Question

本章按功能分组介绍了各常用的API。许多的API包含示例代码，这些示例代码演示了API的基本用法。

这些示例都是使用NumPy并且是通过这种方式导入NumPy：

>>> import numpy as np

提示

执行示例的一种便捷的方法是 IPython 的 %doctest_mode 模式，它允许粘贴多行示例并保留缩进。

• 创建数组
  • Ones and zeros
  • From existing data
  • Creating record arrays (numpy.rec)
  • Creating character arrays (numpy.char)
  • Numerical ranges
  • Building matrices
  • The Matrix class
• 数组操作
  • 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
• 二进制操作
  • Elementwise bit operations
  • Bit packing
  • Output formatting
• 字符串操作
  • String operations
  • Comparison
  • String information
  • Convenience class
• C-Types外部函数接口(numpy.ctypeslib)
• 时间日期相关
  • numpy.datetime_as_stringopen in new window
  • numpy.datetime_dataopen in new window
  • Business Day Functions
• 数据类型相关
  • numpy.can_castopen in new window
  • numpy.promote_typesopen in new window
  • numpy.min_scalar_typeopen in new window
  • numpy.result_typeopen in new window
  • numpy.common_typeopen in new window
  • numpy.obj2sctypeopen in new window
  • Creating data types
  • Data type information
  • Data type testing
  • Miscellaneous
• 可选的Scipy加速支持(numpy.dual)
  • Linear algebra
  • FFT
  • Other
• 具有自动域的数学函数(numpy.emath)
• 浮点错误处理
  • Setting and getting error handling
  • Internal functions
• 离散傅立叶变换(numpy.fft)
  • Standard FFTs
  • Real FFTs
  • Hermitian FFTs
  • Helper routines
  • Background information
  • Implementation details
  • Normalization
  • Real and Hermitian transforms
  • Higher dimensions
  • References
  • Examples
• 财金相关
  • Simple financial functions
• 实用的功能
  • numpy.apply_along_axisopen in new window
  • numpy.apply_over_axesopen in new window
  • numpy.vectorizeopen in new window
  • numpy.frompyfuncopen in new window
  • numpy.piecewiseopen in new window
• 特殊的NumPy帮助功能
  • Finding help
  • Reading help
• 索引相关
  • Generating index arrays
  • Indexing-like operations
  • Inserting data into arrays
  • Iterating over arrays
• 输入和输出
  • 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
• 线性代数(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
• 逻辑函数
  • Truth value testing
  • Array contents
  • Array type testing
  • Logical operations
  • Comparison
• 操作掩码数组
  • Constants
  • Creation
  • Inspecting the array
  • Manipulating a MaskedArray
  • Operations on masks
  • Conversion operations
  • Masked arrays arithmetics
• 数学函数
  • 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
• 矩阵库(numpy.matlib)
  • numpy.matlib.emptyopen in new window
  • numpy.matlib.zerosopen in new window
  • numpy.matlib.onesopen in new window
  • numpy.matlib.eyeopen in new window
  • numpy.matlib.identityopen in new window
  • numpy.matlib.repmatopen in new window
  • numpy.matlib.randopen in new window
  • numpy.matlib.randnopen in new window
• 杂项
  • Performance tuning
  • Memory ranges
  • Array mixins
  • NumPy version comparison
  • Utility
  • Matlab-like Functions
• 填充数组
  • numpy.padopen in new window
• 多项式
  • Transition notice
• 随机抽样(numpy.random)
  • Quick Start
  • Introduction
  • Concepts
  • Features
• 集合操作
  • Making proper sets
  • Boolean operations
• 排序、搜索和计数
  • Sorting
  • Searching
  • Counting
• 统计相关
  • Order statistics
  • Averages and variances
  • Correlating
  • Histograms
• 测试支持(numpy.testing)
  • Asserts
  • Decorators
  • Test Running
  • Guidelines
• 窗口函数
  • Various windows