I regularly perform pandas operations on data frames in excess of 15 million or so rows and I'd love to have access to a progress indicator for particular operations.
Does a text based progress indicator for pandas split-apply-combine operations exist?
For example, in something like:
df_users.groupby(['userID', 'requestDate']).apply(feature_rollup)
where feature_rollup
is a somewhat involved function that take many DF columns and creates new user columns through various methods. These operations can take a while for large data frames so I'd like to know if it is possible to have text based output in an iPython notebook that updates me on the progress.
So far, I've tried canonical loop progress indicators for Python but they don't interact with pandas in any meaningful way.
I'm hoping there's something I've overlooked in the pandas library/documentation that allows one to know the progress of a split-apply-combine. A simple implementation would maybe look at the total number of data frame subsets upon which the apply
function is working and report progress as the completed fraction of those subsets.
Is this perhaps something that needs to be added to the library?
Best Answer
Due to popular demand, I've added
pandas
support intqdm
(pip install "tqdm>=4.9.0"
). Unlike the other answers, this will not noticeably slow pandas down -- here's an example forDataFrameGroupBy.progress_apply
:In case you're interested in how this works (and how to modify it for your own callbacks), see the examples on GitHub, the full documentation on PyPI, or import the module and run
help(tqdm)
. Other supported functions includemap
,applymap
,aggregate
, andtransform
.EDIT
To directly answer the original question, replace:
with:
Note: tqdm <= v4.8: For versions of tqdm below 4.8, instead of
tqdm.pandas()
you had to do: