com/group/pydata pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental perform split-apply-combine operations on data sets, for both ag- gregating and transforming data • Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into Python data analysis toolkit, Release 0.17.0 loss of information, by specifying which columns/rows make up the MultiIndex in the header and index_col parameters (GH4679) See the documentation for more

com/group/pydata pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental perform split-apply-combine operations on data sets, for both ag- gregating and transforming data • Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into g. a value that exceeds the length of the object being indexed. These will be excluded. This will make pandas conform more with python/numpy indexing of out-of-bounds values. A single indexer that is out-of-bounds

com/group/pydata pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental perform split-apply-combine operations on data sets, for both ag- gregating and transforming data • Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into argument from all expanding_ functions (see list), as the results produced when center=True did not make much sense. (GH7925) • Added optional ddof argument to expanding_cov() and rolling_cov(). The default

com/group/pydata pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental perform split-apply-combine operations on data sets, for both ag- gregating and transforming data • Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into argument from all expanding_ functions (see list), as the results produced when center=True did not make much sense. (GH7925) • Added optional ddof argument to expanding_cov() and rolling_cov(). The default

com/group/pydata pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental perform split-apply-combine operations on data sets, for both ag- gregating and transforming data • Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into reindex,reindex_axis,take * truncate (moved to become part of NDFrame) • These are API changes which make Panel more consistent with DataFrame – swapaxes on a Panel with the same axes specified now return

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 3.6.5 Finally, make the pull request . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 3.6.6 Delete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035 29.2.4 Why not make NumPy like R? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035 29.3 Integer com/group/pydata pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 3.6.5 Finally, make the pull request . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 3.6.6 Delete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1037 29.2.4 Why not make NumPy like R? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1037 29.3 Integer com/group/pydata pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 3.6.5 Finally, make the pull request . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 3.6.6 Delete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1173 28.3.4 Why not make NumPy like R? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1173 28.4 Differences com/group/pydata pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 3.6.5 Finally, make the pull request . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 3.6.6 Delete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137 28.3.4 Why not make NumPy like R? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137 28.4 Differences com/group/pydata pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 3.6.5 Finally, make the pull request . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 viii 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135 28.3.4 Why not make NumPy like R? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135 28.4 Differences com/group/pydata pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental