useless=['Overall rank','Score','Generosity','Perceptions of corruption']

happiness_report.drop(useless,axis=1,inplace=True)

happiness_report.set_index("Country or region",inplace=True)

Here we have dropped unnecessary features like latitude , longitude and province/state as it had null values. In happiness report we have dropped few columns as they were not necessary. To read a csv file we write pd.read_csv('filename.csv') and to drop the columns we use dataframe.drop(columns=[list],axis=1,inplace=True) axis=1 states that we want to delete whole column. Inplace =True argument stands for the data frame has to make changes permanent . I have stored latitude and longitude into a list to plot world map .

We have grouped the data region wise using groupby('attribute').sum() or .mean() etc.

geo=[Point(xy) for xy in zip(lon,lat)]

g=gpd.GeoDataFrame(geometry=geo)

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

fig,ax=plt.subplots(figsize=(15,10))

world.plot(ax=ax,color='green')

g.plot(ax=ax,color='pink')

plt.title('confirm cases in the world',color='white')

plt.axis('off')

plt.show()

The goal of GeoPandas is to make working with geospatial data in python easier. It combines the capabilities of pandas and shapely, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in python that would otherwise require a spatial database such as PostGIS. refer this

country=[i for i in confirm_agg.index]

maximum_rate=[]

for i in country:

maximum_rate.append(confirm_agg.loc[i].diff().max())

confirm_agg['maximum_rate']=maximum_rate

country=[i for i in death_agg.index]

max_death_rate=[]

for i in country:

max_death_rate.append(death_agg.loc[i].diff().max())

death_agg['max_dead']=max_death_rate

corona_data=pd.DataFrame(confirm_agg["maximum_rate"])

death_data=pd.DataFrame(death_agg["max_dead"])

data=corona_data.join(happiness_report,how='inner')

data=death_data.join(data,how='inner')

we have found out the maximum confirmed casee and death cases of each country and created one more column to store it. Using join() we have combined the three datasets. Inner join is the most common type of join you'll be working with. It returns a dataframe with only those rows that have common characteristics. An inner join requires each row in the two joined dataframes to have matching column values. This is similar to the intersection of two sets.