Converting numpy dtypes to native python types

python

numpy

data-conversion

best-practices

byAlex Kataev·Oct 10, 2024

For a fast conversion of numpy types to Python types, leverage the .item() method for single values and .tolist() method for entire arrays. A native Python integer can be obtained from a single value as np_int64_var.item(). To convert whole arrays, simply use numpy_arr.tolist(), ensuring every element is transformed into its plain Python equivalent.

Example for a scalar:

# Converting a numpy int to Python int
# The magic happens here. See? No rabbit out of a hat!
python_int = np.int64(10).item()  # Returns a Python integer: 10

Example for an array:

# Morphing numpy array into Python list. Talk about going undercover!
python_list = np.array([1, 2, 3], dtype=np.int64).tolist()  # Yeilds a Python list: [1, 2, 3]

Overcoming quirky cases

While working with conversions, you might encounter certain edge cases or nuances, like when the element is already a native Python format or when the puzzle is more complex. No worries. There's always a way out!

Reality check before conversion: To know if a value is a numpy scalar or a native Python type, use isinstance(val, np.generic).
Auto-magic with asscalar(): numpy.asscalar() is like your fairy godmother! It safely converts a size-one array into its corresponding native Python type.
For the conversion buffs - Dictionary mapping: You can construct conversion maps using dictionary comprehension. It's like having a personalized guidebook!

Example of pre-conversion check:

import numpy as np
val = np.int64(10)
# It's like checking the water before diving in!
if isinstance(val, np.generic):
    val = val.item()  # Conversion happens ONLY if val is a NumPy type

Polish your skills with best practices

The .tolist() and .item() methods are pretty convenient, but let's talk about optimization for bonus points, especially when dealing with large data sets or performance-critical scenarios.

The wise usage of tolist(): tolist() is a handy tool for converting both scalars and arrays. Just be careful with huge arrays - you don't want a performance cliffhanger!
Lambda to the rescue: Create lambda functions with tolist to handle either scalars or arrays like a pro.

Example of a lambda function simplifying conversion:

# Who wouldn't want code that simplifies life?
to_python_type = lambda x: x.tolist() if isinstance(x, np.ndarray) else x.item()

The method maze: Which to use and when?

The best method to apply for conversion wholly depends on the context. Gain insight through these analyzed scenarios:

Single Scalar Values: Use .item() when you have a lone numpy scalar begging to be converted to a Python type.
Full Arrays: When you need a list representing your entire array, go with .tolist().
Large Arrays: For large arrays, consider chunk conversions or streaming methods to avoid memory collapse.

Here's a code snippet for converting large arrays in chunks:

def chunked_to_list(numpy_arr, chunk_size=100000):
    """Convert large numpy array to a list in chunks.
    It's like eating an elephant. How? Piece by piece!
    """
    return sum((numpy_arr[i:i+chunk_size].tolist() for i in range(0, len(numpy_arr), chunk_size)), [])

Grasping the mappings

Understanding how NumPy dtypes map to Python types is crucial to prevent hiccups during conversion.

Precision loss: Beware of precision loss during conversion, as some NumPy types hold more precision than Python types.
Alien types: Certain NumPy types do not have actual Python equivalents. Choose the closest analog we have on Earth!

Here's how you can create your mapping table from NumPy dtypes to Python types:

# It's like translating alien language to human language!
dtypes_mapping = {dtype: np.zeros(1, dtype).tolist()[0].__class__ for dtype in np.sctypes['others']}