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This article will explain in detail what is the use of the concat function in pandas. The editor thinks it is very practical, so I share it for you as a reference. I hope you can get something after reading this article.

Concat

Concat is a function dedicated to data connection merging in pandas. It is very powerful and supports vertical merging and horizontal merging. By default, vertical merging can be set through parameters.

Pd.concat (objs: 'Iterable [NDFrame] | Mapping [Hashable, NDFrame]', axis=0, join='outer', ignore_index: 'bool' = False, keys=None, levels=None, names=None, verify_integrity:' bool' = False, sort: 'bool' = False, copy:' bool' = True,)-> 'FrameOrSeriesUnion'

In the function method, the meanings of the parameters are as follows:

Objs: data for connection, which can be a list of DataFrame or Series

Axis=0: the way to connect. Default is 0, that is, vertical connection. Optional 1 is horizontal connection.

Join='outer': merging method. Default is inner, which means intersection. Optional outer is union.

Ignore_index: whether to keep the original index

Keys=None: join relationship, using the passed value as the primary index

Levels=None: used to construct multi-level indexes

Names=None: the name of the index

Verify_integrity: check whether the index is duplicated. If it is True, an error will be reported if the index is duplicated.

Sort: sort columns in union and merge mode

Copy: whether to make a deep copy

This is the end of this article on "what is the use of the concat function in pandas". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it out for more people to see.

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