NumPy NumPy参考

• 数组对象
• 常数
• 通用功能（ufunc）
• 例行程序
•    数组创建例程
•    数组操作例程
•    Binary operations
•    String operations
•    C-Types Foreign Function Interface (numpy.ctypeslib)
•    Datetime Support Functions
•    Data type routines
•    Optionally Scipy-accelerated routines (numpy.dual)
•    Mathematical functions with automatic domain (numpy.emath)
•    Floating point error handling
•    Discrete Fourier Transform (numpy.fft)
•    Financial functions
•    Functional programming
•    NumPy-specific help functions
•    Indexing routines
•    输入输出
•    线性代数（numpy.linalg）
•    逻辑功能
•    Mathematical functions
•          numpy.sin
•          numpy.cos
•          numpy.tan
•          numpy.arcsin
•          numpy.arccos
•          numpy.arctan
•          numpy.hypot
•          numpy.arctan2
•          numpy.degrees
•          numpy.radians
•          numpy.unwrap
•          numpy.deg2rad
•          numpy.rad2deg
•          numpy.sinh
•          numpy.cosh
•          numpy.tanh
•          numpy.arcsinh
•          numpy.arccosh
•          numpy.arctanh
•          numpy.around
•          numpy.round_
•          numpy.rint
•          numpy.fix
•          numpy.floor
•          numpy.ceil
•          numpy.trunc
•          numpy.prod
•          numpy.sum
•          numpy.nanprod
•          numpy.nansum
•          numpy.cumprod
•          numpy.cumsum
•          numpy.nancumprod
•          numpy.nancumsum
•          numpy.diff
•          numpy.ediff1d
•          numpy.gradient
•          numpy.cross
•          numpy.trapz
•          numpy.exp
•          numpy.expm1
•          numpy.exp2
•          numpy.log
•          numpy.log10
•          numpy.log2
•          numpy.log1p
•          numpy.logaddexp
•          numpy.logaddexp2
•          numpy.i0
•          numpy.sinc
•          numpy.signbit
•          numpy.copysign
•          numpy.frexp
•          numpy.ldexp
•          numpy.nextafter
•          numpy.spacing
•          numpy.lcm
•          numpy.gcd
•          numpy.add
•          numpy.reciprocal
•          numpy.positive
•          numpy.negative
•          numpy.multiply
•          numpy.divide
•          numpy.power
•          numpy.subtract
•          numpy.true_divide
•          numpy.floor_divide
•          numpy.float_power
•          numpy.fmod
•          numpy.mod
•          numpy.modf
•          numpy.remainder
•          numpy.divmod
•          numpy.angle
•          numpy.real
•          numpy.imag
•          numpy.conj
•          numpy.conjugate
•          numpy.convolve
•          numpy.clip
•          numpy.sqrt
•          numpy.cbrt
•          numpy.square
•          numpy.absolute
•          numpy.fabs
•          numpy.sign
•          numpy.heaviside
•          numpy.maximum
•          numpy.minimum
•          numpy.fmax
•          numpy.fmin
•          numpy.nan_to_num
•          numpy.real_if_close
•          numpy.interp
•    Matrix library (numpy.matlib)
•    Miscellaneous routines
•    Padding Arrays
•    Polynomials
•    Random sampling (numpy.random)
•    Set routines
•    Sorting, searching, and counting
•    Statistics
•    Test Support (numpy.testing)
•    Window functions
• Packaging (numpy.distutils)
• NumPy Distutils - Users Guide
• NumPy C-API
• NumPy内部
• NumPy和SWIG

numpy.conjugate（X，/，出=无，*，其中=真，铸造= 'same_kind' ，为了= 'K' ，D型细胞=无，subok =真[，签名，extobj ] ）= <ufunc '缀合物'> ¶

逐元素返回复共轭。

复数的复共轭是通过改变其虚部的符号而获得的。

参量

x array_like

输入值。

out ndarray，None或ndarray和None的元组，可选

结果存储的位置。如果提供，它必须具有输入广播到的形状。如果未提供或没有，则返回一个新分配的数组。元组（只能作为关键字参数）的长度必须等于输出的数量。

其中array_like，可选

此条件通过输入广播。在条件为True的位置，将out数组设置为ufunc结果。在其他地方，out数组将保留其原始值。请注意，如果通过default创建未初始化的out数组out=None，则条件为False的数组中的 位置将保持未初始化。

**夸克

有关其他仅关键字的参数，请参见 ufunc docs。

退货

ÿ ndarray

x的复共轭，与y的 dtype相同。如果x是标量，则这是标量。

笔记

conj是以下内容的别名conjugate：

>>> np.conj is np.conjugate
True

例子

>>> np.conjugate(1+2j)
(1-2j)

>>> x = np.eye(2) + 1j * np.eye(2)
>>> np.conjugate(x)
array([[ 1.-1.j,  0.-0.j],
       [ 0.-0.j,  1.-1.j]])