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DOC: redocument the subset arg in styler for clarity of use #41566

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Merged
merged 6 commits into from
May 21, 2021
Merged

DOC: redocument the subset arg in styler for clarity of use #41566

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0aeef48
redocument styler subset
attack68 May 19, 2021
add8759
optional
attack68 May 19, 2021
18b3dbb
format method also
attack68 May 19, 2021
4f5d271
no subs docstrings
attack68 May 20, 2021
ce5e793
no subs docstrings
attack68 May 20, 2021
7316d1a
Merge remote-tracking branch 'upstream/master' into subset_doc_def
attack68 May 20, 2021
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99 changes: 69 additions & 30 deletions pandas/io/formats/style.py
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Original file line number Diff line number Diff line change
Expand Up @@ -614,9 +614,10 @@ def apply(
Apply to each column (``axis=0`` or ``'index'``), to each row
(``axis=1`` or ``'columns'``), or to the entire DataFrame at once
with ``axis=None``.
subset : IndexSlice
A valid indexer to limit ``data`` to *before* applying the
function. Consider using a pandas.IndexSlice.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
**kwargs : dict
Pass along to ``func``.

Expand All @@ -642,10 +643,20 @@ def apply(
--------
>>> def highlight_max(x, color):
... return np.where(x == np.nanmax(x.to_numpy()), f"color: {color};", None)
>>> df = pd.DataFrame(np.random.randn(5, 2))
>>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"])
>>> df.style.apply(highlight_max, color='red')
>>> df.style.apply(highlight_max, color='blue', axis=1)
>>> df.style.apply(highlight_max, color='green', axis=None)

Using ``subset`` to restrict application to a single column or multiple columns

>>> df.style.apply(highlight_max, color='red', subset="A")
>>> df.style.apply(highlight_max, color='red', subset=["A", "B"])

Using a 2d input to ``subset`` to select rows in addition to columns

>>> df.style.apply(highlight_max, color='red', subset=([0,1,2], slice(None))
>>> df.style.apply(highlight_max, color='red', subset=(slice(0,5,2), "A")
"""
self._todo.append(
(lambda instance: getattr(instance, "_apply"), (func, axis, subset), kwargs)
Expand Down Expand Up @@ -675,9 +686,10 @@ def applymap(
----------
func : function
``func`` should take a scalar and return a scalar.
subset : IndexSlice
A valid indexer to limit ``data`` to *before* applying the
function. Consider using a pandas.IndexSlice.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
**kwargs : dict
Pass along to ``func``.

Expand All @@ -699,8 +711,18 @@ def applymap(
--------
>>> def color_negative(v, color):
... return f"color: {color};" if v < 0 else None
>>> df = pd.DataFrame(np.random.randn(5, 2))
>>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"])
>>> df.style.applymap(color_negative, color='red')

Using ``subset`` to restrict application to a single column or multiple columns

>>> df.style.applymap(color_negative, color='red', subset="A")
>>> df.style.applymap(color_negative, color='red', subset=["A", "B"])

Using a 2d input to ``subset`` to select rows in addition to columns

>>> df.style.applymap(color_negative, color='red', subset=([0,1,2], slice(None))
>>> df.style.applymap(color_negative, color='red', subset=(slice(0,5,2), "A")
"""
self._todo.append(
(lambda instance: getattr(instance, "_applymap"), (func, subset), kwargs)
Expand Down Expand Up @@ -732,9 +754,10 @@ def where(
Applied when ``cond`` returns true.
other : str
Applied when ``cond`` returns false.
subset : IndexSlice
A valid indexer to limit ``data`` to *before* applying the
function. Consider using a pandas.IndexSlice.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
**kwargs : dict
Pass along to ``cond``.

Expand Down Expand Up @@ -1072,9 +1095,9 @@ def hide_columns(self, subset: Subset) -> Styler:

Parameters
----------
subset : IndexSlice
An argument to ``DataFrame.loc`` that identifies which columns
are hidden.
subset : label, array-like, IndexSlice
A valid 1d input or single key along the appropriate axis within
`DataFrame.loc[]`, to limit ``data`` to *before* applying the function.

Returns
-------
Expand Down Expand Up @@ -1127,8 +1150,10 @@ def background_gradient(
Apply to each column (``axis=0`` or ``'index'``), to each row
(``axis=1`` or ``'columns'``), or to the entire DataFrame at once
with ``axis=None``.
subset : IndexSlice
A valid slice for ``data`` to limit the style application to.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
text_color_threshold : float or int
Luminance threshold for determining text color in [0, 1]. Facilitates text
visibility across varying background colors. All text is dark if 0, and
Expand Down Expand Up @@ -1251,8 +1276,10 @@ def set_properties(self, subset: Subset | None = None, **kwargs) -> Styler:

Parameters
----------
subset : IndexSlice
A valid slice for ``data`` to limit the style application to.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
**kwargs : dict
A dictionary of property, value pairs to be set for each cell.

Expand Down Expand Up @@ -1349,8 +1376,10 @@ def bar(

Parameters
----------
subset : IndexSlice, optional
A valid slice for `data` to limit the style application to.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
axis : {0 or 'index', 1 or 'columns', None}, default 0
Apply to each column (``axis=0`` or ``'index'``), to each row
(``axis=1`` or ``'columns'``), or to the entire DataFrame at once
Expand Down Expand Up @@ -1431,8 +1460,10 @@ def highlight_null(
Parameters
----------
null_color : str, default 'red'
subset : label or list of labels, default None
A valid slice for ``data`` to limit the style application to.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.

.. versionadded:: 1.1.0

Expand Down Expand Up @@ -1477,8 +1508,10 @@ def highlight_max(

Parameters
----------
subset : IndexSlice, default None
A valid slice for ``data`` to limit the style application to.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
color : str, default 'yellow'
Background color to use for highlighting.
axis : {0 or 'index', 1 or 'columns', None}, default 0
Expand Down Expand Up @@ -1526,8 +1559,10 @@ def highlight_min(

Parameters
----------
subset : IndexSlice, default None
A valid slice for ``data`` to limit the style application to.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
color : str, default 'yellow'
Background color to use for highlighting.
axis : {0 or 'index', 1 or 'columns', None}, default 0
Expand Down Expand Up @@ -1580,8 +1615,10 @@ def highlight_between(

Parameters
----------
subset : IndexSlice, default None
A valid slice for ``data`` to limit the style application to.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
color : str, default 'yellow'
Background color to use for highlighting.
axis : {0 or 'index', 1 or 'columns', None}, default 0
Expand Down Expand Up @@ -1688,8 +1725,10 @@ def highlight_quantile(

Parameters
----------
subset : IndexSlice, default None
A valid slice for ``data`` to limit the style application to.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
color : str, default 'yellow'
Background color to use for highlighting
axis : {0 or 'index', 1 or 'columns', None}, default 0
Expand Down
7 changes: 4 additions & 3 deletions pandas/io/formats/style_render.py
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Original file line number Diff line number Diff line change
Expand Up @@ -417,9 +417,10 @@ def format(
----------
formatter : str, callable, dict or None
Object to define how values are displayed. See notes.
subset : IndexSlice
An argument to ``DataFrame.loc`` that restricts which elements
``formatter`` is applied to.
subset : label, array-like, IndexSlice, optional
A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
na_rep : str, optional
Representation for missing values.
If ``na_rep`` is None, no special formatting is applied.
Expand Down