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

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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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

. . . . . 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

. . . . . . 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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, indexes. Data alignment / join operations work according to SQL join semantics (including, if application, index duplication in many-to-many joins) 1.8.2 NumPy datetime64 dtype and 1.6 dependency Time

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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, indexes. Data alignment / join operations work according to SQL join semantics (including, if application, index duplication in many-to-many joins) 76 Chapter 1. What’s New pandas: powerful Python data