Python and Data Visualization: Unemployment Rate Analysis Exercise

Introduction
Prerequisites
Setup
Data Preparation
Data Visualization
Conclusion

Introduction

In this tutorial, we will learn how to analyze and visualize unemployment rate data using Python. By the end of this tutorial, you will be able to retrieve unemployment rate data, perform basic data preparation tasks, and generate various visualizations to gain insights from the data.

Prerequisites

To follow along with this tutorial, you should have a basic understanding of Python programming and the matplotlib library. Knowledge of pandas library for data manipulation would also be helpful.

Setup

Before we begin, make sure you have the necessary libraries installed. You can install them using pip with the following command: python pip install matplotlib pandas Additionally, we will be using data from the Bureau of Labor Statistics (BLS) to analyze the unemployment rate. You can download the data from the BLS website in the form of a CSV file.

Data Preparation

Start by importing the required libraries:

 import pandas as pd
 import matplotlib.pyplot as plt

Load the data into a pandas DataFrame:

 data = pd.read_csv('unemployment_data.csv')

Explore the data by displaying the first few rows:
```
 print(data.head())
```
Check the data types of each column:
```
 print(data.dtypes)
```

Convert the date column to a datetime format:

 data['Date'] = pd.to_datetime(data['Date'])

Set the date column as the index:
```
 data.set_index('Date', inplace=True)
```
Select only the unemployment rate column for analysis:
```
 unemployment_rate = data['Unemployment Rate']
```

Resample the data to a monthly frequency by calculating the mean:

 unemployment_rate_monthly = unemployment_rate.resample('M').mean()

Check the resulting data:

 print(unemployment_rate_monthly.head())

Data Visualization

Now that we have prepared our data, let’s dive into the process of visualizing the unemployment rate.

Start by plotting a line chart of the unemployment rate over time:
```
 plt.figure(figsize=(12, 6))
 plt.plot(unemployment_rate.index, unemployment_rate)
 plt.title('Unemployment Rate over Time')
 plt.xlabel('Date')
 plt.ylabel('Unemployment Rate')
 plt.show()
```
In this code snippet, we first create a figure with a specified size. Then, we plot the unemployment rate data against the corresponding dates. Finally, we add a title, labels for the x and y axes, and display the plot.
Next, let’s create a bar chart to compare the monthly unemployment rates:
```
 plt.figure(figsize=(12, 6))
 plt.bar(unemployment_rate_monthly.index, unemployment_rate_monthly)
 plt.title('Monthly Unemployment Rate')
 plt.xlabel('Date')
 plt.ylabel('Unemployment Rate')
 plt.show()
```
Similar to the previous example, we create a figure, but this time we use the bar function to create a bar chart. This type of visualization allows us to easily compare the unemployment rates across different months.
Another popular visualization is a pie chart. Let’s create a pie chart to represent the distribution of unemployment rates by year:
```
 unemployment_rate_by_year = unemployment_rate.resample('Y').mean()
	
 plt.figure(figsize=(8, 8))
 plt.pie(unemployment_rate_by_year, labels=unemployment_rate_by_year.index.year, autopct='%1.1f%%')
 plt.title('Unemployment Rate Distribution by Year')
 plt.show()
```
Here, we first calculate the annual average unemployment rate using the resample function with a ‘Y’ frequency. Then, we use the pie function to create a pie chart, providing labels for each slice and specifying the format for the percentage values.
Finally, let’s create a box plot to visualize the distribution of unemployment rates by quarter:
```
 unemployment_rate_quarterly = unemployment_rate.resample('Q').mean()
	
 plt.figure(figsize=(10, 6))
 plt.boxplot(unemployment_rate_quarterly, widths=0.5)
 plt.title('Unemployment Rate Distribution by Quarter')
 plt.xlabel('Quarter')
 plt.ylabel('Unemployment Rate')
 plt.show()
```
In this example, we calculate the average unemployment rate by quarter using the resample function with a ‘Q’ frequency. Then, we use the boxplot function to create a box plot, which displays the minimum, first quartile, median, third quartile, and maximum values of the dataset.

Conclusion

In this tutorial, we have learned how to perform data visualization using Python to analyze unemployment rate data. We covered data preparation tasks such as loading the data, exploring its structure, and performing basic transformations. Additionally, we created various visualizations, including line charts, bar charts, pie charts, and box plots, to gain insights from the data.

By applying these techniques, you can explore and visualize various datasets to extract valuable information. Python, along with libraries like matplotlib and pandas, offers a wide range of tools for data analysis and visualization, making it a powerful language for data scientists and researchers.

Published: 1 October 2019