Endian-ness.
< = little-endian (LSB first)
> = big-endian (MSB first)
https://docs.scipy.org/doc/numpy/reference/generated/numpy.dtype.byteorder.html
Answer from Jason S on Stack OverflowNumPy
numpy.org › doc › stable › reference › arrays.dtypes.html
Data type objects (dtype) — NumPy v2.4 Manual
>>> dt = np.dtype('i4') # 32-bit signed integer >>> dt = np.dtype('f8') # 64-bit floating-point number >>> dt = np.dtype('c16') # 128-bit complex floating-point number >>> dt = np.dtype('S25') # 25-length zero-terminated bytes >>> dt = np.dtype('U25') # 25-character string
Top answer 1 of 3
29
Endian-ness.
< = little-endian (LSB first)
> = big-endian (MSB first)
https://docs.scipy.org/doc/numpy/reference/generated/numpy.dtype.byteorder.html
2 of 3
14
I just had the same question and tried to search the answer online.
'f' is the shorthand for 'float32'.
'f4' also means 'float32' because it has 4 bytes and each byte has 8 bits.
Similarly, 'f8' means 'float64' because 8*8 = 64.
For the difference between '>f4' and '<f4', it is related to how the 32 bits are stored in 4 bytes.
('>')Big Endian Byte Order: The most significant byte (the "big end") of the data is placed at the byte with the lowest address. The rest of the data is placed in order in the next three bytes in memory.
('<')Little Endian Byte Order: The least significant byte (the "little end") of the data is placed at the byte with the lowest address. The rest of the data is placed in order in the next three bytes in memory.
Please see this link for more detailed information: https://chortle.ccsu.edu/AssemblyTutorial/Chapter-15/ass15_3.html
Python Data Science Handbook
jakevdp.github.io › PythonDataScienceHandbook › 02.09-structured-data-numpy.html
Structured Data: NumPy's Structured Arrays | Python Data Science Handbook
# Use a compound data type for ... of maximum length 10," 'i4' translates to "4-byte (i.e., 32 bit) integer," and 'f8' translates to "8-byte (i.e., 64 bit) float."...
SciPy
docs.scipy.org › doc › numpy-1.4.x › reference › arrays.dtypes.html
Data type objects (dtype) - Numpy and Scipy Documentation
July 21, 2010 - >>> dt = np.dtype('i4') # 32-bit signed integer >>> dt = np.dtype('f8') # 64-bit floating-point number >>> dt = np.dtype('c16') # 128-bit complex floating-point number >>> dt = np.dtype('a25') # 25-character string
NumPy
numpy.org › doc › stable › › reference › generated › numpy.dtype.html
numpy.dtype — NumPy v2.4 Manual
>>> np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', 'S10')]) Using comma-separated field formats. The shape is (2,3): >>> np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))]) Using tuples. int is a fixed type, 3 the field’s shape.
NumPy
numpy.org › doc › 2.1 › reference › generated › numpy.dtype.html
numpy.dtype — NumPy v2.1 Manual
>>> np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', 'S10')]) Using comma-separated field formats. The shape is (2,3): >>> np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))]) Using tuples. int is a fixed type, 3 the field’s shape.
NumPy
numpy.org › doc › 2.1 › reference › arrays.dtypes.html
Data type objects (dtype) — NumPy v2.1 Manual
>>> dt = np.dtype('i4') # 32-bit signed integer >>> dt = np.dtype('f8') # 64-bit floating-point number >>> dt = np.dtype('c16') # 128-bit complex floating-point number >>> dt = np.dtype('S25') # 25-length zero-terminated bytes >>> dt = np.dtype('U25') # 25-character string
GitHub
github.com › numpy › numpy › issues › 2207
Different behaviour in ndarray.astype() for scalars and arrays (Trac #1611) · Issue #2207 · numpy/numpy
October 19, 2012 - np.array(42, dtype=">f8").astype('>f8').dtype dtype('float64') np.array([42], dtype=">f8").astype('>f8').dtype dtype('>f8')
Author numpy-gitbot
GeeksforGeeks
geeksforgeeks.org › data-type-object-dtype-numpy-python
Data type Object (dtype) in NumPy Python - GeeksforGeeks
August 11, 2021 - # Python program for demonstrating # the use of fields import numpy as np # A structured data type containing a 16-character string (in field ‘name’) # and a sub-array of two 64-bit floating-point number (in field ‘grades’): dt = np.dtype([('name', np.unicode_, 16), ('grades', np.float64, (2,))]) # Data type of object with field grades print(dt['grades']) # Data type of object with field name print(dt['name']) Output: ('<f8', (2,)) Python ·
GitHub
github.com › pyvista › pyvista › issues › 540
'>f8' dtype arrays not properly converted by VTK · Issue #540 · pyvista/pyvista
January 15, 2020 - When trying to, e.g., scatter plot an array of points with the '>f8' dtype, the bounding box is set to garbage during construction, resulting in a blank plot. points = np.random.randn(100, 3).astype('>f8') poly = pv.PolyData(points) poly...
Author sampotter
JAX Documentation
docs.jax.dev › en › latest › _autosummary › jax.numpy.dtype.html
jax.numpy.dtype — JAX documentation
>>> np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', 'S10')]) Using comma-separated field formats. The shape is (2,3): >>> np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))]) Using tuples. int is a fixed type, 3 the field’s shape.
GitHub
github.com › ml-explore › mlx › issues › 1670
Missing f8 dtypes · Issue #1670 · ml-explore/mlx
December 7, 2024 - However, inspecting the data types list, the smallest float dtype I saw in mlx today was 16 bits (f16 or bf16) Adding 8 bit floats to mlx would effectively double the maximum possible model size. Would this be possible with software or does it need to be a hardware update? If it is possible with software, how could we make it happen? Some options for sensible default f8 e/m split for an 8-bit dtype could be:
Author bionicles
Python Course
python-course.eu › numerical-programming › numpy-data-objects-dtype.php
3. Numpy Data Objects, dtype | Numerical Programming
import numpy as np # Native byte order (depends on system) native = np.dtype('f8') # Explicit little-endian little_endian = np.dtype('<f8') # Explicit big-endian big_endian = np.dtype('>f8') print("Native byte order dtype:", native) print("Little-endian dtype: ", little_endian) print("Big-endian dtype: ", big_endian)
University of Texas at Austin
het.as.utexas.edu › HET › Software › Numpy › reference › generated › numpy.dtype.html
numpy.dtype — NumPy v1.9 Manual
>>> np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', '|S10')]) Using comma-separated field formats. The shape is (2,3): >>> np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))]) Using tuples. int is a fixed type, 3 the field’s shape.
SciPy
docs.scipy.org › doc › numpy-1.12.0 › reference › generated › numpy.dtype.html
numpy.dtype — NumPy v1.12 Manual
January 16, 2017 - >>> np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', '|S10')]) Using comma-separated field formats. The shape is (2,3): >>> np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))]) Using tuples. int is a fixed type, 3 the field’s shape.
SciPy
docs.scipy.org › doc › numpy-1.16.1 › reference › generated › numpy.dtype.html
numpy.dtype — NumPy v1.16 Manual
January 31, 2019 - >>> np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', '|S10')]) Using comma-separated field formats. The shape is (2,3): >>> np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))]) Using tuples. int is a fixed type, 3 the field’s shape.
SciPy
docs.scipy.org › doc › numpy-1.6.0 › reference › generated › numpy.dtype.html
numpy.dtype — NumPy v1.6 Manual (DRAFT)
May 15, 2011 - >>> np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', '|S10')]) Using comma-separated field formats. The shape is (2,3): >>> np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))]) Using tuples. int is a fixed type, 3 the field’s shape.
NumPy
numpy.org › doc › 2.3 › reference › generated › numpy.dtype.html
numpy.dtype — NumPy v2.3 Manual
>>> np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', 'S10')]) Using comma-separated field formats. The shape is (2,3): >>> np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))]) Using tuples. int is a fixed type, 3 the field’s shape.
NumPy
numpy.org › devdocs › reference › generated › numpy.dtype.html
numpy.dtype — NumPy v2.5.dev0 Manual
>>> np.dtype([('a','f8'),('b','S10')]) dtype([('a', '<f8'), ('b', 'S10')]) Using comma-separated field formats. The shape is (2,3): >>> np.dtype("i4, (2,3)f8") dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))]) Using tuples. int is a fixed type, 3 the field’s shape.
Note.nkmk.me
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NumPy: astype() to change dtype of an array | note.nkmk.me
February 4, 2024 - The usage is the same as np.iinfo(). The arguments can be a type object (e.g., np.float64), a string ('float64', 'f8'), or a value (0.1). You can output an overview with print() or obtain the values of various attributes as numbers.