DOC: update the pandas.DataFrame.apply docstring #20202

mdeboc · 2018-03-10T17:05:41Z

Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

PR title is "DOC: update the docstring"
The validation script passes: scripts/validate_docstrings.py <your-function-or-method>
The PEP8 style check passes: git diff upstream/master -u -- "*.py" | flake8 --diff
The html version looks good: python doc/make.py --single <your-function-or-method>
It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

################################################################################
###################### Docstring (pandas.DataFrame.apply) ######################
################################################################################

Apply a function along an axis of the `Series`.

Objects passed to the function are `Series` objects having as index
either the DataFrame's index (`axis=0`)
or the DataFrame's columns (`axis=1`).
If `result_type` is None, the final return type is the return
type of the applied function.
Otherwise, it depends on the `result_type` argument.

Parameters
----------
func : function
    Function to apply to each column or row.
axis : {0 or 'index', 1 or 'columns'}, default 0
    Axis along which the function is applied:

    * 0 or 'index': apply function to each column.
    * 1 or 'columns': apply function to each row.
broadcast : boolean, optional
    Only relevant for aggregation functions:

    * `False` or `None` : returns a `Series` whose length is the length
      of the index or the number of columns (based on the `axis`
      parameter)
    * `True` : results will be broadcast to the original shape
      of the frame, the original index and columns will be retained.

    .. deprecated:: 0.23.0
       This argument will be removed in a future version, replaced
       by result_type='broadcast'.

raw : boolean, default False
    * `False` : passes each row or column into a `Series` to the
      function.
    * `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,
    :meth:`apply` will use reduce to determine whether the result
    should be a `Series` or a `DataFrame`. If reduce is None (the
    default), :meth:`apply`'s return value will be guessed by calling
    func on an empty `Series`
    (note: 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.

    .. deprecated:: 0.23.0.
       This argument will be removed in a future version, replaced
       by result_type='reduce'.

result_type : {'expand', 'reduce', 'broadcast', None}
    These only act when `axis=1` (columns):

    * 'expand' : list-like results will be turned into columns.
    * 'reduce' : returns a `Series` if possible rather than expanding
      list-like results. This is the opposite of 'expand'.
    * 'broadcast' : results will be broadcast to the original shape
      of the `DataFrame`, the original index and columns will be
      retained.

    The default behaviour (`None`) depends on the return value of the
    applied function: list-like results will be returned as a `Series`
    of those. However if the apply function returns a `Series` these
    are expanded to columns.

    .. versionadded:: 0.23.0.

args : tuple
    Positional arguments to pass to function in addition to the
    array/series.
kwds :
    Additional keyword arguments to pass as keywords to the function.

Notes
-----
In the current implementation apply calls func twice on the
first column/row to decide whether it can take a fast or slow
code path. This can lead to unexpected behavior if func has
side-effects, as they will take effect twice for the first
column/row.

Examples
--------

We use this DataFrame to illustrate

>>> df = pd.DataFrame(np.tile(np.arange(3), 6).reshape(6, -1) + 1,
...                   columns=['A', 'B', 'C'])
>>> df
   A  B  C
0  1  2  3
1  1  2  3
2  1  2  3
3  1  2  3
4  1  2  3
5  1  2  3

Using a numpy universal function (in this case the same as
``np.sqrt(df)``):

>>> df.apply(np.sqrt)
     A         B         C
0  1.0  1.414214  1.732051
1  1.0  1.414214  1.732051
2  1.0  1.414214  1.732051
3  1.0  1.414214  1.732051
4  1.0  1.414214  1.732051
5  1.0  1.414214  1.732051

Using a reducing function on either axis

>>> df.apply(np.sum, axis=0)
A     6
B    12
C    18
dtype: int64

>>> df.apply(np.sum, axis=1)
0    6
1    6
2    6
3    6
4    6
5    6
dtype: int64

Retuning a list-like will result in a Series

>>> df.apply(lambda x: [1, 2], axis=1)
0    [1, 2]
1    [1, 2]
2    [1, 2]
3    [1, 2]
4    [1, 2]
5    [1, 2]
dtype: object

Passing result_type='expand' will expand list-like results
to columns of a Dataframe

>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
   0  1
0  1  2
1  1  2
2  1  2
3  1  2
4  1  2
5  1  2

Returning a Series inside the function is similar to passing
``result_type='expand'``. The resulting column names
will be the Series index.

>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
   foo  bar
0    1    2
1    1    2
2    1    2
3    1    2
4    1    2
5    1    2

Passing ``result_type='broadcast'`` will ensure the same shape
result, whether list-like or scalar is returned by the function,
and broadcast it along the axis. The resulting column names will
be the originals.

>>> df.apply(lambda x: [1, 2, 3], axis=1, result_type='broadcast')
   A  B  C
0  1  2  3
1  1  2  3
2  1  2  3
3  1  2  3
4  1  2  3
5  1  2  3

See also
--------
DataFrame.applymap: For elementwise operations
DataFrame.aggregate: only perform aggregating type operations
DataFrame.transform: only perform transformating type operations

Returns
-------
applied : Series or DataFrame

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Errors in parameters section
		Parameters {'kwds'} not documented
		Unknown parameters {'kwds :'}
		Parameter "raw" description should start with capital letter
		Parameter "kwds :" has no type

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.

jorisvandenbossche

Thanks for the PR! Added some first comments

jorisvandenbossche · 2018-03-10T19:16:56Z

pandas/core/frame.py

@@ -4818,66 +4818,82 @@ def aggregate(self, func, axis=0, *args, **kwargs):

    def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
              result_type=None, args=(), **kwds):
-        """Applies function along an axis of the DataFrame.
+        """
+        Apply a function along an axis of the `Series`.


This is the dataframe docs, so Series -> DataFrame

(also it is not needed to put single backticks around Series and DataFrame, same for the rest of the docstring)

add PR review suggestions from @jorisvandenbossche into commit c593a70

... and commit 4d3e8cf (i.e. the same for DataFrame)

jorisvandenbossche · 2018-03-10T19:18:29Z

pandas/core/frame.py

+              of the index or the number of columns (based on the `axis`
+              parameter)
+            * `True` : results will be broadcast to the original shape
+              of the frame, the original index and columns will be retained.


Since this keyword is deprecated, I am not sure it is that important to add such a detailed description (I would rather expand the explanation of the new keyword result_type)

@jorisvandenbossche OK. But <<results will be broadcast to the original shape of the frame, the original index and columns will be retained.>> is already copied from the explanation of the new keyword result_type. So nothing to add in the paragraph of this new parameter.

Shall I revert to the previous wording for broadcast parameter, i.e.:

''' broadcast : boolean, optional For aggregation functions, return object of same size with values propagated '''

(...which is rather fuzzy),
or not ?

jorisvandenbossche · 2018-03-10T19:19:15Z

pandas/core/frame.py

-            passed function will receive ndarray objects instead. If you are
-            just applying a NumPy reduction function this will achieve much
-            better performance
+            * `False` : passes each row or column into a `Series` to the


"passes each row or column into a Series" -> "passes each row or column as a Series"

add this PR review suggestions into commit 8caf5f6

jorisvandenbossche · 2018-03-10T19:20:28Z

pandas/core/frame.py

-        Final return type depends on the return type of the applied function,
-        or on the `result_type` argument.
+        Objects passed to the function are `Series` objects having as index
+        either the DataFrame's index (`axis=0`)


can you use double backticks around axis=0 (as ``axis=0``) (because it is a small code snippet)

add this PR review suggestions into commit c1214b4

jorisvandenbossche · 2018-03-10T19:21:10Z

pandas/core/frame.py

-            array/series
-        Additional keyword arguments will be passed as keywords to the function
+            array/series.
+        kwds :


kwds -> **kwds (and the colon is not needed)

add this PR review suggestions into commit 2432ff5

datapythonista

Looks quite nice, added some comments.

datapythonista · 2018-03-10T19:15:48Z

pandas/core/frame.py

@@ -4818,66 +4818,82 @@ def aggregate(self, func, axis=0, *args, **kwargs):

    def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
              result_type=None, args=(), **kwds):
-        """Applies function along an axis of the DataFrame.
+        """
+        Apply a function along an axis of the `Series`.


Why did you change DataFrame by Series? The file is frame.py I think it's apply for DataFrame, isn't it?

add this PR review suggestions into commit c593a70

datapythonista · 2018-03-10T19:16:15Z

pandas/core/frame.py

+        Objects passed to the function are `Series` objects having as index
+        either the DataFrame's index (`axis=0`)
+        or the DataFrame's columns (`axis=1`).
+        If `result_type` is None, the final return type is the return


None in backticks

add PR review suggestions from @datapythonista into commit da64c86
Maybe result_type is None is better there, because it is a small code snippet.

datapythonista · 2018-03-10T19:18:46Z

pandas/core/frame.py

+            Axis along which the function is applied:
+
+            * 0 or 'index': apply function to each column.
+            * 1 or 'columns': apply function to each row.
        broadcast : boolean, optional


bool instead of boolean (also in the next 2)

add this PR review suggestions into commit dbd90c1

datapythonista · 2018-03-10T19:20:43Z

pandas/core/frame.py

            are expanded to columns.

-            .. versionadded:: 0.23.0
+            .. versionadded:: 0.23.0.

        args : tuple


args is always a tuple, so we remove it. The validation script will probably complain, but the convention is to use *args and **kwargs (with the stars)

In this case we need to keep it as args and the 'tuple' type description is correct. This is a special case because it actually is a keyword with the name args, not *args in the signature

Ok, so I keep

args : tuple

yes, my fault, sorry.

datapythonista · 2018-03-10T19:22:52Z

pandas/core/frame.py

-        Additional keyword arguments will be passed as keywords to the function
+            array/series.
+        kwds :
+            Additional keyword arguments to pass as keywords to the function.


A personal opinion, but for me it'd be a bit more explicit to say "to pass as keyword arguments to func". I think saying "the function" should be clear enough for most people, but specifying which functions avoids any ambiguity. Also por args.

add this PR review suggestions into commit 6cbadd8

TomAugspurger · 2018-03-10T19:17:08Z

pandas/core/frame.py

@@ -4818,66 +4818,82 @@ def aggregate(self, func, axis=0, *args, **kwargs):

    def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
              result_type=None, args=(), **kwds):
-        """Applies function along an axis of the DataFrame.
+        """
+        Apply a function along an axis of the `Series`.


`Series` -> DataFrame

Ai, we were all doing the review at the same time :-)

Yep. Sorry guys :)

@TomAugspurger I took into account your suggestion in the commit c593a70

TomAugspurger · 2018-03-10T19:17:37Z

pandas/core/frame.py

-        either the DataFrame's index (axis=0) or the columns (axis=1).
-        Final return type depends on the return type of the applied function,
-        or on the `result_type` argument.
+        Objects passed to the function are `Series` objects having as index


"having as" -> "whose index is either"

add PR review suggestions from @TomAugspurger into commit eaf1441

TomAugspurger · 2018-03-10T19:18:09Z

pandas/core/frame.py

-        Final return type depends on the return type of the applied function,
-        or on the `result_type` argument.
+        Objects passed to the function are `Series` objects having as index
+        either the DataFrame's index (`axis=0`)


double backticks around axis=0 and axis=1 since they're code snippets.

add this PR review suggestions into commit c1214b4

TomAugspurger · 2018-03-10T19:20:09Z

pandas/core/frame.py

-            passed function will receive ndarray objects instead. If you are
-            just applying a NumPy reduction function this will achieve much
-            better performance
+            * `False` : passes each row or column into a `Series` to the


Maybe "passes each row or column as a Series to the function"

add this PR review suggestions into commit 8caf5f6

TomAugspurger · 2018-03-10T19:20:41Z

pandas/core/frame.py

+              function.
+            * `True` : the passed function will receive ndarray objects
+              instead.
+            If you are just applying a NumPy reduction function this will


This applies to raw=Ture so keep it under the item formed by that *

add this PR review suggestions into commit 3794d19

TomAugspurger · 2018-03-10T19:21:42Z

pandas/core/frame.py

-            DataFrame will always be returned.
-
-            .. deprecated:: 0.23.0
+            Try to apply reduction procedures. If the `DataFrame` is empty,


Don't need backticsk around Series / DataFrame

add this PR review suggestions into commit c593a70

TomAugspurger · 2018-03-10T19:22:04Z

pandas/core/frame.py

-
-            .. deprecated:: 0.23.0
+            Try to apply reduction procedures. If the `DataFrame` is empty,
+            :meth:`apply` will use reduce to determine whether the result


I think this resolves to pandas.apply, which doesn't exist. SEe if you can rewreite to avoid the reference to apply

After running

python make.py html --single pandas.DataFrame.apply

the :meth:apply link resolves to pandas.DataFrame.apply

@TomAugspurger, if it is not the case for you, I shall revert to a wording more like: apply() or DataFrame.apply()... Let me know.

TomAugspurger · 2018-03-10T19:22:19Z

pandas/core/frame.py

-            .. deprecated:: 0.23.0
+            Try to apply reduction procedures. If the `DataFrame` is empty,
+            :meth:`apply` will use reduce to determine whether the result
+            should be a `Series` or a `DataFrame`. If reduce is None (the


backticsk around parameter names (reduce)

add this PR review suggestions into commit da64c86
Maybe reduce is None is better there, because it is a small code snippet

TomAugspurger · 2018-03-10T19:25:32Z

pandas/core/frame.py

-            array/series
-        Additional keyword arguments will be passed as keywords to the function
+            array/series.
+        kwds :


Just updated our docs on this, we're doing **kwds

add this PR review suggestions into commit 2432ff5

jreback · 2018-03-11T14:05:56Z

pandas/core/frame.py

+            :meth:`apply` will use `reduce` to determine whether the result
+            should be a Series or a DataFrame. If ``reduce is None`` (the
+            default), :meth:`apply`'s return value will be guessed by calling
+            `func` on an empty Series


@jorisvandenbossche can you look at this formatting

@mdeboc Although :meth:`apply` works correctly in sphinx rendering, I would not use the referencing syntax, as it is then just adding a reference to itself ?
I would just do `apply` in this case

@jorisvandenbossche I add this PR review suggestions into commit ee6919e

jreback · 2018-03-11T14:06:31Z

pandas/core/frame.py


-            The default behaviour (None) depends on the return value of the
+            The default behaviour (`None`) depends on the return value of the


no parens here

PR review suggestions from @jreback was added into commit c4da1ea

jorisvandenbossche · 2018-03-11T13:50:05Z

pandas/core/frame.py

+        or the DataFrame's columns (``axis=1``).
+        If ``result_type is None``, the final return type is the return
+        type of the applied function.
+        Otherwise, it depends on the `result_type` argument.


Can you reflow this paragraph so each line is more or less <=79 chars?

add this PR review suggestions into commit 23be01b

jorisvandenbossche · 2018-03-11T13:51:23Z

pandas/core/frame.py

+        either the DataFrame's index (``axis=0``)
+        or the DataFrame's columns (``axis=1``).
+        If ``result_type is None``, the final return type is the return
+        type of the applied function.


The final return type is not exactly the same, but it is "inferred from the return type of the applied function"

add this PR review suggestions into commit 51ad0cb

jorisvandenbossche · 2018-03-12T11:09:02Z

pandas/core/frame.py

+            :meth:`apply` will use `reduce` to determine whether the result
+            should be a Series or a DataFrame. If ``reduce is None`` (the
+            default), :meth:`apply`'s return value will be guessed by calling
+            `func` on an empty Series


@mdeboc Although :meth:`apply` works correctly in sphinx rendering, I would not use the referencing syntax, as it is then just adding a reference to itself ?
I would just do `apply` in this case

codecov · 2018-03-12T12:18:55Z

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The diff coverage is 100%.

@@            Coverage Diff            @@
##             master   #20202   +/-   ##
=========================================
  Coverage          ?    91.7%           
=========================================
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  Branches          ?        0           
=========================================
  Hits              ?    45089           
  Misses            ?     4078           
  Partials          ?        0

Flag	Coverage Δ
#multiple	`90.09% <100%> (?)`
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datapythonista

Good work, added some comments, mainly to parts of the original docstring that can be improved.

datapythonista · 2018-03-12T13:23:14Z

pandas/core/frame.py

            applied function: list-like results will be returned as a Series
            of those. However if the apply function returns a Series these
            are expanded to columns.

-            .. versionadded:: 0.23.0
+            .. versionadded:: 0.23.0.


There is a bug in the script, you don't need the period at the end.

Ok. see cf2f2d0

datapythonista · 2018-03-12T13:24:11Z

pandas/core/frame.py

            are expanded to columns.

-            .. versionadded:: 0.23.0
+            .. versionadded:: 0.23.0.

        args : tuple


yes, my fault, sorry.

datapythonista · 2018-03-12T13:27:56Z

pandas/core/frame.py

        first column/row to decide whether it can take a fast or slow
-        code path. This can lead to unexpected behavior if func has
+        code path. This can lead to unexpected behavior if `func` has
        side-effects, as they will take effect twice for the first
        column/row.



Can you get rid of the "We use this DataFrame to illustrate" at the beginning of the examples? I don't think it adds any value.

Also, I'd personally prefer to create a smaller (e.g 3 rows, 2 cols) dataframe from a Python list, instead of all that numpy machinery. :) And if the example is with sqrt, I'd use numbers like 9 so the user can quickly see what apply does, which is the goal of this example.

Yes, it's simpler. See d52ac81

@datapythonista In commit 3160fb8, I suppress an unnecessary test, unintentionally introduced in previous commit (d52ac81)

jorisvandenbossche

Added some minor comments. Looking good for the rest!

jorisvandenbossche · 2018-03-12T15:37:21Z

pandas/core/frame.py

+            `func` on an empty Series
+            (note: while guessing, exceptions raised by `func` will be
+            ignored).
+            If ``reduce is True`` a Series will always be returned, and if


"reduce is True" -> "reduce=True" (same on line below and few lines above)

@jorisvandenbossche Changes in dfa0f55, but don't forget that this keyword is deprecated, as you mentioned Saturday. :)

jorisvandenbossche · 2018-03-12T15:38:04Z

pandas/core/frame.py

+              instead.
+              If you are just applying a NumPy reduction function this will
+              achieve much better performance.
+        reduce : bool or `None`, default `None`


no backticks are needed inside the type description part

changes in 037c729

jorisvandenbossche · 2018-03-12T15:41:08Z

pandas/core/frame.py

+           A  B
+        0  1  2
+        1  1  2
+        2  1  2

        See also
        --------


Can you move the Returns and See Also sections to just after the Parameters section?

Sure
changes in d757960
@jorisvandenbossche maybe the teams working with functions: combine, combine_first, applymap, join and round should do the same...

pep8speaks · 2018-03-12T16:45:31Z

Hello @mdeboc! Thanks for updating the PR.

Cheers ! There are no PEP8 issues in this Pull Request. 🍻

Comment last updated on March 12, 2018 at 16:46 Hours UTC

jorisvandenbossche · 2018-03-12T16:48:03Z

@mdeboc Thanks a lot!

DOC: update the pandas.DataFrame.apply docstring

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DOC: update the pandas.DataFrame.apply docstring

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DOC: update the pandas.DataFrame.apply docstring

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DOC: update the pandas.DataFrame.apply docstring

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DOC: update the pandas.DataFrame.apply docstring

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DOC: update the pandas.DataFrame.apply docstring

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DOC: update the pandas.DataFrame.apply docstring

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DOC: update the pandas.DataFrame.apply docstring

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DOC: update the pandas.DataFrame.apply docstring

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DOC: update the pandas.DataFrame.apply docstring

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DOC: update the pandas.DataFrame.apply docstring

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Update frame.py

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pep8

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jorisvandenbossche merged commit 5f244d8 into pandas-dev:master Mar 12, 2018

jorisvandenbossche added this to the 0.23.0 milestone Mar 12, 2018

mdeboc deleted the apply_doc_sprint branch March 13, 2018 13:39

jorisvandenbossche mentioned this pull request Mar 13, 2018

DOC: Apply doc sprint #20111

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		The default behaviour (None) depends on the return value of the
		The default behaviour (`None`) depends on the return value of the

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DOC: update the pandas.DataFrame.apply docstring #20202

DOC: update the pandas.DataFrame.apply docstring #20202

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mdeboc commented Mar 10, 2018

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mdeboc Mar 10, 2018 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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mdeboc Mar 10, 2018 •

edited

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mdeboc Mar 10, 2018 •

edited

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