In this article, we will learn all about *numpy.count_nonzero() *function in python and see how to use it to count values based on conditions in 1D or 2D Numpy Arrays.

**Table of Contents**

- Overview of numpy.count_nonzero()
- Count non-zero values in a Numpy Array.
- Count True values in a Numpy Array.
- Count values in a Numpy Array based on conditions.
- Count non-zero values in complete 2D Numpy array or in each row / column.
- Count values in complete 2D Numpy array or in each row / column that satisfy a condition.

## numpy.count_nonzero()

Numpy module in python provides a function to count non-zero values in array,

numpy.count_nonzero(arr, axis=None, keepdims=False)

**Arguments:**

**arr**: array like object- The Array in which we want to count the non zero values

**axis**:*int or tuple, optional*- Axis along which we want to count the values.
- If 1 then it will count non zero values in rows.
- If 0 then it will count non zero values in columns.
- If None then it will flatten the array and then count non zero values in it.

- Axis along which we want to count the values.
**keepdims:**bool- If True, them the axes that are counted are left in the result as dimensions with size one.

**Returns:**

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- int or array of int
- Returns the count of non zero values in numpy array.
- If
**Axis**is provided then returns the array of count of values along the axis.

In Python, True is equivalent to 1 and False is equivalent to 0. So, we can use can use count_nonzero() function to count values in numpy array that satisfy a condition. Let’s learn that step by step with examples.

## Count non zero values in a Numpy Array

Suppose we have a numpy array of integers, which contains some zeros and some non zero values. To count the all the non zeros values in array, use the count_nonzero() function. For example,

import numpy as np # Create a numpy array from list arr = np.array([2, 3, 0, 5, 0, 0, 5, 0, 5]) # Count non zero elements in numpy array count = np.count_nonzero(arr) print('Count of non-zero values in NumPy Array: ', count)

Output:

Count of non-zero values in NumPy Array: 5

## Count True values in a numpy Array

In Python, True is equivalent to 1 and False is equivalent to 0. So, we can use can use count_nonzero() function to count True values in a bool numpy array. For example,

import numpy as np # Create a Numpy Array of bool values arr = np.array([False, True, True, True, False, True, False, True, True]) # Count True elements in numpy array count = np.count_nonzero(arr) print('Count of True values in NumPy Array: ', count)

**Output:**

Count of True values in NumPy Array: 6

Now we will see why counting True values in a bool array is important.

## Count Values in Numpy Array that satisfy a condition

When we apply a condition to a numpy array like **arr > 3,** then it returns a **bool array** of same size as arr. It contains **True **at places where the element in **arr **satisfies the condition i.e. greater than 3 in this case, all other values are **False**. So, if we count **True **values in bool array returned by **arr>3**, then it will give us the count of values that satisfies the condition in array i.e. values greater than 3 in this case. Let’s use this logic to count values in numpy array based on conditions. For example,

**Count Even numbers in a Numpy Array**

import numpy as np # Numpy array of numbers arr = np.array([2, 3, 1, 5, 4, 2, 5, 6, 5]) # Count even number of even elements in array count = np.count_nonzero(arr % 2 == 0) print('Count of Even Numbers in Numpy Array: ', count)

**Output:**

Count of Even Numbers in Numpy Array: 4

## Count Non-Zero Values in 2D Numpy Array

Suppose we have a 2D Numpy array and we want to count all non zero values in it. To do that we can use count_nonzero() function with default value of axis parameter i.e. None. For example,

import numpy as np # Create 2D Numpy ARray arr_2d = np.array( [[2, 3, 0], [5, 0, 0], [5, 0, 5]]) # Get count of non zero values in complete 2D array count = np.count_nonzero(arr_2d) print('Count of non zero values in complete 2D array: ', count)

Output:

Count of non zero values in complete 2D array: 5

### Count Non-Zero Values in each row of 2D Numpy Array

Suppose we have a 2D Numpy array and we want to count all non zero values in each row of it. To do that we can use count_nonzero() function with axis parameter as 1. For example,

import numpy as np # Create 2D Numpy ARray arr_2d = np.array( [[2, 3, 0], [5, 0, 0], [5, 0, 5]]) # Get count of non zero values in each row of 2D array count = np.count_nonzero(arr_2d, axis=1) print('Count of non zero values in each row of 2D array: ', count)

Output:

Count of non zero values in each row of 2D array: [2 1 2]

It returned an array containing count of non zero values in each row.

### Count Non-Zero Values in each column of 2D Numpy Array

Suppose we have a 2D Numpy array and we want to count all non zero values in each column of it. To do that we can use count_nonzero() function with axis parameter as 0. For example,

import numpy as np # Create 2D Numpy ARray arr_2d = np.array( [[2, 3, 0], [5, 0, 0], [5, 0, 5]]) # Get count of non zero values in each column of 2D array count = np.count_nonzero(arr_2d, axis=0) print('Count of non zero values in each column of 2D array: ', count)

**Output:**

Count of non zero values in each column of 2D array: [3 1 1]

It returned an array containing count of non zero values in each column.

## Count values in 2D Numpy array based on condition

To count all the values in 2D array that satisfy a condition, we can use the count_nonzero() function with different values of axis parameter

- axis=None, to count all values in 2D array that satisfy a condition.
- axis=1, to count all values in each row of 2D array that satisfy a condition.
- axis=0, to count all values in each column of 2D array that satisfy a condition.

For example,

import numpy as np # Create 2D Numpy ARray arr_2d = np.array( [[2, 3, 0], [5, 0, 0], [5, 0, 5]]) # Get count of even values in complete 2D array count = np.count_nonzero(arr_2d % 2 == 0) print('Count of even values in complete 2D array: ', count) # Get count of even values in each row of 2D array count = np.count_nonzero(arr_2d % 2 == 0, axis=1) print('Count of even values in each row of 2D array: ', count) # Get count of even values in each column of 2D array count = np.count_nonzero(arr_2d % 2 == 0, axis=0) print('Count of even values in each column of 2D array: ', count)

**Output:**

Count of even values in complete 2D array: 5 Count of even values in each row of 2D array: [2 2 1] Count of even values in each column of 2D array: [1 2 2]

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Thanks for reading.