Unemployment Analysis with Python

Unemployment is measured by the unemployment rate which is the number of people who are unemployed as a percentage of the total labour force. We have seen a sharp increase in the unemployment rate during Covid-19, so analyzing the unemployment rate can be a good data science project. In this article, I will take you through the task of Unemployment analysis with Python.

Unemployment Analysis with Python

The unemployment rate is calculated based on a particular region, so to analyze unemployment I will be using an unemployment dataset of India. The dataset I’m using here contains data on India’s unemployment rate during Covid-19. So let’s start the task of Unemployment analysis by importing the necessary Python libraries and the dataset:

           Region         Date  Frequency  ...  Region.1  longitude  latitude
0  Andhra Pradesh   31-01-2020          M  ...     South    15.9129     79.74
1  Andhra Pradesh   29-02-2020          M  ...     South    15.9129     79.74
2  Andhra Pradesh   31-03-2020          M  ...     South    15.9129     79.74
3  Andhra Pradesh   30-04-2020          M  ...     South    15.9129     79.74
4  Andhra Pradesh   31-05-2020          M  ...     South    15.9129     79.74

[5 rows x 9 columns]

Let’s see if this dataset contains missing values or not:

print(data.isnull().sum())
Region                                      0
Date                                        0
 Frequency                                  0
 Estimated Unemployment Rate (%)            0
 Estimated Employed                         0
 Estimated Labour Participation Rate (%)    0
Region.1                                    0
longitude                                   0
latitude                                    0
dtype: int64

While analyzing the missing values, I found that the column names are not correct. So, for a better understanding of this data, I will rename all the columns:

Now let’s have a look at the correlation between the features of this dataset:

correlation

Unemployment Rate Analysis: Data Visualization

Now let’s visualize the data to analyze the unemployment rate. I will first take a look at the estimated number of employees according to different regions of India:

Employment Rate

Now let’s see the unemployment rate according to different regions of India:

Unemployment Analysis

Now let’s create a dashboard to analyze the unemployment rate of each Indian state by region. For this, I’ll use a sunburst plot:

Summary

So this is how you can analyze the unemployment rate by using the Python programming language. Unemployment is measured by the unemployment rate which is the number of people who are unemployed as a percentage of the total labour force. I hope you liked this article on unemployment rate analysis with Python. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
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