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Today, I will talk to you about how to reconstruct a wide table into a long table in Python data analysis, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following contents for you. I hope you can get something according to this article.

Today, let's take a look at the top-level function part of the Pandas package, one of which is the melt () function, which is located at the top level of the Pandas package and is structured as follows:

Pandas

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Melt ()

The prototype of the melt () function is as follows:

Frame is a list parameter, followed by a position parameter. The former is the usual meaning of * args, and the latter is * * kwargs.

It is not difficult for frame to guess the two-dimensional array structure DataFrame of Pandas, and the meaning of other parameters is observed by the following examples.

The structure of df is as follows:

Do the following:

Pd.melt (df, id_vars= ['A'], value_vars= ['baked,' C'])

The results are as follows:

Observe

After the change, the number of df rows becomes more, and the name of column A remains the same.

The column name of the second column becomes variable, and the values become B and C (exactly equal to the third argument value_vars of the melt function)

The third column is named value and takes the values of the B and C columns of the original df.

Re-verification

Pd.melt (df2, id_vars= ['a'], value_vars= ['baked,' cased,'d'])

After the change, the number of structural rows has changed significantly, consisting of 9 rows, but fewer columns, only 3 columns.

This leads to two concepts:

Wide table (wide format): a large number of columns

Long table (long format): more rows

Go back to check the functions and parameters of the officially given melt

Pay attention to the use of words: unpivot changes the DataFrame from wide format to long format, selectively preserving the label column, which actually refers to the id_vars parameter.

These are the two main parameters, and the second value_vars refers to the column that requires upivot.

Thinking

The function of the melt () function, which can change a wide table into a long table. When we do feature analysis with a large number of columns, that is, a wide table, we might as well choose some columns as unpivot columns, so as to reduce the dimension and increase the row data to reconstruct the data.

The official explanation is that the word change in melt () uses unpivot, so it boldly guesses that its reverse operation is called pivot ().

After reading the above, do you have any further understanding of how wide tables are reconstructed into long tables in Python data analysis? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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