VLOOKUP in 2 Specified Columns: Merging DataFrames with Pandas

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As a data scientist, working with data frames is an essential skill. When it comes to merging two data frames based on specific columns, the task can be challenging. In this article, we’ll explore how to perform a vlookup-style merge using pandas and join operations.

Introduction

The problem at hand involves creating a new column in a Pandas DataFrame HC that contains the grouping of cost centers from another DataFrame called grouping. The grouping is based on two columns: “Previous Cost Center” and “Current Cost Center” in HC, which should be matched against the “Cost Center” column in grouping. We’ll delve into the pandas library to achieve this.

Background

The code provided as an example already offers a solution using the apply function to perform a lookup based on the cost centers. However, we can improve the readability and efficiency of our code by utilizing the join operation.

DataFrames

HC = pd.DataFrame({
    'ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
    'Cost Center Previous': ['A', None, None, 'D', 'F', None, 'A', None, 'A', 'B', 'D', 'C'],
    'Current Cost Center': ['NaN', 'C', 'NaN', 'D', 'F', 'A', 'B', 'B', 'C', 'D', 'F', 'NaN']
})

grouping = pd.DataFrame({
    'Cost Center': ['A', 'B', 'C', 'D', 'E', 'F', 'G'],
    'Grouping': ['XERX', 'ID', 'BLOC', 'KLRO', 'PPP', 'SDF', 'D']
})

Solution

To perform the merge, we need to set the “Cost Center” column in grouping as the index and then use this indexed DataFrame for lookups.

## Step 1: Set Cost Center as Index
grouping = grouping.set_index('Cost Center')

## Step 2: Merge HC with Grouping
HC['Grouping Previous'] = HC.apply(lambda row: grouping.loc[row['Previous Cost Center'], 'Grouping'], axis=1)
HC['Grouping Current'] = HC.apply(lambda row: grouping.loc[row['Current Cost Center'], 'Grouping'], axis=1)

Explanation

The apply function is used to perform a lookup for each row in the DataFrame. The axis=1 argument tells pandas to apply this operation to each row, not each column.

Using the indexed DataFrame allows us to directly access the “Grouping” values by their corresponding “Cost Center”.

This approach is more efficient than using the apply function with a lambda expression because it avoids creating intermediate results and can take advantage of optimized C code for lookups.

Additional Considerations

In this example, we’re dealing with two data frames of relatively small size. However, if you need to work with larger datasets, you should be aware that the time complexity of the join operation grows linearly with the size of your data.

Furthermore, if your “Cost Center” column contains duplicate values, pandas will not perform an exact match but instead use the first occurrence for lookups.

To mitigate these issues, consider indexing your data frames before performing joins or using more complex data structures like dictionaries to map cost centers to their respective groupings.

Conclusion

Merging DataFrames based on specified columns can be challenging but is a common requirement in data analysis and scientific computing. In this article, we explored how to use the join operation with pandas to perform vlookup-style lookups.

We demonstrated how to set one column as an index and then used this indexed DataFrame for lookups. This approach is more efficient than using apply functions and can help improve performance when working with larger datasets.

Remember to consider data structure choices, indexing strategies, and potential duplicate values when dealing with large or complex datasets.

Last modified on 2023-07-10