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After applying this function to an input matrix x, each row of x will be a vector of unit length (meaning length 1). See the numpy documentation . def normalize_rows ( x : numpy . ndarray ): """ function that normalizes each row of the matrix x to have unit length.

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numpy.mean()传送门 numpy.mean(a, axis=None, dtype=None, out=None, keepdims=) a：为array形的数据 axis： 科普下，axis=0表示纵轴的方向，axis=1表示横轴的方向 1）axis为二维array时：axis可为0,1两个方向轴 不填时默认为a全部元素的平均值 axis=0 表示纵轴平均，输出的是格式（1，x）的格式... Try watching this video on www.youtube.com, or enable JavaScript if it is disabled in your browser.

# Numpy axis 1 meaning

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The following information will be displayed in another window (I mean if we can close the help window (by typing 'q'), our command shell will reappear: Type: builtin_function_or_method String Form:<built-in function concatenate> Docstring: concatenate((a1, a2, ...), axis=0) Join a sequence of arrays together. With numpy, the std() function calculates the standard deviation for a given data set. In the code below, we show how to calculate the standard deviation for a data set. import numpy as np dataset= [2,6,8,12,18,24,28,32] sd= np.std(dataset) print(sd) 10.268276389