pandas dataframe drop duplicates keep

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Title: Pandas DataFrame drop_duplicates() with keep parameter - A Comprehensive Tutorial
Introduction:
Pandas is a powerful data manipulation library in Python, and one of its essential functionalities is handling duplicate values in a DataFrame. The drop_duplicates() method allows us to remove duplicate rows, and the keep parameter provides flexibility in choosing which duplicates to retain. In this tutorial, we will explore the drop_duplicates() function with various keep parameter options.
Before we begin, ensure you have Pandas installed. If not, install it using:
Now, import Pandas in your Python script or Jupyter notebook:
Let's create a sample DataFrame to work with throughout the tutorial:
This will be the DataFrame we use for illustrating the drop_duplicates() method.
By default, drop_duplicates() considers all columns and keeps the first occurrence of each duplicated row. Run the following code:
This will output the DataFrame without duplicate rows.
The default behavior is equivalent to specifying keep='first'. However, you can be explicit:
To retain the last occurrence of each duplicate row, set keep='last':
Setting keep=False removes all occurrences of duplicate rows, leaving only the first occurrence:
Conclusion:
In this tutorial, we explored the drop_duplicates() method in Pandas DataFrame with different keep parameter options. Understanding how to manage duplicate values is crucial for data cleaning and analysis. Experiment with these examples to gain a deeper understanding of how the drop_duplicates() method works in various scenarios.
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