about what’s in the library. 2 CONTENTS CHAPTER ONE WHAT’S NEW These are new features and improvements of note in each release. 1.1 v0.12.0 (July 24, 2013) This is a major release from 0.11.0 and method can append levels to an existing Index/MultiIndex (GH1569, GH1577) 1.7.2 Performance improvements • Improved implementation of rolling min and max (thanks to Bottleneck !) • Add accelerated pandas: powerful Python data analysis toolkit, Release 0.12.0 1.8.3 Time series changes and improvements Note: With this release, legacy scikits.timeseries users should be able to port their code to

• 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

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 However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from dependencies for pandas. These dependencies are often not installed by default, but will offer speed improvements if present. 896 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release

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 However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from dependencies for pandas. These dependencies are often not installed by default, but will offer speed improvements if present. 2.23.1 Cython (writing C extensions for pandas) For many use cases writing pandas

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 However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from dependencies for pandas. These dependencies are often not installed by default, but will offer speed improvements if present. 2.23.1 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 However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from

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 However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from

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 However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from

operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2372 5.8.7 Documentation improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2373 5.8.8 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 However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from