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pandas-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, pandas-profiling delivers an extended analysis of a DataFrame while alllowing 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.

Key features

  • Type inference: automatic detection of columns’ data types (Categorical, Numerical, Date, etc.)

  • Warnings: A summary of the problems/challenges in the data that you might need to work on (missing data, inaccuracies, skewness, etc.)

  • Univariate analysis: including descriptive statistics (mean, median, mode, etc) and informative visualizations such as distribution histograms

  • Multivariate analysis: including correlations, a detailed analysis of missing data, duplicate rows, and visual support for variables pairwise interaction

  • Time-Series: including different statistical information relative to time dependent data such as auto-correlation and seasonality, along ACF and PACF plots.

  • Text analysis: most common categories (uppercase, lowercase, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic)

  • File and Image analysis: file sizes, creation dates, dimensions, indication of truncated images and existence of EXIF metadata

  • Compare datasets: one-line solution to enable a fast and complete report on the comparison of datasets

  • Flexible output formats: all analysis can be exported to an HTML report that can be easily shared with different parties, as JSON for an easy integration in automated systems and as a widget in a Jupyter Notebook.

The report contains three additional sections:

  • Overview: mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint)

  • Alerts: a comprehensive and automatic list of potential data quality issues (high correlation, imbalance, skewness, uniformity, zeros, missing values, constant values, between others)

  • Reproduction: technical details about the analysis (time, version and configuration)

The package can be used via code but also directly as a CLI utility. The generated interactive report can be consumed and shared as regular HTML or embedded in an interactive way inside Jupyter Notebooks.


🎁 Latest features
  • Looking for how you can do an EDA for Time-Series 🕛 ? Check this blogpost

  • You want to compare 2 datasets and get a report? Check this blogpost