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