Dataframe Merging without Duplicating Columns

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When working with dataframes, merging two datasets can be a straightforward process. However, when one dataframe contains duplicate columns and the other does not, things become more complicated. In this article, we will explore how to merge two dataframes without duplicating columns.

Background and Prerequisites

To dive into the topic of merging dataframes, it’s essential to understand what a dataframe is and how they are used in data analysis. A dataframe is a two-dimensional table of data with rows and columns. It is similar to an Excel spreadsheet or a CSV file.

Dataframes can be created from various sources such as databases, CSV files, or even user input. They provide a convenient way to manipulate and analyze data.

In Python, the pandas library is widely used for working with dataframes. The pandas library provides efficient data structures and operations for manipulating and analyzing data.

Problem Description

Let’s consider an example where we have two dataframes:

BseDataframe

ResultDataframe

We want to merge the two dataframes based on the COMPANY column. However, we notice that there is no match between the TOKEN and SYMBOL columns in both dataframes.

Solution

To solve this problem, we can use the pandas library’s merge function. The merge() function allows us to combine two dataframes based on a common column(s).

However, by default, the merge() function will try to match rows between the two dataframes and will result in duplicated columns.

We need to avoid duplicating columns when merging the two dataframes. We can achieve this by using the how='left' parameter.

How to Use left Join

When we use a left join, pandas will only return rows from the right dataframe if there is a match with the left dataframe. If not, it will fill in NaN values for the matched column(s).

# import necessary libraries
import pandas as pd

# create two dataframes
bse_df = pd.DataFrame({
    'COMPANY': ['Company A', 'Company B'],
    'TOKEN': ['Token1', 'Token2'],
    'SYMBOL': ['Symbol1', 'Symbol2']
})

result_df = pd.DataFrame({
    'COMPANY': ['Company A', 'Company C'],
    'TOKEN': ['Token3', 'Token5'],
    'SYMBOL': ['Symbol4', 'Symbol6']
})

# merge the dataframes using left join
merged_df = pd.merge(result_df, bse_df, on='COMPANY', how='left')

print(merged_df)

Output:

As we can see, the TOKEN and SYMBOL columns from the bse_df are filled in for the rows where there is a match.

How to Use Outer Join

We can also use an outer join when merging two dataframes. The outer join will return all rows from both dataframes.

# merge the dataframes using outer join
merged_df_outer = pd.merge(result_df, bse_df, on='COMPANY', how='outer')

print(merged_df_outer)

Output:

As we can see, the TOKEN and SYMBOL columns from the bse_df are filled in for all rows.

Conclusion

Merging two dataframes without duplicating columns is a common task when working with data analysis. We have discussed how to use the pandas library’s merge function with different join types to achieve this goal.

We have covered both the left and outer joins, providing examples of each. By understanding these concepts, you will be able to efficiently merge your own dataframes without duplicating columns.

Last modified on 2023-10-28