Sai A Sai A
Updated date Feb 12, 2024
In this blog, we will learn how to convert NumPy arrays to YAML format in Python using simple methods. This blog explores three approaches, providing code examples and explanations for each, catering to different array sizes and performance needs.

Introduction:

Data serialization is an important aspect of programming, especially when dealing with large datasets or transferring data between different systems. One common format for serialization is YAML (YAML Ain't Markup Language), known for its human-readable and easy-to-write syntax. In this blog, we will explore how to convert a NumPy array, a popular data structure in Python for numerical computations, into YAML format. We will cover multiple methods, each with its advantages and use cases.

Method 1: Using PyYAML Library

PyYAML is a popular Python library for working with YAML files. We can use it to easily convert a NumPy array into YAML format.

import numpy as np
import yaml

# Sample NumPy array
data = np.array([[1, 2, 3], [4, 5, 6]])

# Convert NumPy array to YAML
yaml_data = yaml.dump(data.tolist())

print(yaml_data)

Output:

- [1, 2, 3]
- [4, 5, 6]

In this method, we first convert the NumPy array to a regular Python list using the tolist() method. Then, we use yaml.dump() to serialize the list into YAML format.

Method 2: Using NumPy's built-in tofile() and fromfile() functions

NumPy provides built-in functions to save arrays to disk and load them back. We can leverage these functions to save the array in a binary format and then convert it to YAML.

import numpy as np
import yaml

# Sample NumPy array
data = np.array([[1, 2, 3], [4, 5, 6]])

# Save NumPy array to a binary file
data.tofile('numpy_array.bin')

# Load the array back
loaded_data = np.fromfile('numpy_array.bin', dtype=data.dtype)
loaded_data = loaded_data.reshape(data.shape)

# Convert NumPy array to YAML
yaml_data = yaml.dump(loaded_data.tolist())

print(yaml_data)

Output:

- [1, 2, 3]
- [4, 5, 6]

In this method, we first save the NumPy array to a binary file using tofile(). Then, we load the array back using fromfile() and reshape it to its original shape. Finally, we convert the loaded array to YAML format.

Method 3: Using NumPy's ndarray.tobytes() and numpy.frombuffer() functions

Another approach is to convert the NumPy array to a byte string using tobytes() and then reconstruct the array from the byte string using frombuffer().

import numpy as np
import yaml

# Sample NumPy array
data = np.array([[1, 2, 3], [4, 5, 6]])

# Convert NumPy array to byte string
byte_string = data.tobytes()

# Reconstruct the array from the byte string
reconstructed_data = np.frombuffer(byte_string, dtype=data.dtype)
reconstructed_data = reconstructed_data.reshape(data.shape)

# Convert NumPy array to YAML
yaml_data = yaml.dump(reconstructed_data.tolist())

print(yaml_data)

Output:

- [1, 2, 3]
- [4, 5, 6]

In this method, we convert the NumPy array to a byte string using tobytes(). Then, we reconstruct the array from the byte string using frombuffer() and reshape it to its original shape. Finally, we convert the reconstructed array to YAML format.

Conclusion:

In this blog, we have covered  multiple methods to convert a NumPy array to YAML format in Python. The first method using PyYAML library is simple and suitable for small to medium-sized arrays. The second and third methods, using NumPy's built-in functions, are more efficient for large arrays and offer better performance.

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