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In the last few years there has been a large amount of redundancies in the tech sector around the world and it keeps on going.
In this example we will look at a dataset with layoff data and we will use python and various libraries to analyze this data and display some information in graphs
Code
As usual we will import the required libraries and analyse the data
x
import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport matplotlib import seaborn as snsimport warningsdf = pd.read_csv('layoffs.csv')#show the dataprint(df.head())print(df.describe().T)#any empty dataprint(df.isna().sum())This shows a lot of empty cells
company 0
location 0
industry 2
total_laid_off 799
percentage_laid_off 851
date 2
stage 6
country 0
funds_raised 248
Lets fill these with values and check again
#fill empty valuesdf['total_laid_off'] = df['total_laid_off'].fillna(0)df['percentage_laid_off'] = df['percentage_laid_off'].fillna(0)df['stage'] = df['stage'].fillna(0)df['funds_raised'] = df['funds_raised'].fillna(0)df['industry'] = df['industry'].fillna(0)#show the dataprint(df.head())print(df.describe().T)#any empty dataprint(df.isna().sum())company 0
location 0
industry 0
total_laid_off 0
percentage_laid_off 0
date 2
stage 0
country 0
funds_raised 0
Its always handy to visualize the data, here are some examples
#top 10 companies by layoff numbersdf.groupby('company').total_laid_off.sum().sort_values(ascending=False)[:10]fig = px.bar(df.groupby('company').total_laid_off.sum().sort_values(ascending=False)[:10],text_auto=True,title='Top 10 companies that laid off from 2020 to 2022', labels={"x":"Company","y":"Layoffs"})fig.show()#top sectorssectors = np.array(df.groupby('industry')['total_laid_off'].sum().sort_values(ascending=False).head())arr = np.array(['transportation','Consumer','Retail','Finance','Food'])plt.figure(figsize= (8 ,6))plt.bar(arr,sectors)plt.xlabel('Sectors',fontdict={'size':12,'color':'orange'})plt.ylabel('count',fontdict={'size':12,'color':'orange'})plt.title('Top Sectors by layoffs',fontdict={'size':18,'color':'orange'})plt.show()#top countriescountries = np.array(df.groupby('country')['total_laid_off'].sum().sort_values(ascending=False).head())arr1 = np.array(['US','India','Brazil','Singapore','Canada'])plt.figure(figsize=(7,7))ax = sns.barplot(x=countries, y=arr1, data=df)plt.title('Most Affected Countries')plt.show()Links
The examples are available from