![]() newdf df( df.duplicated(subset'A', 'B', 'C', keepFalse) & df'A'.eq('foo') ).copy() Also, if you don't wish to write out columns by name, you can pass slices of df.columnsto subset. 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. For example, to drop all duplicated rows only if column A is equal to 'foo', you can use the following code. In the next section, you’ll see the steps to apply this syntax in practice. Pandas DataFrame class provides the methods dropna(), dropduplicates() to handle these cases in a comprehensive manner. The method dropduplicates can keep either 0 or 1 rows so it didn't help me. So we have duplicated rows based on column A, so for 'foo' I want to drop 2 duplicates rows for example and for 'xxx' I want to drop just one row. It drops the duplicates except for the first occurrence by default. A B C 0 foo 2 3 1 foo nan 9 2 foo 1 4 3 bar 8 nan 4 xxx 9 10 5 xxx 4 4 6 xxx 9 6. Syntax of dropduplicates DataFrame.dropduplicates(subsetNone, keep'first', inplaceFalse, ignoreindexFalse) subset Column label or sequence of labels. If so, you can apply the following syntax to remove duplicates from your DataFrame: df.drop_duplicates() It returns a dataframe with the duplicate rows removed. Below is the syntax of the DataFrame.dropduplicates () function that removes duplicate rows from the pandas DataFrame. It is super helpful when you want to make. ![]() Need to remove duplicates from Pandas DataFrame? You can use the following basic syntax to drop duplicates from a pandas DataFrame but keep the row with the latest timestamp: df df.sortvalues('time').dropduplicates( 'item', keep'last') This particular example drops rows with duplicate values in the item column, but keeps the row with the latest timestamp in the time column. Pandas DataFrame.dropduplicates() will remove any duplicate rows (or duplicate subset of rows) from your DataFrame.
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