Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you necessary to keep those in sync with the parent container’s labels. This should not have any visible user/API behavior changes (GH6745) 1.5.1 API changes • read_excel uses 0 as the default sheet (GH6573) the na_position parameter. (GH3917) • accept TextFileReader in concat, which was affecting a common user idiom (GH6583), this was a regression from 0.13.1 50 Chapter 1. What’s New pandas: powerful Python

Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you groupby('key')['data'].sum() Releasing of the GIL could benefit an application that uses threads for user interactions (e.g. QT), or performing multi-threaded computations. A nice example of a library that er which caused reading of valid S3 files to fail if the bucket also contained keys for which the user does not have read permission (GH10604) • Bug in vectorised setting of timestamp columns with python

Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you previous versions (GH14204) • Compat with Cython 0.25 for building (GH14496) • Fixed regression where user-provided file handles were closed in read_csv (c engine) (GH14418). • Fixed regression in DataFrame only stores the start, stop, and step values for the index. It will transparently interact with the user API, converting to Int64Index if needed. This will now be the default constructed index for NDFrame

Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you only stores the start, stop, and step values for the index. It will transparently interact with the user API, converting to Int64Index if needed. This will now be the default constructed index for NDFrame always returns a DataFrame, which is more consistent and less confusing from the per- spective of a user. Currently the default is expand=None which gives a FutureWarning and uses expand=False. To avoid

Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you necessary to keep those in sync with the parent container’s labels. This should not have any visible user/API behavior changes (GH6745) 1.4.1 API changes • read_excel uses 0 as the default sheet (GH6573) the na_position parameter. (GH3917) • accept TextFileReader in concat, which was affecting a common user idiom (GH6583), this was a regression from 0.13.1 • Added factorize functions to Index and Series

Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you toolkit, Release 0.20.3 • Addition of an IntervalIndex and Interval scalar type, see here • Improved user API when grouping by index levels in .groupby(), see here • Improved support for UInt64 dtypes, see easier extension, see the example notebook (GH15649) • Styler.render() now accepts **kwargs to allow user-defined variables in the template (GH15649) • Compatibility with Jupyter notebook 5.0; MultiIndex

Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you deprecated, see here • Addition of an IntervalIndex and Interval scalar type, see here • Improved user API when grouping by index levels in .groupby(), see here • Improved support for UInt64 dtypes, see easier extension, see the example notebook (GH15649) • Styler.render() now accepts **kwargs to allow user-defined variables in the template (GH15649) • Compatibility with Jupyter notebook 5.0; MultiIndex

Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you (GH18301). We’re working with the matplotlib developers to make this easier. We’re trying to balance user convenience (auto- matically registering the converters) with import performance and best practices including a new top-level read_parquet() function and DataFrame. to_parquet() method, see here. • New user-facing pandas.api.types.CategoricalDtype for specifying categoricals independent of the data, see

Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you Recommended Dependencies 1.2.1 Selection Choices Starting in 0.11.0, object selection has had a number of user-requested additions in order to support more explicit location based indexing. Pandas now supports get_near_stock_price now allows the user to specify the month for which to get rele- vant options data. – Options.get_forward_data now has optional kwargs near and above_below. This allows the user to specify if they

and data analysis tools for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data rolling.apply and expanding.apply We’ve added an engine keyword to apply() and apply() that allows the user to execute the routine using Numba instead of Cython. Using the Numba engine can yield significant (or Kleene logic). For example: In [8]: pd.NA | True Out[8]: True For more, see NA section in the user guide on missing data. 1.3.2 Dedicated string data type We’ve added StringDtype, an extension type