. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.5 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 6.6 Reindexing statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.5 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 6.6 Reindexing statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 6.5 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.6 Reindexing statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 8.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 8.7 statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) 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,

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 9.7 statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) 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,

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 4.1.1 Why more than 1 data structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 4.2 Mutability and copying . . . . . . 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 . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 4.1.1 Why more than 1 data structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 4.2 Mutability and copying . . . . . . 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 . . . . . . . . . . . . . . . . . .

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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 4.1.1 Why more than one data structure? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 4.2 Mutability and copying of . . . . . . 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 . . . . . . . . . . . . . . . . . .