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DOC: Update Series.repeat docstring #20242

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DOC: Improved the docstring of Series.repeat
Mar 10, 2018
b89d4f4
Merge remote-tracking branch 'upstream/master' into repeat
Mar 10, 2018
6607f79
Spacing, variables referencing, *args and **kwargs
pedropazzini Mar 10, 2018
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209 changes: 74 additions & 135 deletions pandas/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -918,12 +918,53 @@ def _set_values(self, key, value):
@deprecate_kwarg(old_arg_name='reps', new_arg_name='repeats')
def repeat(self, repeats, *args, **kwargs):
"""
Repeat elements of an Series. Refer to `numpy.ndarray.repeat`
for more information about the `repeats` argument.
Repeat elements of a Series.

Each element of the Series is repeated `repeats` times.

Parameters
----------
repeats : int
The number of repetitions for each element.
*args, **kwargs
Additional keywords have no effect but might be accepted for
compatibility with numpy.

See also
--------
numpy.ndarray.repeat
Series.append : Concatenate two or more Series.
pandas.concat : Concatenate two or more DataFrames.
Index.repeat : Eqivalent function for Index.
numpy.repeat : Repeat elements of an array.

Returns
-------
Series
The repeated version of the Series.

Notes
-----
Unlike numpy.repeat, `axis` is not a valid argument.

Examples
--------
>>> df = pd.DataFrame({'col1': ['A', 'B', 'C'], 'col2': [0, 1, 2]})
>>> df
col1 col2
0 A 0
1 B 1
2 C 2
>>> df.col1.repeat(3)
0 A
0 A
0 A
1 B
1 B
1 B
2 C
2 C
2 C
Name: col1, dtype: object
"""
nv.validate_repeat(args, kwargs)
new_index = self.index.repeat(repeats)
Expand Down Expand Up @@ -1316,77 +1357,8 @@ def unique(self):

return result

@Appender(base._shared_docs['drop_duplicates'] % _shared_doc_kwargs)
def drop_duplicates(self, keep='first', inplace=False):
"""
Return Series with duplicate values removed.

Parameters
----------
keep : {'first', 'last', ``False``}, default 'first'
- 'first' : Drop duplicates except for the first occurrence.
- 'last' : Drop duplicates except for the last occurrence.
- ``False`` : Drop all duplicates.
inplace : boolean, default ``False``
If ``True``, performs operation inplace and returns None.

Returns
-------
deduplicated : Series

See Also
--------
Index.drop_duplicates : equivalent method on Index
DataFrame.drop_duplicates : equivalent method on DataFrame
Series.duplicated : related method on Series, indicating duplicate
Series values.

Examples
--------
Generate an Series with duplicated entries.

>>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'],
... name='animal')
>>> s
0 lama
1 cow
2 lama
3 beetle
4 lama
5 hippo
Name: animal, dtype: object

With the 'keep' parameter, the selection behaviour of duplicated values
can be changed. The value 'first' keeps the first occurrence for each
set of duplicated entries. The default value of keep is 'first'.

>>> s.drop_duplicates()
0 lama
1 cow
3 beetle
5 hippo
Name: animal, dtype: object

The value 'last' for parameter 'keep' keeps the last occurrence for
each set of duplicated entries.

>>> s.drop_duplicates(keep='last')
1 cow
3 beetle
4 lama
5 hippo
Name: animal, dtype: object

The value ``False`` for parameter 'keep' discards all sets of
duplicated entries. Setting the value of 'inplace' to ``True`` performs
the operation inplace and returns ``None``.

>>> s.drop_duplicates(keep=False, inplace=True)
>>> s
1 cow
3 beetle
5 hippo
Name: animal, dtype: object
"""
return super(Series, self).drop_duplicates(keep=keep, inplace=inplace)

@Appender(base._shared_docs['duplicated'] % _shared_doc_kwargs)
Expand Down Expand Up @@ -2765,54 +2737,28 @@ def reindex_axis(self, labels, axis=0, **kwargs):
return self.reindex(index=labels, **kwargs)

def memory_usage(self, index=True, deep=False):
"""
Return the memory usage of the Series.

The memory usage can optionally include the contribution of
the index and of elements of `object` dtype.
"""Memory usage of the Series

Parameters
----------
index : bool, default True
Specifies whether to include the memory usage of the Series index.
deep : bool, default False
If True, introspect the data deeply by interrogating
`object` dtypes for system-level memory consumption, and include
it in the returned value.
index : bool
Specifies whether to include memory usage of Series index
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption

Returns
-------
int
Bytes of memory consumed.
scalar bytes of memory consumed

See Also
--------
numpy.ndarray.nbytes : Total bytes consumed by the elements of the
array.
DataFrame.memory_usage : Bytes consumed by a DataFrame.
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False

Examples
See Also
--------

>>> s = pd.Series(range(3))
>>> s.memory_usage()
104

Not including the index gives the size of the rest of the data, which
is necessarily smaller:

>>> s.memory_usage(index=False)
24

The memory footprint of `object` values is ignored by default:

>>> s = pd.Series(["a", "b"])
>>> s.values
array(['a', 'b'], dtype=object)
>>> s.memory_usage()
96
>>> s.memory_usage(deep=True)
212
numpy.ndarray.nbytes
"""
v = super(Series, self).memory_usage(deep=deep)
if index:
Expand Down Expand Up @@ -2840,21 +2786,20 @@ def _take(self, indices, axis=0, convert=True, is_copy=False):

def isin(self, values):
"""
Check whether `values` are contained in Series.

Return a boolean Series showing whether each element in the Series
matches an element in the passed sequence of `values` exactly.
Return a boolean :class:`~pandas.Series` showing whether each element
in the :class:`~pandas.Series` is exactly contained in the passed
sequence of ``values``.

Parameters
----------
values : set or list-like
The sequence of values to test. Passing in a single string will
raise a ``TypeError``. Instead, turn a single string into a
list of one element.
``list`` of one element.

.. versionadded:: 0.18.1

Support for values as a set.
Support for values as a set

Returns
-------
Expand All @@ -2863,37 +2808,31 @@ def isin(self, values):
Raises
------
TypeError
* If `values` is a string
* If ``values`` is a string

See Also
--------
pandas.DataFrame.isin : equivalent method on DataFrame
pandas.DataFrame.isin

Examples
--------

>>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama',
... 'hippo'], name='animal')
>>> s.isin(['cow', 'lama'])
>>> s = pd.Series(list('abc'))
>>> s.isin(['a', 'c', 'e'])
0 True
1 True
1 False
2 True
3 False
4 True
5 False
Name: animal, dtype: bool
dtype: bool

Passing a single string as ``s.isin('lama')`` will raise an error. Use
Passing a single string as ``s.isin('a')`` will raise an error. Use
a list of one element instead:

>>> s.isin(['lama'])
>>> s.isin(['a'])
0 True
1 False
2 True
3 False
4 True
5 False
Name: animal, dtype: bool
2 False
dtype: bool

"""
result = algorithms.isin(com._values_from_object(self), values)
return self._constructor(result, index=self.index).__finalize__(self)
Expand Down