DOC: resolved mistakes in examples series (#15625)

pandas/core/generic.py

@@ -668,6 +668,7 @@ def swaplevel(self, i=-2, j=-1, axis=0):
         dtype: int64
         >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
         >>> df.rename(2)
+        Traceback (most recent call last):
         ...
         TypeError: 'int' object is not callable
         >>> df.rename(index=str, columns={"A": "a", "B": "c"})
@@ -1115,7 +1116,7 @@ def __setstate__(self, state):
     to the existing workbook.  This can be used to save different
     DataFrames to one workbook:
 
-    >>> writer = ExcelWriter('output.xlsx')
+    >>> writer = pd.ExcelWriter('output.xlsx')
     >>> df1.to_excel(writer,'Sheet1')
     >>> df2.to_excel(writer,'Sheet2')
     >>> writer.save()
@@ -2260,7 +2261,7 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
         ...      'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
         ...       index=index)
         >>> df
-                    http_status  response_time
+                   http_status  response_time
         Firefox            200           0.04
         Chrome             200           0.02
         Safari             404           0.07
@@ -2275,11 +2276,11 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
         ...             'Chrome']
         >>> df.reindex(new_index)
                        http_status  response_time
-        Safari                 404           0.07
+        Safari               404.0           0.07
         Iceweasel              NaN            NaN
         Comodo Dragon          NaN            NaN
-        IE10                   404           0.08
-        Chrome                 200           0.02
+        IE10                 404.0           0.08
+        Chrome               200.0           0.02
 
         We can fill in the missing values by passing a value to
         the keyword ``fill_value``. Because the index is not monotonically

pandas/core/series.py

@@ -369,10 +369,10 @@ def values(self):
         Timezone aware datetime data is converted to UTC:
 
         >>> pd.Series(pd.date_range('20130101', periods=3,
-                                    tz='US/Eastern')).values
-        array(['2013-01-01T00:00:00.000000000-0500',
-               '2013-01-02T00:00:00.000000000-0500',
-               '2013-01-03T00:00:00.000000000-0500'], dtype='datetime64[ns]')
+        ...                         tz='US/Eastern')).values
+        array(['2013-01-01T05:00:00.000000000',
+               '2013-01-02T05:00:00.000000000',
+               '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
 
         """
         return self._data.external_values()
@@ -1550,6 +1550,8 @@ def append(self, to_append, ignore_index=False, verify_integrity=False):
         With `verify_integrity` set to True:
 
         >>> s1.append(s2, verify_integrity=True)
+        Traceback (most recent call last):
+        ...
         ValueError: Indexes have overlapping values: [0, 1, 2]
 
 
@@ -1919,8 +1921,19 @@ def nlargest(self, n=5, keep='first'):
         --------
         >>> import pandas as pd
         >>> import numpy as np
-        >>> s = pd.Series(np.random.randn(1e6))
+        >>> s = pd.Series(np.random.randn(10**6))
         >>> s.nlargest(10)  # only sorts up to the N requested
+        219921    4.644710
+        82124     4.608745
+        421689    4.564644
+        425277    4.447014
+        718691    4.414137
+        43154     4.403520
+        283187    4.313922
+        595519    4.273635
+        503969    4.250236
+        121637    4.240952
+        dtype: float64
         """
         return algorithms.select_n_series(self, n=n, keep=keep,
                                           method='nlargest')
@@ -1958,8 +1971,19 @@ def nsmallest(self, n=5, keep='first'):
         --------
         >>> import pandas as pd
         >>> import numpy as np
-        >>> s = pd.Series(np.random.randn(1e6))
+        >>> s = pd.Series(np.random.randn(10**6))
         >>> s.nsmallest(10)  # only sorts up to the N requested
+        288532   -4.954580
+        732345   -4.835960
+        64803    -4.812550
+        446457   -4.609998
+        501225   -4.483945
+        669476   -4.472935
+        973615   -4.401699
+        621279   -4.355126
+        773916   -4.347355
+        359919   -4.331927
+        dtype: float64
         """
         return algorithms.select_n_series(self, n=n, keep=keep,
                                           method='nsmallest')
@@ -2052,21 +2076,24 @@ def unstack(self, level=-1, fill_value=None):
 
         Examples
         --------
+        >>> s = pd.Series([1, 2, 3, 4],
+        ...     index=pd.MultiIndex.from_product([['one', 'two'], ['a', 'b']]))
         >>> s
-        one  a   1.
-        one  b   2.
-        two  a   3.
-        two  b   4.
+        one  a    1
+             b    2
+        two  a    3
+             b    4
+        dtype: int64
 
         >>> s.unstack(level=-1)
-             a   b
-        one  1.  2.
-        two  3.  4.
+             a  b
+        one  1  2
+        two  3  4
 
         >>> s.unstack(level=0)
            one  two
-        a  1.   2.
-        b  3.   4.
+        a    1    3
+        b    2    4
 
         Returns
         -------
@@ -2102,15 +2129,16 @@ def map(self, arg, na_action=None):
 
         >>> x = pd.Series([1,2,3], index=['one', 'two', 'three'])
         >>> x
-        one   1
-        two   2
-        three 3
+        one      1
+        two      2
+        three    3
+        dtype: int64
 
         >>> y = pd.Series(['foo', 'bar', 'baz'], index=[1,2,3])
         >>> y
-        1  foo
-        2  bar
-        3  baz
+        1    foo
+        2    bar
+        3    baz
 
         >>> x.map(y)
         one   foo
@@ -2215,6 +2243,7 @@ def apply(self, func, convert_dtype=True, args=(), **kwds):
         >>> import numpy as np
         >>> series = pd.Series([20, 21, 12], index=['London',
         ... 'New York','Helsinki'])
+        >>> series
         London      20
         New York    21
         Helsinki    12