# Solved: numpy split to chunks of equal size

Numpy is a powerful library for numerical computing in Python. One common task in numerical computing and data analysis is to split an array into chunks of equal size. This article will explore how to achieve this using Numpy and provide a comprehensive guide on the steps involved. Let’s dive in!

To solve the problem of splitting a large Numpy array into smaller chunks of equal size, we can utilize the numpy.split function. This function enables us to split an array into multiple sub-arrays that have equal size along a specified axis. Let’s dive into the solution and understand the code step-by-step.

```import numpy as np

def numpy_split_to_chunks(array, chunk_size):
return np.array_split(array, chunk_size, axis=0)

large_array = np.random.randint(0, 100, size=(10, 4))
chunk_size = 2
chunks = numpy_split_to_chunks(large_array, chunk_size)
```

First, we import the numpy library, and then we define a function called numpy_split_to_chunks that takes two input parameters: the numpy array that needs to be split and the desired chunk size. The function returns a list of numpy arrays, which are the chunks.

Here, we utilize the numpy function array_split to split the input array. We also specify the axis along which we want to split the array. In our example, we set axis=0, which means we want to split the array along the rows.

Finally, we create a random numpy array of integers (large_array) and define a chunk size (in this case, 2). We call our numpy_split_to_chunks function to get the list of chunks.

## Numpy Library

• The Numpy library is the core library for scientific computing in Python.
• It is widely used for tasks related to linear algebra, statistics and data analysis.
• It provides a high-performance multidimensional array object and tools to work with arrays.

The Numpy library has a wide range of features and functions that are useful for various mathematical and computational purposes. Its capabilities include array manipulation, mathematical operations on arrays, and statistical functions. Numpy is often combined with other libraries such as Matplotlib for data visualization, making it a staple for data scientists and engineers working in Python.

## Numpy Array Splitting

• Numpy has several functions to split arrays, such as numpy.split, numpy.array_split, numpy.hsplit and numpy.vsplit.
• These functions allow us to divide an array into several parts along a specified axis.
• They are useful in distributing data, parallel computations, and organizing data.

The different array splitting functions provided by Numpy enable developers to work efficiently with large datasets, extract specific portions of data, or divide data across multiple parallel tasks. These functions are powerful tools in data analysis and manipulation tasks, and they are essential in programming workflows for numerical computing in Python.

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