. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Out[64]: A 1 B 3 C 4 In [65]: df1.idxmax(axis=1) Out[65]: 0 C 1 C 2 C 3 A 4 B 6.5 Function application Arbitrary functions can be applied along the axes of a DataFrame or Panel using the apply method

. . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1369 34.3.6 Function application, GroupBy & Window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1370 34.3.7 Computations

. . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1349 34.3.6 Function application, GroupBy & Window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1350 34.3.7 Computations

. . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1428 34.3.6 Function application, GroupBy & Window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1429 34.3.7 Computations

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, 0.0]] Length: 20 Categories (2, interval[float64]): [(-inf, 0.0] < (0.0, inf]] 3.3.6 Function application To apply your own or another library’s functions to pandas objects, you should be aware of the Function Application: pipe() 2. Row or Column-wise Function Application: apply() 3. Aggregation API: agg() and transform() 4. Applying Elementwise Functions: applymap() Tablewise function application DataFrames

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, inf]] Length: 20 Categories (2, interval[float64]): [(-inf, 0.0] < (0.0, inf]] 3.3.6 Function application To apply your own or another library’s functions to pandas objects, you should be aware of the Function Application: pipe() 2. Row or Column-wise Function Application: apply() 3. Aggregation API: agg() and transform() 4. Applying Elementwise Functions: applymap() Tablewise function application DataFrames

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. 48 Chapter 2. Getting started pandas: powerful inf]] Length: 20 Categories (2, interval[float64]): [(-inf, 0.0] < (0.0, inf]] 2.4.6 Function application To apply your own or another library’s functions to pandas objects, you should be aware of the Function Application: pipe() 2. Row or Column-wise Function Application: apply() 3. Aggregation API: agg() and transform() 4. Applying Elementwise Functions: applymap() Tablewise function application DataFrames

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 2.3.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 2.3.7 Reindexing functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1252 3.3.6 Function application, GroupBy & window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1253 3.3.7 Computations functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1745 3.4.6 Function application, GroupBy & window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1747 vi 3.4.7 Computations

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 2.3.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 2.3.7 Reindexing functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1252 3.3.6 Function application, GroupBy & window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1253 3.3.7 Computations functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1745 3.4.6 Function application, GroupBy & window . . . . . . . . . . . . . . . . . . . . . . . . . . . 1747 vi 3.4.7 Computations

background_gradient() now also supports tablewise application (in addition to rowwise and column- wise) with axis=None (GH15204) • bar() now also supports tablewise application (in addition to rowwise and columnwise) 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, 0.0]] Length: 20 Categories (2, interval[float64]): [(-inf, 0.0] < (0.0, inf]] 3.3.6 Function application To apply your own or another library’s functions to pandas objects, you should be aware of the