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.

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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.

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:

- Defines a sample dataset (
`dataset`

) and an indexing variable (`indexing_variable`

). - Finds unique values in the indexing variable to determine the number of subsets.
- Splits the dataset into equal-sized subsets based on the indexing variable.
- Calculates the mean for each subset using NumPy’s
`mean()`

function. - Prints the original dataset, indexing variable, subsets, and means.

I hope this explanation helps you.