Numpy has many useful functions that allow you to do mathematical calculations over an array efficiently. In fact, It creates an array that performs calculations very fast. There is a Numpy random choice method that creates a random sample array from the given 1D NumPy array. In this entire tutorial, I will discuss it.

## Syntax of the Numpy Random Choice Method

Before going to the example part, let’s know the syntax of the function.

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

**An explanation of the parameters is below.**

aYour input 1D Numpy array.sizeThe number of elements you want to generate.replaceIt Allows you for generating unique elements. The Default is true and is with replacement.pThe probabilities of each element in the array to generate.

## Examples of Numpy Random Choice Method

### Example 1: Uniform random Sample within the range

You can generate an array within a range using the *random ** choice() *method. Here You have to input a single value in a parameter. Then define the number of elements you want to generate. The array will be generated.

`np.random.choice(10, 5)`

**Output**

You can see in the figure. The five elements have been generated within the range. But there is a repeated element also. And it is 8.

How you can avoid it? You can do so by using the ** replace **argument. It generates unique elements within the range.

Execute the below lines of code.

`np.random.choice(10, 5,replace=True)`

**Output**

You can see that all the generated elements are unique.

### Example 2: Non -Uniform random Sample within the range

The above case was generating a uniform random sample. Now let’s generate a non-uniform sample. Here each element has some probabilities. The sample will be created according to it. And if you generate the sample using it then * random.choice()* method, then it includes elements using it.

Secondly, Let p is the list of probabilities of each element.

Run the code given below.

```
p=[0.1, 0, 0.3, 0.6, 0]
np.random.choice(5,4,p=p)
```

**Output**

You can see it in the figure again, the duplicates elements have been included. If you want to get only unique elements then you have to use the * replace *argument.

```
p=[0.1, 0, 0.3, 0.6, 0]
np.random.choice(5,3,replace=False,p=p)
```

**Output**

### Example 3: Random sample from 1D Numpy array

Firstly, Now let’s generate a random sample from the 1D Numpy array. In this example first I will create a sample array. And then use the NumPy random choice method to generate a sample.

Execute the below lines of code to generate it.

```
array_1d = np.array([1,2,3,4,5,6])
np.random.choice(array_1d,3)
```

**Output**

## Conclusion

That’s all for now. The numpy random choice method is able to generate both a random sample that is a uniform or non-uniform sample. Hope the above examples have cleared your understanding on how to apply it.

Even,Further, if you have any queries then you can contact us for getting more help.

Source:

Numpy Random Choice Documentation

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