. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.5 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 6.6 Reindexing anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas will soon become a dependency of cases (i.e. when getting slices, etc.), regression from prior versions • Friendlier error message in setup.py if NumPy not installed • Use common set of NA-handling operations (sum, mean, etc.) in Panel class

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.5 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 6.6 Reindexing anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas will soon become a dependency of cases (i.e. when getting slices, etc.), regression from prior versions • Friendlier error message in setup.py if NumPy not installed • Use common set of NA-handling operations (sum, mean, etc.) in Panel class

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 6.5 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.6 Reindexing anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas will soon become a dependency of cases (i.e. when getting slices, etc.), regression from prior versions • Friendlier error message in setup.py if NumPy not installed • Use common set of NA-handling operations (sum, mean, etc.) in Panel class

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 8.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 8.7 anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas is a dependency of statsmodels, (GH2713). • Function to reset Google Analytics token store so users can recover from improperly setup client secrets (GH2687). • Fixed groupby bug resulting in segfault when passing in MultiIndex (GH2706)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 9.7 anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas is a dependency of statsmodels, Pickling. • Refactor of series.py/frame.py/panel.py to move common code to generic.py – added _setup_axes to created generic NDFrame structures – moved methods 1.2. v0.13.0 (January 3, 2014) 31 pandas:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 ii anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas is a dependency of statsmodels, Pickling. • Refactor of series.py/frame.py/panel.py to move common code to generic.py – added _setup_axes to created generic NDFrame structures – moved methods * from_axes,_wrap_array,axes,ix,loc,iloc

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 9.7 anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas is a dependency of statsmodels, Pickling. • Refactor of series.py/frame.py/panel.py to move common code to generic.py – added _setup_axes to created generic NDFrame structures – moved methods * from_axes,_wrap_array,axes,ix,loc,iloc

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 9.7 anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas is a dependency of statsmodels, Pickling. • Refactor of series.py/frame.py/panel.py to move common code to generic.py – added _setup_axes to created generic NDFrame structures – moved methods * from_axes,_wrap_array,axes,ix,loc,iloc

. . . . . 457 10.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458 10.6.1 Tablewise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458 10.6.2 Row or Column-wise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . 459 10.6.3 Applying elementwise Python functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1250 35.3.6 Function application, GroupBy & Window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1251 35.3.7 Computations

. . . . . 459 10.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 10.6.1 Tablewise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 10.6.2 Row or Column-wise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . 461 10.6.3 Applying elementwise Python functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1253 35.3.6 Function application, GroupBy & Window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1254 35.3.7 Computations