pandas-profiling
⚠️ pandas-profiling
package naming was changed. To continue profiling data use ydata-profiling
instead!
This repo implements the brownout strategy for deprecating the pandas-profiling package on PyPI.⚠️
🎊 New year, new face, more functionalities!
Thank you for using and following pandas-profiling
developments. Yet, we have a new exciting feature - we are now thrilled to announce
that Spark is now part of the Data Profiling family from version 4.0.0 onwards
With its introduction, there was also the need for a new naming, one that will allow to decouple the concept of profiling from the Pandas Dataframes - ydata-profiling
!
But fear not, pip install pandas-profiling
will still be a valid for a while, and we will keep investing in growing the best open-source for data profiling, so you can use it for even more use cases.
How to fix the error for the main use cases
- use
pip install ydata-profiling
rather than pip install pandas-profiling
- replace
pandas-profiling
by ydata-profiling
in your pip requirements files (requirements.txt, setup.py, setup.cfg, Pipfile, etc ...) - if the
pandas-profiling
package is used by one of your dependencies it would be great if you take some time to track which package uses pandas_profiling
instead of ydata_profiling
for the imports
Schedule for deprecation
ydata-profiling
was launched in February 1st.pip install pandas-profiling
will still be supported until April 1st, but a warning will be thrown. from pandas_profiling import ProfileReport
will be supported until April 1st.- After April 1st, an error will be thrown if
pip install pandas-profiling
is used. Use pip install ydata-profiling
instead. - After April 1st, an error will be thrown if
from pandas_profiling import ProfileReport
is used. Use from ydata_profiling import ProfileReport
instead.
About pandas-profiling
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.
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