How to split a dataset into equal-sized subsets based on an indexing variable and calculate the mean using NumPy?

Python
#1

Hey, I am seeking assistance in an assignment involving code to split a dataset into equal-sized subsets based on an indexing variable and compute the mean for each subset using NumPy. Struggling to interpret the logic, any guidance or solution is appreciated.

1 Like
#2

Hello @safiaa.02, I have provided a solution below that splits a random dataset of 100 entries into groups based on unique random indexes and then calculates the mean for each group.

  • The code generates an array of 100 random numbers between 0 and 1, which serves as our sample dataset. It also generates an array of 100 random integers between 0 and 9 to be used as an indexing variable.
  • Next, the unique values in S and their corresponding indices are found using np.unique(). An array of zeros with the same length as the number of unique values in S is created to store the mean of D values for each unique value.
  • The code then loops over each unique value in S and calculates the mean of the D values that correspond to the current unique value in S. This is done using boolean indexing to select the D values with the same index as the current unique value.
#3

Yes, you can use this simple example code and you can just adjust the dataset and indexing_variable according to your actual data.

This code does the following:

  1. Defines a sample dataset (dataset) and an indexing variable (indexing_variable).
  2. Finds unique values in the indexing variable to determine the number of subsets.
  3. Splits the dataset into equal-sized subsets based on the indexing variable.
  4. Calculates the mean for each subset using NumPy’s mean() function.
  5. Prints the original dataset, indexing variable, subsets, and means.

I hope this explanation helps you.