Standard Deviation of NumPy Array in Python – np.std() Function (3 Examples)
In this tutorial, I’ll demonstrate how to find the standard deviation of a NumPy array using the np.std function in the Python programming language.
Creation of Example Data
import numpy # Load numpy |
import numpy # Load numpy
x = np.array([[8, 2, 1, 7, 7, 5], # Constructing a NumPy array in Python [4, 10, 5, 9, 1, 4]]) print(x) # [[ 8 2 1 7 7 5] # [ 4 10 5 9 1 4]] |
x = np.array([[8, 2, 1, 7, 7, 5], # Constructing a NumPy array in Python [4, 10, 5, 9, 1, 4]]) print(x) # [[ 8 2 1 7 7 5] # [ 4 10 5 9 1 4]]
Example 1: Calculate Standard Deviation of All Array Values in Python
print(np.std(x)) # Computing the standard deviation of all values # 2.890357532670771 |
print(np.std(x)) # Computing the standard deviation of all values # 2.890357532670771
Example 2: Calculate Standard Deviation by Array Rows in Python
print(np.std(x, axis = 1)) # Computing the standard deviation by rows # [2.64575131 3.09569594] |
print(np.std(x, axis = 1)) # Computing the standard deviation by rows # [2.64575131 3.09569594]
Example 3: Calculate Standard Deviation by Array Columns in Python
print(np.std(x, axis = 0)) # Computing the standard deviation by columns # [2. 4. 2. 1. 3. 0.5] |
print(np.std(x, axis = 0)) # Computing the standard deviation by columns # [2. 4. 2. 1. 3. 0.5]