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DOC: docstring to series.unique #20474

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Merged
merged 11 commits into from
Mar 27, 2018
Merged

DOC: docstring to series.unique #20474

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0a8ba45
remove _shared_docs and add doctring to series.unique
minggli Mar 23, 2018
fc7049b
Merge branch 'master' into doc/unique
minggli Mar 24, 2018
308b279
add examples and wording
minggli Mar 24, 2018
fb86b7f
line break in example docstrings
minggli Mar 24, 2018
10d940d
descriptions in See Also
minggli Mar 24, 2018
93b1ffd
minor changes
minggli Mar 24, 2018
2dff831
remove backward slash
minggli Mar 25, 2018
e024635
rephrase See Also section
minggli Mar 25, 2018
e325fef
simpler return description.
minggli Mar 26, 2018
bd1f114
rephase return description
minggli Mar 27, 2018
4931fcf
Update series.py
jorisvandenbossche Mar 27, 2018
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24 changes: 0 additions & 24 deletions pandas/core/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1020,30 +1020,6 @@ def value_counts(self, normalize=False, sort=True, ascending=False,
normalize=normalize, bins=bins, dropna=dropna)
return result

_shared_docs['unique'] = (
"""
Return unique values in the object. Uniques are returned in order
of appearance, this does NOT sort. Hash table-based unique.

Parameters
----------
values : 1d array-like

Returns
-------
unique values.
- If the input is an Index, the return is an Index
- 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
--------
unique
Index.unique
Series.unique
""")

@Appender(_shared_docs['unique'] % _indexops_doc_kwargs)
def unique(self):
values = self._values

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45 changes: 44 additions & 1 deletion pandas/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1429,8 +1429,51 @@ def mode(self):
# TODO: Add option for bins like value_counts()
return algorithms.mode(self)

@Appender(base._shared_docs['unique'] % _shared_doc_kwargs)
def unique(self):
"""
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it might be worth trying to share this doc-string with pd.unique (at least the examples) no?

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good point but they have some differences:

  • pd.unique takes param and Series.unique doesn't take.
  • pd.unique handles 1d array-like objects including Index and Series.unique applies on self.
  • pd.unique examples contain more and Series.unique only series examples

do we have pattern somewhere in regards to conditionally show docstring lines?

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https://github.com/pandas-dev/pandas/pull/20361/files is doing something similar for factorize. It's somewhat complex, since we have pd.unique, Series/Index.unique, and Categorical.unique. I'd be OK with improving the docstring here, and merging theme later.

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agree, this PR is mainly about improve docstring. and should be another PR synthesising docs of all unique methods.

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that's fine too

Return unique values of Series object.

Uniques are returned in order of appearance. Hash table-based unique,
therefore does NOT sort.

Returns
-------
ndarray or Categorical
The unique values returned as a NumPy array. In case of categorical
data type, returned as a Categorical.

See Also
--------
pandas.unique : top-level unique method for any 1-d array-like object.
Index.unique : return Index with unique values from an Index object.

Examples
--------
>>> pd.Series([2, 1, 3, 3], name='A').unique()
array([2, 1, 3])

>>> pd.Series([pd.Timestamp('2016-01-01') for _ in range(3)]).unique()
array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

>>> pd.Series([pd.Timestamp('2016-01-01', tz='US/Eastern')
... for _ in range(3)]).unique()
array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')],
dtype=object)

An unordered Categorical will return categories in the order of
appearance.

>>> pd.Series(pd.Categorical(list('baabc'))).unique()
[b, a, c]
Categories (3, object): [b, a, c]

An ordered Categorical preserves the category ordering.

>>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'),
... ordered=True)).unique()
[b, a, c]
Categories (3, object): [a < b < c]
"""
result = super(Series, self).unique()

if is_datetime64tz_dtype(self.dtype):
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