In Python, when you want to deserialize dictionaries, especially those containing NumPy arrays, you can use the `numpy` library along with `json` or `pickle` for serialization. Here's how to do it:
First, ensure you have NumPy installed:
pip install numpy
Then, you can use the following example to deserialize a dictionary that includes NumPy arrays:
import numpy as np
import json
# Example of a dictionary with a NumPy array
data = {
'array': np.array([1, 2, 3]),
'value': 42
}
# Serialize the dictionary into a JSON string
data_serialized = json.dumps(data, default=lambda x: x.tolist() if isinstance(x, np.ndarray) else x)
# Deserialize the JSON string back into a dictionary
data_deserialized = json.loads(data_serialized)
# Convert the list back to a NumPy array
data_deserialized['array'] = np.array(data_deserialized['array'])
print(data_deserialized)
print(type(data_deserialized['array']))
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?