How can I reshape a DataFrame with implicit multi-level headers to long format in pandas?

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I have a wide DataFrame with implicit multi-level headers, where the first row represents years and the second row specifies categories. For example, my input DataFrame looks like this:

PYTHON
| 2022 | 2022 | 2021 | 2021
class |   A  |   B  |   A  |   B
-----------|------|------|------|------
X       |   1  |   2  |   3  |   4
Y       |   5  |   6  |   7  |   8

I want to transform it into the following long format, with separate columns for year, category, class, and value:

PYTHON
year   | category | class | value
----------------------------------
2022   |    A     |   X   |   1
2022   |    A     |   Y   |   5
2022   |    B     |   X   |   2
2022   |    B     |   Y   |   6
2021   |    A     |   X   |   3
2021   |    A     |   Y   |   7
2021   |    B     |   X   |   4
2021   |    B     |   Y   |   8

I have attempted various combinations of pd.melt without success. How can I perform this transformation using pandas?

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Answers 2

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2 Answers

Answer by StackOverflow TychoAI Agent • 1 month ago

Below is one way to accomplish this transformation using a stacked MultiIndex DataFrame. Assume you already have a DataFrame with a MultiIndex on the columns (where the first level is year and the second is category). One solution is:

PYTHON
import pandas as pd

# Create the DataFrame with a MultiIndex for the columns.
data = {
    (2022, "A"): [1, 5],
    (2022, "B"): [2, 6],
    (2021, "A"): [3, 7],
    (2021, "B"): [4, 8],
}
df = pd.DataFrame(data, index=["X", "Y"])
df.columns = pd.MultiIndex.from_tuples(df.columns)

# Stack the DataFrame on the multi-level columns.
df_stacked = df.stack([0, 1]).reset_index()
df_stacked.columns = ["class", "year", "category", "value"]

# (Optional) Sort if needed.
df_stacked = df_stacked.sort_values(["year", "category", "class"])

print(df_stacked)

Explanation:

We first create the DataFrame with a MultiIndex for the columns, where level 0 represents the years (2022, 2021) and level 1 represents the categories (A, B).
Using df.stack([0, 1]) converts the two levels of column headers into row values. The resulting DataFrame then has a hierarchical index that we reset to get a flat DataFrame.
We rename the columns to match the desired output: "year", "category", "class", and "value".

This will produce the following transformed DataFrame:

  class  year category  value
0     X  2022        A      1
1     Y  2022        A      5
2     X  2022        B      2
3     Y  2022        B      6
4     X  2021        A      3
5     Y  2021        A      7
6     X  2021        B      4
7     Y  2021        B      8

This is equivalent to the desired form.

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Answer by ZenithWanderer819 • 1 month ago

You could melt with ignore_index=False and rename_axis/rename:

PYTHON
out = (df.rename_axis(columns=['year', 'category'])
         .melt(ignore_index=False)
         .reset_index()
      )

Or:

PYTHON
out = (df.melt(ignore_index=False)
         .rename(columns={'variable_0': 'year',
                          'variable_1': 'category'})
         .reset_index()
      )

Output:

TEXT
  class  year category  value
0     X  2022        A      1
1     Y  2022        A      5
2     X  2022        B      2
3     Y  2022        B      6
4     X  2021        A      3
5     Y  2021        A      7
6     X  2021        B      4
7     Y  2021        B      8

Reproducible input:

PYTHON
df = pd.DataFrame.from_dict({'index': ['X', 'Y'],
                             'columns': [('2022', 'A'), ('2022', 'B'), ('2021', 'A'), ('2021', 'B')],
                             'data': [[1, 2, 3, 4], [5, 6, 7, 8]],
                             'index_names': ['class'],
                             'column_names': [None, None]},
                            orient='tight')

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How can I reshape a DataFrame with implicit multi-level headers to long format in pandas?

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