# numpy random float array between 0 and 1 with code examples

NumPy is a popular library in Python that is mainly used in scientific computing and data analysis. It has a wide range of features including mathematical functions, random number generators and functions for working with arrays. The NumPy random module in Python provides various functions for generating random numbers. In this article, we will explore NumPy random float array between 0 and 1 with code examples.

Creating a NumPy random float array between 0 and 1 is really easy. All you need to do is import the NumPy library and use the random module to generate random numbers. Here is the code to generate a random float array between 0 and 1:

```import numpy as np

random_array = np.random.rand(3, 4)
print(random_array)
```

In the above code, we have used the `rand()` function to generate a 3 x 4 array which contains random float values between 0 and 1. The `rand()` function is used to generate random numbers and takes in the shape of the array as an argument. The `shape` parameter can be a tuple of integers that specifies the dimensions of the array.

The output of the above code will be:

```[[0.4522448  0.97341466 0.02358413 0.19213766]
[0.71288379 0.64443967 0.12317118 0.63788772]
[0.4477646  0.61379415 0.50391605 0.1322487 ]]
```

As you can see, the output contains random float values between 0 and 1.

Another function that can be used to generate random float array between 0 and 1 is `random_sample()`. Here is the code to generate a random float array using the `random_sample()` function:

```import numpy as np

random_array = np.random.random_sample((3, 4))
print(random_array)
```

In the above code, we have used the `random_sample()` function to generate a 3 x 4 random float array between 0 and 1. This function takes the shape of the array as an argument.

The output of the above code will be similar to the output of the `rand()` function:

```[[0.74136566 0.94823607 0.31894177 0.19137608]
[0.52493831 0.51044109 0.14526857 0.70922166]
[0.961418   0.42134059 0.23946012 0.25400807]]
```

Both the `rand()` and `random_sample()` functions can be used to generate a random float array between 0 and 1. The `rand()` function is a convenient function to use as it is quicker and more efficient, but if you require a larger range of precision, then it is better to use the `random_sample()` function.

In conclusion, generating a NumPy random float array between 0 and 1 is quite simple using the `rand()` and `random_sample()` functions provided by the NumPy library. These functions take in the shape of the array as an argument and return an array of random float values between 0 and 1. With these functions, generating random float arrays between 0 and 1 is a breeze.

Generating a random float array between 0 and 1 with NumPy provides a lot of flexibility and control over the size, shape, and the distribution of the generated random numbers. In addition to the `rand()` and `random_sample()` functions, NumPy provides several other functions for generating random numbers.

For example, the `random()` function is used to generate random numbers between 0 and 1 with uniform distribution. Here's an example code:

```import numpy as np

array = np.random.random((3,4))
print(array)
```

The output would be a 3 x 4 array of random values between 0 and 1:

```[[0.58538981 0.78879697 0.90888516 0.11203968]
[0.24922325 0.71186209 0.11872474 0.07993317]
[0.19866749 0.87867879 0.25784741 0.42756782]]
```

You can also generate random float arrays between other ranges using the `uniform()` function. The `uniform()` function takes the minimum and maximum values of the range as arguments, along with the size of the output array. Here's an example code:

```import numpy as np

array = np.random.uniform(-5, 5, size=(2, 3))
print(array)
```

The output would be a 2 x 3 array of random values between -5 and 5:

```[[-3.93142534  3.13351424 -2.15255454]
[ 3.8222802  -3.10122315 -1.41940262]]
```

If you want to generate random float arrays with a normal distribution, you can use the `normal()` function. The `normal()` function takes the mean and standard deviation values of the normal distribution as arguments, along with the size of the output array. Here's an example code:

```import numpy as np

array = np.random.normal(0, 1, size=(3, 2))
print(array)
```

The output would be a 3 x 2 array of random values with a normal distribution around 0 and a standard deviation of 1:

```[[-0.47583488 -0.52354376]
[-0.86722652 -1.01013525]
[ 0.4722479   1.39997503]]
```

Overall, NumPy provides a lot of flexibility and control over the generation of random float arrays. These functions make it easy to generate arrays with different sizes, shapes, and distributions.

## Popular questions

1. What is NumPy?
• NumPy is a popular library in Python that is mainly used in scientific computing and data analysis.
1. How can you generate a random float array between 0 and 1 with NumPy?
• You can use the `rand()` or `random_sample()` function to generate a random float array between 0 and 1.
1. What is the difference between the `rand()` and `random_sample()` functions?
• The `rand()` and `random_sample()` functions can both be used to generate a random float array between 0 and 1. The `rand()` function is a convenient function to use as it is quicker and more efficient, but if you require a larger range of precision, then it is better to use the `random_sample()` function.
1. What is the `uniform()` function used for?
• The `uniform()` function is used to generate random float arrays between other ranges. It takes the minimum and maximum values of the range as arguments, along with the size of the output array.
1. How can you generate a random float array with a normal distribution in NumPy?
• You can use the `normal()` function to generate random float arrays with a normal distribution. It takes the mean and standard deviation values of the normal distribution as arguments, along with the size of the output array.

### Tag

"Randomness"

##### Deeksha Sudhakar
As a seasoned software engineer, I bring over 7 years of experience in designing, developing, and supporting Payment Technology, Enterprise Cloud applications, and Web technologies. My versatile skill set allows me to adapt quickly to new technologies and environments, ensuring that I meet client requirements with efficiency and precision. I am passionate about leveraging technology to create a positive impact on the world around us. I believe in exploring and implementing innovative solutions that can enhance user experiences and simplify complex systems. In my previous roles, I have gained expertise in various areas of software development, including application design, coding, testing, and deployment. I am skilled in various programming languages such as Java, Python, and JavaScript and have experience working with various databases such as MySQL, MongoDB, and Oracle.
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