Joining / merging on duplicate keys can cause a returned frame that is the. This can significantly reduce the memory usage of drop_duplicates(). Solution working if type and date pairs are unique in both DataFrames. We then processed each chunk separately, and finally concatenated them back into a single DataFrame. In this example, we first split the DataFrame into chunks of 10,000 rows. # Concatenate the chunks back into a single DataFrame Here’s an example: import pandas as pdĬhunks = for i in range(0, df.shape, 10000)] One way to mitigate this is by processing your DataFrame in chunks. This is because drop_duplicates() needs to create a new DataFrame, which can be memory-intensive. When working with large datasets, you might encounter memory errors. Let’s discuss some common problems and their solutions. While pandas’ drop_duplicates() function is an incredibly useful tool, you might encounter some issues along the way. Which one to use depends on your specific needs and level of comfort with pandas. While drop_duplicates() is simpler and more straightforward, using duplicated() with boolean indexing can give you more control over the process. More complex, requires understanding of boolean indexing duplicated()īoth methods can effectively remove duplicates, but there are some differences: MethodĮasy to use, customizable with parametersĪllows more control, modifies the original DataFrame The result is a DataFrame without duplicates. ![]() We then used the ~ operator to flip these True/False values, and used this to index our DataFrame. In this example, df.duplicated() returned a Boolean Series where True indicates a duplicate row. # Using duplicated() with boolean indexing Here’s a simple example:ĭf = pd.DataFrame() The drop_duplicates() function in pandas is your go-to tool. This will remove all of the duplicate rows from the data frame and only return the. Ready to master drop_duplicates()? Let’s dive in and start eliminating those pesky duplicates. In line 23, we use the function dropduplicates() on the entire data frame. Whether you’re a beginner just starting out or an experienced data scientist looking for a refresher, we’ve got you covered. In this guide, we’ll walk you through the use of drop_duplicates() in Python’s pandas library. Like a skilled detective, this function can help you spot and eliminate these duplicates, ensuring your data analysis is accurate and reliable. Thankfully, Python’s pandas library has a solution: the drop_duplicates() function. Duplicate data can be a common but frustrating problem that can throw off your data analysis. ![]() Struggling with duplicate data in your pandas DataFrame? You’re not alone.
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