. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.2 Performance Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.3 Bug Fixes . . version deprecations/changes . . . . . . . . . . . . . . . . . . . . . . . . 42 1.3.6 Performance Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 1.3.7 Bug Fixes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 1.4.2 Performance Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 1.4.3 Bug Fixes . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.2 Performance Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.3 Bug Fixes . . version deprecations/changes . . . . . . . . . . . . . . . . . . . . . . . . 40 1.2.6 Performance Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 1.2.7 Bug Fixes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 1.3.2 Performance Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 1.3.3 Bug Fixes . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2.1 Improvements to the Parquet IO functionality . . . . . . . . . . . . . . . . . . . . 4 1.1.2.2 Other Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.4 Performance Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.5 Bug Fixes . . version deprecations/changes . . . . . . . . . . . . . . . . . . . . . . . . 27 1.2.5 Performance Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.2.6 Documentation

• Added to the list of default NA values for read_csv() (GH30821) 1.5.15 Documentation Improvements • Added new section on Scaling to large datasets (GH28315). • Added sub-section on Query MultiIndex DatetimeIndex will now return an object array of tz-aware Timestamp (GH24596) • 1.8 Performance improvements • Performance improvement in DataFrame arithmetic and comparison operations with scalars (GH24990 or higher. Note: You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Optional dependencies Pandas has many optional

deprecated convert keyword argument in Series.take() and DataFrame. take() (GH17352) 1.5 Performance improvements • Significant speedup in SparseArray initialization that benefits most operations, fixing performance or higher. Note: You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. 2.4.2 Optional dependencies Pandas has many optional branch (optional) 7.1.1 Where to start? All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. If you are brand new to pandas or open-source development

deprecated convert keyword argument in Series.take() and DataFrame. take() (GH17352) 1.5 Performance improvements • Significant speedup in SparseArray initialization that benefits most operations, fixing performance or higher. Note: You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. 2.5.2 Optional dependencies Pandas has many optional branch (optional) 7.1.1 Where to start? All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. If you are brand new to pandas or open-source development

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2481 ix 4.8.7 Documentation improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2481 4.8.8 Performance monitoring or higher. Note: You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. 8 Chapter 1. Getting started pandas: powerful performance 841 pandas: powerful Python data analysis toolkit, Release 1.1.1 will offer speed improvements if present. 2.21.1 Cython (writing C extensions for pandas) For many use cases writing pandas

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2481 ix 4.8.7 Documentation improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2481 4.8.8 Performance monitoring or higher. Note: You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. 8 Chapter 1. Getting started pandas: powerful performance 841 pandas: powerful Python data analysis toolkit, Release 1.1.0 will offer speed improvements if present. 2.21.1 Cython (writing C extensions for pandas) For many use cases writing pandas

operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2386 4.8.7 Documentation improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2387 4.8.8 Package docstring or higher. Note: You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Optional dependencies Pandas has many optional branch (optional) 4.1.1 Where to start? All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. If you are brand new to pandas or open-source development

operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2382 4.8.7 Documentation improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2383 4.8.8 Package docstring or higher. Note: You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Optional dependencies Pandas has many optional branch (optional) 4.1.1 Where to start? All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. If you are brand new to pandas or open-source development