Covid-19 India Data Analysis Project

This is a practice Data Analysis Project by me in the field of Covid-19 Virus spreading in India. The Data/Datasets used in this is project are provided by Kaggle.

Following is the link to the Dataset in Kaggle's official website: Covid-19 India Datasets

The main motive behind this project was just practicing and learning new things in the field of Data Analysis for me. The whole project has been done in Jupyter Notebook Covid19 Data Analysis Project.ipynb provided in my Github Repository named Covid-19-India-Data-Analysis-Project.

Modules used in this project are:

• [Pandas](https://pandas.pydata.org/)
• [Numpy](https://numpy.org/)
• [Matplotlib](https://matplotlib.org/)
• [Seaborn](https://seaborn.pydata.org/)
• [Plotly](https://plotly.com/python/)
• [Datetime](https://docs.python.org/3/library/datetime.html)

Things I have done in this Project:

Firstly I imported all the downloaded datasets into the project using the pd.read_csv("dataset.csv") command into 3 different datasets. After that I used some data exploration commands like df.head(), df.tail(), df.info() as well as df.describe() to explore the data and see for NULL values and anomaly.

After the data exploration part was over it was time to divide and sort the datasets into suitable form to draw insights and reprent it in graphical form for better usderstanding of the numbers.

At last I used a mixture of all graphical modules in python to draw graphs and pie charts to represent the data in a more cleaner and understandable way than a excel file or a csv file.

Graphical Insights from the Data:

Confirmed vs Recovery Rate vs Mortality Rate Demographic according to States

statewise = pd.pivot_table(covid_df, values = ["Confirmed", "Deaths", "Cured"], index = "State/UnionTerritory", aggfunc = max)
statewise["Recovery_Rate"] = statewise["Cured"] * 100 / statewise["Confirmed"]
statewise["Mortality_Rate"] = statewise["Deaths"] * 100 / statewise["Confirmed"]
statewise = statewise.sort_values(by = "Confirmed", ascending = False)
statewise.style.background_gradient(cmap = "Blues")

Top 10 States with Most Active Cases

top_10_active_cases_statewise = covid_df.groupby(by = 'State/UnionTerritory').max()[['Active_Cases', 'Date']].sort_values(by = ['Active_Cases'], ascending = False).reset_index()

fig = plt.figure(figsize = (20, 11))

plt.title("Top 10 States with Most Active Cases", size = 25)

ax = sns.barplot(data = top_10_active_cases_statewise.iloc[:10], y = 'Active_Cases', x = 'State/UnionTerritory', linewidth = 2, edgecolor = 'red')

plt.xlabel("States")
plt.ylabel("Total Active Cases")
plt.show()

Top 10 States with Least Active Cases

top_10_deaths_statewise = covid_df.groupby(by = 'State/UnionTerritory').max()[['Deaths', 'Date']].sort_values(by = ['Deaths'], ascending = False).reset_index()

fig = plt.figure(figsize = (18, 10))

plt.title("Top 10 States with Most Deaths", size = 25)

ax = sns.barplot(data = top_10_deaths_statewise.iloc[:12], y = 'Deaths', x = 'State/UnionTerritory', linewidth = 2, edgecolor = 'red')

plt.xlabel("States")
plt.ylabel("Total Deaths")
plt.show()

Top 5 States Affected by Covid-19

fig = plt.figure(figsize = (20,6))

ax = sns.lineplot(data = covid_df[covid_df['State/UnionTerritory'].isin(['Maharashtra', 'Kerala', 'West Bengal', 'Madhya Pradesh', 'Delhi'])], x = 'Date', y = 'Active_Cases', hue = 'State/UnionTerritory')

ax.set_title("Top 5 Covid Affected States in India", size = 16)

Male vs Female Vaccination

male = vaccination['Male(Individuals Vaccinated)'].sum()
female = vaccination['Female(Individuals Vaccinated)'].sum()
px.pie(names = ['Male', 'Female'], values = [male, female], title = 'Male vs Female Vaccination')

Top 5 States with Most Vaccinated Individuals

max_vaccine = vaccine_no_india_state.groupby('State')['Total'].sum().to_frame('Total')
max_vaccine = max_vaccine.sort_values('Total', ascending = False)[:5]

fig = plt.figure(figsize = (20,10))
plt.title('Top 5 States with Most number of Vaccinated Individuals', size = 30)
ax = sns.barplot(data = max_vaccine.iloc[:10], y = max_vaccine.Total, x = max_vaccine.index, linewidth = 2, edgecolor = 'blue')
plt.xlabel("State")
plt.ylabel("Vaccinated Individuals")
plt.show()

Top 5 States with Least Vaccinated Individuals

min_vaccine = vaccine_no_india_state.groupby('State')['Total'].sum().to_frame('Total')
min_vaccine = min_vaccine.sort_values('Total', ascending = True)[:5]

fig = plt.figure(figsize = (20,10))
plt.title('Top 5 States with Least number of Vaccinated Individuals', size = 30)
ax = sns.barplot(data = min_vaccine.iloc[:10], y = min_vaccine.Total, x = min_vaccine.index, linewidth = 2, edgecolor = 'blue')
plt.xlabel("State")
plt.ylabel("Vaccinated Individuals")
plt.show()