. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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, metacharacters were being treated as regexs even when regex=False (GH6777). • Bug in timedelta ops on 32-bit platforms (GH6808) • Bug in setting a tz-aware index directly via .index (GH6785) • Bug in expressions

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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, Out[22]: a int64 dtype: object Keep in mind that DataFrame(np.array([1,2])) WILL result in int32 on 32-bit platforms! Upcasting Gotchas Performing indexing operations on integer type data can easily upcast

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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, dtypes a int64 dtype: object Keep in mind that DataFrame(np.array([1,2])) WILL result in int32 on 32-bit platforms! Upcasting Gotchas Performing indexing operations on integer type data can easily upcast

. . . . . . 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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, is_superperiod and is_subperiod cannot handle higher frequencies than S (GH7760, GH7772, GH7803) • Bug in 32-bit platforms with Series.shift (GH8129) • Bug in PeriodIndex.unique returns int64 np.ndarray (GH7540)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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, is_superperiod and is_subperiod cannot handle higher frequencies than S (GH7760, GH7772, GH7803) • Bug in 32-bit platforms with Series.shift (GH8129) • Bug in PeriodIndex.unique returns int64 np.ndarray (GH7540)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 10.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 10.7 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, : np.random.randn(N) }) df.groupby('key')['data'].sum() Releasing of the GIL could benefit an application that uses threads for user interactions (e.g. QT), or performing multi-threaded computations. A

. . . . . 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