Pandas Tutorial Part #15 - Merging or Concatenating DataFrames.Pandas Tutorial Part #14 - Sorting DataFrame by Rows or Columns.Pandas Tutorial Part #13 - Iterate over Rows & Columns of DataFrame.Pandas Tutorial Part #12 - Handling Missing Data or NaN values.Pandas Tutorial Part #11 - DataFrame attributes & methods.Pandas Tutorial Part #10 - Add/Remove DataFrame Rows & Columns.Pandas Tutorial Part #9 - Filter DataFrame Rows.Pandas Tutorial Part #8 - DataFrame.iloc - Select Rows / Columns by Label Names.Pandas Tutorial Part #7 - DataFrame.loc - Select Rows / Columns by Indexing.Pandas Tutorial Part #6 - Introduction to DataFrame.Pandas Tutorial Part #5 - Add or Remove Pandas Series elements.Pandas Tutorial Part #4 - Attributes & methods of Pandas Series.Pandas Tutorial Part #3 - Get & Set Series values.Pandas Tutorial Part #2 - Basics of Pandas Series.Pandas Tutorial Part #1 - Introduction to Data Analysis with Python.Pandas Tutorials -Learn Data Analysis with Python In this article, we discussed how to drop duplicate rows from the dataframe using drop_duplicates() with three scenarios and using groupby() function. # Drop dupicates rows by multiple columnsĭf = df.groupby().first() first() is used to get the first values from the grouped dataĮxample: Here, we are going to remove duplicates in ‘one’, ‘five’,’three’ columns import pandas as pd.columns are the column names where duplicate data is removed base on the multiple columns.We can remove duplicate rows by multiple columns At last we have to use first() method to get the data only once. Here we are going to use groupby() function to get unique rows from the dataframe by removing the duplicate rows. import pandas as pdġ 0 1 0 1 56 Drop duplicate rows from dataframe using groupby() For that we can simply provide drop_duplicates() method with no parametersĮxample: In this example, we are going to drop duplicates rows from the entire dataframe. We are going to drop duplicate rows from all columns. subset is the list of columns names from which duplicates need to be removed.Įxample: In this example, we are going to drop first three columns based – ‘one’,’two’ and ‘three’ import pandas as pdĭf = df.drop_duplicates(subset=)ġ 0 1 0 1 56 Drop duplicate rows from dataframe by all column Syntax is as follows: df.drop_duplicates(subset=)Ģ. We are going to drop duplicate rows from multiple columns using drop_duplicates() method. column is the column name from which duplicates need to be removed.Įxample: In this example, we are going to drop duplicate rows from the one column import pandas as pdĠ 0 0 0 0 34 Drop duplicate rows from dataframe by multiple columns We are going to use drop_duplicates() method to drop duplicate rows from one column. False – it will consider all same values as duplicate valuesĭrop Duplicate Rows from Dataframe by one column.last – it will consider the last value as the unique value and remaining as duplicate values.first – it is the default value and considers first value as the unique value and remaining as duplicate values.keep is a parameter that will controls which duplicate to keep and we can specify only three distinct value.subset takes an input list that contains the column labels to be included while identifying duplicates.Where, df is the input dataframe and other parameters are as follows: Syntax is as follows:Īdvertisements df.drop_duplicates(subset=None, keep) For that, we are going to use is drop_duplicates() method of the dataframe. The drop means removing the data from the given dataframe and the duplicate means same data occurred more than once. # Create dataframe with 4 rows and 5 columnsģ 0 0 0 0 34 Drop duplicate rows from DataFrame using drop_duplicates() Let’s create a dataframe with 4 rows and 5 columns. ![]() We can create a DataFrame using pandas.DataFrame() method.
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