Applied "black formatting" on the example code

Author: ShaharNaveh

pandas/core/algorithms.py

@@ -375,48 +375,60 @@ def unique(values):
     >>> pd.unique(pd.Series([2] + [1] * 5))
     array([2, 1])
 
-    >>> pd.unique(pd.Series([pd.Timestamp('20160101'),
-    ...                     pd.Timestamp('20160101')]))
+    >>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
     array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
 
-    >>> pd.unique(pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
-    ...                      pd.Timestamp('20160101', tz='US/Eastern')]))
+    >>> pd.unique(
+    ...     pd.Series(
+    ...         [
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...         ]
+    ...     )
+    ... )
     <DatetimeArray>
     ['2016-01-01 00:00:00-05:00']
     Length: 1, dtype: datetime64[ns, US/Eastern]
 
-    >>> pd.unique(pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
-    ...                     pd.Timestamp('20160101', tz='US/Eastern')]))
+    >>> pd.unique(
+    ...     pd.Index(
+    ...         [
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...         ]
+    ...     )
+    ... )
     DatetimeIndex(['2016-01-01 00:00:00-05:00'],
             dtype='datetime64[ns, US/Eastern]',
             freq=None)
 
-    >>> pd.unique(list('baabc'))
+    >>> pd.unique(list("baabc"))
     array(['b', 'a', 'c'], dtype=object)
 
     An unordered Categorical will return categories in the
     order of appearance.
 
-    >>> pd.unique(pd.Series(pd.Categorical(list('baabc'))))
+    >>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
     ['b', 'a', 'c']
     Categories (3, object): ['a', 'b', 'c']
 
-    >>> pd.unique(pd.Series(pd.Categorical(list('baabc'),
-    ...                                    categories=list('abc'))))
+    >>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))
     ['b', 'a', 'c']
     Categories (3, object): ['a', 'b', 'c']
 
     An ordered Categorical preserves the category ordering.
 
-    >>> pd.unique(pd.Series(pd.Categorical(list('baabc'),
-    ...                                    categories=list('abc'),
-    ...                                    ordered=True)))
+    >>> pd.unique(
+    ...     pd.Series(
+    ...         pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
+    ...     )
+    ... )
     ['b', 'a', 'c']
     Categories (3, object): ['a' < 'b' < 'c']
 
     An array of tuples
 
-    >>> pd.unique([('a', 'b'), ('b', 'a'), ('a', 'c'), ('b', 'a')])
+    >>> pd.unique([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")])
     array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
     """
     values = _ensure_arraylike(values)