Skip to content

DOC: Document the args of DataFrame.apply in the same order as in the signature #11541

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Nov 8, 2015
Merged

DOC: Document the args of DataFrame.apply in the same order as in the signature #11541

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions pandas/core/frame.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3882,6 +3882,11 @@ def apply(self, func, axis=0, broadcast=False, raw=False, reduce=None,
broadcast : boolean, default False
For aggregation functions, return object of same size with values
propagated
raw : boolean, default False
If False, convert each row or column into a Series. If raw=True the
passed function will receive ndarray objects instead. If you are
just applying a NumPy reduction function this will achieve much
better performance
reduce : boolean or None, default None
Try to apply reduction procedures. If the DataFrame is empty,
apply will use reduce to determine whether the result should be a
Expand All @@ -3890,11 +3895,6 @@ def apply(self, func, axis=0, broadcast=False, raw=False, reduce=None,
while guessing, exceptions raised by func will be ignored). If
reduce is True a Series will always be returned, and if False a
DataFrame will always be returned.
raw : boolean, default False
If False, convert each row or column into a Series. If raw=True the
passed function will receive ndarray objects instead. If you are
just applying a NumPy reduction function this will achieve much
better performance
args : tuple
Positional arguments to pass to function in addition to the
array/series
Expand Down