Hey, I had work assigned to me in school which to basically write a code that was splitting a dataset into equal-sized subsets based on some indexing variable and then computing the mean of each subset. I had a hard time interpreting the logic for this, can anyone please provide me with a solution for this?
How to split a dataset into equal-sized subsets based on an indexing variable and calculate the mean using NumPy?
Hello @safiaa.02, I have provided a solution below which 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
100random numbers between
1, which serves as our sample dataset. It also generates an array of
100random integers between
9to be used as an indexing variable.
- Next, the unique values in
Sand their corresponding indices are found using
np.unique(). An array of zeros with the same length as the number of unique values in
Sis created to store the mean of
Dvalues for each unique value.
- The code then loops over each unique value in
Sand calculates the mean of the
Dvalues that correspond to the current unique value in
S. This is done using boolean indexing to select the
Dvalues with the same index as the current unique value.