How do you transform a wide Pandas DataFrame into a long format with values representing variables and dates?

Reshaping from Wide Data:

In the realm of data manipulation, reshaping a wide dataset into a long one is a crucial operation for data integration and analysis. Consider the following scenario:

You have a dataframe in pandas with daily values for variables AA, BB, and CC, indexed by dates.

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

You wish to transform this data into a format where each row represents a variable and date, as seen below:

+------+---------+--------+
| var  | date    | value  |
+------+---------+--------+
| AA   | 05/03   | 1      |
| AA   | 06/03   | 4      |
| AA   | 07/03   | 7      |
| AA   | 08/03   | 5      |
| BB   | 05/03   | 2      |
| BB   | 06/03   | 5      |
| BB   | 07/03   | 8      |
| BB   | 08/03   | 7      |
| CC   | 05/03   | 3      |
| CC   | 06/03   | 6      |
| CC   | 07/03   | 9      |
| CC   | 08/03   | 1      |
+------+---------+--------+

This restructuring is a typical task in data integration and will enable you to merge this dataframe with another with matching dates and initial column names (AA, BB, CC).

Method: Pandas' Melt Function

Fortunately, pandas offers a straightforward method to perform this transformation: pandas.melt or DataFrame.melt. Here's an example:

import pandas as pd

df = pd.DataFrame({
    'date' : ['05/03', '06/03', '07/03', '08/03'],
    'AA' : [1, 4, 7, 5],
    'BB' : [2, 5, 8, 7],
    'CC' : [3, 6, 9, 1]
})
df.set_index('date', inplace=True)

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

This will transform your dataframe into the desired long format:

     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

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