# numpy.random.choice in Python : Learn in 7 Steps

Numpy is the best Python package for creating a numpy array and doing mathematical calculations in an efficient way. In this tutorial, you will learn how to implement numpy.random.choice in Python.

## Syntax of the numpy.random.choice function

``numpy.random.choice(a, size=None, replace=True, p=None)``

## Steps to Implement numpy.random.choice in Python

Certainly! Here’s a step-by-step tutorial on implementing numpy.random.choice.

### Step 1: Install NumPy

If you haven’t installed NumPy yet, you can install it using the following command in your terminal or command prompt. If you have already installed then move to the second step.

``````pip install numpy # For python 2.xx
pip3 install numpy # For python 3.xx``````

### Step 2: Import NumPy

The first step is to import the numpy package. In your Python script or Jupyter Notebook, start by importing the NumPy library using the below line of code.

``import numpy as np``

### Step 3: Create an Array of Elements

Define an array from which you want to draw random samples. This can be a list or another iterable converted to a NumPy array.

``elements = np.array([10, 20, 30, 40, 50])``

### Step 4: Use numpy.random.choice

Now, you can use the numpy.random.choice function to generate random samples. Here’s a basic example.

``````# Draw a random sample of size 3 with replacement
random_sample = np.random.choice(elements, size=3, replace=True)

print("Random Sample with Replacement:", random_sample)``````

In this example, `size=3` specify that you want to generate three random samples from the array, and `replace=True` indicate that you want to draw with replacement.

### Step 5: Draw Without Replacement

If you want to draw without replacement, set `replace` to `False`:

``````# Draw a random sample of size 3 without replacement
random_sample_without_replacement = np.random.choice(elements, size=3, replace=False)

print("Random Sample without Replacement:", random_sample_without_replacement)``````

### Step 6: Specify Probabilities

You can also specify probabilities for each element using the `p` parameter. Below is an example of it.

``````# Specify probabilities for each element
probabilities = [0.1, 0.2, 0.3, 0.2, 0.2]

# Draw a random sample of size 3 with replacement and specified probabilities
random_sample_with_probabilities = np.random.choice(elements, size=3, replace=True, p=probabilities)

print("Random Sample with Probabilities:", random_sample_with_probabilities)``````

### Step 7: Explore More Options

NumPy’s `numpy.random.choice` provides various options for customizing your random sampling. Explore additional parameters and options in the NumPy documentation.

And that’s it! You’ve successfully implemented random sampling using NumPy’s `numpy.random.choice`. Feel free to experiment with different parameters and options based on your specific use case.

## Conclusion

If you want to generate random samples of statistical calculation then the above step will help you to create a random numpy array. Just follow the steps for deep understanding.

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