• 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 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 see the Cython docs on compiler directives. 2.23.2 Numba (JIT compilation) An alternative to statically compiling Cython code is to use a dynamic just-in-time (JIT) compiler with Numba. Numba allows

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 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 cause memory access isn’t checked. For more about boundscheck and wraparound, see the Cython docs on compiler directives. 2.23. Enhancing performance 939 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 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 cause memory access isn’t checked. For more about boundscheck and wraparound, see the Cython docs on compiler directives. 940 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release 1

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 dependencies for pandas. These dependencies are often not installed by default, but will offer speed improvements if present. 844 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release wraparound, see the Cython docs on compiler directives. 2.23.2 Using Numba A recent alternative to statically compiling Cython code, is to use a dynamic jit-compiler, Numba. Numba gives you the power

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 dependencies for pandas. These dependencies are often not installed by default, but will offer speed improvements if present. 2.23. Enhancing performance 845 pandas: powerful Python data analysis toolkit, Release wraparound, see the Cython docs on compiler directives. 2.23.2 Using Numba A recent alternative to statically compiling Cython code, is to use a dynamic jit-compiler, Numba. Numba gives you the power

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 because memory access isn’t checked. For more about boundscheck and wraparound, see the Cython docs on compiler directives. 2.18. Enhancing performance 827 pandas: powerful Python data analysis toolkit, Release

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 because memory access isn’t checked. For more about boundscheck and wraparound, see the Cython docs on compiler directives. 826 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release 1