I am working on how I can detect duplicate values from a Python list efficiently and I came up with the following code but I believe this won’t be efficient for large lists:
The code loops through each item and checks the count of the item and checks if it was already not appended to the list named duplicates. This works completely fine but I was curious if there are alternate methods of doing the same thing, if there are, please provide them below and an explanation with an example code will greatly help too.
Hi, @mubashir_rizvi I was stuck with a similar problem, and here’s how I dealt with it:
This method uses list comprehension to create a list of duplicate values by looping through each item in the defined list and checking if its count is greater than 1 using the count() method.
The resulting list may contain duplicates itself, so we use the set() function to remove any duplicates and convert them into a set. Finally, we check if the final result contains any items or not and print them if it does.
The advantage of this method is that it is more concise and efficient than the previous method using a loop. Since the list comprehension is only executed once, we avoid the potential O(n^2) time complexity of the count() function. Additionally, using the set() function makes it easy to remove duplicates from the list.
Hey @mubashir_rizvi , this can be done by using NumPy library. Here we use NumPy’s np.unique() function to find the unique elements in the list and their corresponding counts. The return_counts=True parameter is passed to return the frequency counts of each unique element. Next, we use NumPy’s indexing capabilities to filter the unique values whose count is greater than 1, which indicates that they are duplicates. Finally, we print the duplicates if they are present.
The advantage of this method is that it is very flexible and can handle various types of data, including non-numeric and mixed data types. Additionally, Pandas provides a wide range of data manipulation and analysis functions that can be useful in more complex data analysis tasks.
However, it is important to note that Pandas can be slower than some of the other methods, especially for smaller datasets. Additionally, Pandas require more memory and may not be suitable for very large datasets.