Joining a Single-Level Table to a Multi-Level Table in Python

When working with dataframes, it’s not uncommon to encounter different types of tables. In this article, we’ll explore how to join a single-level table to a multi-level table in Python.

Introduction

In the world of data science and machine learning, dataframes are a fundamental concept. A dataframe is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet or a SQL database. When working with dataframes, it’s often necessary to combine multiple tables into one. In this article, we’ll focus on joining a single-level table to a multi-level table in Python.

Problem Statement

Let’s assume we have two dataframes: df1 and df2. df1 is a multi-level table with expenses, while df2 is a single-level table with money. We want to append the second table to the first one but ensure that only the client total level is matched.

Creating the Dataframes

To demonstrate the solution, we’ll create the two dataframes:

import pandas as pd

# Create df1 (multi-level table)
df1 = pd.DataFrame({'Expenses': [60, 10, 20, 30, 65, 40, 10, 15]},
          index=pd.MultiIndex(levels=[['a', 'b'],
                                     ['', 'EUR', 'AUD', 'USD', 'GBP', 'HKD']],
                              labels=[[0, 0, 0, 0, 1, 1, 1, 1], [0, 1, 2, 3, 0, 1, 4, 5]],
                              names=['Person', 'Currency']))

# Create df2 (single-level table)
df2 = pd.DataFrame({'Money': [100, 200]}, index=pd.Index(['a', 'b'], name='Person'))

Merging the Dataframes

To merge the dataframes, we’ll use the pd.merge function. We’ll specify that we want to merge on both the left and right indexes:

new_df = pd.merge(df1, df2, left_index=True, right_index=True)

Result

The resulting dataframe will have all the columns from both dataframes, but with some issues:

new_df

#                       Expenses  Money
# Person    Currency    
#      a                60        100
#                EUR    10        100
#                AUD    20        100
#                USD    30        100
#      b                65        200
#                EUR    40        200
#                GBP    10        200
#                HKD    15        200

As you can see, the Money value from df2 is copied to all rows for the same person. This isn’t exactly what we want.

Solution

To fix this issue, we’ll need to find the rows that shouldn’t have a value (those in which there is no Currency value) and replace the Money value with something else. We can do this in two steps:

1. Select all the rows with no value in Currency:

no_change = new_df.loc[(slice(None), slice(’’)), :]

2.  Modify the `Money` value for the other rows:
    ```markdown
new_df.loc[~new_df.index.isin(no_change.index), 'Money'] = '-'

Result

After applying these steps, we’ll get the desired output:

new_df

#                       Expenses  Money
# Person    Currency    
#      a                60        100
#                EUR    10        -
#                AUD    20        -
#                USD    30        -
#      b                65        200
#                EUR    40        -
#                GBP    10        -
#                HKD    15        -

As you can see, the Money value is now correctly matched only to the client total level.

Conclusion

Joining a single-level table to a multi-level table in Python requires some careful planning and execution. By using the pd.merge function and applying the necessary steps, we can achieve our desired output. This solution highlights the importance of data manipulation and visualization when working with complex data structures.

Last modified on 2024-01-16