Numpy.Floor — Numpy V1.18.Dev0 Manual
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numpy.choose ¶ numpy.choose(a, choices, out=None, mode=’raise‘) [源代码] ¶ 从索引数组和一组要选择的数组构造数组。 首先，如果混淆或不确定，一定要看一下例子——在它的全部概括性
Highlights ¶ The NumPy 1.12.0 release contains a large number of fixes and improvements, but few that stand out above all others. That makes picking out the highlights somewhat arbitrary NumPy 1.24 Release Notes # The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the
numpy.broadcast # class numpy.broadcast [source] # Produce an object that mimics broadcasting. Parameters: in1, in2, array_like Input parameters. Returns: bbroadcast object numpy.log2 ¶ numpy.log2(x, /, out=None, *, where=True, casting=’same_kind‘, order=’K‘, dtype=None, subok=True[, signature, extobj]) =
numpy.floor — NumPy v1.21 Manual
Writing custom array containers # Numpy’s dispatch mechanism, introduced in numpy version v1.16 is the recommended approach for writing custom N-dimensional array containers that are
NumPy user guide # This guide is an overview and explains the important features; details are found in NumPy Reference. NumPy 1.18.0发行说明 ¶ 除了常见的bug修复之外，这个NumPy版本还清理并记录了新的随机C-API，终止了大量旧的不赞成意见，并改进了文档的外观。支持的Python版本是3.5-3.8。这是
NumPy 1.8.2 Release Notes # This is a bugfix only release in the 1.8.x series. Issues fixed # gh-4836: partition produces wrong results for multiple selections in equal ranges gh-4656: Make NumPy 1.14.5 Release Notes ¶ This is a bugfix release for bugs reported following the 1.14.4 release. The most significant fixes are: fixes for compilation errors on alpine and NetBSD The numpy.fmod ¶ numpy.fmod(x1, x2, /, out=None, *, where=True, casting=’same_kind‘, order=’K‘, dtype=None, subok=True[, signature, extobj]) =
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- numpy.tanh — NumPy v2.4.dev0 Manual
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numpy.tanh # numpy.tanh(x, /, out=None, *, where=True, casting=’same_kind‘, order=’K‘, dtype=None, subok=True[, signature]) =
NumPy 1.18.1 Release Notes # This release contains fixes for bugs reported against NumPy 1.18.0. Two bugs in particular that caused widespread problems downstream were: The cython This is documentation for an old release of NumPy (version 1.18). Read this page in the documentation of the latest stable release (version 2.3). NumPy v1.18 Manual Welcome!
numpy.stack # numpy.stack(arrays, axis=0, out=None, *, dtype=None, casting=’same_kind‘) [source] # Join a sequence of arrays along a new axis. The axis parameter specifies the index NumPy 1.18.0 Release Notes # In addition to the usual bug fixes, this NumPy release cleans up and documents the new random C-API, expires a large number of old deprecations, and Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples. ndarray.itemsize the size in bytes of each element of the array. For example,
Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples. ndarray.itemsize the size in bytes of each element of the array. For example,
numpy.fix ¶ numpy.fix(x, out=None) [源代码] ¶ 四舍五入到接近零的整数。 将浮点元素数组向上舍入到接近零的整数。四舍五入的值作为浮点数返回。 参数 xarray_like 要舍入的浮点数组
numpy.remainder(x1, x2, /, out=None, *, where=True, casting=’same_kind‘, order=’K‘, dtype=None, subok=True[, signature, extobj]) =
numpy.dstack # numpy.dstack(tup) [source] # Stack arrays in sequence depth wise (along third axis). This is equivalent to concatenation along the third axis after 2-D arrays of shape (M,N) numpy.cov # numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None, *, dtype=None) [source] # Estimate a covariance matrix, given data and numpy.floor_divide ¶ numpy.floor_divide(x1, x2, /, out=None, *, where=True, casting=’same_kind‘, order=’K‘, dtype=None, subok=True[, signature, extobj]) = This is documentation for an old release of NumPy (version 1.17.0). Search for this page in the documentation of the latest stable release (version > 1.17). The mypy plugin, introduced in numpy/numpy#17843, has again been expanded: the plugin now is now responsible for setting the platform-specific precision of numpy.ctypeslib.c_intp, the numpy.lexsort # numpy.lexsort(keys, axis=-1) # Perform an indirect stable sort using a sequence of keys. Given multiple sorting keys, lexsort returns an array of integer indices that describes numpy.floor ¶ numpy.floor(x, /, out=None, *, where=True, casting=’same_kind‘, order=’K‘, dtype=None, subok=True[, signature, extobj]) = numpy.dot ¶ numpy. dot (a, b, out=None) ¶ Dot product of two arrays. For 2-D arrays it is equivalent to matrix multiplication, and for 1-D arrays to inner product of vectors (without numpy.rint # numpy.rint(x, /, out=None, *, where=True, casting=’same_kind‘, order=’K‘, dtype=None, subok=True[, signature]) = NumPy 1.17.0 Release Notes # This NumPy release contains a number of new features that should substantially improve its performance and usefulness, see Highlights below for a Scalars # Python defines only one type of a particular data class (there is only one integer type, one floating-point type, etc.). This can be convenient in applications that don’t need to be numpy.conjugate # numpy.conjugate(x, /, out=None, *, where=True, casting=’same_kind‘, order=’K‘, dtype=None, subok=True[, signature]) =