BetterDocs
- 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.asarray()
This method is similar to np.array() .
Method:
np.asarray(a, dtype=None, order='K', *, device=None, copy=True, like=None)
Creates a NumPy array with the elements passed to a parameter.
Returns:
np.ndarray
Parameters:
a: (list, tuple)-
The input array-like data to be converted into a NumPy array.
If a is already a np.ndarray (NumPy array) with the requested data type and memory layout, np.asarray() will return it as is (without copying)
If a is not a np.ndarray or has a different data type, np.asarray() will create a new array based on a.
dtype: data-type, Optional-
Specifies the data-type of the array elements. If not provided, it’s inferred from the input.
import numpy as np
array = np.asarray([1, 2, 3], dtype=np.float32)
print(array)
# Output: [1., 2., 3.]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).
device: None, Optional-
In NumPy 2.1, the device parameter in np.asarray() 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.
order: {'C', 'F', 'K', 'A'}, Optional-
Specifies the memory layout order for multi-dimensional arrays.
order = K (default) +
Preserves the original memory layout (C-order or F-order) as much as possible.
order = C +
Row-major order (C-style), where rows are flattened first.
order = F +
Column-major order (Fortran-style), where columns are flattened first.
order = A +
Chooses row-major (C-order) unless the array is Fortran-contiguous.
.ravel() is a NumPy function used to flatten a multi-dimensional array into a 1D array.
copy: (True or False) Optional-
The copy parameter specifies whether to create a new array (copy) or if NumPy can reuse the existing memory from the input array (i.e., return a view ).
copy = True (default) +
A new NumPy array is created, and the data is copied.
import numpy as np
# Original array
original_array = np.asarray([1, 2, 3])
# Creating a new array with copy=True (default behavior)
new_array = np.asarray(original_array, copy=True)
# Modify the new array
new_array[0] = 99
# Print both arrays
print("Original Array:", original_array) # Output: [1 2 3]
print("New Array:", new_array) # Output: [99 2 3]copy = False +
NumPy avoids copying and returns a view of the original array, meaning changes to the new array will also affect the original array.
import numpy as np
# Original array
original_array = np.asarray([1, 2, 3])
# Creating a new array with copy=False (view)
new_array = np.asarray(original_array, copy=False)
# Modify the new array
new_array[0] = 99
# Print both arrays
print("Original Array:", original_array) # Output: [99 2 3]
print("New Array:", new_array) # Output: [99 2 3]Modification to the original_array or new_array will affect both the arrays.
like: None, Optional-
The like parameter in np.asarray() 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).