Merging Dataframes Horizontally with Pandas

In this article, we’ll explore the process of merging two dataframes horizontally using pandas. We’ll delve into the different ways to achieve this and provide examples to illustrate each method.

Understanding Dataframes

Before diving into the merge process, let’s briefly review what dataframes are and how they’re used in pandas. A dataframe is a 2-dimensional labeled data structure with columns of potentially different types. It’s similar to an Excel spreadsheet or a table in a relational database.

In pandas, you can create a dataframe from a dictionary, a list of lists, or other sources. The dataframe has rows and columns, just like a spreadsheet. Each column represents a variable, and each row represents an observation.

Merging Dataframes

Merging dataframes involves combining two or more dataframes into one based on common columns. When merging horizontally, you want to combine the rows of both dataframes based on a common column.

The question provides us with two example dataframes: mydata_old and mydata_new. We want to merge these dataframes horizontally, keeping only the newest data available when there are duplicates in the common column.

Using pandas.concat

One way to achieve this is by using pandas.concat, which concatenates two or more dataframes along a specified axis. In our case, we want to concatenate mydata_old and mydata_new.

However, simply concatenating the dataframes won’t automatically remove duplicates based on the common column. To do that, we need to use conditional indexing.

# Concat frames and if data is available in both, keep data from mydata_new
mydata = pd.concat(
    [
        mydata_old.loc[~mydata_old['x'].isin(mydata_new['x'])],
        mydata_new
    ],
    axis=0)

In the above code:

• mydata_old.loc[~mydata_old['x'].isin(mydata_new['x'])]: This line creates a boolean mask to select rows from mydata_old that don’t have values in mydata_new. The ~ operator is used to negate the set of values, and isin() checks if each value exists in another series.
• mydata_new: This line includes all rows from mydata_new.
• axis=0: By default, pd.concat() concatenates along columns (axis=1). In this case, we’re concatenating along rows (axis=0).

This approach ensures that only the newest data is kept when there are duplicates in the common column.

Using pandas.merge

Another way to achieve horizontal merging is by using pandas.merge, which performs an inner join on two dataframes. However, since we want to keep both original dataframes and their modifications, this method isn’t ideal for our use case.

That being said, you can use the how='outer' parameter to perform an outer join:

# Perform an outer join to get all rows from both dataframes
mydata = pd.merge(mydata_new, mydata_old, how='outer')

However, this approach will duplicate the index values and won’t remove duplicates based on the common column.

Best Practice: Using pandas.concat

In our example, we used pandas.concat to achieve the desired result. This method is more straightforward and efficient than using pandas.merge.

Keep in mind that when merging dataframes, it’s essential to consider the data types of the columns involved. If you’re working with large datasets or need to perform complex joins, make sure to check the data types of your columns beforehand.

Handling Missing Values

When dealing with missing values in your data, it’s crucial to decide how you want to handle them during the merge process.

• pd.merge() will drop rows containing missing values if specified using the how parameter.
• pandas.concat() does not inherently handle missing values. You might need to use additional methods or libraries like numpy to impute or fill missing values.

Handling Duplicate Index Values

When merging dataframes with duplicate index values, you’ll want to decide how you want to handle them:

• If you’re doing an inner join (how='inner') and there are duplicate rows in the common column, those duplicates will be removed from the resulting dataframe.
• If you’re performing an outer join (how='outer') or using pandas.concat(), the resulting dataframe might contain all duplicate index values.

To avoid duplicate index values, make sure to remove them before merging:

# Drop duplicate rows based on the common column
mydata_old = mydata_old.drop_duplicates(subset=['x'])

After removing duplicates, you can perform the merge:

# Perform a left join to get all rows from both dataframes
mydata = pd.merge(mydata_new, mydata_old, how='left')

Conclusion

Merging dataframes horizontally using pandas is an essential skill for data analysis and manipulation. By understanding how to use pandas.concat and handling common issues like missing values and duplicate index values, you can effectively merge your dataframes and achieve the desired results.

Remember to choose the correct merging strategy based on the specifics of your problem and consider potential pitfalls or edge cases that might arise during the process. With practice and experience, you’ll become proficient in merging dataframes efficiently and accurately.

Last modified on 2025-02-04