Saving NumPy Arrays as YAML Lists: A Simple Guide
You've got a powerful NumPy array packed with data and want to store it in a human-readable format like YAML. But, YAML isn't designed for directly handling NumPy arrays. This can lead to confusion when you try to save the array as a YAML file and end up with a representation that's difficult to interpret or load back into Python. This article will guide you through saving a NumPy array as a regular list within a YAML file, ensuring both readability and easy retrieval.
The Problem: NumPy Arrays and YAML Compatibility
Let's illustrate the challenge with an example:
import numpy as np
import yaml
data = np.array([1, 2, 3, 4, 5])
with open('data.yaml', 'w') as f:
yaml.dump(data, f)
Running this code will produce a data.yaml file containing:
!!python/object/apply:numpy.core.multiarray.array
- !!python/object/apply:numpy.core.multiarray._reconstruct
args:
- &id001 !!python/object/apply:numpy.core.multiarray.dtype
args:
- i
- 0
- 1
- -1
- &id002 !!python/object/apply:numpy.core.multiarray.empty
args:
- -1
- 1
- i
- 0
- 1
state: !!python/tuple
- 1
- 2
- 3
- 4
- 5
- 0
- 0
- 0
- 0
- 0
- -1
- -1
- -1
- -1
- -1
- !!python/object/apply:numpy.core.multiarray.empty
args:
- 0
- 1
- i
- 0
- 1
state: !!python/tuple
- 1
- 2
- 3
- 4
- 5
- 0
- 0
- 0
- 0
- 0
- -1
- -1
- -1
- -1
- -1
This YAML representation, while technically valid, is complex and not easily readable. It's also challenging to load back into Python as a NumPy array without additional parsing.
The Solution: Convert to a List
The key to storing a NumPy array in YAML lies in converting it to a regular Python list before saving it. This ensures a straightforward YAML representation that can be easily parsed back to a NumPy array.
import numpy as np
import yaml
data = np.array([1, 2, 3, 4, 5])
data_list = data.tolist()
with open('data.yaml', 'w') as f:
yaml.dump(data_list, f)
This code will produce a data.yaml file containing:
- 1
- 2
- 3
- 4
- 5
This YAML representation is much simpler and more readable. It's also easy to load back into Python using yaml.safe_load and then convert it back to a NumPy array:
import yaml
import numpy as np
with open('data.yaml', 'r') as f:
data_list = yaml.safe_load(f)
data = np.array(data_list)
Beyond Simple Arrays
This approach works seamlessly with multi-dimensional NumPy arrays as well. The tolist() method will convert them into nested lists that YAML can handle gracefully.
import numpy as np
import yaml
data = np.array([[1, 2], [3, 4]])
data_list = data.tolist()
with open('data.yaml', 'w') as f:
yaml.dump(data_list, f)
This will result in a data.yaml file containing:
-
- 1
- 2
-
- 3
- 4
Conclusion
Saving NumPy arrays as YAML lists is the simplest and most readable way to handle this task. It ensures data integrity and easy loading back into Python for further processing. By understanding the core concept of conversion to a list before YAML serialization, you gain the flexibility to work with NumPy data within the YAML ecosystem.
