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DOC: Added additional example for groupby by indexer. #13276

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Jun 28, 2016
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DOC: Added additional example for groupby by indexer. #13276

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DOC: Added additional example for groupby by indexer.
pfrcks May 25, 2016
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DOC: Increased documentation
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16 changes: 16 additions & 0 deletions doc/source/groupby.rst
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Expand Up @@ -1014,6 +1014,22 @@ Regroup columns of a DataFrame according to their sum, and sum the aggregated on
df
df.groupby(df.sum(), axis=1).sum()

Groupby by Indexer to 'resample' data.
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This needs a fair bit more explanation of the why and how this does what it does. Maybe show the intent of a resample, then show how one can go about the same idea using non-datetimelike indices.

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need a markdown line here like the other examples

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Can you underline this with ~~~~ to make this a header (see a few lines above this for an example)

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I think you forgot this one


Resampling produces new hypothetical samples(resamples) from already existing observed data or from a data generating mechanism which resemble the underlying population.
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"data generating mechanism which resemble the underlying population" seems a bit difficult explanation.


In order to resample to work on indices that are non-datetimelike , the following procedure can be utilized.

In the following examples, **df.index / 5** returns a binary array which is used to determine what get's selected for the groupby operation.
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you can show this (add another ipython block), df.index / 5


.. note:: The above example shows how we can downsample. Downsampling refers to the throwing away of samples. Here by using **df.index / 5**, we are aggregating the samples in bins. By applying **std()** function, we aggregate the information contained in many samples into a small subset of values which is their standard deviation. Hence we can reduce the number of samples by creating bins by clubbing together samples.
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'above example' -> do you mean 'below'?

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also, Can you try to shorten this a little bit?


.. ipython:: python

df = pd.DataFrame(np.random.randn(10,2))
df
df.index / 5
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This will not work as desired in python 3. Can you make this df.index // 5 to do a floor division explicitly?

df.groupby(df.index / 5).std()

Returning a Series to propagate names
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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