Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation with anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas will soon become a dependency to rename a DataFrame in place (ENHed) • Enable unstacking by name (PR142) • Enable sortlevel to work by level (PR141) 1.6.2 Performance Enhancements • Altered binary operations on differently-indexed

Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation with anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas will soon become a dependency to rename a DataFrame in place (ENHed) • Enable unstacking by name (PR142) • Enable sortlevel to work by level (PR141) 1.7.2 Performance Enhancements • Altered binary operations on differently-indexed

Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation with anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas will soon become a dependency to rename a DataFrame in place (ENHed) • Enable unstacking by name (PR142) • Enable sortlevel to work by level (PR141) 16 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2693 4.1.4 Contributing your changes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2694 4.1.5 Tips for performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2733 4.4.9 Documenting your code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2734 4.5 pandas code reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2693 4.1.4 Contributing your changes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2694 4.1.5 Tips for performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2733 4.4.9 Documenting your code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2734 4.5 pandas code reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2785 4.1.4 Contributing your changes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2786 4.1.5 Tips for performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2826 4.4.9 Documenting your code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2827 4.5 pandas code reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2787 4.1.4 Contributing your changes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2788 4.1.5 Tips for performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2828 4.4.9 Documenting your code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2829 4.5 pandas code reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2957 4.1.4 Contributing your changes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2958 4.1.5 Tips for test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2998 4.4.10 Documenting your code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2999 4.5 pandas maintenance reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615 4.1.4 Contributing your changes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2616 4.1.5 Tips for performance test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2654 4.4.9 Documenting your code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2655 4.5 pandas code reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts.

base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2444 4.1.6 Contributing your changes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2457 4.1.7 Tips for or databases, pandas is the right tool for you. pandas will help you to explore, clean and process your data. In pandas, a data table is called a DataFrame. To introduction tutorial To user guide Straight tutorial... pandas provides plotting your data out of the box, using the power of Matplotlib. You can pick the plot type (scatter, bar, boxplot,. ..) corresponding to your data. To introduction tutorial