Pandas Profiling

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:

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

Examples

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

Installation

Using pip

You can install using the pip package manager by running

pip install pandas-profiling

Using conda

You can install using the conda package manager by running

conda install -c anaconda 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

Usage

The profile report is written in HTML5 and CSS3, which means pandas-profiling requires a modern browser.

Documentation

The documentation for pandas_profiling can be found here. The documentaion is generated using pdoc3. If you are contribution to this project, you can rebuild the documentation using:

make docs

or on Windows:

make.bat docs

Jupyter Notebook

We recommend generating reports interactively by using the Jupyter notebook.

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

import numpy as np
import pandas as pd
import pandas_profiling

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

To display the report in a Jupyter notebook, run:

df.profile_report()

To retrieve the list of variables which are rejected due to high correlation:

profile = df.profile_report()
rejected_variables = profile.get_rejected_variables(threshold=0.9)

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

profile = df.profile_report(title='Pandas Profiling Report')
profile.to_file(output_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

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).
minify_html (boolean): Whether to minify the output HTML.

More settings can be found in the default configuration file.

Example

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

Dependencies

python (>= 3.5)
pandas (>=0.19)
matplotlib (>=1.4)
missingno
confuse
requests
jinja2
numpy
htmlmin (optional)
phik (optional)

For development and testing we use additional packages which you can find in the requirements-dev.txt and requirements-test.txt.

hmartiniano / pandas-profiling