broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many project to maintain compatibility. 2.2 Binary installers 2.2.1 All platforms Stable installers available on PyPI Preliminary builds and installers on the Pandas download page . 65 pandas: powerful Python evaluations. bottleneck uses specialized cython routines to achieve large speedups. Note: You are highly encouraged to install these libraries, as they provide large speedups, especially if working with

compiled. Installation instructions for Anaconda can be found here. A full list of the packages available as part of the Anaconda distribution can be found here. Another advantage to installing Anaconda install the full Anaconda distribution: conda install anaconda If you need packages that are available to pip but not conda, then install pip, and then use pip to install those packages: conda install routines to achieve large speedups. If installed, must be Version 1.2.1 or higher. Note: You are highly encouraged to install these libraries, as they provide speed improvements, especially when working

broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many support or someone contributing to the project to maintain compatibility. 2.2 Binary installers Available on PyPI 2.3 Dependencies • NumPy: 1.4.0 or higher. Recommend 1.5.1 or higher • python-dateutil 0.7.1 Note: Without the optional dependencies, many useful features will not work. Hence, it is highly recommended that you install these. A packaged distribution like the Enthought Python Distribution

broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many support or someone contributing to the project to maintain compatibility. 2.2 Binary installers Available on PyPI 2.3 Dependencies • NumPy: 1.4.0 or higher. Recommend 1.5.1 or higher • python-dateutil 0.7.2 Note: Without the optional dependencies, many useful features will not work. Hence, it is highly recommended that you install these. A packaged distribution like the Enthought Python Distribution

broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many support or someone contributing to the project to maintain compatibility. 2.2 Binary installers Available on PyPI 2.3 Dependencies • NumPy: 1.4.0 or higher. Recommend 1.5.1 or higher • python-dateutil 0.7.3 Note: Without the optional dependencies, many useful features will not work. Hence, it is highly recommended that you install these. A packaged distribution like the Enthought Python Distribution

Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601 11.5 Available Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many to the respective engine (GH18216) 1.1.2.2 Other Enhancements • Timestamp.timestamp() is now available in Python 2.7. (GH17329) • Grouper and TimeGrouper now have a friendly repr output (GH18203).

5]: array([1, 2, 3]) We haven’t removed or deprecated Series.values or DataFrame.values, but we highly recommend and using .array or .to_numpy() instead. See Dtypes and Attributes and Underlying Data release will be the last release to support Python 2. The released package will continue to be available on PyPI and through conda. Starting January 1, 2019, all new feature releases (> 0.24) will be 59 pandas: powerful Python data analysis toolkit, Release 0.24.0 A full list of the packages available as part of the Anaconda distribution can be found here. Another advantage to installing Anaconda

aggregations (Deprecate groupby.agg() with a dictionary when renaming). A similar approach is now available for Series groupby objects as well. Because there’s no need for column selection, the values can release will be the last release to support Python 2. The released package will continue to be available on PyPI and through conda. Starting January 1, 2019, all new feature releases (> 0.24) will be 35 pandas: powerful Python data analysis toolkit, Release 0.25.0 A full list of the packages available as part of the Anaconda distribution can be found here. Another advantage to installing Anaconda

aggregations (Deprecate groupby.agg() with a dictionary when renaming). A similar approach is now available for Series groupby objects as well. Because there’s no need for column selection, the values can compiled. Installation instructions for Anaconda can be found here. A full list of the packages available as part of the Anaconda distribution can be found here. Another advantage to installing Anaconda install the full Anaconda distribution: conda install anaconda If you need packages that are available to pip but not conda, then install pip, and then use pip to install those packages: conda install

core.index has been deprecated and will be removed in a future version, the public classes are available in the top-level namespace (GH19711) • pandas.json_normalize() is now exposed in the top-level compiled. Installation instructions for Anaconda can be found here. A full list of the packages available as part of the Anaconda distribution can be found here. Another advantage to installing Anaconda install the full Anaconda distribution: conda install anaconda If you need packages that are available to pip but not conda, then install pip, and then use pip to install those packages: conda install