pandas-profiling-3.2.0.tar.gz

# Pandas Profiling  [](https://github.com/ydataai/pandas-profiling/actions/workflows/tests.yml) [](https://codecov.io/gh/ydataai/pandas-profiling) [](https://github.com/ydataai/pandas-profiling/releases) [](https://pypi.org/project/pandas-profiling/) [](https://github.com/python/black) <p align="center"> <a href="https://pandas-profiling.ydata.ai/docs/master/rtd/">Documentation</a> | <a href="https://slack.ydata.ai">Slack</a> | <a href="https://stackoverflow.com/questions/tagged/pandas-profiling">Stack Overflow</a> | <a href="https://pandas-profiling.ydata.ai/docs/master/rtd/pages/changelog.html#changelog">Latest changelog</a> </p> 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 **Spark backend in progress**: We can happily announce that we're nearing v1 for the Spark backend for generating profile reports. Beta testers wanted! The Spark backend will be released as a pre-release for this package. **Monitoring time series?**: I'd like to draw your attention to [popmon](https://github.com/ing-bank/popmon). Whereas pandas-profiling allows you to explore patterns in a single dataset, popmon allows you to uncover temporal patterns. It's worth checking out! --- _Contents:_ **[Examples](#examples)** | **[Installation](#installation)** | **[Documentation](#documentation)** | **[Large datasets](#large-datasets)** | **[Command line usage](#command-line-usage)** | **[Advanced usage](#advanced-usage)** | **[Support](#support)** | **[Go beyond](#go-beyond)** | **[Support the project](#supporting-open-source)** | **[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.ydata.ai/examples/master/census/census_report.html) (US Adult Census data relating income) * [NASA Meteorites](https://pandas-profiling.ydata.ai/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.ydata.ai/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.ydata.ai/examples/master/nza/nza_report.html) (open data from the Dutch Healthcare Authority) * [Stata Auto](https://pandas-profiling.ydata.ai/examples/master/stata_auto/stata_auto_report.html) (1978 Automobile data) * [Vektis](https://pandas-profiling.ydata.ai/examples/master/vektis/vektis_report.html) (Vektis Dutch Healthcare data) * [Colors](https://pandas-profiling.ydata.ai/examples/master/colors/colors_report.html) (a simple colors dataset) * [UCI Bank Dataset](https://pandas-profiling.ydata.ai/examples/master/bank_marketing_data/uci_bank_marketing_report.html) (banking marketing dataset) * [RDW](https://pandas-profiling.ydata.ai/examples/master/rdw/rdw.html) (RDW, the Dutch DMV's vehicle registration 10 million rows, 71 features) Specific features: * [Russian Vocabulary](https://pandas-profiling.ydata.ai/examples/master/features/russian_vocabulary.html) (demonstrates text analysis) * [Cats and Dogs](https://pandas-profiling.ydata.ai/examples/master/features/cats-and-dogs.html) (demonstrates image analysis from the file system) * [Celebrity Faces](https://pandas-profiling.ydata.ai/examples/master/features/celebrity-faces.html) (demonstrates image analysis with EXIF information) * [Website Inaccessibility](https://pandas-profiling.ydata.ai/examples/master/features/website_inaccessibility_report.html) (demonstrates URL analysis) * [Orange prices](https://pandas-profiling.ydata.ai/examples/master/features/united_report.html) and [Coal prices](https://pandas-profiling.ydata.ai/examples/master/features/flatly_report.html) (showcases report themes) Tutorials: * [Tutorial: report structure using Kaggle data (advanced)](https://pandas-profiling.ydata.ai/examples/master/tutorials/modify_report_structure.ipynb) (modify the report's structure) [](https://colab.research.google.com/github/pandas-profiling/pandas-profiling/blob/master/examples/tutorials/modify_report_structure.ipynb) [](https://mybinder.org/v2/gh/pandas-profiling/pandas-profiling/master?filepath=examples%2Ftutorials%2Fmodify_report_structure.ipynb) ## Installation ### Using pip [](https://pepy.tech/project/pandas-profiling) [](https://pepy.tech/project/pandas-profiling/month) [](https://pypi.org/project/pandas-profiling/)