Omicron-covid19

This Python 3 environment comes with many helpful analytics libraries installed

It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python

For example, here's several helpful packages to load

import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

Input data files are available in the read-only "../input/" directory

For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename))

You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"

You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

Importing necessary files

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns

Reading Omicron variant in a variable named 'train'

train=pd.read_csv("../input/omicron-covid19-variant-daily-cases/covid-variants.csv")

Displaying first 5 rows of our data

train.head()

Checking out null values if any

train.isnull().sum()

Counting number of values corresponding to each location

train['location'].value_counts()

Creating separate datasets with respect to OMICRON,DELTA,BETA Variants.

omicron=train[train['variant']=='Omicron'] delta=train[train['variant']=='Delta'] beta=train[train['variant']=='Beta']

Adding up cases on same date using sum() method

omicron_sum=omicron.groupby("date").sum() delta_sum=delta.groupby("date").sum() beta_sum=beta.groupby("date").sum() CASES AROUND WORLD

Lineplot Showing Omicron Cases Over World

Using log as it returns natural logarithm of that particular column

plt.figure(figsize=(15,6)) plt.xticks(rotation=90) sns.lineplot(x=omicron_sum.index,y=np.log1p(omicron_sum['num_sequences']),color='red',linewidth='3') plt.title('Omicron Cases All Over World')

Lineplot Showing Delta Cases Over World

Using log as it returns natural logarithm of that particular column

plt.figure(figsize=(15,6)) plt.xticks(rotation=90) sns.lineplot(x=delta_sum.index,y=np.log1p(delta_sum['num_sequences']),color='red',linewidth='3') plt.title('Delta Cases All Over World')

Lineplot Showing Beta Cases Over World

Using log as it returns natural logarithm of that particular column

plt.figure(figsize=(15,6)) plt.xticks(rotation=90) sns.lineplot(x=beta_sum.index,y=np.log1p(beta_sum['num_sequences']),color='red',linewidth='3') plt.title('Beta Cases All Over World') Beta Variant

Lineplot showing different increase of beta variant around different countries

Using log as it returns natural logarithm of that particular column

plt.figure(figsize=(15,6)) plt.xticks(rotation=90) sns.lineplot(x=beta_india['date'],y=np.log1p(beta_india['num_sequences']),label='India',linewidth='3') sns.lineplot(x=beta_Nepal['date'],y=np.log1p(beta_Nepal['num_sequences']),label='Nepal',linewidth='3') sns.lineplot(x=beta_Pakistan['date'],y=np.log1p(beta_Pakistan['num_sequences']),label='Pakistan',linewidth='3') sns.lineplot(x=beta_Bangladesh['date'],y=np.log1p(beta_Bangladesh['num_sequences']),label='Bangladesh',linewidth='3') plt.title('Beta variant around different countries')