Python NumPy 函数及例程

例程

在本章中，将显示常规文档字符串，按功能分组。许多文档字符串包含示例代码，它演示了例程的基本用法。

---

>>> 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
  • 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 (numpy.emath)
• 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
  • Normalization
  • Real and Hermitian transforms
  • Higher dimensions
  • References
  • Examples
• Financial functions
  • Simple financial functions
• Functional programming
  • numpy.apply_along_axis
  • numpy.apply_over_axes
  • numpy.vectorize
  • numpy.frompyfunc
  • numpy.piecewise
• NumPy-specific help functions
  • Finding help
  • Reading help
• Indexing routines
  • Generating index arrays
  • Indexing-like operations
  • Inserting data into arrays
  • Iterating over arrays
• 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)
  • 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 arithmetics
• 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
  • 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
  • Buffer objects
  • Performance tuning
  • Memory ranges
  • Array mixins
  • NumPy version comparison
• Padding Arrays
  • numpy.pad
• Polynomials
  • Transition notice
• Random sampling (numpy.random)
  • Simple random data
  • Permutations
  • Distributions
  • Random generator
• Set routines
  • 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
  • Decorators
  • Test Running
  • Guidelines
• Window functions
  • Various windows