# Pandas Profiling
[](https://travis-ci.com/pandas-profiling/pandas-profiling)
[](https://codecov.io/gh/pandas-profiling/pandas-profiling)
[](https://github.com/pandas-profiling/pandas-profiling/releases)
[](https://pypi.org/project/pandas-profiling/)
[](https://github.com/python/black)
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](#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 v2.8.0 released
News for users working with image datasets: ``pandas-profiling`` now has build-in supports for Files and Images.
Moreover, the text analysis features have also been reworked, providing more informative statistics.
For a better feel, have a look at the [examples](https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/pages/examples.html#showcasing-specific-features) section in the docs or read the changelog for a complete view of the changes.
### Version v2.7.0 released
#### Performance
There were several performance regressions pointed out to me recently when comparing 1.4.1 to 2.6.0.
To that end, we benchmarked the code and found several minor features introducing disproportionate computational complexity.
Version 2.7.0 optimizes these, giving significant performance improvements!
Moreover, the default configuration is tweaked for towards the needs of the average user.
#### Phased builds and lazy loading
A report is built in phases, which allows for new exciting features such as caching, only re-rendering partial reports and lazily computing the report.
Moreover, the progress bar provides more information on the building phase and step.
#### Documentation
This version introduces [more elaborate documentation](https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/index.html) powered by Sphinx. The previously used pdoc3 has been adequate initially, however misses functionality and extensibility. Several recurring topics are now documented, for instance the configuration parameters are documented and there are pages on big datasets, sensitive data, integrations and resources.
#### 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](https://github.com/sponsors/sbrugman)!
It's extra exciting that GitHub **matches your contribution** for the first year.
Find more information here:
- [Changelog v2.7.0](https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/pages/changelog.html#changelog-v2-7-0)
- [Changelog v2.8.0](https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/pages/changelog.html#changelog-v2-8-0)
- [Sponsor the project on GitHub](https://github.com/sponsors/sbrugman)
*May 7, 2020 ����*
---
_Contents:_ **[Examples](#examples)** |
**[Installation](#installation)** | **[Documentation](#documentation)** |
**[Large datasets](#large-datasets)** | **[Command line usage](#command-line-usage)** |
**[Advanced usage](#advanced-usage)** |
**[Types](#types)** | **[How to contribute](#contributing)** |
**[Editor Integration](#editor-integration)** | **[Dependencies](#dependencies)**
---
## Examples
The following examples can give you an impression of what the package can do:
* [Census Income](https://pandas-profiling.github.io/pandas-profiling/examples/master/census/census_report.html) (US Adult Census data relating income)
* [NASA Meteorites](https://pandas-profiling.github.io/pandas-profiling/examples/master/meteorites/meteorites_report.html) (comprehensive set of meteorite landings) [](https://colab.research.google.com/github/pandas-profiling/pandas-profiling/blob/master/examples/meteorites/meteorites.ipynb) [](https://mybinder.org/v2/gh/pandas-profiling/pandas-profiling/master?filepath=examples%2Fmeteorites%2Fmeteorites.ipynb)
* [Titanic](https://pandas-profiling.github.io/pandas-profiling/examples/master/titanic/titanic_report.html) (the "Wonderwall" of datasets) [](https://colab.research.google.com/github/pandas-profiling/pandas-profiling/blob/master/examples/titanic/titanic.ipynb) [](https://mybinder.org/v2/gh/pandas-profiling/pandas-profiling/master?filepath=examples%2Ftitanic%2Ftitanic.ipynb)
* [NZA](https://pandas-profiling.github.io/pandas-profiling/examples/master/nza/nza_report.html) (open data from the Dutch Healthcare Authority)
* [Stata Auto](https://pandas-profiling.github.io/pandas-profiling/examples/master/stata_auto/stata_auto_report.html) (1978 Automobile data)
* [Vektis](https://pandas-profiling.github.io/pandas-profiling/examples/master/vektis/vektis_report.html) (Vektis Dutch Healthcare data)
* [Colors](https://pandas-profiling.github.io/pandas-profiling/examples/master/colors/colors_report.html) (a simple colors dataset)
Specific features:
* [Russian Vocabulary](https://pandas-profiling.github.io/pandas-profiling/examples/master/features/russian_vocabulary.html) (demonstrates text analysis)
* [Cats and Dogs](https://pandas-profiling.github.io/pandas-profiling/examples/master/features/cats-and-dogs.html) (demonstrates image analysis from the file system)
* [Celebrity Faces](https://pandas-profiling.github.io/pandas-profiling/examples/master/features/celebrity-faces.html) (demonstrates image analysis with EXIF information)
* [Website Inaccessibility](https://pandas-profiling.github.io/pandas-profiling/examples/master/features/website_inaccessibility_report.html) (demonstrates URL analysis)
* [Orange prices](https://pandas-profiling.github.io/pandas-profiling/examples/master/themes/united
