Generating random number list in Python
Sometimes, in making programs for gaming or gambling, we come across the task of creating a list all with random numbers in Python. This task is to perform in general using loop and appending the random numbers one by one. But there is always a requirement to perform this in the most concise manner. Let’s discuss certain ways in which this can be done.
Random Number Using random module
Python Random module is an in-built module of Python which is used to generate random numbers. This module can be used to perform random actions such as generating random numbers, printing random a value for a list or string, etc.
Method 1: Using the random.randint()
By using random.randint() we can add random numbers into a list.
[9, 3, 3, 6, 8, 5, 4, 6, 3, 7]
Method 2: Using random.sample()
This single utility function performs the exact required as asked by the problem statement, it generated N no. of random numbers in a list in the specified range and returns the required list.
Random number list is : [49, 20, 23, 34, 6, 29, 35]
Method 3: Using list comprehension + randrange()
The naive method to perform this particular task can be shortened using list comprehension. randrange function is used to perform the task of generating the random numbers.
Random number list is : [32, 16, 9, 28, 19, 31, 21]
Method 4: using loop + randint()
[3, 11, 48, 2, 48, 2, 8, 51, 8, 5]
Random Number Using Numpy
The random function provided by the Numpy module can be more useful for you as it provides little better functionality and performance as compared to the random module.
Method 1: Generating a list of random integers using numpy.random.randint function
This function returns random integers from the “discrete uniform” distribution of the integer data type.
Output: [5, 3, 6, 7, 4, 5, 7, 7, 7, 7] [0, 2, 1, 2, 1]
Method 2. Generating a list of random floating values using numpy.random.random_sample function
This function return random float values in half open interval [0.0, 1.0).
output: [0.08035145 0.94966245 0.92860366 0.22102797] [[0.02937499 0.50073572 0.58278742 0.02577903] [0.37892104 0.60267882 0.33774815 0.28425059] [0.57086088 0.07445422 0.86236614 0.33505317] [0.83514508 0.82818536 0.1917555 0.76293027]]
The benefit of using numpy.random over the random module of Python is that it provides a few extra probability distributions which can help in scientific research.
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