Pandas Profiling in Python

What is Pandas Profiling?

Pandas profiling is an extension of the Pandas library in Python. It is used to generate a comprehensive report of the dataset that we are giving as input. It helps in data analysis. In this report, we will get information about

• Basic statistics of the data
   
• Missing values, if any
   
• Data types
   
• Correlation
   
• Data visualization
   

Pandas profiling is the easiest way to get information about your data. Without Pandas profiling to get the same information about our data, we need to write complex codes. It helps us to access the information in a few lines of code.

Now, you might be wondering about how to use it. To use it, we need to first Install the profiling Library.

Installation of Pandas Profiling in Python

We need to write the following command in our terminal to install Pandas profiling:

pip install pandas-profiling

After running this command, we will see downloading gets started:

Now, let’s see its syntax.

Syntax of Pandas Profiling in Python

After installing the Pandas profiling library, we can create a profile report by using the ProfileReport() function:

import pandas_profiling
pandas_profiling.ProfileReport(our_dataframe, other_parameters)

You can also try this code with Online Python Compiler

Run Code

Note: In the place of other_parameters we can pass other parameters in this ProfileReport() function.

Now, let us understand what parameters we can pass in this function.

Parameters

There are several parameters that we can use to customize the report, and a few of them are mentioned below:

1. title: Using this parameter, we can give a title for the report
    
2. explorative: We can use this parameter as a toggle between a full exploratory analysis or a faster overview
    
3. minimal: We can use this to produce a minimal report. If we use this, it will exclude the visualization part
    
4. correlation_threshold: We can use this to set a threshold for correlation matrix visualization
    

Now, you might be wondering how we can use it. Let us understand this with the help of an example.

Example 

Suppose we are using a dataset called StudentsPerformance.csv. Now, we want to generate a report of this dataset. 

Now, we need to write the program to generate the report:

• Python

Python

import pandas as pd

import pandas_profiling as profiling



# Loading the dataset StudentsPerformance

studentPerformance = pd.read_csv('StudentsPerformance.csv')



# Creating a profile report of the dataset

pReport = profiling.ProfileReport(studentPerformance)


# Saving the report to an HTML file

pReport.to_file('StudentsPerformanceReport.html')

You can also try this code with Online Python Compiler

Run Code

After running the code, we will see:

To check the report, we can simply open the created HTML file.

Our report will look like this:

We can go to different tabs and check the result:

Why Use Pandas Profiling in Python?

Using Pandas profiling in Python can help us with the following:

• Time-saving: We don't need to write separate code snippets to explore data. By using Pandas Profiling, we can generate a detailed report in seconds
   
• Gives better data understanding: The report offers a quick understanding of our dataset's characteristics. It allows us to make better decisions
   
• Provides visual insights: It provides visualizations like histograms, scatter plots, and correlation matrices. It helps in spotting trends and patterns
   
• Provides quick data quality check: It can help us in identifying missing values, duplicate rows, and unique values
   
• Offers effortless data exploration: Pandas Profiling paves the way for better data exploration, even if we're new to data analysis

Frequently Asked Questions

Can we say Pandas profiling is a replacement for Pandas?

No, we cannot say Pandas profiling is a replacement for Pandas. It is an extension that complements Pandas. It helps in data analysis and visualization, and Pandas helps in data manipulation.

How can we customize the generated report?

We can customize the generated report with the help of adjusting parameters. We can adjust the report title, level of exploration, and more to customize the report according to our requirements.

Can we use Pandas profiling in Python for large datasets?

Yes, we can use Pandas profiling for large datasets, but it will take time to generate the report. We can still use it to get an initial overview but consider using it selectively or sampling data for faster results.

In which formats we can export the report?

We can export the report in HTML, JSON, and interactive widgets for Jupyter Notebooks. This flexibility enables you to share and access insights conveniently.

Conclusion

In this blog, we have discussed about Pandas profiling in Python. We have discussed about how to use it to generate a report. We have also taken an example to understand this. If you want to learn more about the Python modules, then you can check out our blogs:

• How to Install Pandas in Python?
   
• Import Pandas as pd
   
• Pandas Read Excel
   
• Pandas dataframe.shift()
   

We hope this article helped you to gain knowledge about Pandas profiling in Python. You can refer to our guided paths on the Codestudio platform. You can check our course to learn more about DSA, DBMS, Competitive Programming, Python, Java, JavaScript, etc. 

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