CODEWITHSUNDEEP
pythonnumpymean
Alex Müller
Alex Müller

Reputation: 33

Compute the mean of each row in a Numpy Matrix

I would like to implement phython code which does the following:

 For a collections of clusters calculate the mean for each cluster

Args:
    clusters (List[np.ndarray]): A list of 2d arrays
    
Returns:
    List[np.ndarray]: A matrix where each row represents a mean of a cluster
    
Example: 
    >>> tiny_clusters = [
        np.array([[0.2, 0.3], [0.1, 0.2]]),
        np.array([[0.8, 0.9], [0.7, 0.5], [0.6, 0.7]]),
    ]
    >>> calc_means(tiny_clusters)
    [array([0.15, 0.25]), array([0.7,0.7])]

My current solution is the following:

    i = 0 
    return [clusters[i].mean(axis=1) for i in range(np.amax(i)+1)]

However, I only get the following output:

    [array([0.25, 0.15])]

    (Input: [
    np.array([[0.2, 0.3], [0.1, 0.2]]),
    np.array([[0.8, 0.9], [0.7, 0.5], [0.6, 0.7]]),
    ])

So, I only get the mean of the first row computed, but unfortunately not for the seconder row. Do you have any idea which improves my code?

Thanks!

Upvotes: 0

Views: 1479

Answers (1)

Christian K.
Christian K.

Reputation: 2823

The list comprehension should be

[q.mean(axis=0) for q in clusters]

It is a bit confusing that the output is called a matrix. It is a list of rank 1 arrays.

Upvotes: 3

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