Python distribution for data analytics and scientific computing. After running the installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else 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 cross-sectional data sets. When using ndarrays to store 2- and 3-dimensional data, a burden is placed on the user to consider the orientation of the data set when writing functions; axes are considered more or less

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

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 all users upgrade to this version. • Highlights include: – Officially support Python 3.4 – SQL interfaces updated to use sqlalchemy, See Here. – Display interface changes, See Here – MultiIndexing Using necessary to keep those in sync with the parent container’s labels. This should not have any visible user/API behavior changes (GH6745) 1.1.1 API changes • read_excel uses 0 as the default sheet (GH6573)

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 all users upgrade to this version. • Highlights include: – Officially support Python 3.4 – SQL interfaces updated to use sqlalchemy, See Here. – Display interface changes, See Here – MultiIndexing Using 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)

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 all users upgrade to this version. • Highlights include: – Officially support Python 3.4 – SQL interfaces updated to use sqlalchemy, See Here. – Display interface changes, See Here – MultiIndexing Using 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)

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 statistical mode(s) by axis/Series. (GH5367) • Chained assignment will now by default warn if the user is assigning to a copy. This can be changed with the option mode.chained_assignment, allowed options Recommended Dependencies 1.4.1 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

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

In [9]: from pandas.io.json import json_normalize In [10]: data = [{ ....: 'CreatedBy': {'Name': 'User001'}, ....: 'Lookup': {'TextField': 'Some text', (continues on next page) 6 Chapter 1. What’s new json_normalize(data, max_level=1) Out[11]: CreatedBy.Name Lookup.TextField Lookup.UserField Image.a 0 User001 Some text {'Id': 'ID001', 'Name': 'Name001'} b [1 rows x 4 columns] 1.1.6 Series.explode to split read_excel() supports reading OpenDocument tables. Specify engine='odf' to enable. Consult the IO User Guide for more details (GH9070) • Interval, IntervalIndex, and IntervalArray have gained an is_empty

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