The np.random.randint and np.random.rand Numpy Functions
The np.random module in NumPy provides functions for generating random numbers. Two commonly used functions are np.random.randint and np.random.rand.
np.random.randint
Purpose: Generates random integers from a specified range.
Usage: np.random.randint(low, high=None, size=None, dtype=int)
Parameters:
low: The lowest integer (inclusive).
high: The highest integer (exclusive). If not provided, the range is [0, low).
size: The shape of the output array. If not provided, a single integer is returned.
dtype: The desired data type of the output array.
Example:
import numpy as np
random_integers = np.random.randint(1, 10, size=(3, 2))
print(random_integers)
This might output:
[[3 7]
[1 9]
[4 6]]
np.random.rand
Purpose: Generates random numbers from a uniform distribution over the interval [0, 1).
Usage: np.random.rand(d0, d1, ..., dn)
Parameters: The dimensions of the returned array.
Example: 1-D Array
import numpy as np
random_array_1d = np.random.rand(5)
print(random_array_1d)
This will out put a one dimensional array of random floating point numbers such as:
[0.5488135 0.71518937 0.60276338 0.54488318 0.4236548]
Example: Multi-Dimensional Array
import numpy as np
random_array_multi = np.random.rand(3, 2)
print(random_array_multi)
This might output:
[[0.5488135 0.71518937]
[0.60276338 0.54488318]
[0.4236548 0.64589411]]
Both functions are useful for different purposes: np.random.randint for generating random integers and np.random.rand for random floats. They are essential tools for simulations, random sampling, and various other applications in data science and machine learning.
