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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...

In this section, we introduce the Series-like operation of DataFrame.

First, create a DataFrame

1. Type conversion method

2, file import method

3, column by column generation method

Second, the index of DataFrame

DataFrame's index is very similar to Series, supporting subscript index, label index and Boolean index (label refers to index or columns). This can usually be achieved in the following ways: square brackets [], period symbols, loc,iloc,ix. The most commonly used are square brackets, loc, and iloc.

1, select only rows

2, select only columns

3, select both rows and columns

4, Boolean index

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