How to show duplicates in python
Return boolean Series denoting duplicate rows. Considering
certain columns is optional. Only consider certain columns for identifying duplicates, by default use all of the columns. Determines which duplicates (if any) to mark. False : Mark all duplicates as Boolean series for each duplicated rows. Examples Consider dataset containing ramen rating. >>> df = pd.DataFrame({ ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'], ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'], ... 'rating': [4, 4, 3.5, 15, 5] ... }) >>> df brand style rating 0 Yum Yum cup 4.0 1 Yum Yum cup 4.0 2 Indomie cup 3.5 3 Indomie pack 15.0 4 Indomie pack 5.0 By default, for each set of duplicated values, the first occurrence is set on False and all others on True. >>> df.duplicated() 0 False 1 True 2 False 3 False 4 False dtype: bool By using ‘last’, the last occurrence of each set of duplicated values is set on False and all others on True. >>> df.duplicated(keep='last') 0 True 1 False 2 False 3 False 4 False dtype: bool By setting >>> df.duplicated(keep=False) 0 True 1 True 2 False 3 False 4 False dtype: bool To find duplicates on specific column(s), use >>> df.duplicated(subset=['brand']) 0 False 1 True 2 False 3 True 4 True dtype: bool You can use
or if you only want one of each duplicate this can be combined with
It can also handle unhashable elements (however at the cost of performance):
That's something that only a few of the other approaches here can handle. BenchmarksI did a quick benchmark containing most (but not all) of the approaches mentioned here. The first benchmark included only a small range of list-lengths because some approaches have In the graphs the y-axis represents the time, so a lower value means better. It's also plotted log-log so the wide range of values can be visualized better: Removing the As you can see the One additional interesting thing to note here is that the pandas approaches are very slow for small lists but can easily compete for longer lists. However as these benchmarks show most of the approaches perform roughly equally, so it doesn't matter much which one is used (except for the 3 that had
Benchmark 1
Benchmark 2
Disclaimer1 This is from a third-party library I have written: How do you show duplicates?Find and remove duplicates. Select the cells you want to check for duplicates. ... . Click Home > Conditional Formatting > Highlight Cells Rules > Duplicate Values.. In the box next to values with, pick the formatting you want to apply to the duplicate values, and then click OK.. How do you check for duplicates in Python string?We can use different methods of Python to achieve our goal. First, we will find the duplicate characters of a string using the count method.. Initialize a string.. Initialize an empty list.. Loop over the string. Check whether the char frequency is greater than one or not using the count method.. How can I see duplicates in pandas?You can use the duplicated() function to find duplicate values in a pandas DataFrame.
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