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
  • Ones and zeros
  • From existing data
  • Creating record arrays (numpy.rec)
  • Creating character arrays (numpy.char)
  • Numerical ranges
  • Building matrices
  • The Matrix class
• Array manipulation routines
  • 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
• Indexing routines
  • Generating index arrays
  • Indexing-like operations
  • Inserting data into arrays
  • Iterating over arrays
• 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
• Input and output
  • NPZ files
  • Text files
  • String formatting
  • Memory mapping files
  • Text formatting options
  • Base-n representations
  • Data sources
• Discrete Fourier Transform (numpy.fft)
  • Standard FFTs
  • Real FFTs
  • Hermitian FFTs
  • Helper routines
  • Background information
• Linear algebra (numpy.linalg)
  • Matrix and vector products
  • Decompositions
  • Matrix eigenvalues
  • Norms and other numbers
  • Solving equations and inverting matrices
  • Exceptions
• Random sampling (numpy.random)
  • Simple random data
  • Permutations
  • Distributions
  • Random generator
• Sorting, searching, and counting
  • Sorting
  • Searching
  • Counting
• Logic functions
  • Truth value testing
  • Array contents
  • Array type testing
  • Logical operations
  • Comparison
• Binary operations
  • Elementwise bit operations
  • Bit packing
  • Output formatting
• Statistics
  • Extremal values
  • Averages and variances
  • Correlating
  • Histograms
• Mathematical functions
  • Trigonometric functions
  • Hyperbolic functions
  • Rounding
  • Sums, products, differences
  • Exponents and logarithms
  • Other special functions
  • Floating point routines
  • Arithmetic operations
  • Handling complex numbers
  • Miscellaneous
• Functional programming
  • numpy.apply_along_axis
  • numpy.apply_over_axes
  • numpy.vectorize
  • numpy.frompyfunc
  • numpy.piecewise
• Polynomials
  • Basics
  • Fitting
  • Calculus
  • Arithmetic
  • Warnings
• Financial functions
  • Simple financial functions
• Set routines
  • Making proper sets
  • Boolean operations
• Window functions
  • Various windows
• Floating point error handling
  • Setting and getting error handling
  • Internal functions
• Masked array operations
  • Constants
  • Creation
  • Inspecting the array
  • Manipulating a MaskedArray
  • Operations on masks
  • Conversion operations
  • Masked arrays arithmetics
• Numpy-specific help functions
  • Finding help
  • Reading help
• Miscellaneous routines
  • Buffer objects
  • Performance tuning
• Test Support (numpy.testing)
• Asserts
  • numpy.testing.assert_almost_equal
  • numpy.testing.assert_approx_equal
  • numpy.testing.assert_array_almost_equal
  • numpy.testing.assert_array_equal
  • numpy.testing.assert_array_less
  • numpy.testing.assert_equal
  • numpy.testing.assert_raises
  • numpy.testing.assert_warns
  • numpy.testing.assert_string_equal
  • Decorators
  • Test Running
• Mathematical functions with automatic domain (numpy.emath)
• Matrix library (numpy.matlib)
• Optionally Scipy-accelerated routines (numpy.dual)
  • Linear algebra
  • FFT
  • Other
• Numarray compatibility (numpy.numarray)
• Old Numeric compatibility (numpy.oldnumeric)
• C-Types Foreign Function Interface (numpy.ctypeslib)
• String operations
  • String operations
  • Comparison
  • String information
  • Convenience class