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.lcm（X1，X2，/，出=无，*，其中=真，铸造= 'same_kind' ，为了= 'K' ，D型细胞=无，subok =真[，签名，extobj ] ）= <ufunc 'LCM'> ¶

返回|x1|和的最小公倍数|x2|

参量

x1，x2 array_like，int

值数组。如果为，则必须将它们广播为通用形状（成为输出的形状）。x1.shape != x2.shape

退货

Ÿ ndarray或标

输入的绝对值的最小公倍数如果x1和x2均为标量，则这是标量。

也可以看看

gcd

最大公约数

例子

>>> np.lcm(12, 20)
60
>>> np.lcm.reduce([3, 12, 20])
60
>>> np.lcm.reduce([40, 12, 20])
120
>>> np.lcm(np.arange(6), 20)
array([ 0, 20, 20, 60, 20, 20])