How do you efficiently transform wide data to a long format in Pandas?

Reshaping Wide Data to Long in Pandas

Many data manipulation tasks require datasets to be in a specific format, often referred to as long or wide data. In pandas, reshaping from wide to long can be achieved through the pd.melt or DataFrame.melt functions.

Original Data:

Consider the following wide dataframe, where the dates are the index and the columns represent different variables:

         AA  BB  CC
date
05/03     1   2   3
06/03     4   5   6
07/03     7   8   9
08/03     5   7   1

Reshaping to Long:

To reshape this dataframe into a long format, where each row represents a single date and variable combination, we can use:

df = df.reset_index().melt(id_vars='date')

This transforms the dataframe into:

     date variable  value
0   05/03       AA      1
1   06/03       AA      4
2   07/03       AA      7
3   08/03       AA      5
4   05/03       BB      2
5   06/03       BB      5
6   07/03       BB      8
7   08/03       BB      7
8   05/03       CC      3
9   06/03       CC      6
10  07/03       CC      9
11  08/03       CC      1

Alternatively, the reset_index step can be omitted by specifying ignore_index=False in the melt function:

dfm = df.melt(ignore_index=False)

This ensures that the row index is preserved in the transformed dataframe.

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