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JosPolfliet committed Jan 9, 2016
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  2. +21 −0 LICENSE
  3. +40 −0 README.md
  4. +316 −0 __init__.py
  5. +33 −0 formatters.py
  6. +69 −0 pandas_profiling.mplstyle
  7. +17 −0 setup.py
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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
*.egg-info/
.installed.cfg
*.egg
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*,cover
.hypothesis/
# Translations
*.mo
*.pot
# Django stuff:
*.log
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# gedit backup files
*~
21 LICENSE
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The MIT License (MIT)
Copyright (c) 2016 Jos Polfliet
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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#pandas-profiling
Generates profile reports from a pandas DataFrame. The df.describe() function is great but a little basic for serious exploratory data analysis.
For each column the following statistics - if relevant for the column type - are presented in a nice interactive HTML report:
* ** Essentials**: type, unique values (# and %), missing values (# and %)
* ** 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**
## Demo
Here is a demo of a report that has been generated
## Usage
The profile report is written in HTML5 and CSS3, which means pandas-profiling requires a modern browser.
### Jupyter Notebook (formerly IPython)
We recommend generating reports interactively by using the Jupyter notebook.
With a DataFrame df defined as follows:
import pandas as pd
### Command line
You can also use the command line interface to generate a report file on your local disk from a CSV file and subsequently open it.
## Dependencies
* **An internet connection.** Pandas-profiling requires an internet connection to download the Bootstrap and JQuery libraries. I might change this in the future, let me know if you want that sooner than later.
* pandas
* matplotlib
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