Series.to_sparse()

Series.to_sparse(kind='block', fill_value=None) [source] Convert Series to SparseSeries Parameters: kind : {?block?, ?integer?} fill_value : float, defaults to NaN (missing) Returns: sp : SparseSeries

DatetimeIndex.is_numeric()

DatetimeIndex.is_numeric() [source]

DatetimeIndex.hour

DatetimeIndex.hour The hours of the datetime

DatetimeIndex.transpose()

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

Panel4D.blocks

Panel4D.blocks Internal property, property synonym for as_blocks()

GroupBy.indices

GroupBy.indices dict {group name -> group indices}

DataFrame.to_latex()

DataFrame.to_latex(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=True, column_format=None, longtable=None, escape=None, encoding=None, decimal='.') [source] Render a DataFrame to a tabular environment table. You can splice this into a LaTeX document. Requires usepackage{booktabs}. to_latex-specific options: bold_rows : boolean, default TrueMake the row labels bold in the output co

TimedeltaIndex.total_seconds()

TimedeltaIndex.total_seconds() [source] Total duration of each element expressed in seconds. New in version 0.17.0.

Index[source]

class pandas.Index [source] Immutable ndarray implementing an ordered, sliceable set. The basic object storing axis labels for all pandas objects Parameters: data : array-like (1-dimensional) dtype : NumPy dtype (default: object) copy : bool Make a copy of input ndarray name : object Name to be stored in the index tupleize_cols : bool (default: True) When True, attempt to create a MultiIndex if possible Notes An Index instance can only contain hashable objects Attributes T return th

Enhancing Performance

Cython (Writing C extensions for pandas) For many use cases writing pandas in pure python and numpy is sufficient. In some computationally heavy applications however, it can be possible to achieve sizeable speed-ups by offloading work to cython. This tutorial assumes you have refactored as much as possible in python, for example trying to remove for loops and making use of numpy vectorization, it?s always worth optimising in python first. This tutorial walks through a ?typical? process of cyt