How can you keep only the frequent values and replace others in a Pandas Series?

Can anyone please provide some techniques by which I can keep only the frequent values in my series and replace all others with some other value? The value can be anything that is not the issue, I know there is a method in Pandas value_counts() which counts the occurrences of each unique value in a series, but how can I use this in conjunction with other methods or functions to achieve my task?

Here is what value_counts() does:

my_series = [1, 2, 1, 3]

1        2
2        1
3        1

@mubashir_rizvi , I also believe that there are many alternatives and efficient solutions present for every problem. For your query, I have the solution too.
In NumPy, np.where() is a function that allows you to select elements from an array based on a condition, and return a new array with the selected elements replaced by a specified value.
You can use np.where() to check the condition using isin(), if the elements are found in the top_two list, they are returned as it is, and other values are returned as Other. Let me show you how to work with it.

Hi @mubashir_rizvi! One approach you could take to solve your problem is to use the value_counts() method in Pandas to count the occurrences of each unique value in your series, and then replace all other values with a designated value.

One way to do this is to use a lambda function with the apply() method to replace all values that are not in the top n most frequent values with a designated value, as shown in the code snippet below:

I hope this helps! Let me know if you have any questions.

Hey @mubashir_rizvi , you can use the apply() method to apply a function to a Pandas DataFrame or Series along a specified axis. This function can be any kind of function, including a built-in function, a lambda function, or a user-defined function.

In the example you provided, a custom user-defined function is applied to a Pandas Series using apply(). The function is applied to each element in the Series, and the resulting values are returned as a new Series. This can be a useful approach when you need to perform a specific operation on each element in a Series, and want to avoid using a for loop to iterate through the elements.