How to Merge Large Data Frames Efficiently with Pandas

Let's learn how to merge large DataFrames in Pandas efficiently.

Preparation

Make sure you have the Pandas package installed in your environment. Otherwise, you can install it via pip using the following code:

With the Pandas package installed, we will learn more in the next part.

Efficiently Merge with Pandas

Pandas is an open-source data manipulation package that is used by many people in the data community. It is a flexible package that can handle many data-related tasks, including data merging. Merging, on the other hand, refers to the activity of combining two or more data sets based on common columns or indexes. It is mainly used if we have multiple data sets and we want to combine their information.

In real-world situations, we are likely to see several large tables. When we convert the table into Pandas DataFrames, we can manipulate and merge them. However, a larger size would be resource-intensive and computationally intensive.

That's why there are some methods to improve the efficiency of merging large Pandas DataFrames.

First, if applicable, let's use a type that uses memory more efficiently, such as a category type and a smaller float type.

df1('object1') = df1('object1').astype('category')
df2('object2') = df2('object2').astype('category')

df1('numeric1') = df1('numeric1').astype('float32')
df2('numeric2') = df2('numeric2').astype('float32')

Then try setting the key columns to be merged as index. This is because index-based merge is faster.

df1.set_index('key', inplace=True) 
df2.set_index('key', inplace=True)

Next, we use the DataFrame .merge method instead of pd.merge function, as it is much more efficient and optimized for performance.

merged_df = df1.merge(df2, left_index=True, right_index=True, how='inner')

Finally, you can debug the entire process to understand which rows come from which DataFrame.

merged_df_debug = pd.merge(df1.reset_index(), df2.reset_index(), on='key', how='outer', indicator=True)

Using this method, you can improve the efficiency of merging large DataFrames.

Additional Resources

Cornellius Yudha Wijaya Cornellius is a Data Science Assistant Manager and Data Writer. While working full-time at Allianz Indonesia, he loves sharing Python and data tips through social media and writing. Cornellius writes on a variety of ai and machine learning topics.