In this comprehensive guide, we learned how to identify and remove duplicates from a DataFrame using Pandas. In this example, the duplicates are removed from df itself. If you want to remove duplicates from the original DataFrame, you can use the inplace parameter: df.drop_duplicates(inplace=True) Removing Duplicates Inplaceīy default, drop_duplicates() returns a new DataFrame and does not modify the original. Hence, only the first occurrence of each name is kept in df_no_duplicates. In this example, the functions only consider the ‘Name’ column. If you want to consider only certain columns, you can pass them as a list: print(df.duplicated(subset=))ĭf_no_duplicates = df.drop_duplicates(subset=) Considering Certain Columnsīy default, duplicated() and drop_duplicates() consider all columns. In this example, the DataFrame df_no_duplicates contains the last occurrence of each duplicate. If you want to keep the last occurrence instead, you can use the keep parameter: df_no_duplicates = df.drop_duplicates(keep='last') Keeping the Last Occurrenceīy default, drop_duplicates() keeps the first occurrence of each duplicate. This function returns a new DataFrame with the duplicates removed: df_no_duplicates = df.drop_duplicates()īy default, drop_duplicates() considers all columns and keeps the first occurrence of each duplicate. To remove duplicates, we use the drop_duplicates() function. Here’s how to use it: print(df.duplicated())īy default, duplicated() considers all columns. Pandas provides the duplicated() function, which returns a Boolean series that is True for each duplicated row. For this guide, we will work with a simple DataFrame: import pandas as pd Creating a DataFrameįirst, let’s create a DataFrame. In this comprehensive guide, we will walk you through different ways to identify and remove duplicates from a DataFrame using Pandas. Fortunately, with the powerful Python library Pandas, it’s relatively easy to deal with duplicates. Duplicate data can distort your results, leading to inaccuracies in your analyses and misleading conclusions. Data duplication is a common problem in data analysis.
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