Pandas: Data Manipulation - wide_to_long() function
Wide panel to long format. Less flexible but more user-friendly than melt.
With stubnames [‘A’, ‘B’], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,…, B-suffix1, B-suffix2,… You specify what you want to call this suffix in the resulting long format with j (for example j=’year’)
Each row of these wide variables are assumed to be uniquely identified by i (can be a single column name or a list of column names)
All remaining variables in the data frame are left intact.
pandas.wide_to_long(df, stubnames, i, j, sep='', suffix='\d+')
|Name||Description||Type||Default Value||Required / Optional|
|df||The wide-format DataFrame||DataFrame||Required|
|stubnames||The stub name(s). The wide format variables are assumed to start with the stub names.||str or list-like||Required|
|i||Column(s) to use as id variable(s)||str or list-like||Required|
|j||The name of the sub-observation variable. What you wish to name your suffix in the long format.||str||Required|
|sep||A character indicating the separation of the variable names in the wide format, to be stripped from the names in the long format. For example, if your column names are A-suffix1, A-suffix2, you can strip the hyphen by specifying sep=’-‘||str||Default: “”||Required|
|suffix||A regular expression capturing the wanted suffixes. ‘\d+’ captures numeric suffixes. Suffixes with no numbers could be specified with the negated character class ‘\D+’. You can also further disambiguate suffixes, for example, if your wide variables are of the form A-one, B-two,.., and you have an unrelated column A-rating, you can ignore the last one by specifying suffix=’(!?||str||Default: ‘\d+’||Required|
Returns:DataFrame - A DataFrame that contains each stub name as a variable, with new index (i, j).
Notes: All extra variables are left untouched. This simply uses pandas.melt under the hood, but is hard-coded to “do the right thing” in a typical case.
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