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- np.array()
- np.asarray()
- np.zeros()
- np.ones()
- np.empty()
- np.arange()
- np.linspace()
- np.eye()
- np.full()
- np.fromfunction()
- np.empty_like()
- np.identity()
- np.ones_like()
- np.zeros_like()
- np.full_like()
- np.frombuffer()
- np.from_dlpack()
- np.fromfile()
- np.fromiter()
- np.fromstring()
- np.loadtxt()
- np.rec.array()
- np.logspace()
- np.geomspace()
- np.meshgrid()
- np.mgrid()
- np.ogrid()
- np.diag()
- np.diagflat()
- np.tri()
- np.tril()
- np.triu()
- np.vander()
Creation | np.full()
Method:
np.full(shape, fill_value, dtype=None, order='C', *, device=None, like=None)
Creates an array filled with the specified fill_value.
Returns:
np.ndarray
Parameters:
shape: dimension-
fill_value: (int, str, bool, array-like)-
Value to fill the array with.
fill_value = int +
fill_value = str +
fill_value = bool +
When passing array-like to fill_value, if the fill_value is multi-dimensional, the values passed must be less than or equal to shape parameter.
When passing array-like to fill_value, the values in fill_value must be of the same dtype, if not, all the values will be converted to str.
dtype: None, Optional-
The desired data-type for the NumPy array is inferred from the fill_value.
import numpy as np
# 2x2 array filled with 4
full_array = np.full((2, 2), 4, dtype=np.float64)
print(full_array)
'''
[[4. 4.]
[4. 4.]]
'''Values: +
Integer Types
np.int8: 8-bit signed integer (range: -128 to 127).
np.int16: 16-bit signed integer (range: -32,768 to 32,767).
np.int32: 32-bit signed integer (range: -2,147,483,648 to 2,147,483,647).
np.int64: 64-bit signed integer (large integer range).
np.uint8: 8-bit unsigned integer (range: 0 to 255).
np.uint16: 16-bit unsigned integer (range: 0 to 65,535).
np.uint32: 32-bit unsigned integer (range: 0 to 4,294,967,295).
np.uint64: 64-bit unsigned integer (large positive integer range).
Floating Types
np.float16: Half precision floating-point (16-bit, for low-precision computations).
np.float32: Single precision floating-point (32-bit).
np.float64: Double precision floating-point (64-bit, the default float in NumPy).
np.float128: Extended precision floating-point (128-bit, availability depends on system).
Comples Number Types
np.complex64: Complex number represented by two 32-bit floats (for real and imaginary parts).
np.complex128: Complex number represented by two 64-bit floats (default complex dtype).
np.complex256: Complex number represented by two 128-bit floats (system-dependent).
Boolean Type
np.bool_: Boolean type, can be either True or False (stored as 1-bit but takes up a full byte).
String Types
np.str_: Fixed-length Unicode string, specified by S + length (e.g., S10 for a 10-character string).
np.unicode_: Fixed-length Unicode string with support for multiple characters (uses U).
Object Type
np.object_: Allows storing any Python object, including mixed types, strings, or other arrays. Useful for heterogeneous data but slower than native NumPy types.
Datetime & Timedelta Types
np.datetime64: Stores dates and times with varying precisions (e.g., Y, M, D, h, m, s, ms, us, ns, ps, fs, as). Example: np.datetime64('2003-10-02')
np.timedelta64: Represents time durations with units (same units as datetime64).
By passing the dtype, you are implicitly converting the values to the passed dtype.
order: {'C', 'F'}, Optional-
Specifies the memory layout order for multi-dimensional arrays.
order = C (default) +
Row-major order (C-style), where rows are flattened first.
order = F +
Column-major order (Fortran-style), where columns are flattened first.
.ravel() is a NumPy function used to flatten a multi-dimensional array into a 1D array.
device: None, Optional-
In NumPy 2.1, the device parameter in np.full() is still in an early stage and currently only accepts "cpu" as a valid option. At this time, it is primarily there for future development, allowing the function to eventually support GPUs or other devices, but for now, it doesn't support GPU IDs directly.
like: None, Optional-
The like parameter in np.full() was introduced to make array creation more flexible, particularly for compatibility with libraries that extend NumPy, like Dask, CuPy, or other libraries that create arrays compatible with np.ndarray but optimized for different backends (e.g., parallel processing or GPU-based arrays).