<jupyter_start><jupyter_text>Uploading the dataset to google colab<jupyter_code>from google.colab import files
uploaded = files.upload()<jupyter_output><empty_output><jupyter_text>Importing the libraries<jupyter_code>import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
%matplotlib inline
import io<jupyter_output><empty_output><jupyter_text>Reading and printing the dataset<jupyter_code>df = pd.read_csv(io.BytesIO(uploaded['Latest Covid-19 India Status.csv']))
print(df)
df.head(36)<jupyter_output><empty_output><jupyter_text>Analysing information, statistical and about the datatypes<jupyter_code>df.shape
df.info()
df.describe()
#correlation (tabular)
df.corr()<jupyter_output><empty_output><jupyter_text>Checking if the dataset is clean by looking for missing or duplicate values<jupyter_code>df.isnull().sum()
df.duplicated().sum()<jupyter_output><empty_output><jupyter_text>Since all the data is clean, we can visually analyze the data using the plot library and seaborn library  **Correlation** and **Statewise analysis**<jupyter_code>#correlation using heatmap visuals
f,ax = plt.subplots(figsize=(12, 8))
sns.heatmap(df.corr(), annot=True, ax=ax)
plt.show()
#Top 5 highestCases
highestCases = df.sort_values(by ='Total Cases', ascending = False).head(5)
highestCases
#Top 5 highestRecoveryRate
highestRecoveryRate = df.sort_values(by ='Discharged', ascending = False).head(5)
highestRecoveryRate
#comparison of the active and death ratio
px.scatter(df, x='Active Ratio',y='Death Ratio', color=df['State/UTs'])
plt.subplot(1,3,1)
plt.plot(np.log(df['Total Cases']), np.log(df['Active']), 'go')
plt.title("Active VS Total Cases ",fontdict={'size':12,'color':'green'})
plt.xlabel("Total Cases")
plt.ylabel("Active")

plt.subplot(1,3,2)
plt.plot(np.log(df['Total Cases']), np.log(df['Discharged']), 'b*')
plt.title("Discharged VS Total Cases",fontdict={'size':12,'color':'blue'})
plt.xlabel("Total Cases")
plt.ylabel("Discharged")

plt.subplot(1,3,3)
plt.plot(np.log(df['Total Cases']), np.log(df['Deaths']), 'go')
plt.title("Deaths VS Total Cases",fontdict={'size':12,'color':'red'})
plt.xlabel("Total Cases")
plt.ylabel("Deaths")

plt.tight_layout()
plt.show<jupyter_output><empty_output>
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