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This article mainly introduces "what is one-dimensional series in python". In daily operation, I believe that many people have doubts about what one-dimensional series in python refers to. The editor consulted all kinds of data and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the questions of "what is one-dimensional series in python"! Next, please follow the editor to study!

A brief introduction to 0pandas

Pandas is one of the most classic libraries in the field of python data analysis, which is based on numpy.

The data structures commonly used in pandas are:

1century Series: an one-dimensional array with index. Only data of the same type is allowed to be stored in Series.

2Magna DataFrame: two-dimensional tabular data structure. You can think of DataFrame as a container for Series.

3Jing Panel: a three-dimensional array. Can be understood as a container of DataFrame.

Did you find the relationship between the name of the pandas library and the names of these three data structures? In this section, we introduce Series. The summary of Series is as follows:

Series can be thought of as an one-dimensional array with an index attached.

Series is very much like an ordered dictionary, and index is the equivalent of keys.

Series can use time series indexes and multi-level indexes.

First, create a Series

1. Type conversion method

2, create a time series index

3, create a multi-level index

Second, the index of Series

1, subscript and subscript slice index

2 the index of index slices and the index of quotation

3, Boolean index

4, multi-level index

Third, common operations of Series

1, vectorization operation

2. Adjust the order of index

At this point, the study of "what does one-dimensional series in python refer to" is over. I hope I can solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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