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Fix error GL07 #24126

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Dec 7, 2018
Merged

Fix error GL07 #24126

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20543e1
Fix error GL07
alexander-ponomaroff Dec 6, 2018
776f021
Fix: error W293: blank line contains whitespace
alexander-ponomaroff Dec 6, 2018
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10 changes: 5 additions & 5 deletions pandas/core/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -289,6 +289,11 @@ def unique(values):
- If the input is a Categorical dtype, the return is a Categorical
- If the input is a Series/ndarray, the return will be an ndarray

See Also
--------
pandas.Index.unique
pandas.Series.unique

Examples
--------
>>> pd.unique(pd.Series([2, 1, 3, 3]))
Expand Down Expand Up @@ -338,11 +343,6 @@ def unique(values):

>>> pd.unique([('a', 'b'), ('b', 'a'), ('a', 'c'), ('b', 'a')])
array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)

See Also
--------
pandas.Index.unique
pandas.Series.unique
"""

values = _ensure_arraylike(values)
Expand Down
76 changes: 38 additions & 38 deletions pandas/core/arrays/categorical.py
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Expand Up @@ -273,6 +273,16 @@ class Categorical(ExtensionArray, PandasObject):
If an explicit ``ordered=True`` is given but no `categories` and the
`values` are not sortable.

See Also
--------
pandas.api.types.CategoricalDtype : Type for categorical data.
CategoricalIndex : An Index with an underlying ``Categorical``.

Notes
-----
See the `user guide
<http://pandas.pydata.org/pandas-docs/stable/categorical.html>`_ for more.

Examples
--------
>>> pd.Categorical([1, 2, 3, 1, 2, 3])
Expand All @@ -293,16 +303,6 @@ class Categorical(ExtensionArray, PandasObject):
Categories (3, object): [c < b < a]
>>> c.min()
'c'

Notes
-----
See the `user guide
<http://pandas.pydata.org/pandas-docs/stable/categorical.html>`_ for more.

See Also
--------
pandas.api.types.CategoricalDtype : Type for categorical data.
CategoricalIndex : An Index with an underlying ``Categorical``.
"""

# For comparisons, so that numpy uses our implementation if the compare
Expand Down Expand Up @@ -827,11 +827,6 @@ def set_categories(self, new_categories, ordered=None, rename=False,
dtypes on python3, which does not considers a S1 string equal to a
single char python string.

Raises
------
ValueError
If new_categories does not validate as categories

Parameters
----------
new_categories : Index-like
Expand All @@ -850,6 +845,11 @@ def set_categories(self, new_categories, ordered=None, rename=False,
-------
cat : Categorical with reordered categories or None if inplace.

Raises
------
ValueError
If new_categories does not validate as categories

See Also
--------
rename_categories
Expand Down Expand Up @@ -882,12 +882,6 @@ def rename_categories(self, new_categories, inplace=False):
"""
Renames categories.

Raises
------
ValueError
If new categories are list-like and do not have the same number of
items than the current categories or do not validate as categories

Parameters
----------
new_categories : list-like, dict-like or callable
Expand Down Expand Up @@ -922,6 +916,12 @@ def rename_categories(self, new_categories, inplace=False):
With ``inplace=False``, the new categorical is returned.
With ``inplace=True``, there is no return value.

Raises
------
ValueError
If new categories are list-like and do not have the same number of
items than the current categories or do not validate as categories

See Also
--------
reorder_categories
Expand Down Expand Up @@ -979,12 +979,6 @@ def reorder_categories(self, new_categories, ordered=None, inplace=False):
`new_categories` need to include all old categories and no new category
items.

Raises
------
ValueError
If the new categories do not contain all old category items or any
new ones

Parameters
----------
new_categories : Index-like
Expand All @@ -1000,6 +994,12 @@ def reorder_categories(self, new_categories, ordered=None, inplace=False):
-------
cat : Categorical with reordered categories or None if inplace.

Raises
------
ValueError
If the new categories do not contain all old category items or any
new ones

See Also
--------
rename_categories
Expand All @@ -1022,12 +1022,6 @@ def add_categories(self, new_categories, inplace=False):
`new_categories` will be included at the last/highest place in the
categories and will be unused directly after this call.

Raises
------
ValueError
If the new categories include old categories or do not validate as
categories

Parameters
----------
new_categories : category or list-like of category
Expand All @@ -1040,6 +1034,12 @@ def add_categories(self, new_categories, inplace=False):
-------
cat : Categorical with new categories added or None if inplace.

Raises
------
ValueError
If the new categories include old categories or do not validate as
categories

See Also
--------
rename_categories
Expand Down Expand Up @@ -1072,11 +1072,6 @@ def remove_categories(self, removals, inplace=False):
`removals` must be included in the old categories. Values which were in
the removed categories will be set to NaN

Raises
------
ValueError
If the removals are not contained in the categories

Parameters
----------
removals : category or list of categories
Expand All @@ -1089,6 +1084,11 @@ def remove_categories(self, removals, inplace=False):
-------
cat : Categorical with removed categories or None if inplace.

Raises
------
ValueError
If the removals are not contained in the categories

See Also
--------
rename_categories
Expand Down
20 changes: 10 additions & 10 deletions pandas/core/arrays/datetimes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -892,6 +892,11 @@ def to_period(self, freq=None):
When converting a DatetimeArray/Index with non-regular values,
so that a frequency cannot be inferred.

See Also
--------
PeriodIndex: Immutable ndarray holding ordinal values.
DatetimeIndex.to_pydatetime: Return DatetimeIndex as object.

Examples
--------
>>> df = pd.DataFrame({"y": [1,2,3]},
Expand All @@ -908,11 +913,6 @@ def to_period(self, freq=None):
>>> idx.to_period()
PeriodIndex(['2017-01-01', '2017-01-02'],
dtype='period[D]', freq='D')

See Also
--------
PeriodIndex: Immutable ndarray holding ordinal values.
DatetimeIndex.to_pydatetime: Return DatetimeIndex as object.
"""
from pandas.core.arrays import PeriodArray

Expand Down Expand Up @@ -1087,17 +1087,17 @@ def date(self):
by 6. This method is available on both Series with datetime
values (using the `dt` accessor) or DatetimeIndex.

Returns
-------
Series or Index
Containing integers indicating the day number.

See Also
--------
Series.dt.dayofweek : Alias.
Series.dt.weekday : Alias.
Series.dt.day_name : Returns the name of the day of the week.

Returns
-------
Series or Index
Containing integers indicating the day number.

Examples
--------
>>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series()
Expand Down
84 changes: 41 additions & 43 deletions pandas/core/arrays/interval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,21 +84,21 @@
set_closed
%(extra_methods)s\

%(examples)s\

Notes
------
See the `user guide
<http://pandas.pydata.org/pandas-docs/stable/advanced.html#intervalindex>`_
for more.

See Also
--------
Index : The base pandas Index type.
Interval : A bounded slice-like interval; the elements of an %(klass)s.
interval_range : Function to create a fixed frequency IntervalIndex.
cut : Bin values into discrete Intervals.
qcut : Bin values into equal-sized Intervals based on rank or sample quantiles.

Notes
------
See the `user guide
<http://pandas.pydata.org/pandas-docs/stable/advanced.html#intervalindex>`_
for more.

%(examples)s\
"""


Expand Down Expand Up @@ -236,18 +236,18 @@ def _from_factorized(cls, values, original):

.. versionadded:: 0.23.0

See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex.
%(klass)s.from_arrays : Construct from a left and right array.
%(klass)s.from_tuples : Construct from a sequence of tuples.

Examples
--------
>>> pd.%(klass)s.from_breaks([0, 1, 2, 3])
%(klass)s([(0, 1], (1, 2], (2, 3]]
closed='right',
dtype='interval[int64]')

See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex.
%(klass)s.from_arrays : Construct from a left and right array.
%(klass)s.from_tuples : Construct from a sequence of tuples.
"""

@classmethod
Expand Down Expand Up @@ -281,14 +281,6 @@ def from_breaks(cls, breaks, closed='right', copy=False, dtype=None):
-------
%(klass)s

Notes
-----
Each element of `left` must be less than or equal to the `right`
element at the same position. If an element is missing, it must be
missing in both `left` and `right`. A TypeError is raised when
using an unsupported type for `left` or `right`. At the moment,
'category', 'object', and 'string' subtypes are not supported.

Raises
------
ValueError
Expand All @@ -304,6 +296,13 @@ def from_breaks(cls, breaks, closed='right', copy=False, dtype=None):
%(klass)s.from_tuples : Construct an %(klass)s from an
array-like of tuples.

Notes
-----
Each element of `left` must be less than or equal to the `right`
element at the same position. If an element is missing, it must be
missing in both `left` and `right`. A TypeError is raised when
using an unsupported type for `left` or `right`. At the moment,
'category', 'object', and 'string' subtypes are not supported.

Examples
--------
Expand Down Expand Up @@ -339,6 +338,16 @@ def from_arrays(cls, left, right, closed='right', copy=False, dtype=None):

..versionadded:: 0.23.0

See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex.
%(klass)s.from_arrays : Construct an %(klass)s from a left and
right array.
%(klass)s.from_breaks : Construct an %(klass)s from an array of
splits.
%(klass)s.from_tuples : Construct an %(klass)s from an
array-like of tuples.

Examples
--------
>>> pd.%(klass)s.from_intervals([pd.Interval(0, 1),
Expand All @@ -352,16 +361,6 @@ def from_arrays(cls, left, right, closed='right', copy=False, dtype=None):
>>> pd.Index([pd.Interval(0, 1), pd.Interval(1, 2)])
%(klass)s([(0, 1], (1, 2]]
closed='right', dtype='interval[int64]')

See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex.
%(klass)s.from_arrays : Construct an %(klass)s from a left and
right array.
%(klass)s.from_breaks : Construct an %(klass)s from an array of
splits.
%(klass)s.from_tuples : Construct an %(klass)s from an
array-like of tuples.
"""

_interval_shared_docs['from_tuples'] = """
Expand All @@ -381,20 +380,19 @@ def from_arrays(cls, left, right, closed='right', copy=False, dtype=None):

..versionadded:: 0.23.0


Examples
--------
>>> pd.%(klass)s.from_tuples([(0, 1), (1, 2)])
%(klass)s([(0, 1], (1, 2]],
closed='right', dtype='interval[int64]')

See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex.
%(klass)s.from_arrays : Construct an %(klass)s from a left and
right array.
%(klass)s.from_breaks : Construct an %(klass)s from an array of
splits.

Examples
--------
>>> pd.%(klass)s.from_tuples([(0, 1), (1, 2)])
%(klass)s([(0, 1], (1, 2]],
closed='right', dtype='interval[int64]')
"""

@classmethod
Expand Down Expand Up @@ -1052,6 +1050,10 @@ def repeat(self, repeats, **kwargs):
ndarray
Boolean array positionally indicating where an overlap occurs.

See Also
--------
Interval.overlaps : Check whether two Interval objects overlap.

Examples
--------
>>> intervals = %(constructor)s.from_tuples([(0, 1), (1, 3), (2, 4)])
Expand All @@ -1071,10 +1073,6 @@ def repeat(self, repeats, **kwargs):

>>> intervals.overlaps(pd.Interval(1, 2, closed='right'))
array([False, True, False])

See Also
--------
Interval.overlaps : Check whether two Interval objects overlap.
"""

@Appender(_interval_shared_docs['overlaps'] % _shared_docs_kwargs)
Expand Down
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