CODEWITHSUNDEEP
pythonlistcount
Ben2pop
Ben2pop

Reputation: 756

Extract the count of positive and negative values from an array

I need to work with an array to make some computations. I have the following data: 

x = [[81, 68, 71, 71, 67, -72], [79, 77, 88, 88, 59, -71], [67, 71, 68, 68, 85, -66]]

and I would need to play with the data and extract from each column the number of positive and negative values so the output should be like:

positive_value = [3,3,3,3,0]
negative_vaue = [0,0,0,0,3]

I gave it a try using a for loop with no success and with Numpy as well, but I do not really know how to use it.

What is the best way to get that result?

Upvotes: 8

Views: 35907

Answers (6)

Châu Hồng Lĩnh
Châu Hồng Lĩnh

Reputation: 1823

The code in the answer from splash58 above works, and beautifully written:

matrix_2d = [[81, 68, 71, 71, 67, 72], [79, 77, 88, 88, 59, -71], [67, 71, 68, 68, 85, -66]]

pos = [sum(y >= 0 for y in x)  for x in zip(*matrix_2d)]
neg = [len(matrix_2d) - x for x in pos]

print('Positive counts by columns: ', pos)
print('Nagetive counts by columns: ', neg)

However, if you want to look deeper in the working of algorithms, here is a simpler version, although lengthier and more verbose:

matrix_2d = [[81, 68, 71, 71, 67, 72], [79, 77, 88, 88, 59, -71], [67, 71, 68, 68, 85, -66]]

matrix_rows = len(matrix_2d)
matrix_cols = len(matrix_2d[0])

positive_counts = [0] * matrix_cols
negative_counts = [0] * matrix_cols

for col_idx in range(matrix_cols):
  for row_idx in range(matrix_rows):
    if matrix_2d[row_idx][col_idx] < 0:
      negative_counts[col_idx] += 1
    else:
      positive_counts[col_idx] += 1


print('Positive counts by columns: ', positive_counts)
print('Nagetive counts by columns: ', negative_counts)

I change the input of the matrix_2d[0, 5], so the expected result is:

Positive counts by columns:  [3, 3, 3, 3, 3, 1]
Nagetive counts by columns:  [0, 0, 0, 0, 0, 2]

Upvotes: 0

user12300428
user12300428

Reputation: 

twoD = [[4,3,2,-1],[3,2,1,-1],[1,1,-1,-2],[-1,-1,-2,-3]]
pos = neg = 0
for row in twoD:
   for col in row:
      if col < 0:
         neg += 1
      else:
         pos += 1
print('Number of positive integers are', pos, 'Number of negative integers are', neg)

Upvotes: 0

Nipun Kumar Goel
Nipun Kumar Goel

Reputation: 201

you can use count_nonzero function ,for this you may need to modify your array

>>> np.count_nonzero(np.eye(4))  #identity matrix              
4            
>>> np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]])   
5           
>>> np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=0)         
array([1, 1, 1, 1, 1])         
>>> np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=1)        
array([2, 3])

Upvotes: 3

Michael
Michael

Reputation: 1180

>>> x = [[81, 68, 71, 71, 67, -72], [79, 77, 88, 88, 59, -71], [67, 71, 68, 68, 85, -66]]
>>> zipped = list(zip(*x))
>>> for items in zipped:
    pos = len(list(filter(lambda i: i > 0, items)))
    neg = len(list(filter(lambda i: i < 0, items)))
    positive_values.append(pos)
    negative_values.append(neg)


>>> positive_values
[3, 3, 3, 3, 3, 0]
>>> negative_values
[0, 0, 0, 0, 0, 3]

Upvotes: 1

splash58
splash58

Reputation: 26153

without any library

pos = [ sum(y>=0 for y in x)  for x in zip(*mylist) ]
neg = [ len(mylist)-x for x in pos]
print(pos, neg)

demo

Upvotes: 5

willeM_ Van Onsem
willeM_ Van Onsem

Reputation: 477065

Probably the most elegant way is to convert it to a array first, then perform a condition >= 0 on it, and then calculate the sum(..) over the first axis:

import numpy as np

np.sum(np.array(x) >= 0, axis=0)

This then yields:

>>> np.sum(np.array(x) >= 0, axis=0)
array([3, 3, 3, 3, 3, 0])

So by using np.array(x) >= 0, we obtain a 2d-array of booleans:

>>> np.array(X) >= 0
array([[ True,  True,  True,  True,  True, False],
       [ True,  True,  True,  True,  True, False],
       [ True,  True,  True,  True,  True, False]], dtype=bool)

Since True counts as one, and False as zero, by calculing the sum per column, we thus count the number of positive numbers.

In case you want to count strictly positive numbers (so only larger than zero), you should omit the = in >=:

>>> np.sum(np.array(x) > 0, axis=0)
array([3, 3, 3, 3, 3, 0])

Upvotes: 20

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