A novel Corona virus is a category of pathogens, which mostly attacks on the respiratory system of human beings. Corona virus outbreaks are emerging earlier also in the form of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS).Now, in the present times, it emerges as a COVID-19, which is caused by the SARS2 Coronavirus and poses a significant risk to the human race.
In December 2019, a number of patients with pneumonia symptoms were reported in Wuhan and Hubei Province of China and later identified as symptoms caused due to the spread of corona virus. Epidemiologically, these patients were later on found to be linked to an animal and seafood market of Wuhan. Later on, the Wuhan city of China was recognized as the epicentre of the COVID-19 disease and claimed for spreading the disease across the world. Around 41 lab confirmed COVID-19 patients reported and admitted to hospital up to January 2, 2020 in China. These patients have symptoms of coughing, sneezing, breathing problems, chest pain, indigestion, and respiratory illness. It was also observed that most of these patients are already suffering from varied diseases such as hypertension, diabetes and cardiovascular.
According to China National Health Commission, 17 deaths reported in China with COVID-19 up to January 22, 2020 and within four days death rate raised to triple with 5502 confirmed cases. By the end of January 2020, 7734 confirmed cases have been reported in China along with 90 cases in other countries such as Thailand, Japan, Malaysia, Iran, Italy, India, USA, Canada, Taiwan, Vietnam, France, Nepal, Cambodia, Germany, Singapore, Korea, United Arab Emirates, Sri Lanka, The Philippines, Australia and Finland.
Furthermore, WHO on 30 Jan declares Public health emergency of international concern due to the severity of the disease. The International Committee on Taxonomy of Viruses named this corona virus as a Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and the disease caused by the virus designated as Coronavirus Disease-19 (COVID-19) by WHO. The COVID-19 has strewed in more than 114 countries with 118,326 active cases and 4292 deaths on March 11, 2020 and declared as pandemic by WHO. The situation becomes worse within a week after the pandemic declaration and Italy become the second most affected country after china.
Presently, about 216 countries are affected by COVID-19 across the globe and shattered the economic growth of developed as well as developing countries. The WHO report documented on 05 October 2020 claimed 7,637,066 cases United States of America, 6,626,291 cases in India, 4,915,289 in Brazil, 1,225,889 in Russia, 855,052 cases in Colombia, much higher cases in comparison to China from where the disease actually originated (85,470)
This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.
The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".
Install Conda by following these instructions. Add Conda binaries to your system PATH
, so you can use the conda
command on your terminal.
Create a Conda environment and install the required libraries by running these commands on the terminal:
conda create -n zerotopandas -y python=3.8
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade
jovian clone notebook-owner/notebook-id
cd directory-name
and start the Jupyter notebook.jupyter notebook
You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb
extension) to open it.
!pip install jovian opendatasets --upgrade --quiet
Let's begin by downloading the data, and listing the files within the dataset.
# Change this
dataset_url = 'https://www.kaggle.com/sudalairajkumar/covid19-in-india'
import opendatasets as od
od.download(dataset_url)
Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds
Your Kaggle username: abhijeetraj22
Your Kaggle Key: ········
100%|██████████| 147k/147k [00:00<00:00, 37.2MB/s]
Downloading covid19-in-india.zip to ./covid19-in-india
The dataset has been downloaded and extracted.
# Change this
data_dir = './covid19-in-india'
import os
os.listdir(data_dir)
['covid_19_india.csv', 'StatewiseTestingDetails.csv']
Let us save and upload our work to Jovian before continuing.
project_name = "zerotopandas-course-project-covid19-in-india" # change this (use lowercase letters and hyphens only)
!pip install jovian --upgrade -q
import jovian
jovian.commit(project=project_name)
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API KEY: ········
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import pandas as pd
import numpy as np
covid_india_df = pd.read_csv(data_dir + '/covid_19_india.csv')
covid_india_df
covid_india_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7086 entries, 0 to 7085
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Sno 7086 non-null int64
1 Date 7086 non-null object
2 Time 7086 non-null object
3 State/UnionTerritory 7086 non-null object
4 ConfirmedIndianNational 7086 non-null object
5 ConfirmedForeignNational 7086 non-null object
6 Cured 7086 non-null int64
7 Deaths 7086 non-null int64
8 Confirmed 7086 non-null int64
dtypes: int64(4), object(5)
memory usage: 498.4+ KB
covid_india_df.describe()
#Drop Lat & Long
covid_india_df.drop(['ConfirmedIndianNational', 'ConfirmedForeignNational','Sno'], inplace=True, axis=1)
#Rename Cured to Recovered
covid_india_df.rename(columns = {'Cured':'Recovered'}, inplace = True)
#Rename State/UnionTerritory to Province_State
covid_india_df.rename(columns = {'State/UnionTerritory':'Province_State'}, inplace = True)
#Date format change
covid_india_df['Date'] = pd.to_datetime(covid_india_df['Date'],format='%d/%m/%y').dt.date
#Add New Cases
covid_india_df['Prev_Confirmed'] = covid_india_df.groupby('Province_State')['Confirmed'].shift(1)
covid_india_df['New Cases'] = covid_india_df['Confirmed'] - covid_india_df['Prev_Confirmed']
covid_india_df.drop('Prev_Confirmed',inplace = True,axis=1)
#Add New Recovered Cases
covid_india_df['Prev_Recovered'] = covid_india_df.groupby('Province_State')['Recovered'].shift(1)
covid_india_df['New Recovered'] = covid_india_df['Recovered'] - covid_india_df['Prev_Recovered']
covid_india_df.drop('Prev_Recovered',inplace = True,axis=1)
#Add New Deaths Cases
covid_india_df['Prev_Deaths'] = covid_india_df.groupby('Province_State')['Deaths'].shift(1)
covid_india_df['New Deaths'] = covid_india_df['Deaths'] - covid_india_df['Prev_Deaths']
covid_india_df.drop('Prev_Deaths',inplace = True,axis=1)
#check Null
covid_india_df.isnull().sum()
Date 0
Time 0
Province_State 0
Recovered 0
Deaths 0
Confirmed 0
New Cases 42
New Recovered 42
New Deaths 42
dtype: int64
#few Null data So, Remove it
covid_india_df['New Cases'].fillna(0, inplace=True)
covid_india_df['New Recovered'].fillna(0, inplace=True)
covid_india_df['New Deaths'].fillna(0, inplace=True)
#Type Check
covid_india_df['New Deaths'].dtypes
dtype('float64')
#Type Change into integer
covid_india_df['New Deaths'] = covid_india_df['New Deaths'].astype(int)
covid_india_df['New Cases'] = covid_india_df['New Cases'].astype(int)
covid_india_df['New Recovered'] = covid_india_df['New Recovered'].astype(int)
covid_india_df['Province_State'].unique()
array(['Kerala', 'Telengana', 'Delhi', 'Rajasthan', 'Uttar Pradesh',
'Haryana', 'Ladakh', 'Tamil Nadu', 'Karnataka', 'Maharashtra',
'Punjab', 'Jammu and Kashmir', 'Andhra Pradesh', 'Uttarakhand',
'Odisha', 'Puducherry', 'West Bengal', 'Chhattisgarh',
'Chandigarh', 'Gujarat', 'Himachal Pradesh', 'Madhya Pradesh',
'Bihar', 'Manipur', 'Mizoram', 'Andaman and Nicobar Islands',
'Goa', 'Unassigned', 'Assam', 'Jharkhand', 'Arunachal Pradesh',
'Tripura', 'Nagaland', 'Meghalaya', 'Dadar Nagar Haveli',
'Cases being reassigned to states', 'Sikkim', 'Daman & Diu',
'Dadra and Nagar Haveli and Daman and Diu', 'Telangana',
'Telangana***', 'Telengana***'], dtype=object)
#Remove non State data
#Drop 'Cases being reassigned to states' & 'Unassigned'
df_dump = covid_india_df['Province_State']=='Unassigned'
covid_india_df.drop(covid_india_df[df_dump].index,inplace=True)
df_dump = covid_india_df['Province_State']=='Cases being reassigned to states'
covid_india_df.drop(covid_india_df[df_dump].index,inplace=True)
def change_state_name(state):
if state == 'Odisha':
return 'Orissa'
elif state == 'Telengana':
return 'Telangana'
return state
covid_india_df['Province_State'] = covid_india_df.apply(lambda x: change_state_name(x['Province_State']), axis=1)
last_date = covid_india_df.Date.max()
state_cases = covid_india_df.copy()
state_cases = state_cases[state_cases['Date']==last_date]
state_cases.drop(['Date','Time', 'New Cases','New Recovered','New Deaths'],inplace = True,axis=1)
#Add Active, Deaths/Recovered, Mortality & Recovered Rate(per 100)
state_cases['Active'] = state_cases['Confirmed'] - (state_cases['Deaths']+state_cases['Recovered'])
state_cases['Active'] = state_cases['Active'].astype(int)
state_cases["Mortality Rate(per 100)"] = np.round(100*state_cases["Deaths"]/state_cases["Confirmed"],2)
state_cases["Recovered Rate(per 100)"] = np.round(100*state_cases["Recovered"]/state_cases["Confirmed"],2)
state_cases.reset_index(drop=True,inplace=True)
state_cases.head()
#Day wise Copy Data
covid_india_dayswise_df = covid_india_df.groupby('Date').sum().reset_index()
covid_india_dayswise_df.head()
#check Null
covid_india_dayswise_df.isnull().sum()
Date 0
Recovered 0
Deaths 0
Confirmed 0
New Cases 0
New Recovered 0
New Deaths 0
dtype: int64
#Add Deaths/Recovered, Mortality & Recovered Rate(per 100)
covid_india_df["Mortality Rate(per 100)"] = np.round(100*covid_india_df["Deaths"]/covid_india_df["Confirmed"],2)
covid_india_df["Recovered Rate(per 100)"] = np.round(100*covid_india_df["Recovered"]/covid_india_df["Confirmed"],2)
#check Null
covid_india_df.isnull().sum()
Date 0
Time 0
Province_State 0
Recovered 0
Deaths 0
Confirmed 0
New Cases 0
New Recovered 0
New Deaths 0
Mortality Rate(per 100) 7
Recovered Rate(per 100) 7
dtype: int64
#few Null data So, Remove it
covid_india_df['Mortality Rate(per 100)'] = covid_india_df['Mortality Rate(per 100)'].replace(np.nan, 0)
covid_india_df['Recovered Rate(per 100)'] = covid_india_df['Recovered Rate(per 100)'].replace(np.nan, 0)
#check Null
covid_india_df.isnull().sum()
Date 0
Time 0
Province_State 0
Recovered 0
Deaths 0
Confirmed 0
New Cases 0
New Recovered 0
New Deaths 0
Mortality Rate(per 100) 0
Recovered Rate(per 100) 0
dtype: int64
import jovian
jovian.commit()
[jovian] Attempting to save notebook..
[jovian] Updating notebook "abhijeetraj22/zerotopandas-course-project-covid19-in-india" on https://jovian.ml/
[jovian] Uploading notebook..
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Install plotly-express
!pip install plotly-express
Requirement already satisfied: plotly-express in /srv/conda/envs/notebook/lib/python3.8/site-packages (0.4.1)
Requirement already satisfied: pandas>=0.20.0 in /srv/conda/envs/notebook/lib/python3.8/site-packages (from plotly-express) (1.1.2)
Requirement already satisfied: scipy>=0.18 in /srv/conda/envs/notebook/lib/python3.8/site-packages (from plotly-express) (1.5.2)
Requirement already satisfied: statsmodels>=0.9.0 in /srv/conda/envs/notebook/lib/python3.8/site-packages (from plotly-express) (0.12.0)
Requirement already satisfied: patsy>=0.5 in /srv/conda/envs/notebook/lib/python3.8/site-packages (from plotly-express) (0.5.1)
Requirement already satisfied: plotly>=4.1.0 in /srv/conda/envs/notebook/lib/python3.8/site-packages (from plotly-express) (4.11.0)
Requirement already satisfied: numpy>=1.11 in /srv/conda/envs/notebook/lib/python3.8/site-packages (from plotly-express) (1.19.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /srv/conda/envs/notebook/lib/python3.8/site-packages (from pandas>=0.20.0->plotly-express) (2.8.1)
Requirement already satisfied: pytz>=2017.2 in /srv/conda/envs/notebook/lib/python3.8/site-packages (from pandas>=0.20.0->plotly-express) (2020.1)
Requirement already satisfied: six in /srv/conda/envs/notebook/lib/python3.8/site-packages (from patsy>=0.5->plotly-express) (1.15.0)
Requirement already satisfied: retrying>=1.3.3 in /srv/conda/envs/notebook/lib/python3.8/site-packages (from plotly>=4.1.0->plotly-express) (1.3.3)
Let's begin by importingmatplotlib.pyplot
and seaborn
.
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import plotly_express as px
%matplotlib inline
sns.set_style('darkgrid')
matplotlib.rcParams['font.size'] = 14
matplotlib.rcParams['figure.figsize'] = (9, 5)
matplotlib.rcParams['figure.facecolor'] = '#00000000'
total_cases = state_cases.copy()
total_cases.drop(['Province_State','Mortality Rate(per 100)','Recovered Rate(per 100)'],inplace = True,axis=1)
total = total_cases.sum()
total.name = "Total"
df_t = pd.DataFrame(total,dtype=float).transpose()
df_t["Mortality Rate(per 100)"] = np.round(100*df_t["Deaths"]/df_t["Confirmed"],2)
df_t.style.background_gradient(cmap='Purples',axis=1).format("{:.2f}").format("{:.0f}",subset=["Confirmed","Deaths","Recovered","Active"])
india_confirmed = total.Confirmed
india_active = total.Active
india_recovered = total.Recovered
india_deaths = total.Deaths
labels = ['Active','Recovered','Deaths']
sizes = [india_active,india_recovered,india_deaths]
color= ['#66b3ff','green','red']
explode = []
for i in labels:
explode.append(0.02)
plt.figure(figsize= (15,5))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=9, explode =explode,colors = color)
centre_circle = plt.Circle((0,0),0.30,fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.title('India COVID-19 Cases',fontsize = 20)
plt.axis('equal')
plt.tight_layout()
state_cases_df = state_cases.copy()
state_cases_df = state_cases_df.set_index("Province_State")
state_cases_df.sort_values('Confirmed', ascending= False).fillna(0).style.background_gradient(cmap='Blues',subset=["Confirmed"])\
.background_gradient(cmap='Reds',subset=["Deaths"])\
.background_gradient(cmap='Greens',subset=["Recovered"])\
.background_gradient(cmap='Purples',subset=["Active"])\
.background_gradient(cmap='YlOrBr',subset=["Mortality Rate(per 100)"])\
.background_gradient(cmap='Greens_r',subset=["Recovered Rate(per 100)"])
fig, ax = plt.subplots(figsize=(15, 10))
ax.clear()
#fig = sns.lineplot(x="Date",y="Active",data = covid_india_dayswise_df ,color="y",label="Active")
fig = sns.lineplot(x="Date",y="Recovered",data = covid_india_dayswise_df ,color="green",label="Recovered")
fig = sns.lineplot(x="Date",y="Deaths",data = covid_india_dayswise_df ,color="r",label="Deaths")
fig = sns.lineplot(x="Date",y="Confirmed",data = covid_india_dayswise_df ,color="c",label="Confirmed")
fig.set_xlabel('Date\n',size=15,color='#4bb4f2')
fig.set_ylabel('Number of Cases\n',size=15,color='#4bb4f2')
fig.set_title('India Covid-19 Cases',size=25,color='navy')
fig.ticklabel_format(style='plain', axis='y',useOffset=False)
df1 = covid_india_dayswise_df.melt(id_vars='Date', value_vars=['New Cases','New Deaths','New Recovered'],
var_name='Cases', value_name='Cases Count')
fig = px.line(df1, x="Date", y="Cases Count",color='Cases')
fig.update_layout(title="India Covid-19 Daily Cases", xaxis_title="", yaxis_title="")
fig.show()
fig = px.line(covid_india_df,x='Date', y='Confirmed', color='Province_State',title='India growth COVID19 Cases ')
fig.show()
def plot_hbar(df, col, n, hover_data=[]):
fig = px.bar(df.sort_values(col).tail(n),
x=col, y="Province_State", color='Province_State',
text=col, orientation='h', width=700, hover_data=hover_data,
color_discrete_sequence = px.colors.qualitative.Dark24)
fig.update_layout(title=col, xaxis_title="", yaxis_title="",
yaxis_categoryorder = 'total ascending',
uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()
plot_hbar(state_cases, 'Confirmed', 10)
plot_hbar(state_cases, 'Deaths', 10)
plot_hbar(state_cases, 'Recovered', 10)
plot_hbar(state_cases, 'Active', 10)
plot_hbar(state_cases, 'Mortality Rate(per 100)', 10)
plot_hbar(state_cases, 'Recovered Rate(per 100)', 10)
fig = px.scatter(state_cases.sort_values('Deaths', ascending=False).iloc[:20, :],
x='Confirmed', y='Deaths', color='Province_State', size='Confirmed',
height=700, text='Province_State', log_x=True, log_y=True,
title='Confirmed vs Deaths (Scale in log10)')
fig.update_traces(textposition='top center')
fig.update_layout(showlegend=False)
fig.update_layout(xaxis_rangeslider_visible=True)
fig.show()
Let us save and upload our work to Jovian before continuing
import jovian
jovian.commit()
[jovian] Attempting to save notebook..
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total_cases = state_cases.copy()
total_cases.drop(['Province_State','Mortality Rate(per 100)','Recovered Rate(per 100)'],inplace = True,axis=1)
total = total_cases.sum()
total.name = "Total"
df_t = pd.DataFrame(total,dtype=float).transpose()
df_t["Mortality Rate(per 100)"] = np.round(100*df_t["Deaths"]/df_t["Confirmed"],2)
df_t.style.background_gradient(cmap='Purples',axis=1).format("{:.2f}").format("{:.0f}",subset=["Confirmed","Deaths","Recovered","Active"])
print("A total of {a} COVID-19 confirmed cases have been reported in Indian \
as from {date} with {b} active cases ({ap:.1f}%),{c} cured/discharged ({rp:.1f}%),\
and {d} deaths ({dp:.1f}%).".format(a = india_confirmed,date = last_date.strftime("%d. %B %Y"),b = india_active,ap=(india_active/india_confirmed)*100 \
,c = india_recovered,rp=(india_recovered/india_confirmed)*100 \
,d = india_deaths,dp=(india_deaths/india_confirmed)*100))
A total of 6757131 COVID-19 confirmed cases have been reported in Indian as from 07. October 2020 with 907883 active cases (13.4%),5744693 cured/discharged (85.0%),and 104555 deaths (1.5%).
top_state = state_cases.sort_values('Deaths', ascending= False).head(5)
#top_state
top_state.reset_index(drop=True,inplace=True)
top_state.style.background_gradient(cmap='Blues',subset=["Confirmed"])\
.background_gradient(cmap='Reds',subset=["Deaths"])\
.background_gradient(cmap='Greens',subset=["Recovered"])\
.background_gradient(cmap='Purples',subset=["Active"])\
.background_gradient(cmap='YlOrBr',subset=["Mortality Rate(per 100)"])\
.background_gradient(cmap='Greens_r',subset=["Recovered Rate(per 100)"])
print("{state_name[0]} state is the most recent with COVID-19 Deaths cases {num_cases[0]}".format(state_name = top_state['Province_State'],num_cases=top_state['Deaths']))
Maharashtra state is the most recent with COVID-19 Deaths cases 38717
top_state = state_cases.sort_values('Mortality Rate(per 100)', ascending= False).head(5)
#top_state
top_state.reset_index(drop=True,inplace=True)
top_state.style.background_gradient(cmap='Blues',subset=["Confirmed"])\
.background_gradient(cmap='Reds',subset=["Deaths"])\
.background_gradient(cmap='Greens',subset=["Recovered"])\
.background_gradient(cmap='Purples',subset=["Active"])\
.background_gradient(cmap='YlOrBr',subset=["Mortality Rate(per 100)"])\
.background_gradient(cmap='Greens_r',subset=["Recovered Rate(per 100)"])
print("{state_name[0]} state is the High Mortality Rate(per 100) {num_cases[0]} in COVID-19 ".format(state_name = top_state['Province_State'],num_cases=top_state['Mortality Rate(per 100)']))
Punjab state is the High Mortality Rate(per 100) 3.07 in COVID-19
top_state = state_cases.sort_values('Recovered Rate(per 100)', ascending= False).head(5)
#top_state
top_state.reset_index(drop=True,inplace=True)
top_state.style.background_gradient(cmap='Blues',subset=["Confirmed"])\
.background_gradient(cmap='Reds',subset=["Deaths"])\
.background_gradient(cmap='Greens',subset=["Recovered"])\
.background_gradient(cmap='Purples',subset=["Active"])\
.background_gradient(cmap='YlOrBr',subset=["Mortality Rate(per 100)"])\
.background_gradient(cmap='Greens_r',subset=["Recovered Rate(per 100)"])
print("{state_name[0]} state is the High Recovered Rate(per 100) {num_cases[0]} in COVID-19 ".format(state_name = top_state['Province_State'],num_cases=top_state['Recovered Rate(per 100)']))
Dadra and Nagar Haveli and Daman and Diu state is the High Recovered Rate(per 100) 96.68 in COVID-19
covid_india_dayswise_df["Mortality Rate(per 100)"] = np.round(100*covid_india_dayswise_df["Deaths"]/covid_india_dayswise_df["Confirmed"],2)
covid_india_dayswise_df[["Date","Mortality Rate(per 100)"]]
fig = px.line(covid_india_dayswise_df, x="Date", y="Mortality Rate(per 100)", color_discrete_sequence=['lightcoral'],height=600)
fig.update_layout(title="India Covid-19 Mortality Rate (per 100)", xaxis_title="", yaxis_title="")
fig.show()
covid_india_dayswise_df["Recovered Rate(per 100)"] = np.round(100*covid_india_dayswise_df["Recovered"]/covid_india_dayswise_df["Confirmed"],2)
covid_india_dayswise_df[["Date","Recovered Rate(per 100)"]]
fig = px.line(covid_india_dayswise_df, x="Date", y="Recovered Rate(per 100)", color_discrete_sequence=['darkseagreen'],height=600)
fig.update_layout(title="India Covid-19 Recovered Rate(per 100)", xaxis_title="", yaxis_title="")
fig.show()
covid_india_dayswise_df["Incidence Rate(per 100)"] = np.round(100*covid_india_dayswise_df["New Cases"]/covid_india_dayswise_df["Confirmed"],2)
covid_india_dayswise_df[["Date","Incidence Rate(per 100)"]]
fig = px.line(covid_india_dayswise_df, x="Date", y="Incidence Rate(per 100)", color_discrete_sequence=['red'],height=600)
fig.update_layout(title="India Covid-19 Incidence Rate(per 100)", xaxis_title="", yaxis_title="")
fig.show()
Let us save and upload our work to Jovian before continuing.
import jovian
jovian.commit()
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import jovian
jovian.commit()
[jovian] Attempting to save notebook..
[jovian] Updating notebook "abhijeetraj22/zerotopandas-course-project-covid19-in-india" on https://jovian.ml/
[jovian] Uploading notebook..
[jovian] Capturing environment..
[jovian] Committed successfully! https://jovian.ml/abhijeetraj22/zerotopandas-course-project-covid19-in-india
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import jovian
jovian.commit()
[jovian] Attempting to save notebook..
[jovian] Updating notebook "abhijeetraj22/zerotopandas-course-project-covid19-in-india" on https://jovian.ml/
[jovian] Uploading notebook..
[jovian] Capturing environment..
[jovian] Committed successfully! https://jovian.ml/abhijeetraj22/zerotopandas-course-project-covid19-in-india