Documentation | Discord | Stack Overflow | Latest changelog
Do you like this project? Show us your love and give feedback!
ydata-profiling primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Like pandas df.describe() function, that is so handy, ydata-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as html and json.
The package outputs a simple and digested analysis of a dataset, including time-series and text.
Looking for a scalable solution that can fully integrate with your database systems?
Leverage YData Fabric Data Catalog to connect to different databases and storages (Oracle, snowflake, PostGreSQL, GCS, S3, etc.) and leverage an interactive and guided profiling experience in Fabric. Check out the Community Version.
pip install ydata-profiling
or
conda install -c conda-forge ydata-profiling
Start by loading your pandas DataFrame as you normally would, e.g. by using:
import numpy as np
import pandas as pd
from ydata_profiling import ProfileReport
df = pd.DataFrame(np.random.rand(100, 5), columns=["a", "b", "c", "d", "e"])
To generate the standard profiling report, merely run:
profile = ProfileReport(df, title="Profiling Report")
The report contains three additional sections:
Spark support has been released, but we are always looking for an extra pair of hands 👐. Check current work in progress!.
YData-profiling can be used to deliver a variety of different use-case. The documentation includes guides, tips and tricks for tackling them:
| Use case | Description |
|---|---|
| Comparing datasets | Comparing multiple version of the same dataset |
| Profiling a Time-Series dataset | Generating a report for a time-series dataset with a single line of code |
| Profiling large datasets | Tips on how to prepare data and configure ydata-profiling for working with large datasets |
| Handling sensitive data | Generating reports which are mindful about sensitive data in the input dataset |
| Dataset metadata and data dictionaries | Complementing the report with dataset details and column-specific data dictionaries |
| Customizing the report's appearance | Changing the appearance of the report's page and of the contained visualizations |
| Profiling Databases | For a seamless profiling experience in your organization's databases, check Fabric Data Catalog, which allows to consume data from different types of storages such as RDBMs (Azure SQL, PostGreSQL, Oracle, etc.) and object storages (Google Cloud Storage, AWS S3, Snowflake, etc.), among others. |
There are two interfaces to consume the report inside a Jupyter notebook: through widgets and through an embedded HTML report.
The above is achieved by simply displaying the report as a set of widgets. In a Jupyter Notebook, run:
profile.to_widgets()
The HTML report can be directly embedded in a cell in a similar fashion:
profile.to_notebook_iframe()
To generate a HTML report file, save the ProfileReport to an object and use the to_file() function:
profile.to_file("your_report.html")
Alternatively, the report's data can be obtained as a JSON file:
# As a JSON string
json_data = profile.to_json()
# As a file
profile.to_file("your_report.json")
For standard formatted CSV files (which can be read directly by pandas without additional settings), the ydata_profiling executable can be used in the command line. The example below generates a report named Example Profiling Report, using a configuration file called default.yaml, in the file report.html by processing a data.csv dataset.
ydata_profiling --title "Example Profiling Report" --config_file default.yaml data.csv report.html
Additional details on the CLI are available on the documentation.
The following example reports showcase the potentialities of the package across a wide range of dataset and data types:
Additional details, including information about widget support, are available on the documentation.
You can install using the pip package manager by running:
pip install -U ydata-profiling
The package declares "extras", sets of additional dependencies.
[notebook]: support for rendering the report in Jupyter notebook widgets.[unicode]: support for more detailed Unicode analysis, at the expense of additional disk space.[pyspark]: support for pyspark for big dataset analysisInstall these with e.g.
pip install -U ydata-profiling[notebook,unicode,pyspark]
You can install using the conda package manager by running:
conda install -c conda-forge ydata-profiling
Download the source code by cloning the repository or click on Download ZIP to download the latest stable version.
Install it by navigating to the proper directory and running:
pip install -e .
The profiling report is written in HTML and CSS, which means a modern browser is required.
You need Python 3 to run the package. Other dependencies can be found in the requirements files:
| Filename | Requirements |
|---|---|
| requirements.txt | Package requirements |
| requirements-dev.txt | Requirements for development |
| requirements-test.txt | Requirements for testing |
| setup.py | Requirements for widgets etc. |
To maximize its usefulness in real world contexts, ydata-profiling has a set of implicit and explicit integrations with a variety of other actors in the Data Science ecosystem:
| Integration type | Description |
|---|---|
| Other DataFrame libraries | How to compute the profiling of data stored in libraries other than pandas |
| Great Expectations | Generating Great Expectations expectations suites directly from a profiling report |
| Interactive applications | Embedding profiling reports in Streamlit, Dash or Panel applications |
| Pipelines | Integration with DAG workflow execution tools like Airflow or Kedro |
| Cloud services | Using ydata-profiling in hosted computation services like Lambda, Google Cloud or Kaggle |
| IDEs | Using ydata-profiling directly from integrated development environments such as PyCharm |
Need help? Want to share a perspective? Report a bug? Ideas for collaborations? Reach out via the following channels:
Need Help?
Get your questions answered with a product owner by booking a Pawsome chat! 🐼
❗ Before reporting an issue on GitHub, check out Common Issues.
Learn how to get involved in the Contribution Guide.
A low-threshold place to ask questions or start contributing is the Data Centric AI Community's Discord.
A big thank you to all our amazing contributors!
Contributors wall made with contrib.rocks.
FAQs
Generate profile report for pandas DataFrame
We found that ydata-profiling demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.
Security News
The Ultralytics' PyPI Package was compromised four times in one weekend through GitHub Actions cache poisoning and failure to rotate previously compromised API tokens.