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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:
With your help, we got approved for GitHub Sponsors! It's extra exciting that GitHub matches your contribution for the first year. Therefore, we welcome you to support the project through GitHub!
The v2.5.0 release includes many new features and stability improvements.
February 14, 2020 💘
Contents: Examples | Installation | Documentation | Large datasets | Command line usage | Advanced usage | Types | How to contribute | Editor Integration | Dependencies
The following examples can give you an impression of what the package can do:
You can install using the pip package manager by running
pip install pandas-profiling[notebook,html]
Alternatively, you could install the latest version directly from Github:
pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip
You can install using the conda package manager by running
conda install -c conda-forge pandas-profiling
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
The documentation for pandas_profiling can be found here.
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', html={'style':{'full_width':True}})
We recommend generating reports interactively by using the Jupyter notebook. There are two interfaces (see animations below): through widgets and through a HTML report.
This is achieved by simply displaying the report. In the Jupyter Notebook, run:
profile.to_widgets()
The HTML report can be included in a Juyter notebook:
Run the following code:
profile.to_notebook_iframe()
If you want to generate a HTML report file, save the ProfileReport to an object and use the to_file() function:
profile.to_file(output_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(output_file="your_report.json")
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_file="output.html")
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.
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.More settings can be found in the default configuration file, minimal configuration file and dark themed configuration file.
Example
profile = df.profile_report(title='Pandas Profiling Report', plot={'histogram': {'bins': 8}})
profile.to_file(output_file="output.html")
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:
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!
The package is actively maintained and developed as open-source software.
If pandas-profiling was helpful or interesting to you, you might want to get involved.
There are several ways of contributing and helping our thousands of users.
If you would like to be a industry partner or sponsor, please drop us a line.
The documentation is generated using pdoc3.
If you are contributing to this project, you can rebuild the documentation using:
make docs
or on Windows:
make.bat docs
Read more on getting involved in the Contribution Guide.
Install pandas-profiling via the instructions above
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
In Pycharm, go to Settings (or Preferences on macOS) > Tools > External tools
Click the + icon to add a new external tool
Insert the following values
To use the PyCharm Integration, right click on any dataset file: External Tools > Pandas Profiling.
Other editor integrations may be contributed via pull requests.
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. |
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