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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.

Announcements

Version v2.11.0 released featuring an exciting integration with Great Expectations that many of you requested (see details below).

Spark backend in progress: We can happily announce that we're nearing v1 for the 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 directly through GitHub Sponsors! Please help me to continue to support this package. It's extra exciting that GitHub matches your contribution for the first year.

Find more information here:

February 20, 2021 💘


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


Examples

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

Specific features:

Tutorials:

Installation

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 https://github.com/pandas-profiling/pandas-profiling/archive/master.zip

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 setup.py install

Documentation

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:

profile.to_widgets()

The HTML report can be included in a Jupyter notebook:

HTML

Run the following code:

profile.to_notebook_iframe()

Saving the report

If you want 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, you can obtain the data as JSON:

# As a string
json_data = profile.to_json()

# As a file
profile.to_file("your_report.json")

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)
profile.to_file("output.html")

Command line usage

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

Run the following for information about options and arguments.

pandas_profiling -h

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.
  • infer_dtypes (bool): When True (default) the dtype of variables are inferred using visions using the typeset logic (for instance a column that has integers stored as string will be analyzed as if being numeric).

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

You find the configuration docs on the advanced usage page here

Example

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

Integrations

Great Expectations

Great Expectations

Profiling your data is closely related to data validation: often validation rules are defined in terms of well-known statistics. For that purpose, pandas-profiling integrates with Great Expectations. This a world-class open-source library that helps you to maintain data quality and improve communication about data between teams. Great Expectations allows you to create Expectations (which are basically unit tests for your data) and Data Docs (conveniently shareable HTML data reports). pandas-profiling features a method to create a suite of Expectations based on the results of your ProfileReport, which you can store, and use to validate another (or future) dataset.

You can find more details on the Great Expectations integration here

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 without 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, Brian Lee, Stephanie Rivera, abdulAziz, gramster

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

Types

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!

Contributing

Read on getting involved in the Contribution Guide.

A low threshold place to ask questions or start contributing is by reaching out on the pandas-profiling Slack. Join the Slack community.

Editor integration

PyCharm integration

  1. Install pandas-profiling via the instructions above
  2. Locate your pandas-profiling executable.
    • On macOS / Linux / BSD:
      $ which pandas_profiling
      (example) /usr/local/bin/pandas_profiling
    • On Windows:
      $ 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.

Dependencies

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
setup.py Requirements for Widgets etc.