@@ -1910,20 +1910,20 @@ def to_hdf(self, path_or_buf, key, **kwargs):
19101910 Identifier for the group in the store.
19111911 mode : {'a', 'w', 'r+'}, default 'a'
19121912 Mode to open file:
1913-
1913+
19141914 - 'w': write, a new file is created (an existing file with
1915- the same name would be deleted).
1915+ the same name would be deleted).
19161916 - 'a': append, an existing file is opened for reading and
1917- writing, and if the file does not exist it is created.
1917+ writing, and if the file does not exist it is created.
19181918 - 'r+': similar to 'a', but the file must already exist.
19191919 format : {'fixed', 'table'}, default 'fixed'
19201920 Possible values:
1921-
1921+
19221922 - 'fixed': Fixed format. Fast writing/reading. Not-appendable,
1923- nor searchable.
1923+ nor searchable.
19241924 - 'table': Table format. Write as a PyTables Table structure
1925- which may perform worse but allow more flexible operations
1926- like searching / selecting subsets of the data.
1925+ which may perform worse but allow more flexible operations
1926+ like searching / selecting subsets of the data.
19271927 append : bool, default False
19281928 For Table formats, append the input data to the existing.
19291929 data_columns : list of columns or True, optional
@@ -5795,10 +5795,11 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None,
57955795 * None: (default) no fill restriction
57965796 * 'inside' Only fill NaNs surrounded by valid values (interpolate).
57975797 * 'outside' Only fill NaNs outside valid values (extrapolate).
5798- .. versionadded:: 0.21.0
57995798
58005799 If limit is specified, consecutive NaNs will be filled in this
58015800 direction.
5801+
5802+ .. versionadded:: 0.21.0
58025803 inplace : bool, default False
58035804 Update the NDFrame in place if possible.
58045805 downcast : optional, 'infer' or None, defaults to None
@@ -7717,6 +7718,7 @@ def truncate(self, before=None, after=None, axis=None, copy=True):
77177718
77187719 The index values in ``truncate`` can be datetimes or string
77197720 dates.
7721+
77207722 >>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
77217723 >>> df = pd.DataFrame(index=dates, data={'A': 1})
77227724 >>> df.tail()
@@ -7960,7 +7962,7 @@ def abs(self):
79607962 0 1 days
79617963 dtype: timedelta64[ns]
79627964
7963- Select rows with data closest to certian value using argsort (from
7965+ Select rows with data closest to certain value using argsort (from
79647966 `StackOverflow <https://stackoverflow.com/a/17758115>`__).
79657967
79667968 >>> df = pd.DataFrame({
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