Operations on NumPy Arrays
NumPy arrays support a wide range of operations, making it a powerful library for numerical computations. These include:
- Arithmetic operations.
- Universal functions (like
sin(),cos(),log()). - Aggregation operations (like
sum(),mean(),std()).
Arithmetic Operations
NumPy allows element-wise arithmetic operations between arrays or between arrays and scalars.
Examples
import numpy as np
# Create arrays
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
# Addition
print("Addition:", array1 + array2) # Output: [5 7 9]
# Subtraction
print("Subtraction:", array1 - array2) # Output: [-3 -3 -3]
# Multiplication
print("Multiplication:", array1 * array2) # Output: [4 10 18]
# Division
print("Division:", array1 / array2) # Output: [0.25 0.4 0.5 ]
# Scalar operations
print("Scalar Multiplication:", array1 * 2) # Output: [2 4 6]Universal Functions (ufuncs)
Universal functions operate element-wise on arrays. Some commonly used ufuncs are:
np.sin()np.cos()np.log()np.exp()
Examples
# Create an array
array = np.array([0, np.pi / 2, np.pi])
# Sine function
print("Sine:", np.sin(array)) # Output: [0. 1. 0.]
# Cosine function
print("Cosine:", np.cos(array)) # Output: [ 1. 0. -1.]
# Logarithm (natural log)
log_array = np.array([1, np.e, np.e**2])
print("Logarithm:", np.log(log_array)) # Output: [0. 1. 2.]
# Exponential
print("Exponential:", np.exp([1, 2, 3])) # Output: [ 2.71828183 7.3890561 20.08553692]Aggregation Operations
Aggregation functions compute a single value from an array, such as the sum, mean, or standard deviation.
Common Aggregation Functions
| Function | Description |
|---|---|
np.sum() | Sum of all elements |
np.mean() | Mean (average) of elements |
np.std() | Standard deviation |
np.min() | Minimum value |
np.max() | Maximum value |
Examples
# Create an array
array = np.array([1, 2, 3, 4, 5])
# Sum
print("Sum:", np.sum(array)) # Output: 15
# Mean
print("Mean:", np.mean(array)) # Output: 3.0
# Standard Deviation
print("Standard Deviation:", np.std(array)) # Output: 1.4142135623730951
# Min and Max
print("Minimum:", np.min(array)) # Output: 1
print("Maximum:", np.max(array)) # Output: 5Try It Yourself
Problem 1: Perform Arithmetic Operations
Create two arrays and perform addition, subtraction, multiplication, and division. Print the results.
Show Code
import numpy as np
# Arrays
array1 = np.array([10, 20, 30])
array2 = np.array([1, 2, 3])
# Perform operations
print("Addition:", array1 + array2)
print("Subtraction:", array1 - array2)
print("Multiplication:", array1 * array2)
print("Division:", array1 / array2)Problem 2: Use Universal Functions
Create an array of angles (in radians) and compute the sine, cosine, and exponential values.
Show Code
import numpy as np
# Angles in radians
angles = np.array([0, np.pi / 4, np.pi / 2])
# Compute trigonometric values
print("Sine:", np.sin(angles))
print("Cosine:", np.cos(angles))
print("Exponential:", np.exp(angles))Problem 3: Aggregate Array Data
Create an array and find its sum, mean, standard deviation, and maximum value.
Show Code
import numpy as np
# Create an array
array = np.array([4, 7, 1, 8, 3])
# Perform aggregations
print("Sum:", np.sum(array))
print("Mean:", np.mean(array))
print("Standard Deviation:", np.std(array))
print("Maximum:", np.max(array))Pyground
Play with Python!
Output:
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