. . . . . . . . . . . . . . . 1085 29 Contributing to pandas 1125 29.1 Contributing to the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1125 30 Release Notes Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some the structure of the unlocalized data (GH4230), see the docs • DatetimeIndex is now in the API documentation, see the docs • json_normalize() is a new method to allow you to create a flat table from semi-structured

. . . . . . . . . . . . . . . 1195 29 Contributing to pandas 1251 29.1 Contributing to the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1251 30 Release Notes Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection objects

. . . . . . . . . . . . . . . 1388 33 Contributing to pandas 1449 33.1 Contributing to the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1449 34 Internals 1453 Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some 2014-11-22 call AAPL141122C00110000 1.02 2014-11-28 call AAPL141128C00110000 1.32 See the Options documentation in Remote Data • pandas now also registers the datetime64 dtype in matplotlib’s units registry

. . . 2698 4.3 Contributing to the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2701 4.3.1 About the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2719 4.3.3 How to build the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2719 4.3.4 Previewing changes . . . toolkit, Release 1.3.4 Date: Oct 17, 2021 Version: 1.3.4 Download documentation: PDF Version | Zipped HTML Previous versions: Documentation of previous pandas versions is available at pandas.pydata.org.

. . . 2698 4.3 Contributing to the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2701 4.3.1 About the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2719 4.3.3 How to build the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2719 4.3.4 Previewing changes . . . powerful Python data analysis toolkit, Release 1.3.3 Date: Sep 12, 2021 Version: 1.3.3 Download documentation: PDF Version | Zipped HTML Useful links: Binary Installers | Source Repository | Issues & Ideas

. . . 2620 4.3 Contributing to the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2623 4.3.1 About the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2640 4.3.3 How to build the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2640 4.3.4 Previewing changes . . . powerful Python data analysis toolkit, Release 1.3.2 Date: Aug 15, 2021 Version: 1.3.2 Download documentation: PDF Version | Zipped HTML Useful links: Binary Installers | Source Repository | Issues & Ideas

. . . . . . . . . . . . . . . 1410 33 Contributing to pandas 1471 33.1 Contributing to the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1471 34 Internals 1475 Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some fromtimestamp(), and combine() on Timestamp class (GH5351). • Added Google Analytics (pandas.io.ga) basic documentation (GH8835). See here. • Timedelta arithmetic returns NotImplemented in unknown cases, allowing

. . . 2792 4.3 Contributing to the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2795 4.3.1 About the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2813 4.3.3 How to build the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2813 4.3.4 Previewing changes . . . toolkit, Release 1.4.4 Date: Aug 31, 2022 Version: 1.4.4 Download documentation: PDF Version | Zipped HTML Previous versions: Documentation of previous pandas versions is available at pandas.pydata.org.

. . . 2790 4.3 Contributing to the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2793 4.3.1 About the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2811 4.3.3 How to build the pandas documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2811 4.3.4 Previewing changes . . . toolkit, Release 1.4.2 Date: Apr 02, 2022 Version: 1.4.2 Download documentation: PDF Version | Zipped HTML Previous versions: Documentation of previous pandas versions is available at pandas.pydata.org.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 3.4 Contributing to the documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 3.5 Contributing Python. • pandas has been used extensively in production in financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some with the Air Speed Velocity library (GH8361) • Support for reading SAS xport files, see here • Documentation comparing SAS to pandas, see here • Removal of the automatic TimeSeries broadcasting, deprecated