2011) . . . . . . . . . . . . . . . . . . . . . . . . 12 2 Installation 15 2.1 Python version support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Binary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 Getting Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.4 Repository: http://github.com/pydata/pandas Issues & Ideas: https://github.com/pydata/pandas/issues Q&A Support: http://stackoverflow.com/questions/tagged/pandas Developer Mailing List: http://groups.google.

2011) . . . . . . . . . . . . . . . . . . . . . . . . 12 2 Installation 15 2.1 Python version support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Binary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 Getting Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.4 Repository: http://github.com/pydata/pandas Issues & Ideas: https://github.com/pydata/pandas/issues Q&A Support: http://stackoverflow.com/questions/tagged/pandas Developer Mailing List: http://groups.google.

time-series aware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 read_csv has improved support for duplicate column names . . . . . . . . . . . . . . . . . 9 read_csv supports parsing Categorical . . . . . . . . . . . . . . . . . . . . . 101 Support for SAS XPORT files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Support for Math Functions in .eval() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 1.19.5 Updated PyTables Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 1.19.6 N Dimensional Panels

time-series aware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 read_csv has improved support for duplicate column names . . . . . . . . . . . . . . . . . 10 read_csv supports parsing Categorical . . . . . . . . . . . . . . . . . . . . . 103 Support for SAS XPORT files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Support for Math Functions in .eval() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 1.20.5 Updated PyTables Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 1.20.6 N Dimensional Panels

Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 1.5.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 42 1.5.1.6 Pickle file I/O now now supports compression . . . . . . . . . . . . . . . . . . . . . . . 42 1.5.1.7 UInt64 Support Improved . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 1.5.1.8 GroupBy on Categoricals dtypes will not automatically upcast . . . . . . . . . . . . 56 1.5.2.8 Pandas Google BigQuery support has moved . . . . . . . . . . . . . . . . . . . . . 56 1.5.2.9 Memory Usage for Index is more Accurate

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2534 4.9.2 Python support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2535 4.10 Roadmap Version | Zipped HTML Useful links: Binary Installers | Source Repository | Issues & Ideas | Q&A Support | Mailing List pandas is an open source, BSD-licensed library providing high-performance, easy-to-use tables of data. To introduction tutorial To user guide Straight to tutorial... pandas has great support for time series and has an extensive set of tools for working with dates, times, and time- indexed

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2528 4.9.2 Python support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2529 4.10 Roadmap Version | Zipped HTML Useful links: Binary Installers | Source Repository | Issues & Ideas | Q&A Support | Mailing List pandas is an open source, BSD-licensed library providing high-performance, easy-to-use tables of data. To introduction tutorial To user guide Straight to tutorial... pandas has great support for time series and has an extensive set of tools for working with dates, times, and time- indexed

Version | Zipped HTML Useful links: Binary Installers | Source Repository | Issues & Ideas | Q&A Support | Mailing List pandas is an open source, BSD-licensed library providing high-performance, easy-to-use single threshold, set pd.options.display.min_rows = None. 1.1.5 Json normalize with max_level param support json_normalize() normalizes the provided input dict to all nested levels. The new max_level parameter keywords logy, logx and loglog can now accept the value 'sym' for symlog scaling. (GH24867) • Added support for ISO week year format (‘%G-%V-%u’) when parsing datetimes using to_datetime() (GH16607) • Indexing

Version | Zipped HTML Useful links: Binary Installers | Source Repository | Issues & Ideas | Q&A Support | Mailing List pandas is an open source, BSD-licensed library providing high-performance, easy-to-use working with strings. See Text Data Types for more. 1.3.3 Boolean data type with missing values support We’ve added BooleanDtype / BooleanArray, an extension type dedicated to boolean data that can hold encourage use of the extension dtypes StringDtype, BooleanDtype, Int64Dtype, Int32Dtype, etc., that support pd.NA, the methods DataFrame.convert_dtypes() and Series.convert_dtypes() have been introduced. (GH29752)

Version | Zipped HTML Useful links: Binary Installers | Source Repository | Issues & Ideas | Q&A Support | Mailing List pandas is an open source, BSD-licensed library providing high-performance, easy-to-use single threshold, set pd.options.display.min_rows = None. 1.1.5 Json normalize with max_level param support json_normalize() normalizes the provided input dict to all nested levels. The new max_level parameter keywords logy, logx and loglog can now accept the value 'sym' for symlog scaling. (GH24867) • Added support for ISO week year format (‘%G-%V-%u’) when parsing datetimes using to_datetime() (GH16607) • Indexing