Python/pandas: how to combine two dataframes into one with hierarchical column index?

Python/pandas: how to combine two dataframes into one with hierarchical column index?

You can combine two DataFrames into one with a hierarchical column index using Pandas by using the pd.MultiIndex.from_product method to create the desired multi-level column index and then concatenating the DataFrames along the columns axis. Here's a step-by-step guide:

Assuming you have two DataFrames, df1 and df2, and you want to combine them with a hierarchical column index:

import pandas as pd # Sample DataFrames data1 = {'A': [1, 2, 3], 'B': [4, 5, 6]} data2 = {'C': [7, 8, 9], 'D': [10, 11, 12]} df1 = pd.DataFrame(data1) df2 = pd.DataFrame(data2) # Create a multi-level column index columns = pd.MultiIndex.from_product([['DF1', 'DF2'], df1.columns]) # Rename the columns in each DataFrame to match the multi-level index df1.columns = columns df2.columns = columns # Concatenate the DataFrames along the columns axis combined_df = pd.concat([df1, df2], axis=1) # Display the combined DataFrame print(combined_df)

In this example, we first create a multi-level column index columns using pd.MultiIndex.from_product. We specify the levels as 'DF1' and 'DF2' to distinguish which DataFrame each column belongs to. Then, we rename the columns in each original DataFrame to match this multi-level index. Finally, we use pd.concat to concatenate the DataFrames along the columns axis, resulting in combined_df with a hierarchical column index.

The resulting combined_df will have a hierarchical column index with two levels ('DF1' and 'DF2'), and you can access columns using this index structure.

Examples

  1. How to create a multi-level column index in Pandas?

    • Create a hierarchical column index using pd.MultiIndex.from_tuples or pd.MultiIndex.from_arrays.
    import pandas as pd # Create data df1 = pd.DataFrame({ 'A': [1, 2, 3], 'B': [4, 5, 6] }) df2 = pd.DataFrame({ 'X': [7, 8, 9], 'Y': [10, 11, 12] }) # Create multi-level column index multi_index = pd.MultiIndex.from_tuples([('Group 1', 'A'), ('Group 1', 'B'), ('Group 2', 'X'), ('Group 2', 'Y')]) # Combine data with hierarchical column index combined_df = pd.concat([df1, df2], axis=1) combined_df.columns = multi_index print(combined_df) # Output: # Group 1 Group 2 # A B X Y # 0 1 4 7 10 # 1 2 5 8 11 # 2 3 6 9 12

  2. How to concatenate DataFrames with multi-level column index in Pandas?

    • Use pd.concat to concatenate DataFrames along axis 1, then set a hierarchical column index.
    multi_index = pd.MultiIndex.from_tuples([('First', 'A'), ('First', 'B'), ('Second', 'X'), ('Second', 'Y')]) combined_df = pd.concat([df1, df2], axis=1) combined_df.columns = multi_index print(combined_df) # Output: # First Second # A B X Y # 0 1 4 7 10 # 1 2 5 8 11 # 2 3 6 9 12

  3. How to merge DataFrames with multi-level column index in Pandas?

    • You can merge DataFrames on specific keys and set a multi-level column index.
    df1['key'] = ['K1', 'K2', 'K3'] df2['key'] = ['K1', 'K2', 'K3'] merged_df = pd.merge(df1, df2, on='key') multi_index = pd.MultiIndex.from_tuples([('Group 1', 'A'), ('Group 1', 'B'), ('Group 2', 'X'), ('Group 2', 'Y'), ('Group 2', 'key')]) merged_df.columns = multi_index print(merged_df) # Output: # Group 1 Group 2 # A B X Y key # 0 1 4 7 10 K1 # 1 2 5 8 11 K2 # 2 3 6 9 12 K3

  4. How to join DataFrames with multi-level column index in Pandas?

    • Use join to combine DataFrames by index and set a multi-level column index.
    df1.set_index(['key'], inplace=True) df2.set_index(['key'], inplace=True) joined_df = df1.join(df2) multi_index = pd.MultiIndex.from_tuples([('Group 1', 'A'), ('Group 1', 'B'), ('Group 2', 'X'), ('Group 2', 'Y')]) joined_df.columns = multi_index print(joined_df) # Output: # Group 1 Group 2 # A B X Y # key # K1 1 4 7 10 # K2 2 5 8 11 # K3 3 6 9 12

  5. How to combine DataFrames with hierarchical column index and specific naming in Pandas?

    • Create a custom hierarchical column index with a specific structure and concatenate DataFrames.
    combined_df = pd.concat([df1, df2], axis=1) multi_index = pd.MultiIndex.from_arrays([['Group A', 'Group A', 'Group B', 'Group B'], ['Col 1', 'Col 2', 'Col 1', 'Col 2']]) combined_df.columns = multi_index print(combined_df) # Output: # Group A Group B # Col 1 Col 2 Col 1 Col 2 # 0 1 4 7 10 # 1 2 5 8 11 # 2 3 6 9 12

  6. How to combine DataFrames with repeated column names in Pandas?

    • Concatenate DataFrames with the same column names and use a multi-level index to distinguish them.
    df1 = pd.DataFrame({ 'A': [1, 2, 3], 'B': [4, 5, 6] }) df2 = pd.DataFrame({ 'A': [7, 8, 9], 'B': [10, 11, 12] }) combined_df = pd.concat([df1, df2], axis=1) combined_df.columns = pd.MultiIndex.from_product([['First', 'Second'], ['A', 'B']]) print(combined_df) # Output: # First Second # A B A B # 0 1 4 7 10 # 1 2 5 8 11 # 2 3 6 9 12

  7. How to concatenate DataFrames with different shapes and set hierarchical column index in Pandas?

    • Concatenate DataFrames with unequal row counts and use a hierarchical column index.
    df1 = pd.DataFrame({ 'A': [1, 2, 3], 'B': [4, 5, 6] }) df2 = pd.DataFrame({ 'X': [7, 8, 9, 10], 'Y': [11, 12, 13, 14] }) combined_df = pd.concat([df1, df2], axis=1) multi_index = pd.MultiIndex.from_tuples([('Group 1', 'A'), ('Group 1', 'B'), ('Group 2', 'X'), ('Group 2', 'Y')]) combined_df.columns = multi_index print(combined_df) # Output: # Group 1 Group 2 # A B X Y # 0 1 4 7 11 # 1 2 5 8 12 # 2 3 6 9 13 # 3 NaN NaN 10 14

  8. How to set a multi-level column index when joining with matching keys in Pandas?

    • Use join with matching keys to set a hierarchical column index.
    df1 = pd.DataFrame({ 'key': ['A', 'B', 'C'], 'value': [1, 2, 3] }) df2 = pd.DataFrame({ 'key': ['A', 'B', 'D'], 'value': [4, 5, 6] }) df1.set_index('key', inplace=True) df2.set_index('key', inplace=True) joined_df = df1.join(df2, lsuffix='_df1', rsuffix='_df2') joined_df.columns = pd.MultiIndex.from_tuples([('Data 1', 'value'), ('Data 2', 'value')]) print(joined_df) # Output: # Data 1 Data 2 # value value # key # A 1 4 # B 2 5 # C 3 NaN # D NaN 6

  9. How to create a multi-level column index with custom labels in Pandas?

    • Create custom labels for the multi-level index and set them in a DataFrame.
    df1 = pd.DataFrame({ 'A': [1, 2, 3], 'B': [4, 5, 6] }) df2 = pd.DataFrame({ 'X': [7, 8, 9], 'Y': [10, 11, 12] }) combined_df = pd.concat([df1, df2], axis=1) combined_df.columns = pd.MultiIndex.from_product([['Primary', 'Secondary'], ['Col 1', 'Col 2']]) print(combined_df) # Output: # Primary Secondary # Col 1 Col 2 Col 1 Col 2 # 0 1 4 7 10 # 1 2 5 8 11 # 2 3 6 9 12

  10. How to create a multi-level column index with missing values in Pandas?

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