This process involves transforming a dataset by first optionally organizing its rows based on certain criteria. It then identifies unique values or combinations thereof within specified columns, creating groups based on these unique identifiers. For each group, specific functions are applied to the rows to summarize or condense their information. The result is a new dataset where each row represents a unique group, and each column corresponds to the outcome of a distinct summarization function applied across the grouped data. For more information, refer to the pandas melt documentation.

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id_vars
[string, array[string]]
required

Identifier columns. The column(s) to use as identifier variables.

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value_vars
[string, array[string]]
required

Value columns. The column(s) that are considered as value variables.

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var_name
string
default: "variable"

Variable column name. Name of the variable column if not provided, we will use the name ‘variable’.

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value_name
string
default: "value"

Value column name. Name of the value column if not provided, we will use the name ‘value’.

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