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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