Automate Python Data Analysis With YData Profiling

The YData Profiling package generates an exploratory data analysis (EDA) report with a few lines of code. The report provides dataset and column-level analysis, including plots and summary statistics to help you quickly understand your dataset. These reports can be exported to HTML or JSON so you can share them with other stakeholders.

By the end of this tutorial, you’ll understand that:

• YData Profiling generates interactive reports containing EDA results, including summary statistics, visualizations, correlation matrices, and data quality warnings from DataFrames.
• ProfileReport creates a profile you can save with .to_file() for HTML or JSON export, or display inline with .to_notebook_iframe().
• Setting tsmode=True and specifying a date column with sortby enables time series analysis, including stationarity tests and seasonality detection.
• The .compare() method generates side-by-side reports highlighting distribution shifts and statistical differences between datasets.

To get the most out of this tutorial, you’ll benefit from having knowledge of pandas.

You can install this package using pip:

$ python -m pip install ydata-profiling

Once installed, you’re ready to transform any pandas DataFrame into an interactive report. To follow along, download the example dataset you’ll work with by clicking the link below:

The following example generates a profiling report from the 2024 flight delay dataset and saves it to disk:

import pandas as pd
from ydata_profiling import ProfileReport
df = pd.read_csv("flight_data_2024_sample.csv")
profile = ProfileReport(df)
profile.to_file("flight_report.html")

This code generates an HTML file containing interactive visualizations, statistical summaries, and data quality warnings:

You can open the file in any browser to explore your data’s characteristics without writing additional analysis code.

There are a number of tools available for high-level dataset exploration, but not all are built for the same purpose. The following table highlights a few common options and when each one is a good fit:

Overall, YData Profiling is best used as an exploratory report creation tool. If you’re looking to generate an overview for a large dataset, using SkimPy or a built-in DataFrame library method may be more efficient. Other tools, like Pandera, are more appropriate for data validation.

If YData Profiling looks like the right choice for your use case, then keep reading to learn about its most important features.

Take the Quiz: Test your knowledge with our interactive “Automate Python Data Analysis With YData Profiling” quiz. You’ll receive a score upon completion to help you track your learning progress:

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Interactive Quiz

Automate Python Data Analysis With YData Profiling

Test your knowledge of YData Profiling, including report creation, customization, performance optimization, time series analysis, and comparisons.

Building a Report With YData Profiling