statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629 22.7 Trellis plotting interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 630 23 IO Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755 28 rpy2 / R interface 757 28.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . .

statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 22.7 Trellis plotting interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642 23 IO Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769 28 rpy2 / R interface 771 28.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 10.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 23.7 Trellis plotting interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 740 24 IO Tools . . . . . 891 29 rpy2 / R interface 893 29.1 Updating your code to use rpy2 functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 29.2 R interface with rpy2 . . . . . . . .

groupby(..).nth() changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 numpy function compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Using .apply . . . . . . . . . . . 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 .

groupby(..).nth() changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 numpy function compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Using .apply . . . . . . . . . . . 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 .

groupby(..).nth() changes . . . . . . . . . . . . . . . . . . . . . . . . . . 99 1.6.3.2 numpy function compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 1.6.3.3 Using .apply . . . . . . . . . . . 507 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 9.6.1 Tablewise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 9.6.2 Row or Column-wise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . 510 9.6.3 Aggregation API . . . . . . . . . . . . .

groupby(..).nth() changes . . . . . . . . . . . . . . . . . . . . . . . . . . 101 1.7.3.2 numpy function compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 1.7.3.3 Using .apply . . . . . . . . . . . 509 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510 9.6.1 Tablewise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 9.6.2 Row or Column-wise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . 512 9.6.3 Aggregation API . . . . . . . . . . . . .

groupby(..).nth() changes . . . . . . . . . . . . . . . . . . . . . . . . . . 130 1.9.3.2 numpy function compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 1.9.3.3 Using .apply . . . . . . . . . . . 535 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536 9.6.1 Tablewise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537 9.6.2 Row or Column-wise Function Application . . . . . . . . . . . . . . . . . . . . . . . . . . 538 9.6.3 Aggregation API . . . . . . . . . . . . .

and the second element is the aggregation function to apply. Pandas provides the pandas.NamedAgg namedtuple to make it clearer what the arguments to the function are, but plain tuples are accepted as well group only once The implementation of DataFrameGroupBy.apply() previously evaluated the supplied function consistently twice on the first group to infer if it is safe to use a fast code path. Particularly timezone aware and timezone unaware DatetimeIndex (GH21671) • Bug when applying a numpy reduction function (e.g. numpy.minimum()) to a timezone aware Series (GH15552) 1.6.5 Numeric • Bug in to_numeric()

and the second element is the aggregation function to apply. Pandas provides the pandas.NamedAgg namedtuple to make it clearer what the arguments to the function are, but plain tuples are accepted as well group only once The implementation of DataFrameGroupBy.apply() previously evaluated the supplied function consistently twice on the first group to infer if it is safe to use a fast code path. Particularly timezone aware and timezone unaware DatetimeIndex (GH21671) • Bug when applying a numpy reduction function (e.g. numpy.minimum()) to a timezone aware Series (GH15552) 1.6.5 Numeric • Bug in to_numeric()