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np.uint8 represents integers from 0 through 255, inclusive. Converting values outside that range is not one uniform operation: current NumPy may raise OverflowError when constructing an array from out-of-range Python integers, while casting existing NumPy values can overflow. To preserve values, check the bounds before conversion and, where supported, use astype(..., casting="same_value").
What is the range of np.uint8?
np.uint8 (also written numpy.uint8) is an unsigned integer dtype with a fixed width of 8 bits. Because it has no sign, its 256 possible bit patterns represent the inclusive range 0–255. NumPy’s data types guide identifies uint8 as an unsigned 8-bit type and documents numpy.iinfo for inspecting integer limits.
import numpy as np
info = np.iinfo(np.uint8)
print(info.min, info.max) # 0 255Use uint8 when an explicitly sized, non-negative 8-bit representation is intended. Some C-like integer aliases vary by platform; the explicitly sized name makes the width clear.
What happens when converting a negative number to np.uint8?
The outcome depends on the conversion path, so do not assume that a negative Python integer will always wrap to a value such as 255. Current NumPy’s array-creation documentation demonstrates that constructing an integer array from a Python integer outside the requested dtype’s range can raise OverflowError. Its example uses int8; for uint8, the corresponding valid bounds are 0 and 255.
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Casting an existing NumPy array is a different operation. NumPy’s dtype guide explains that casts follow C casting rules and can overflow. Its example casts 300 to int8, yielding 44; that example illustrates the casting behavior, not a guarantee that every constructor or conversion API wraps the same way. Avoid using an unchecked conversion as a way to define what should happen to out-of-range input.
| Operation | What the documentation establishes | Practical implication |
|---|---|---|
| Construct a typed array from Python integers | Current array-creation documentation shows an out-of-range Python integer can raise OverflowError. |
Validate Python inputs against 0–255; do not rely on wraparound. |
| Cast an existing NumPy array | The dtype guide says NumPy casts follow C casting rules and can overflow. | Use a value-preserving cast when changing values would be an error. |
How do I convert to uint8 without overflow?
Check every input against the inclusive range, then perform a cast that requires the values to remain unchanged. NumPy documents casting="same_value" for astype in its dtype guide.
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info = np.iinfo(np.uint8)
if np.any((values < info.min) | (values > info.max)):
raise ValueError("values outside uint8 range")
result = np.asarray(values).astype(np.uint8, casting="same_value")The explicit range check states the input contract and gives a clear error before conversion. The same_value option adds a conversion guard, but check the NumPy version in environments that must support older releases because current documentation may describe options those releases do not provide.
If values must remain valid outside 0–255, keep them as Python int or choose a wider representation that can hold them. Do not discard information merely to fit a narrower dtype.
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Can uint8 arithmetic overflow?
Yes. NumPy integer dtypes have fixed precision, so arithmetic can exceed the dtype’s representable range. The NumPy promotion guide says scalar overflow warns, but array overflow may not; it specifically notes that np.array(100, dtype=np.uint8) + 100 does not warn.
Since NumPy 2.0, promotion with Python scalar values considers their kind but ignores their precision when selecting a result dtype. A Python integer combined with a low-precision NumPy integer therefore does not necessarily widen the calculation, and an out-of-range Python integer can fail during coercion for a NumPy scalar operation. For calculations that may exceed 255, widen before performing the operation or validate inputs and results against the intended limits. Do not use the presence or absence of a warning as a bounds check.
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Why does numpy.can_cast not prove a value is in range?
numpy.can_cast checks dtype-level cast compatibility; it is not a substitute for checking an individual value. Since NumPy 2.0, it does not accept Python scalars, and it does not apply value-based logic to 0-D arrays or NumPy scalars. Use np.iinfo(np.uint8) and compare the actual values when range matters. See the numpy.can_cast reference.
Which NumPy version’s behavior should I expect?
The promotion behavior described above has a version boundary: NumPy 2.0 changed how promotion works with Python scalars. The current stable NumPy manual documents the behavior and APIs cited here; code intended for older NumPy releases should be checked against the documentation for those releases, especially when it relies on casting="same_value". The fixed-width range of uint8 remains 0–255.
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