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numpy.distutilsuser guide...onfig.add_subpackage(subpackage_name,subpackage_path=None) — add SciPy subpackage configuration to the current one. The meaning and usage of arguments is explained above, see config.get_subpackage() method. config.add_data_files(*files) — p...
- Array API
...he array. PyObject *PyArray_BASE(PyArrayObject *arr) This returns the base object of the array. In most cases, this means the object which owns the memory the array is pointing at. If you are constructing an array using the C API, and s...
- Array iterator API
...then set every value to the reduction unit before doing the iteration loop. In the case of a buffered reduction, this means you must also specify the flag NPY_ITER_DELAY_BUFALLOC, then reset the iterator after initializing the allocated o...
- Bit generators
...end using small seeds below 32 bits for general use. Using just a small set of seeds to instantiate larger state spaces means that there are some initial states that are impossible to reach. This creates some biases if everyone uses such va...
- Broadcasting
..., the smaller array is “broadcast” across the larger array so that they have compatible shapes. Broadcasting provides a means of vectorizing array operations so that looping occurs in C instead of Python. It does this without making needles...
- Building from source
...build & packaging documentation that is relevant. System-level dependencies NumPy uses compiled code for speed, which means you need compilers and some other system-level (i.e, non-Python / non-PyPI) dependencies to build it on your syste...
- Contributing to NumPy
...st your work locally before committing. A PR must be approved by at least one core team member before merging. Approval means the core team member has carefully reviewed the changes, and the PR is ready for merging. Document changes Beyon...
- Data type promotion in NumPy
...general promotion rules In NumPy promotion refers to what specific functions do with the result and in some cases, this means that NumPy may deviate from what the np.result_type would give. Behavior of sum and prod np.sum and np.prod will...
- Data types
...have used the Python float object as a dtype instead of numpy.float64. NumPy knows that int refers to numpy.int_, bool means numpy.bool, that float is numpy.float64 and complex is numpy.complex128. The other data-types do not have Python...
- Discrete Fourier Transform (
numpy.fft)...mitian, i.e., the component at frequency \(f_k\) is the complex conjugate of the component at frequency \(-f_k\), which means that for real inputs there is no information in the negative frequency components that is not already available fr...
- How does the CPU dispatcher work?
...How does the CPU dispatcher work? NumPy dispatcher is based on multi-source compiling, which means taking a certain source and compiling it multiple times with different compiler flags and also with different C de...
- How to contribute to the NumPy documentation
...are our best guide to what needs fixing. If you write a missing doc you join the front line of open source, but it’s a meaningful contribution just to let us know what’s missing. If you want to compose a doc, run your thoughts by the maili...
- Internal organization of NumPy arrays
...use of the copy method if one really wants to make a new and independent copy of the data buffer. New views into arrays mean the object reference counts for the data buffer increase. Simply doing away with the original array object will not...
- Interoperability with NumPy
...method returns an array or any nested sequence) that implements it can be used as a NumPy array. If possible, this will mean using __array__() to create a NumPy ndarray view of the array-like object. Otherwise, this copies the data into a n...
- Legacy random generation
...aplace([loc, scale, size]) Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay). logistic([loc, scale, size]) Draw samples from a logistic distribution. lognormal([mean, sigm...
- Linear algebra (
numpy.linalg) - Multithreaded generation
...a single thread. In [4]: print(mrng.threads) ...: %timeit mrng.fill() Out[4]: 4 ...: 32.8 ms ± 2.71 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) The single threaded call directly uses the BitGenerator. In [5]: values = n...
- NumPy 1.10.0 Release Notes
- NumPy 1.12.0 Release Notes
- NumPy 1.14.0 Release Notes
...type-like arguments. It might be expected that issubdtype(np.float32, 'float64') and issubdtype(np.float32, np.float64) mean the same thing - however, there was an undocumented special case that translated the former into issubdtype(np.floa...
- NumPy 1.15.0 Release Notes
...failed on python 3 when called with a string. Multidimensional indexing with anything but a tuple is deprecated. This means that the index list in ind = [slice(None), 0]; arr[ind] should be changed to a tuple, e.g., ind = [slice(None), 0]...
- NumPy 1.16.0 Release Notes
...ing point values, while before it was equal to the size of the type. For many users (for instance on x64/unix/gcc) this means that complex64 is now 4-byte aligned instead of 8-byte aligned. An important consequence is that aligned structure...
- NumPy 1.17.0 Release Notes
...rounding Casting from a different floating point precision to float16 used incorrect rounding in some edge cases. This means in rare cases, subnormal results will now be rounded up instead of down, changing the last bit (ULP) of the result...
- NumPy 1.19.0 Release Notes
...ixed scalars and arrays in PyArray_ConvertToCommonType has been changed to adhere to those used by np.result_type. This means that input such as (1000, np.array([1], dtype=np.uint8))) will now return uint16 dtypes. In most cases the behavio...
- NumPy 1.20.0 Release Notes
- NumPy 1.21.0 Release Notes
- NumPy 1.22.0 Release Notes
- NumPy 1.23.0 Release Notes
- NumPy 1.24 Release Notes
- NumPy 1.7.0 Release Notes
- NumPy 1.8.0 Release Notes
- NumPy 1.9.0 Release Notes
- NumPy 2.0 migration guide
- NumPy 2.0.0 Release Notes
- NumPy 2.1.0 Release Notes
- NumPy 2.2.0 Release Notes
- NumPy C code explanations
- NumPy for MATLAB users
- NumPy project governance and decision-making
- NumPy quickstart
- NumPy security
- numpy.average
- numpy.bartlett
- numpy.char.chararray.sort
- numpy.fft.rfft
- numpy.frombuffer
- numpy.fromfile
- numpy.gradient
- numpy.histogram_bin_edges
- numpy.i: a SWIG interface file for NumPy
- numpy.loadtxt
- numpy.ma.frombuffer
- numpy.matlib.randn
- numpy.matrix.sort
- numpy.memmap.sort
- numpy.ndarray.sort
- numpy.polynomial.polynomial.polyfit
- numpy.quantile
- numpy.random.chisquare
- numpy.random.choice
- numpy.random.f
- numpy.random.gamma
- numpy.random.Generator.beta
- numpy.random.Generator.chisquare
- numpy.random.Generator.choice
- numpy.random.Generator.f
- numpy.random.Generator.gamma
- numpy.random.Generator.gumbel
- numpy.random.Generator.laplace
- numpy.random.Generator.logistic
- numpy.random.Generator.lognormal
- numpy.random.Generator.multivariate_normal
- numpy.random.Generator.negative_binomial
- numpy.random.Generator.noncentral_f
- numpy.random.Generator.normal
- numpy.random.Generator.poisson
- numpy.random.Generator.rayleigh
- numpy.random.Generator.standard_gamma
- numpy.random.Generator.standard_normal
- numpy.random.Generator.standard_t
- numpy.random.Generator.vonmises
- numpy.random.Generator.wald
- numpy.random.Generator.weibull
- numpy.random.gumbel
- numpy.random.laplace
- numpy.random.logistic
- numpy.random.lognormal
- numpy.random.multivariate_normal
- numpy.random.noncentral_f
- numpy.random.normal
- numpy.random.randn
- numpy.random.RandomState.chisquare
- numpy.random.RandomState.choice
- numpy.random.RandomState.f
- numpy.random.RandomState.gamma
- numpy.random.RandomState.gumbel
- numpy.random.RandomState.laplace
- numpy.random.RandomState.logistic
- numpy.random.RandomState.lognormal
- numpy.random.RandomState.multivariate_normal
- numpy.random.RandomState.noncentral_f
- numpy.random.RandomState.normal
- numpy.random.RandomState.randn
- numpy.random.RandomState.rayleigh
- numpy.random.RandomState.standard_gamma
- numpy.random.RandomState.standard_normal
- numpy.random.RandomState.standard_t
- numpy.random.RandomState.vonmises
- numpy.random.RandomState.wald
- numpy.random.rayleigh
- numpy.random.standard_gamma
- numpy.random.standard_normal
- numpy.random.standard_t
- numpy.random.vonmises
- numpy.random.wald