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Learn how to efficiently merge large DataFrames using Pandas.
Preparation
Ensure that the Pandas package is installed in your environment. If not, you can install it via pip using the following code:
Once Pandas is installed, we will delve deeper into its functionalities in the next section.
Efficiently Merging with Pandas
Pandas is an open-source data manipulation package widely used within the data community. It is a versatile package capable of handling numerous data tasks, including data merging. Merging refers to the activity of combining two or more datasets based on common columns or indices. It is primarily used when we have multiple datasets and want to combine their information.
In real-world scenarios, we often encounter several large tables. When we convert these tables into Pandas DataFrames, we can manipulate and merge them. However, larger sizes require more computation and resources.
Therefore, there are a few methods to enhance the efficiency of merging large Pandas DataFrames.
First, if applicable, use a more memory-efficient type, 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')Next, try setting the key columns to merge as the index. This is because index-based merging is faster.
df1.set_index('key', inplace=True)
df2.set_index('key', inplace=True)Then, use the DataFrame .merge method instead of the 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)By following these methods, you can improve the efficiency of merging large DataFrames.
Additional Resources
Cornellius Yudha Wijaya is the Deputy Director of Data Science and a data writer. While working full-time at Allianz Indonesia, he enjoys sharing Python and data tips through social media and writing. Cornellius writes on a variety of topics related to AI and machine learning.
