statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 8.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.0 • Implement __nonzero__ for NDFrame objects (GH3691, GH3696) • IO api – added top-level function read_excel to replace the following, The original API is deprecated and will be removed in a future pd.read_excel(’path_to_file.xls’, ’Sheet1’, index_col=None, na_values=[’NA’]) – added top-level function read_sql that is equivalent to the following from pandas.io.sql import read_frame read_frame(.

statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a’) with no complaints from pandas about am- biguity of the name a. – The top-level pandas.eval() function does not allow you use the ’@’ prefix and provides you with an error message telling you so. – ensure that the name attribute of the original series is propagated to the result (GH6265). – If the function provided to GroupBy.apply returns a named series, the name of the series will be kept as the name

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

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 .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 10.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . functions map to the intrinsics for the NumExpr engine. For the Python engine, they are mapped to NumPy calls. Changes to Excel with MultiIndex In version 0.16.2 a DataFrame with MultiIndex columns could not Enhancements • Added ability to automatically create a table/dataset using the pandas.io.gbq.to_gbq() function if the destination table/dataset does not exist. (GH8325, GH11121). • Added ability to replace

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 .

statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . or putsYYMMDD. Previously they were saved as callsMMYY and putsMMYY. The next expiry is saved as calls and puts. New features: – The expiry parameter can now be a single date or a list-like object containing parameter to to_sql function. This allows DataFrame to be written in chunks and avoid packet-size overflow errors (GH8062). • Added support for a chunksize parameter to read_sql function. Specifying this

statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 9.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Timedelta.to_timedelta64() method to the public API (GH8884). • Added gbq.generate_bq_schema() function to the gbq module (GH8325). • Series now works with map objects the same way as generators (GH8909) matching is still the default) (GH8904) • Added axvlines boolean option to parallel_coordinates plot function, determines whether vertical lines will be printed, default is True • Added ability to read table