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How to reduce the number of row repetitions in a numpy array


How to reduce the number of row repetitions in a numpy array

By : user2172136
Date : October 24 2020, 08:10 PM
Hope that helps Even though using list appending as an intermediate step is not always a good idea when you already have numpy arrays, in this case it is by far the cleanest way to do it:
code :
def n_uniques(arr, max_uniques):
    uniq, cnts = np.unique(arr, axis=0, return_counts=True)
    arr_list = []
    for i in range(cnts.size):
        num = cnts[i] if cnts[i] <= max_uniques else max_uniques
        arr_list.extend([uniq[i]] * num)
    return np.array(arr_list)

x = np.array([[1,2,3],
              [1,2,3],
              [1,2,3],
              [5,5,5],
              [1,2,3],
              [1,2,3],])

reduced_arr = n_uniques(x, 2)


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3D Array indexing in NumPy to reduce number of rows

3D Array indexing in NumPy to reduce number of rows


By : user3899831
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I have a 3d array with the shape: , You can use slicing to stop 100 rows before the end of the array:
code :
ans = data[:-100]
ans = data[:-100].copy()
Comparing numpy array having repetitions with list

Comparing numpy array having repetitions with list


By : user2900429
Date : March 29 2020, 07:55 AM
it helps some times Following the previous question as follows Comparing list elements with a column in numpy array, I have implemented the following code to compare the list elements with array. , You can using the index which will return the 1st match
code :
l1=[x[0] for x in array]
[array[y] for y in [l1.index(x) for x in l]]
[['M', 360.0, 360.0], ['R', 135.9, 360.0], ['W', 101.4, -125.4]]
How to reduce the number of repetitions for formatting many tables?

How to reduce the number of repetitions for formatting many tables?


By : user3302067
Date : March 29 2020, 07:55 AM
Hope that helps Maybe someone will prove me wrong (it'd be great if they did) but I don't believe there is any "right" answer to your question.
Perhaps the easiest and least redundant way forward would be start by identifying two or more formats that frequently appear together. Then defining a custom data type to pass the formats with one variable.
Match same number of repetitions of character as repetitions of captured group

Match same number of repetitions of character as repetitions of captured group


By : vicyang
Date : March 29 2020, 07:55 AM
it fixes the issue It looks like Python does not support recursive regex. If you can use another language, you could try this:
delete 'nan' or reduce length of numpy array if array contains nan after converting to numpy from pandas

delete 'nan' or reduce length of numpy array if array contains nan after converting to numpy from pandas


By : P. Mayer
Date : March 29 2020, 07:55 AM
hope this fix your issue You can't use np.isnan because the NaNs are strings in your array but you can use boolean indexing by comparing with the string: "nan":
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