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Getting started What is NumPy? Installation NumPy quickstart NumPy: the absolute basics for beginners Fundamentals and usage NumPy fundamentals NumPy for MATLAB users NumPy tutorials NumPy how-tos Advanced usage and interoperability Using NumPy C-API F2PY user guide and reference manual Under-the-hood documentation for developers Interoperability with NumPy Extras Glossary Release notes 2.4.0 2.3.

Parameters: a1-D array_likeInput array. If sorter is None, then it must be sorted in ascending order, otherwise sorter must be an array of indices that sort it. varray_likeValues to insert into a. side{‘left’, ‘right’}, optionalIf ‘left’, the index of the first suitable location found is given. If ‘right’, return the last such index. If there is no suitable index, return either 0 or N (where N is

Version: 2.3 Download documentation: Historical versions of documentation Useful links: Installation | Source Repository | Issue Tracker | Q&A Support | Mailing List NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for

numpy.insert# numpy.insert(arr, obj, values, axis=None)[source]# Insert values along the given axis before the given indices. Parameters: arrarray_likeInput array. objslice, int, array-like of ints or boolsObject that defines the index or indices before which values is inserted. Changed in version 2.1.2: Boolean indices are now treated as a mask of elements to insert, rather than being cast to the

numpy.argmax# numpy.argmax(a, axis=None, out=None, *, keepdims=<no value>)[source]# Returns the indices of the maximum values along an axis. Parameters: aarray_likeInput array. axisint, optionalBy default, the index is into the flattened array, otherwise along the specified axis. outarray, optionalIf provided, the result will be inserted into this array. It should be of the appropriate shape and d

numpy.linalg.norm# linalg.norm(x, ord=None, axis=None, keepdims=False)[source]# Matrix or vector norm. This function is able to return one of eight different matrix norms, or one of an infinite number of vector norms (described below), depending on the value of the ord parameter. Parameters: xarray_likeInput array. If axis is None, x must be 1-D or 2-D, unless ord is None. If both axis and ord are

numpy.genfromtxt# numpy.genfromtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=" !#$%&'()*+, -./:;<=>?@[\\]^{|}~", replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_r

Getting started What is NumPy? Installation NumPy quickstart NumPy: the absolute basics for beginners Fundamentals and usage NumPy fundamentals Array creation Indexing on ndarrays I/O with NumPy Data types Broadcasting Copies and views Working with Arrays of Strings And Bytes Structured arrays Universal functions (ufunc) basics NumPy for MATLAB users NumPy tutorials NumPy how-tos Advanced usage an

Translates slice objects to concatenation along the second axis.

numpy.nan_to_num# numpy.nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None)[source]# Replace NaN with zero and infinity with large finite numbers (default behaviour) or with the numbers defined by the user using the nan, posinf and/or neginf keywords. If x is inexact, NaN is replaced by zero or by the user defined value in nan keyword, infinity is replaced by the largest finite floating po

numpy.polyfit# numpy.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False)[source]# Least squares polynomial fit. Note This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide. Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of degree deg to points (x

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 the sort order by multiple keys. The last key in the sequence is used for the primary sort order, ties are broken by the second-to-last key, and so on. Parameters: keys(k, m, n, …) array-likeThe k keys to be s

Version: 2.3 Download documentation: Historical versions of documentation Useful links: Installation | Source Repository | Issue Tracker | Q&A Support | Mailing List NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for

numpy.ndarray# class numpy.ndarray(shape, dtype=float, buffer=None, offset=0, strides=None, order=None)[source]# An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something e

Linear algebra# The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient low level implementations of standard linear algebra algorithms. Those libraries may be provided by NumPy itself using C versions of a subset of their reference implementations but, when possible, highly optimized libraries that take advantage of specialized processor functionality are preferred. Exampl

numpy.pad# numpy.pad(array, pad_width, mode='constant', **kwargs)[source]# Pad an array. Parameters: arrayarray_like of rank NThe array to pad. pad_width{sequence, array_like, int}Number of values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. (before, after) or ((before, after),) yields same before and after pad for each axis. (pa

Getting started What is NumPy? Installation NumPy quickstart NumPy: the absolute basics for beginners Fundamentals and usage NumPy fundamentals Array creation Indexing on ndarrays I/O with NumPy Data types Broadcasting Copies and views Working with Arrays of Strings And Bytes Structured arrays Universal functions (ufunc) basics NumPy for MATLAB users NumPy tutorials NumPy how-tos Advanced usage an

numpy.matrix# class numpy.matrix(data, dtype=None, copy=True)[source]# Returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D array that retains its 2-D nature through operations. It has certain special operators, such as * (matrix multiplication) and ** (matrix power). Note It is no longer recommended to use this class, even for linear algebra. Instead

NumPy reference# Release: 2.3 Date: June 09, 2025 This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. For learning how to use NumPy, see the complete documentation. Python API#

numpy.where# numpy.where(condition, [x, y, ]/)# Return elements chosen from x or y depending on condition. Note When only condition is provided, this function is a shorthand for np.asarray(condition).nonzero(). Using nonzero directly should be preferred, as it behaves correctly for subclasses. The rest of this documentation covers only the case where all three arguments are provided. Parameters: c

numpy.roll# numpy.roll(a, shift, axis=None)[source]# Roll array elements along a given axis. Elements that roll beyond the last position are re-introduced at the first. Parameters: aarray_likeInput array. shiftint or tuple of intsThe number of places by which elements are shifted. If a tuple, then axis must be a tuple of the same size, and each of the given axes is shifted by the corresponding num

The N-dimensional array (ndarray)# An ndarray is a (usually fixed-size) multidimensional container of items of the same type and size. The number of dimensions and items in an array is defined by its shape, which is a tuple of N non-negative integers that specify the sizes of each dimension. The type of items in the array is specified by a separate data-type object (dtype), one of which is associa

numpy.ravel# numpy.ravel(a, order='C')[source]# Return a contiguous flattened array. A 1-D array, containing the elements of the input, is returned. A copy is made only if needed. As of NumPy 1.10, the returned array will have the same type as the input array. (for example, a masked array will be returned for a masked array input) Parameters: aarray_likeInput array. The elements in a are read in t

numpy.corrcoef# numpy.corrcoef(x, y=None, rowvar=True, bias=<no value>, ddof=<no value>, *, dtype=None)[source]# Return Pearson product-moment correlation coefficients. Please refer to the documentation for cov for more detail. The relationship between the correlation coefficient matrix, R, and the covariance matrix, C, is The values of R are between -1 and 1, inclusive. Parameters: xarray_likeA 1

NumPy reference# Release: 2.3 Date: June 09, 2025 This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. For learning how to use NumPy, see the complete documentation. Python API#

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

Routines and objects by topic# In this chapter, routine docstrings are presented, grouped by functionality. Many docstrings contain example code, which demonstrates basic usage of the routine. A convenient way to execute examples is the %doctest_mode mode of IPython, which allows for pasting of multi-line examples and preserves indentation.