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 should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development

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 should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development

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 should be sent to: support@lambdafoundry.com 4.4 Credits pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development

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 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

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) 1.1.1 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 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 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 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