I'm looking for a quick way to do the following: Say I have an array
X = np.array([1,1,1,2,2,2,2,2,3,3,1,1,0,0,0,5])
Instead of a simple frequency of elements I'm looking for the frequency in a row. So first 1 repeats 3 times, than 2 5 times, than 3 2 times, etc. So if freq
is my function than:
Y = freq(X)
Y = np.array([[1,3],[2,5],[3,2],[1,2],[0,3],[5,1]])
For example, I can write this with loops like this:
def freq(X):
i=0
Y=[]
while i<len(X):
el = X[i]
el_count=0
while X[i]==el:
el_count +=1
i+=1
if i==len(X):
break
Y.append(np.array([el,el_count]))
return np.array(Y)
I'm looking for a faster and nicer way to do this. Thanks!
Here's one NumPy way for performance efficiency -
In [14]: m = np.r_[True,X[:-1]!=X[1:],True]
In [21]: counts = np.diff(np.flatnonzero(m))
In [22]: unq = X[m[:-1]]
In [23]: np.c_[unq,counts]
Out[23]:
array([[1, 3],
[2, 5],
[3, 2],
[1, 2],
[0, 3],
[5, 1]])
You can use itertools.groupby
to perform the operation without invoking numpy
.
import itertools
X = [1,1,1,2,2,2,2,2,3,3,1,1,0,0,0,5]
Y = [(x, len(list(y))) for x, y in itertools.groupby(X)]
print(Y)
# [(1, 3), (2, 5), (3, 2), (1, 2), (0, 3), (5, 1)]
If sorted output is OK, there is numpy.unique
X = [1,1,1,2,2,2,2,2,3,3,1,1,0,0,0,5]
import numpy as np
(uniq, freq) = (np.unique(X, return_counts=True))
print(np.column_stack((uniq,freq)))
[[0 3]
[1 5]
[2 5]
[3 2]
[5 1]]
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