In order to drop duplicate records and keep the first row that is duplicated, we can simply call the method using its default parameters. Using Pandas drop_duplicates to Keep the First Row drop_duplicates() method to drop duplicates across all columns. In the following section, you’ll learn how to start using the Pandas. We can see that we have a number of records that are either duplicate records across all columns or only a subset of columns. In the code block above, we loaded a sample Pandas DataFrame with three columns. Simply copy and paste the code below into your code editor of choice: # Loading a Sample Pandas DataFrame If you’re not using your own dataset, I have provided a sample DataFrame below that you can use to follow along. Now that you have a strong understanding of the different parameters that the method provides, let’s dive into how to use the method to drop duplicate records in Pandas. Understanding the parameters of the Pandas. Whether to relabel the resulting index axis Whether to drop duplicates in place or to return a copy of the resulting DataFrame The default value of None will consider all columns. Which column(s) to consider when identifying duplicate recordsĬolumn label or sequence of column labels. The table below breaks down the behavior of each of these parameters: Parameter It’s important to understand what these parameters do. This means that we can simply call the method without needing to provide any additional information. drop_duplicates Methodįrom the code block above, we can see that the method offers four parameters, each with a default argument provided. drop_duplicates() method: # Understanding the Pandas. Let’s first take a look at the different parameters and default arguments in the Pandas. drop_duplicates() method works, it can be helpful to understand what options the method offers. Understanding the Pandas drop_duplicates() Methodīefore diving into how the Pandas. How to Reset an Index When Dropping Duplicate Records in Pandas.Use Pandas to Remove Duplicate Records In Place.Use Pandas drop_duplicates to Keep Row with Max Value.
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