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What is the array-like operation in two-dimensional dataframe? aiming at this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

Introduction to pandas

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? We introduce DataFrame in this section and the following sections. DataFrame is the most widely used data structure of python in the field of data analysis. It has the following advantages:

The data is intuitive-it's like an excel table.

Powerful-extremely rich methods

The summary of DataFrame is as follows:

DataFrame is a Series container that is created and indexed much like Series.

DataFrame can be thought of as an array with index and columns names and supports vectorization.

You can manipulate DataFrame like an excel table: insert rows and columns, sort, filter...

You can manipulate DataFrame in the same way as SQL tables: query, group, join...

Let's introduce the array-like operation of DataFrame.

First, vectorization operation

Second, data type conversion

1, specify the type when you create it

2. Force type conversion

3, convert to the appropriate numeric type

Third, commonly used statistical analysis functions

The answer to the question about the array-like operation in two-dimensional dataframe is shared here. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel to learn more about it.

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