How to count values in an array python

Question

If you are interested in the fastest execution, you know in advance which value(s) to look for, and your array is 1D, or you are otherwise interested in the result on the flattened array (in which case the input of the function should be np.ravel(arr) rather than just arr), then Numba is your friend:

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Use count_nonzero() to count occurrences of a value in a NumPy array
Count all occurrences of value ‘3’ in a numpy array
Use bincount() to count occurrences of a value in a NumPy array
Convert numpy array to list and count occurrences of a value in a array
Select elements from array that matches the value and count them
Count occurrences of a value in 2D NumPy Array
Count occurrences of a value in each row of 2D NumPy Array
Count occurrences of a value in each column of 2D NumPy Array
How do you count items in an array in Python?
How do I count items in a NumPy array?
How do you count numbers in an array?
How do you count the number of times an element appears in an array in Python?

import numba as nb


@nb.jit
def count_nb(arr, value):
    result = 0
    for x in arr:
        if x == value:
            result += 1
    return result

or, for very large arrays where parallelization may be beneficial:

@nb.jit(parallel=True)
def count_nbp(arr, value):
    result = 0
    for i in nb.prange(arr.size):
        if arr[i] == value:
            result += 1
    return result

These can be benchmarked against np.count_nonzero() (which also has a problem of creating a temporary array -- something that is avoided in the Numba solutions) and a np.unique()-based solution (which is actually counting all unique value values contrarily to the other solutions).

import numpy as np


def count_np(arr, value):
    return np.count_nonzero(arr == value)

import numpy as np


def count_np_uniq(arr, value):
    uniques, counts = np.unique(a, return_counts=True)
    counter = dict(zip(uniques, counts))
    return counter[value] if value in counter else 0

Since the support for "typed" dicts in Numba, it is also possible to have a function counting all occurrences of all elements. This competes more directly with np.unique() because it is capable of counting all values in a single run. Here is proposed a version which eventually only returns the number of elements for a single value (for comparison purposes, similarly to what is done in count_np_uniq()):

@nb.jit
def count_nb_dict(arr, value):
    counter = {arr[0]: 1}
    for x in arr:
        if x not in counter:
            counter[x] = 1
        else:
            counter[x] += 1
    return counter[value] if value in counter else 0

The input is generated with:

def gen_input(n, a=0, b=100):
    return np.random.randint(a, b, n)

The timings are reported in the following plots (the second row of plots is a zoom on the faster approaches):

Showing that the simple Numba-based solution is fastest for smaller inputs and the parallelized version is fastest for larger inputs. They NumPy version is reasonably fast at all scales.

When one wants to count all values in an array, np.unique() is more performant than a solution implemented manually with Numba for sufficiently large arrays.

EDIT: It seems that the NumPy solution has become faster in recent versions. In a previous iteration, the simple Numba solution was outperforming NumPy's approach for any input size.

Full code available here.

In this article, we will discuss different ways to count the occurrences of a value in numpy array.

Table of Contents

Use count_nonzero() to count occurrences of a value in a NumPy array
Use sum() to count occurrences of a value in a NumPy array
Use bincount() to count occurrences of a value in a NumPy array
Convert numpy array to list and count occurrences of a value in a array
Select elements from array that matches the value and count them
Count occurrences of a value in 2D NumPy Array
Count occurrences of a value in each row of 2D NumPy Array
Count occurrences of a value in each column of 2D NumPy Array

Use count_nonzero() to count occurrences of a value in a NumPy array

In Python, the numpy module provides a function count_nonzero(arr, axis=None), which returns the count of non zero values in a given numpy array. When the value of axis argument is None, then it returns the count
of non zero values in complete array. But in case you are dealing with multi-dimensional array, then you can use the axis argument to count occurrences of a value along the given axis.

Let’s understand by some examples,

Count all occurrences of value ‘3’ in a numpy array

When we applied a condition to the numpy array like, arr==3, then it applies the condition on each element of the array and stores the result as bool value in a new array. So,

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arr==3

Returns a bool array of same size as arr,

[2 3 4 5 3 4 5 3 5 4 7 8 3 6 2]

This bool array contains True values at the indexes where value is 3 in the original array arr and False where value is not 3.

Now, if we count the True (non zero) values in this array, then we can get the count of value ‘3’ in the array.

import numpy as np

arr = np.array([2, 3, 4, 5, 3, 4, 5, 3, 5, 4, 7, 8, 3, 6, 2])


print('Numpy Array:')
print(arr)

# Count occurrence of element '3' in numpy array
count = np.count_nonzero(arr == 3)

print('Total occurences of "3" in array: ', count)

Output:

Numpy Array:
[2 3 4 5 3 4 5 3 5 4 7 8 3 6 2]
Total occurences of "3" in array:  4

To get the count we used the count_nonzero() function.

Similar to above solution we can apply a condition to numpy array to convert it to a bool array. A bool True is equivalent to 1 in python, so we can add add the True values in array to get the sum of values in array that matches the condition. Let’s use this logic to count all occurrences of value ‘3’ in numpy array,

import numpy as np

arr = np.array([2, 3, 4, 5, 3, 4, 5, 3, 5, 4, 7, 8, 3, 6, 2])

print('Numpy Array:')
print(arr)

# Count occurrence of element '3' in numpy array
count = (arr == 3).sum()

print('Total occurences of "3" in array: ', count)

Output:

Numpy Array:
[2 3 4 5 3 4 5 3 5 4 7 8 3 6 2]
Total occurences of "3" in array:  4

It returned the count of all occurrences of 3 in the array.

Use bincount() to count occurrences of a value in a NumPy array

In python, the numpy module provides a function numpy.bincount(arr), which returns a count of number of occurrences of each value in array of non-negative ints.

Let’s use this to count all occurrences of value ‘3’ in numpy array,

import numpy as np

arr = np.array([2, 3, 4, 5, 3, 4, 5, 3, 5, 4, 7, 8, 3, 6, 2])

count_arr = np.bincount(arr)

# Count occurrence of element '3' in numpy array
print('Total occurences of "3" in array: ', count_arr[3])

# Count occurrence of element '5' in numpy array
print('Total occurences of "5" in array: ', count_arr[5])

Output:

Total occurences of "3" in array:  4
Total occurences of "5" in array:  3

It returned the count of all occurences of 3 in the array.

How did it work?

bincount(arr), returned an array, where ith element contains the occurence of i in arr. For example,

result[1] contains the occurrences of 1 in array
result[2] contains the occurrences of 2 in array
result[3] contains the occurrences of 3 in array

Convert numpy array to list and count occurrences of a value in a array

We can convert the numpy array to a list and then use the count() function of list to get the count of occurrences of an element in it. For example,

import numpy as np

arr = np.array([2, 3, 4, 5, 3, 4, 5, 3, 5, 4, 7, 8, 3, 6, 2])

# Count occurrence of element '3' in numpy array
count = arr.tolist().count(3)

print('Total occurences of "3" in array: ', count)

Output:

Total occurences of "3" in array:  4

It returned the count of all occurences of 3 in the array.

Select elements from array that matches the value and count them

We can select only those elements from numpy array which are equal to given value and then we can can get the length of this new array. It will gives the count of occurrences of the value in original array. For example,

import numpy as np

arr = np.array([2, 3, 4, 5, 3, 4, 5, 3, 5, 4, 7, 8, 3, 6, 2])

# Count occurrence of element '3' in numpy array
count = arr[arr==3].shape[0]

print('Total occurences of "3" in array: ', count)

Output:

Total occurences of "3" in array:  4

It returned the count of all occurences of 3 in the array.

How did it work?

When we applied a condition to the numpy array like, arr==3, then it applies the condition on each element of the array and stores the result as bool value in a new array. Finally returns a bool array of same size as arr. It contains True where value is 3 in array and False where value is not 3. If we pass the bool array to subscript operator [] of numpy array then, it will select elements from array where bool value is True.

It means arr[arr==3], returned an array of 3’s only. Then we checked its length using the shape attribute.

Count occurrences of a value in 2D NumPy Array

To count the occurrences of a value in complete 2D Numpy array or Matrix we can use the count_nonzero() function with axis parameter as None. For example,

import numpy as np

# Create a 2D Numpy Array from list of lists
matrix = np.array( [[2, 3, 4],
                    [5, 3, 4],
                    [5, 3, 5],
                    [4, 7, 8],
                    [3, 6, 2]] )

# Count occurrence of element '3' in complete 2D Numpy Array
count = np.count_nonzero(matrix == 3)

print('Total occurrences of "3" in 2D array:')
print(count)

Output:

Total occurrences of "3" in 2D array: 4

Count occurrences of a value in each row of 2D NumPy Array

To count the occurrences of a value in each row of the 2D NumPy array pass the axis value as 1 in the count_nonzero() function. It will return an array containing the count of occurrences of a value in each row. For example,

import numpy as np

# Create a 2D Numpy Array from list of lists
matrix = np.array( [[2, 3, 4],
                    [5, 3, 4],
                    [5, 3, 5],
                    [4, 7, 8],
                    [3, 6, 2]] )

# Count occurrence of element '3' in each row
count = np.count_nonzero(matrix == 3, axis=1)

print('Total occurrences  of "3" in each row of 2D array: ', count)

Output:

Total occurrences of "3" in each row of 2D array: [1 1 1 0 1]

Count occurrences of a value in each column of 2D NumPy Array

To count the occurrences of a value in each column of the 2D NumPy array pass the axis value as 0 in the count_nonzero() function. It will return an array containing the count of occurrences of a value in each column. For example,

import numpy as np

# Create a 2D Numpy Array from list of lists
matrix = np.array( [[2, 3, 4],
                    [5, 3, 4],
                    [5, 3, 5],
                    [4, 7, 8],
                    [3, 6, 2]] )

# Count occurrence of element '3' in each column
count = np.count_nonzero(matrix == 3, axis=0)

print('Total occurrences  of "3" in each column of 2D array: ', count)

Output:

Total occurrences of "3" in each column of 2D array: [1 3 0]

How do you count items in an array in Python?

Python len() method enables us to find the total number of elements in the array/object. That is, it returns the count of the elements in the array/object.

How do I count items in a NumPy array?

Use count_nonzero() to count True elements in NumPy array. Use sum() to count True elements in a NumPy array. Use bincount() to count True elements in a NumPy array. Count True elements in 2D Array.

How do you count numbers in an array?

Approach used in the below program is as follows.

Input an array let's say, int arr[].

Calculate the length of both the arrays using the length() function that will return an integer value as per the elements in an array..

Start the loop from i to 0 till i less than size of an array..

How do you count the number of times an element appears in an array in Python?

Operator. countOf() is used for counting the number of occurrences of b in a. It counts the number of occurrences of value. It returns the Count of a number of occurrences of value.

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