Examples Consider dataset containing ramen rating. DataFrame.dropduplicates Remove duplicate values from DataFrame. Series.dropduplicates Remove duplicate values from Series. Steps to Remove Duplicates from Pandas DataFrame Step 1: Gather the data that contains the duplicatesįirstly, you’ll need to gather the data that contains the duplicates.įor example, let’s say that you have the following data about boxes, where each box may have a different color or shape: ColorĪs you can see, there are duplicates under both columns.īefore you remove those duplicates, you’ll need to create Pandas DataFrame to capture that data in Python. Series.duplicated Equivalent method on Series. Its syntax is: dropduplicates (self, subsetNone, keep'first', inplaceFalse) subset: column label or sequence of labels to consider for identifying duplicate rows. The tutorial will explain what the technique does, explain the syntax, and it will also show you clear examples. Pandas dropduplicates () function removes duplicate rows from the DataFrame. Pandas Drop Duplicates, Explained Novemby Joshua Ebner This tutorial will show you how to use Pandas drop duplicates to remove duplicate rows from a dataframe. If inplaceTrue is used, it updates the existing DataFrame object and returns None. Pandas dropduplicates () Function Syntax. It takes subset, keep, inplace and ignoreindex as params and returns DataFrame with duplicate rows removed based on the parameters passed. In the next section, you’ll see the steps to apply this syntax in practice. Following is the syntax of the dropduplicates () function. If so, you can apply the following syntax to remove duplicates from your DataFrame: df.drop_duplicates() Need to remove duplicates from Pandas DataFrame?
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