8000 DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. by pandres · Pull Request #20374 · pandas-dev/pandas · GitHub
[go: up one dir, main page]
Skip to content

DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. #20374

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 11 commits into from
Nov 14, 2018
Merged

DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. #20374

Changes from 1 commit
Commits
Show all changes
11 commits
Select commit Hold shift + click to select a range
59355d6
DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring.
pandres Mar 15, 2018
deea3c7
DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring.
pandres Mar 20, 2018
2fcabf7
DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. …
pandres Aug 22, 2018
fdce932
Merge remote-tracking branch 'upstream/master' into docstring_pandas.…
datapythonista Nov 3, 2018
4b55b5f
Minor fixes
datapythonista Nov 3, 2018
e7905b2
Replace Offset with DateOffset.
pandres Nov 4, 2018
a0c5329
Merge branch 'docstring_pandas.core.groupby.DataFrameGroupBy.resample…
pandres Nov 4, 2018
4880d3b
Change args and kwargs description.
pandres Nov 11, 2018
084ac39
Change args and kwargs description.
pandres Nov 11, 2018
73b480b
Merge remote-tracking branch 'upstream/master' into docstring_pandas.…
Nov 13, 2018
f375ceb
Merge remote-tracking branch 'upstream/master' into docstring_pandas.…
Nov 13, 2018
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
Next Next commit
DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring.
  • Loading branch information
@pandres
pandres committed Apr 3, 2018
commit 59355d68122d3f1b1f5a8186b320f46c08d9bfd5
89 changes: 87 additions & 2 deletions pandas/core/groupby/groupby.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1460,8 +1460,93 @@ def describe(self, **kwargs):
@Appender(_doc_template)
def resample(self, rule, *args, **kwargs):
"""
Provide resampling when using a TimeGrouper
Return a new grouper with our resampler appended
Provide resampling when using a TimeGrouper.

Given a grouper the function resamples it according to a string and an
optional list and dictionary of parameters. Returns a new grouper with
our resampler appended.

Parameters
----------
rule : str
The offset string or object representing target grouper conversion.
*args
These parameters will be passed to the get_resampler_for_grouping
function which builds the approriate resampler and checks for
deprecated parameters.
**kwargs
These parameters will be passed to the get_resampler_for_grouping
function which builds the approriate resampler and checks for
deprecated parameters.

Returns
-------
Grouper
Return a new grouper with our resampler appended.

Examples
--------
Start by creating a DataFrame with 9 one minute timestamps.

>>> idx = pd.date_range('1/1/2000', periods=9, freq='T')
>>> df = pd.DataFrame(data=9*[range(4)],
... index=idx,
... columns=['a', 'b', 'c', 'd'])
>>> df.iloc[[6], [0]] = 5 # change a value for grouping
>>> df
a b c d
2000-01-01 00:00:00 0 1 2 3
2000-01-01 00:01:00 0 1 2 3
2000-01-01 00:02:00 0 1 2 3
2000-01-01 00:03:00 0 1 2 3
2000-01-01 00:04:00 0 1 2 3
2000-01-01 00:05:00 0 1 2 3
2000-01-01 00:06:00 5 1 2 3
2000-01-01 00:07:00 0 1 2 3
2000-01-01 00:08:00 0 1 2 3
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Do you think it'd be possible to use a more compact example. May be 4 rows of 1 minute intervals, that can be downsampled to 2 rows of 30 seconds? Also, I think 2 columns should be enough.


Downsample the DataFrame into 3 minute bins and sum the values of
the timestamps falling into a bin.

>>> df.groupby('a').resample('3T').sum()
a b c d
a
0 2000-01-01 00:00:00 0 3 6 9
2000-01-01 00:03:00 0 3 6 9
2000-01-01 00:06:00 0 2 4 6
5 2000-01-01 00:06:00 5 1 2 3

Upsample the series into 30 second bins.

>>> df.groupby('a').resample('30S').sum()
a b c d
a
0 2000-01-01 00:00:00 0 1 2 3
2000-01-01 00:00:30 0 0 0 0
2000-01-01 00:01:00 0 1 2 3
2000-01-01 00:01:30 0 0 0 0
2000-01-01 00:02:00 0 1 2 3
2000-01-01 00:02:30 0 0 0 0
2000-01-01 00:03:00 0 1 2 3
2000-01-01 00:03:30 0 0 0 0
2000-01-01 00:04:00 0 1 2 3
2000-01-01 00:04:30 0 0 0 0
2000-01-01 00:05:00 0 1 2 3
2000-01-01 00:05:30 0 0 0 0
2000-01-01 00:06:00 0 0 0 0
2000-01-01 00:06:30 0 0 0 0
2000-01-01 00:07:00 0 1 2 3
2000-01-01 00:07:30 0 0 0 0
2000-01-01 00:08:00 0 1 2 3
5 2000-01-01 00:06:00 5 1 2 3

Resample by month. Values are assigned to the month of the period.

>>> df.groupby('a').resample('M').sum()
a b c d
a
0 2000-01-31 0 8 16 24
5 2000-01-31 5 1 2 3
"""
from pandas.core.resample import get_resampler_for_grouping
return get_resampler_for_grouping(self, rule, *args, **kwargs)
Expand Down
0