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 Overflow 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
NumPy
numpy.org › doc › stable › reference › arrays.dtypes.html
Data type objects (dtype) — NumPy v2.4 Manual
>>> import numpy as np >>> dt = np.dtype("i4, (2,3)f8, f4") field named f0 containing a 3-character string · field named f1 containing a sub-array of shape (3,) containing 64-bit unsigned integers · field named f2 containing a 3 x 4 sub-array containing 10-character strings ·
GeeksforGeeks
geeksforgeeks.org › python › numpy-data-type-objects
NumPy - Data type Objects(dtype) - GeeksforGeeks
July 23, 2025 - import numpy as np # Custom structured data type x = np.dtype([('name', 'U10'), ('age', 'i4'), ('weight', 'f4')]) a = np.array([('Alice', 25, 55.5), ('Bob', 30, 72.3)], dtype=x) print(a) print(a.dtype) Output · [('Alice', 25, 55.5) ('Bob', 30, 72.3)] [('name', '<U10'), ('age', '<i4'), ('weight', '<f4')] Byte Order : The byte order can be specified using prefixes: < for little-endian.
GitHub
github.com › numpy › numpy › issues › 13444
numpy.concatenate change '>f4' dtype to 'float32' · Issue #13444 · numpy/numpy
April 30, 2019 - I've encountered some strange behavior of numpy.concatenate today. So I have two arrays of data type '>f4', but after concatenate, the combined array type changed to 'float32'. This is confusion and I thought all numpy function keep the ...
Author wang1943
Python for Data Science
python4data.science › en › latest › workspace › numpy › dtype.html
dtype - Python for Data Science
ndarray is a container for homogeneous data, i.e. all elements must be of the same type. Each array has a dtype, an object that describes the data type of the array: NumPy data types:,,, Type, Type...
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((np.int32, (2,2))) # 2 x 2 integer sub-array >>> dt = np.dtype(('S10', 1)) # 10-character string >>> dt = np.dtype(('i4, (2,3)f8, f4', (2,3))) # 2 x 3 record sub-array · (base_dtype, new_dtype) Both arguments must be convertible to data-type objects in this case.
Ufkapano
ufkapano.github.io › scicomppy › week08 › np_dtype.html
NumPy - data type objects
# Using array-scalar types. dt_int ... # "H", "u2", 'uint16'; # "u4", "uint32"; # "u8", "uint64"; dt_float = np.dtype(float) # Python-compatible floating-point number # "f2", 'float16'; # "f", "f4", 'float32' ('float' in 32-bit); # "d", "f8", 'float64' ('double', 'float' in ...
NumPy
numpy.org › doc › 2.1 › reference › arrays.dtypes.html
Data type objects (dtype) — NumPy v2.1 Manual
>>> import numpy as np >>> dt = np.dtype("i4, (2,3)f8, f4") field named f0 containing a 3-character string · field named f1 containing a sub-array of shape (3,) containing 64-bit unsigned integers · field named f2 containing a 3 x 4 sub-array containing 10-character strings ·
Cryosparc
tools.cryosparc.com › api › dtype.html
cryosparc.dtype — cryosparc-tools
Can just be a single string such as “f4”, “3u4” or “O”. A datatype description of a ndarray entry. Can also be the a tuple with a string datatype name and its shape. For example, the following dtypes are equivalent.
GitHub
github.com › tensorflow › tensorflow › issues › 13384
Tensorflow - Error: Cannot convert value dtype('<f4') to a TensorFlow DType · Issue #13384 · tensorflow/tensorflow
September 29, 2017 - File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/dtypes.py", line 584, in as_dtype "Cannot convert value %r to a TensorFlow DType." % type_value) TypeError: Cannot convert value dtype('<f4') to a TensorFlow DType.
Author claudefalbriard
SciPy
docs.scipy.org › doc › numpy-1.14.0 › user › basics.rec.html
Structured arrays — NumPy v1.14 Manual
>>> d = np.dtype([('x', 'i8'), ('y', 'f4')]) >>> d.names ('x', 'y') The field names may be modified by assigning to the names attribute using a sequence of strings of the same length. The dtype object also has a dictionary-like attribute, fields, whose keys are the field names (and Field Titles, see below) and whose values are tuples containing the dtype and byte offset of each field.
TutorialsPoint
tutorialspoint.com › numpy › numpy_data_types.htm
NumPy - Data Types
import numpy as np student = np.dtype([('name', 'S20'), ('age', 'i1'), ('marks', 'f4')]) print(student) We get the output as shown below − ·
GeeksforGeeks
geeksforgeeks.org › data-type-object-dtype-numpy-python
Data type Object (dtype) in NumPy Python - GeeksforGeeks
August 11, 2021 - # dt is a dtype object dt = np.dtype('>i4') print("Byte order is:",dt.byteorder) print("Size is:",dt.itemsize) print("Data type is:",dt.name) ... b1, i1, i2, i4, i8, u1, u2, u4, u8, f2, f4, f8, c8, c16, a (representing bytes, ints, unsigned ints, floats, complex and fixed-length strings of specified byte lengths)
NumPy
numpy.org › devdocs › reference › arrays.dtypes.html
Data type objects (dtype) — NumPy v2.5.dev0 Manual
>>> dt = np.dtype((np.int32, (2,2))) # 2 x 2 integer sub-array >>> dt = np.dtype(('i4, (2,3)f8, f4', (2,3))) # 2 x 3 structured sub-array
Note.nkmk.me
note.nkmk.me › home › python › numpy
NumPy: astype() to change dtype of an array | note.nkmk.me
February 4, 2024 - NumPy arrays (ndarray) hold a data type (dtype). You can set this through various operations, such as when creating an ndarray with np.array(), or change it later with astype(). Data type objects (dty ...
GitHub
github.com › numpy › numpy › issues › 17173
Regression in array creation `np.array([...], dtype='(2,2)f4')` RuntimeError (Zarr testsuite ) · Issue #17173 · numpy/numpy
August 27, 2020 - In [1]: import numpy as np ...: a = np.array([((0, 1), ...: (1, 2)), ...: ((1, 2), ...: (2, 3)), ...: ((2, 3), ...: (3, 4))], dtype='(2,2)f4') --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-1-0a6ed20baa50> in <module> 5 (2, 3)), 6 ((2, 3), ----> 7 (3, 4))], dtype='(2,2)f4') RuntimeError: Inconsistent object during array creation?
Author Carreau
Python Data Science Handbook
jakevdp.github.io › PythonDataScienceHandbook › 02.09-structured-data-numpy.html
Structured Data: NumPy's Structured Arrays | Python Data Science Handbook
dtype([('name', '<U10'), ('age', '<i8'), ('weight', '<f4')]) A compound type can also be specified as a list of tuples: In [12]: np.dtype([('name', 'S10'), ('age', 'i4'), ('weight', 'f8')]) Out[12]: dtype([('name', 'S10'), ('age', '<i4'), ('weight', '<f8')]) If the names of the types do not matter to you, you can specify the types alone in a comma-separated string: In [13]: np.dtype('S10,i4,f8') Out[13]: dtype([('f0', 'S10'), ('f1', '<i4'), ('f2', '<f8')]) The shortened string format codes may seem confusing, but they are built on simple principles.
SciPy
docs.scipy.org › doc › numpy-1.10.1 › user › basics.rec.html
Structured arrays — NumPy v1.10 Manual
>>> x = np.array([(1,2.,'Hello'), (2,3.,"World")], ... dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')]) >>> x array([(1, 2.0, 'Hello'), (2, 3.0, 'World')], dtype=[('foo', '>i4'), ('bar', '>f4'), ('baz', '|S10')])
NumPy
numpy.org › doc › stable › user › basics.rec.html
Structured arrays — NumPy v2.4 Manual
>>> np.dtype([(('my title', 'name'), 'f4')]) dtype([(('my title', 'name'), '<f4')]) When using the first form of dictionary-based specification, the titles may be supplied as an extra 'titles' key as described above. When using the second (discouraged) dictionary-based specification, the title can be supplied by providing a 3-element tuple (datatype, offset, title) instead of the usual 2-element tuple: >>> np.dtype({'name': ('i4', 0, 'my title')}) dtype([(('my title', 'name'), '<i4')])
GitHub
github.com › numpy › numpy › issues › 30890
BUG: common_type(dtype('f4'), dtype('f4')) raises AttributeError unexpectedly · Issue #30890 · numpy/numpy
February 26, 2026 - >>> import numpy as np >>> np.common_type(np.dtype('f4'), np.dtype('f4')) Traceback (most recent call last): File "<python-input-1>", line 1, in <module> np.common_type(np.dtype('f4'), np.dtype('f4')) ~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/opavlyk/miniforge/envs/np_new/...
Author oleksandr-pavlyk