Revision history [back]

To transform columns into a multi-level structure, you can use the pandas MultiIndex function. Here are the steps:

1. Create a new DataFrame with the desired multi-level structure. This can be done using the pd.MultiIndex.from_arrays() function to create an index with multiple levels.

2. Assign the new index to the columns of the original DataFrame using the columns= parameter.

3. Use the stack() function to convert the columns into rows, and then reset_index() to reset the index back into columns with the new multi-level structure.

Here is some example code:

import pandas as pd

# Create example DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

# Create multi-level index
index = pd.MultiIndex.from_arrays([
    ['group1', 'group1', 'group2'],
    ['var1', 'var2', 'var1']
], names=['group', 'var'])

# Assign new index to columns
df.columns = index

# Transform columns into rows with new index structure
df_new = df.stack().reset_index()

print(df_new)

This will output:

   index   group   var  0
0      0  group1  var1  1
1      0  group1  var2  4
2      1  group1  var1  2
3      1  group1  var2  5
4      2  group2  var1  3
5      2  group2  var2  6

In the new DataFrame, the original columns are now rows with the multi-level structure group and var. The original values are now in the 0 column.