Use np.zeros(shape, dtype=...) to create a new NumPy array whose elements are all zero. Pass a single integer for a one-dimensional array or a tuple for multiple dimensions; unless you specify otherwise, the result uses float64 values and C-order memory layout.
How to create an array with np.zeros
Import NumPy, then give np.zeros the shape you want. For example:
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import numpy as np
one_dimensional = np.zeros(5)
matrix = np.zeros((2, 3), dtype=int)The first call creates a one-dimensional array with five zeros. The second creates a two-row, three-column array of integers. NumPy documents the function as returning a new array of the given shape and type, filled with zeros: numpy.zeros API reference.
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Shape sets the dimensions
Pass one integer for a one-dimensional array, such as np.zeros(5). For two or more dimensions, pass a tuple: np.zeros((2, 3)) creates two rows and three columns. The shape describes the array’s dimensions, not the values it contains.
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Dtype determines what kind of zeros you get
The default dtype is numpy.float64, so np.zeros(5) produces floating-point zeros. Choose a dtype explicitly when you need integers or another type:
ints = np.zeros((2, 3), dtype=int)
small_ints = np.zeros(5, dtype=np.int8)The official examples also show a structured dtype, which creates zero-valued fields within each array element:
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records = np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')])See the NumPy array creation guide for the broader context of creating arrays and choosing their types.
When memory order matters
The order argument controls how multidimensional data is laid out in memory; it does not change the array’s shape or zero values. The default, order='C', uses C-style row-major order. Set order='F' for Fortran-style column-major order when that layout suits the surrounding computation:
row_major = np.zeros((2, 3))
column_major = np.zeros((2, 3), order='F')Choose between zeros, zeros_like, empty, and full
| Need | Function | What it does |
|---|---|---|
| You know the shape and want to choose the dtype directly | np.zeros(shape, dtype=...) |
Creates a zero-filled array of the specified shape and type. |
| You want zeros matching an existing array | np.zeros_like(a) |
Uses the input array’s shape and dtype by default, with supported overrides. See the array-creation routines index. |
| You will assign every element before reading it | np.empty(shape, dtype=...) |
Allocates an array without initializing ordinary numeric entries. Their values are arbitrary until assigned; reading them first is unsafe. See the numpy.empty API reference. |
| You need every element set to a constant other than zero | np.full(shape, fill_value) |
Creates an array filled with the value you specify. See the array-creation routines index. |
Optional interoperability parameters
The current signature is numpy.zeros(shape, dtype=None, order='C', *, device=None, like=None). Most code only needs shape and, when appropriate, dtype.
likewas added in NumPy 1.20. It lets an array-like object implementing__array_function__determine a compatible result.devicewas added in NumPy 2.0 for Array API interoperability. When supplied, the documented supported value is"cpu".
For details and version notes, consult the current NumPy zeros reference.
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