![]() But here, instead of keeping the first duplicate row, it kept the last duplicate row. In this dataframe, that applied to row 0 and row 1. Remember: by default, Pandas drop duplicates looks for rows of data where all of the values are the same. Re-assign to the original DataFrame hrdf = hrdf.drop_duplicates(subset = cols)Ĭreate a new DataFrame consisting of unique records hrdf_2 = hrdf. In this example, drop duplicates operated on row 0 and row 1 (the rows for William). Using the inplace=True parameter hrdf.drop_duplicates(subset = cols, inplace=True) In order to do so we can use one of the following: We would like to persist the changes in our DataFrame. Note: If you would like to remove dups across all columns ignoring the index, simply omit the subset parameter: hrdf.drop_duplicates() Step 4: Persist your results in your DataFrame The last record of our DataFrame will be removed: We will do it based on the DataFrame subset we defined in the previous step: hrdf.drop_duplicates(subset = cols) As a solution, you can transform these values to be a frozenset of the tuples, and then use dropduplicates. ![]() We will define a list of DataFrame columns for determining the duplicated records cols = ] Step 3: Drop duplicates on the selected columns We will focus on removing duplicated records based on two columns – in our case the month and the language columns. Step 2: Identify columns to check for duplicates Hr_campaign = dict(month = month, language = language, salary = salary) For this simple example we will use some random HR data. ![]() Step 1: Prepare your pandas DataFrameįirst off you need to acquire the data which you will filter for unique occurrences. ![]() In this guide we will learn how to handle the case in which after invoking the drop_duplicates DataFrame method and removing non-unique records, your DataFrame still shows up duplicates. Can’t remove duplicates from Pandas DataFrame ![]()
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