TimedeltaIndex.strides return the strides of the underlying data

DataFrame memory usage As of pandas version 0.15.0, the memory usage of a dataframe (including the index) is shown when accessing the info method of a dataframe. A configuration option, display.memory_usage (see Options and Settings), specifies if the dataframe?s memory usage will be displayed when invoking the df.info() method. For example, the memory usage of the dataframe below is shown when calling df.info(): In [1]: dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]', ..

Series.get_values() [source] same as values (but handles sparseness conversions); is a view

New in version 0.15. Note While there was pandas.Categorical in earlier versions, the ability to use categorical data in Series and DataFrame is new. This is an introduction to pandas categorical data type, including a short comparison with R?s factor. Categoricals are a pandas data type, which correspond to categorical variables in statistics: a variable, which can take on only a limited, and usually fixed, number of possible values (categories; levels in R). Examples are gender, social c

CategoricalIndex.is_numeric() [source]

Series.itemsize return the size of the dtype of the item of the underlying data

MultiIndex.identical(other) [source] Similar to equals, but check that other comparable attributes are also equal

Index.transpose(*args, **kwargs) [source] return the transpose, which is by definition self

DataFrame.to_dense() [source] Return dense representation of NDFrame (as opposed to sparse)

MultiIndex.reshape(*args, **kwargs) [source] NOT IMPLEMENTED: do not call this method, as reshaping is not supported for Index objects and will raise an error. Reshape an Index.