<jupyter_start><jupyter_text>EDA on covid-19<jupyter_code>## importing all the libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
##READING THE TOP 5 DATA FROM THE DATASET (df)
df=pd.read_csv("covid_19_india.csv")
df.head()<jupyter_output><empty_output><jupyter_text>Information about the dataset<jupyter_code>df.info()
df.describe()
###checking is there any null values
df.isnull()
##Showing is there any null values using graph
sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap="YlGnBu")<jupyter_output><empty_output><jupyter_text>Seaborn is a very powerful data visualizing library<jupyter_code>sns.pairplot(df)<jupyter_output><empty_output><jupyter_text>Gives the no. of death cases in India<jupyter_code>sns.countplot(x="Deaths",data=df)<jupyter_output><empty_output><jupyter_text>Gives the no. of confirmed cases in India with respect  to data<jupyter_code>sns.boxplot("Confirmed","Date",data=df)
sns.violinplot("Confirmed","Date",data=df)
##GIVES THE RELATIONSHIP B/W CURED AND DEATH CASES IN INDIA
sns.jointplot("Cured","Deaths",data=df)<jupyter_output><empty_output><jupyter_text>It shows that the no. of cured people is much higher than the death one.<jupyter_code>df_age=pd.read_csv("AgeGroupDetails.csv")
df_age.head()<jupyter_output><empty_output><jupyter_text>Showing no. of total cases of different age group<jupyter_code>sns.countplot(x="TotalCases",hue="AgeGroup",data=df_age)
plt.bar(df_age["TotalCases"],height=2)<jupyter_output><empty_output>