np.empty(shape, dtype=...) allocates an array with the requested shape and data type, but it does not initialize ordinary element values. Use it when your code will write every element before reading it. A zero-length array is different: a shape such as (0,) contains no elements at all, while still retaining its shape and dtype.
What does np.empty() return?
NumPy documents numpy.empty as returning a new array of a given shape and type without initializing its entries. The array has allocated storage, but for ordinary numeric dtypes its initial values are arbitrary—not guaranteed to be zero or any other particular value. See the NumPy empty API reference.
That makes np.empty suitable when a program is about to overwrite every element. If any element might be read before being assigned, use an initializing constructor instead.
How do shape and dtype work?
The current documented signature is numpy.empty(shape, dtype=None, order='C', *, device=None, like=None). The shape argument can be an integer or a tuple of integers. If dtype is omitted, it defaults to numpy.float64; pass a dtype explicitly when you need another type. The default memory order is C-style, and order='F' requests Fortran-style order. These options affect the array’s type and layout, not whether ordinary values are initialized.
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import numpy as np
# Defaults to float64
x = np.empty(3)
# Specify an integer dtype
counts = np.empty(3, dtype=np.int32)
# Specify shape and memory order
matrix = np.empty((2, 3), dtype=np.float64, order='F')The optional device parameter is documented as new in NumPy 2.0.0; if supplied for Array API interoperability, it must be 'cpu'. The like parameter is documented as new in NumPy 1.20.0. If the reference object supports __array_function__, it can determine a compatible output type. Consult the API reference for the release you use, since signatures may change.
What is a zero-length NumPy array?
A zero-length array has at least one dimension whose length is zero. For example, np.empty((0,)) has shape (0,) and contains zero elements; np.empty((2, 0)) has shape (2, 0) and also contains zero elements. The second example has two dimensions, but the zero extent means there are no element positions to fill. Both arrays still have shape and dtype metadata.
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empty_1d = np.empty((0,))
empty_2d = np.empty((2, 0), dtype=np.int32)
print(empty_1d.shape, empty_1d.size, empty_1d.dtype)
# (0,) 0 float64
print(empty_2d.shape, empty_2d.size, empty_2d.dtype)
# (2, 0) 0 int32A zero-length shape is not an uninitialized nonempty array: there are no values in it to read or initialize. NumPy’s shape contract and array-creation guide describe arrays by their specified dimensions and type; see the array creation guide.
Does np.empty() initialize values to zero?
No. For ordinary numeric arrays, NumPy does not promise zero-filled entries. Fill every slot before using its value when correctness or reproducibility matters:
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z = np.empty(3, dtype=np.float64)
z[:] = [1.0, 2.0, 3.0]
# It is now safe to use the assigned values
print(z.sum())There is a documented exception for object arrays: NumPy says object arrays returned by empty are initialized to None. Do not generalize that behavior to numeric dtypes.
When should you use np.zeros() or another constructor?
Choose a constructor based on the initial values your code requires and whether it will overwrite every element. NumPy’s array creation routines reference lists the alternatives below.
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| Need | Constructor | What it provides |
|---|---|---|
| Allocate an array that you will completely overwrite before reading | np.empty |
Requested shape and dtype; ordinary entries are not initialized. |
| Begin with every value set to zero | np.zeros |
Requested shape filled with zeros; see the NumPy zeros API reference. |
| Use a chosen constant as the initial value | np.full |
Requested shape filled with the chosen value. |
| Begin with ones | np.ones |
Requested shape filled with ones. |
| Create an uninitialized array based on an existing array | np.empty_like |
Uses a prototype array to determine the output shape and type. |
NumPy’s manual notes that skipping initialization may offer a marginal speed advantage in some circumstances, but that is not a performance guarantee. Choose np.empty for its allocation behavior only when you can safely write all required elements first; establish any speed benefit with a benchmark for your own workload and environment.
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