Package pandas_profiling

Main module of pandas-profiling.

Pandas Profiling

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Generates profile reports from a pandas DataFrame. The pandas df.describe() function is great but a little basic for serious exploratory data analysis. pandas_profiling extends the pandas DataFrame with df.profile_report() for quick data analysis.

For each column the following statistics - if relevant for the column type - are presented in an interactive HTML report:

  • Type inference: detect the types of columns in a dataframe.
  • Essentials: type, unique values, missing values
  • Quantile statistics like minimum value, Q1, median, Q3, maximum, range, interquartile range
  • Descriptive statistics like mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness
  • Most frequent values
  • Histogram
  • Correlations highlighting of highly correlated variables, Spearman, Pearson and Kendall matrices
  • Missing values matrix, count, heatmap and dendrogram of missing values
  • Text analysis learn about categories (Uppercase, Space), scripts (Latin, Cyrillic) and blocks (ASCII) of text data.
  • File and Image analysis extract file sizes, creation dates and dimensions and scan for truncated images or those containing EXIF information.


Version v2.9.0 released

The release candidate for v2.9.0 was already out for a while, now v2.9.0 is finally released. See the changelog below to know what has changed.

Spark backend in progress

We can happily announce that we're working on a Spark backend for generating profile reports. Stay tuned.

Support pandas-profiling

The development of pandas-profiling relies completely on contributions. If you find value in the package, we welcome you to support the project through GitHub Sponsors! It's extra exciting that GitHub matches your contribution for the first year.

Find more information here:

September 2, 2020 💘

Contents: Examples | Installation | Documentation | Large datasets | Command line usage | Advanced usage | Support | Types | How to contribute | Editor Integration | Dependencies


The following examples can give you an impression of what the package can do:

  • Census Income (US Adult Census data relating income)
  • NASA Meteorites (comprehensive set of meteorite landings) Open In Colab Binder
  • Titanic (the "Wonderwall" of datasets) Open In Colab Binder
  • NZA (open data from the Dutch Healthcare Authority)
  • Stata Auto (1978 Automobile data)
  • Vektis (Vektis Dutch Healthcare data)
  • Colors (a simple colors dataset)

Specific features: * Russian Vocabulary (demonstrates text analysis) * Cats and Dogs (demonstrates image analysis from the file system) * Celebrity Faces (demonstrates image analysis with EXIF information) * Website Inaccessibility (demonstrates URL analysis) * Orange prices and Coal prices (showcases report themes)

Tutorials: * Tutorial: report structure using Kaggle data (advanced) (modify the report's structure) Open In Colab Binder


Using pip

PyPi Downloads PyPi Monthly Downloads PyPi Version

You can install using the pip package manager by running

pip install pandas-profiling[notebook]

Alternatively, you could install the latest version directly from Github:

pip install <>

Using conda

Conda Downloads Conda Version

You can install using the conda package manager by running

conda install -c conda-forge pandas-profiling

From source

Download the source code by cloning the repository or by pressing 'Download ZIP' on this page. Install by navigating to the proper directory and running

python install


The documentation for pandas_profiling can be found here. Previous documentation is still available here.

Getting started

Start by loading in your pandas DataFrame, e.g. by using

import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport

df = pd.DataFrame(
    np.random.rand(100, 5),
    columns=["a", "b", "c", "d", "e"]

To generate the report, run:

profile = ProfileReport(df, title="Pandas Profiling Report")

Explore deeper

You can configure the profile report in any way you like. The example code below loads the explorative configuration file, that includes many features for text (length distribution, unicode information), files (file size, creation time) and images (dimensions, exif information). If you are interested what exact settings were used, you can compare with the default configuration file.

profile = ProfileReport(df, title='Pandas Profiling Report', explorative=True)

Learn more about configuring pandas-profiling on the Advanced usage page.

Jupyter Notebook

We recommend generating reports interactively by using the Jupyter notebook. There are two interfaces (see animations below): through widgets and through a HTML report.

Notebook Widgets

This is achieved by simply displaying the report. In the Jupyter Notebook, run:


The HTML report can be included in a Jupyter notebook:


Run the following code:


Saving the report

If you want to generate a HTML report file, save the ProfileReport to an object and use the to_file() function:


Alternatively, you can obtain the data as json:

# As a string
json_data = profile.to_json()

# As a file

Large datasets

Version 2.4 introduces minimal mode. This is a default configuration that disables expensive computations (such as correlations and dynamic binning). Use the following syntax:

profile = ProfileReport(large_dataset, minimal=True)

Command line usage

For standard formatted CSV files that can be read immediately by pandas, you can use the pandas_profiling executable. Run

pandas_profiling -h

for information about options and arguments.

Advanced usage

A set of options is available in order to adapt the report generated.

  • title (str): Title for the report ('Pandas Profiling Report' by default).
  • pool_size (int): Number of workers in thread pool. When set to zero, it is set to the number of CPUs available (0 by default).
  • progress_bar (bool): If True, pandas-profiling will display a progress bar.

More settings can be found in the default configuration file, minimal configuration file and dark themed configuration file.


profile = df.profile_report(title='Pandas Profiling Report', plot={'histogram': {'bins': 8}})

Supporting open source

Maintaining and developing the open-source code for pandas-profiling, with millions of downloads and thousands of users, would not be possible with support of our gracious sponsors.

Lambda Labs [Lambda workstations](, servers, laptops, and cloud services power engineers and researchers at Fortune 500 companies and 94% of the top 50 universities. [Lambda Cloud]( offers 4 & 8 GPU instances starting at $1.50 / hr. Pre-installed with TensorFlow, PyTorch, Ubuntu, CUDA, and cuDNN.

We would like to thank our generous Github Sponsors supporters who make pandas-profiling possible:

Martin Sotir, Joseph Yuen, Brian Lee, Stephanie Rivera, nscsekhar, abdulAziz

More info if you would like to appear here: Github Sponsor page


Types are a powerful abstraction for effective data analysis, that goes beyond the logical data types (integer, float etc.). pandas-profiling currently recognizes the following types: Boolean, Numerical, Date, Categorical, URL, Path, File and Image.

We have developed a type system for Python, tailored for data analysis: visions. Selecting the right typeset drastically reduces the complexity the code of your analysis. Future versions of pandas-profiling will have extended type support through visions!


Read on getting involved in the Contribution Guide.

Editor integration

PyCharm integration

  1. Install pandas-profiling via the instructions above
  2. Locate your pandas-profiling executable.

    On macOS / Linux / BSD:

    console $ which pandas_profiling (example) /usr/local/bin/pandas_profiling

    On Windows:

    console $ where pandas_profiling (example) C:\ProgramData\Anaconda3\Scripts\pandas_profiling.exe

  3. In Pycharm, go to Settings (or Preferences on macOS) > Tools > External tools

  4. Click the + icon to add a new external tool
  5. Insert the following values
    • Name: Pandas Profiling
    • Program: The location obtained in step 2
    • Arguments: "$FilePath$" "$FileDir$/$FileNameWithoutAllExtensions$_report.html"
    • Working Directory: $ProjectFileDir$

PyCharm Integration

To use the PyCharm Integration, right click on any dataset file: External Tools > Pandas Profiling.

Other integrations

Other editor integrations may be contributed via pull requests.


The profile report is written in HTML and CSS, which means pandas-profiling requires a modern browser.

You need Python 3 to run this 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 Requirements for Widgets etc.
Expand source code
"""Main module of pandas-profiling.

.. include:: ../../

from pandas_profiling.config import Config, config
from pandas_profiling.controller import pandas_decorator
from pandas_profiling.profile_report import ProfileReport
from pandas_profiling.version import __version__

clear_config = ProfileReport.clear_config



Configuration for the package is handled in this wrapper for confuse.


The controller module handles all user interaction with the package (console, jupyter, etc.).


The model module handles all logic/calculations, e.g. calculate statistics, testing for special conditions.


All functionality concerned with presentation to the user.


Utility functions for the complete package.


This file is auto-generated by, please do not alter.


Code for generating plots