Routines

In this chapter routine docstrings are presented, grouped by functionality. Many docstrings contain example code, which demonstrates basic usage of the routine. The examples assume that NumPy is imported with:

>>> import numpy as np

A convenient way to execute examples is the %doctest_mode mode of IPython, which allows for pasting of multi-line examples and preserves indentation.

• Array creation routines
  • From shape or value
  • From existing data
  • Creating record arrays (numpy.rec)
  • Creating character arrays (numpy.char)
  • Numerical ranges
  • Building matrices
  • The Matrix class
• Array manipulation routines
  • Basic operations
  • Changing array shape
  • Transpose-like operations
  • Changing number of dimensions
  • Changing kind of array
  • Joining arrays
  • Splitting arrays
  • Tiling arrays
  • Adding and removing elements
  • Rearranging elements
• Binary operations
  • Elementwise bit operations
  • Bit packing
  • Output formatting
• String operations
  • String operations
  • Comparison
  • String information
  • Convenience class
• C-Types Foreign Function Interface (numpy.ctypeslib)
• Datetime Support Functions
  • numpy.datetime_as_string
  • numpy.datetime_data
  • Business Day Functions
• Data type routines
  • numpy.can_cast
  • numpy.promote_types
  • numpy.min_scalar_type
  • numpy.result_type
  • numpy.common_type
  • numpy.obj2sctype
  • Creating data types
  • Data type information
  • Data type testing
  • Miscellaneous
• Optionally SciPy-accelerated routines (numpy.dual)
  • Linear algebra
  • FFT
  • Other
• Mathematical functions with automatic domain
  • Functions
• Floating point error handling
  • Setting and getting error handling
  • Internal functions
• Discrete Fourier Transform (numpy.fft)
  • Standard FFTs
  • Real FFTs
  • Hermitian FFTs
  • Helper routines
  • Background information
  • Implementation details
  • Type Promotion
  • Normalization
  • Real and Hermitian transforms
  • Higher dimensions
  • References
  • Examples
• Functional programming
  • numpy.apply_along_axis
  • numpy.apply_over_axes
  • numpy.vectorize
  • numpy.frompyfunc
  • numpy.piecewise
• NumPy-specific help functions
  • Finding help
  • Reading help
• Input and output
  • NumPy binary files (NPY, NPZ)
  • Text files
  • Raw binary files
  • String formatting
  • Memory mapping files
  • Text formatting options
  • Base-n representations
  • Data sources
  • Binary Format Description
• Linear algebra (numpy.linalg)
  • The @ operator
  • Matrix and vector products
  • Decompositions
  • Matrix eigenvalues
  • Norms and other numbers
  • Solving equations and inverting matrices
  • Exceptions
  • Linear algebra on several matrices at once
• Logic functions
  • Truth value testing
  • Array contents
  • Array type testing
  • Logical operations
  • Comparison
• Masked array operations
  • Constants
  • Creation
  • Inspecting the array
  • Manipulating a MaskedArray
  • Operations on masks
  • Conversion operations
  • Masked arrays arithmetic
• Mathematical functions
  • Trigonometric functions
  • Hyperbolic functions
  • Rounding
  • Sums, products, differences
  • Exponents and logarithms
  • Other special functions
  • Floating point routines
  • Rational routines
  • Arithmetic operations
  • Handling complex numbers
  • Extrema Finding
  • Miscellaneous
• Matrix library (numpy.matlib)
  • numpy.matlib.empty
  • numpy.matlib.zeros
  • numpy.matlib.ones
  • numpy.matlib.eye
  • numpy.matlib.identity
  • numpy.matlib.repmat
  • numpy.matlib.rand
  • numpy.matlib.randn
• Miscellaneous routines
  • Performance tuning
  • Memory ranges
  • Array mixins
  • NumPy version comparison
  • Utility
  • Matlab-like Functions
  • Exceptions
• Padding Arrays
  • numpy.pad
• Polynomials
  • Transitioning from numpy.poly1d to numpy.polynomial
  • Documentation for the polynomial Package
  • Documentation for Legacy Polynomials
• Random sampling (numpy.random)
  • Quick Start
  • Introduction
  • Concepts
  • Features
• Set routines
  • numpy.lib.arraysetops
  • Making proper sets
  • Boolean operations
• Sorting, searching, and counting
  • Sorting
  • Searching
  • Counting
• Statistics
  • Order statistics
  • Averages and variances
  • Correlating
  • Histograms
• Test Support (numpy.testing)
  • Asserts
  • Asserts (not recommended)
  • Decorators
  • Test Running
  • Guidelines
• Window functions
  • Various windows

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numpy.ufunc.signature

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Array creation routines