今天查看以前寫的文章時, 發現有個地方理解不了, 就是 np.mean(a) 跟 a.mean() 的區別是什么, 于是就查閱了相關資料:

• 官方doc:

a.mean()

Docstring: a.mean(axis=None, dtype=None, out=None, keepdims=False)Returns the average of the array elements along given axis.Refer to `numpy.mean` for full documentation.See Also -------- numpy.mean : equivalent function Type: builtin_function_or_method

np.mean(a)

Signature: np.mean(a, axis=None, dtype=None, out=None, keepdims=<no value>) Docstring: Compute the arithmetic mean along the specified axis.Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. `float64` intermediate and return values are used for integer inputs.Parameters ---------- a : array_likeArray containing numbers whose mean is desired. If `a` is not anarray, a conversion is attempted. axis : None or int or tuple of ints, optionalAxis or axes along which the means are computed. The default is tocompute the mean of the flattened array... versionadded:: 1.7.0If this is a tuple of ints, a mean is performed over multiple axes,instead of a single axis or all the axes as before. dtype : data-type, optionalType to use in computing the mean. For integer inputs, the defaultis `float64`; for floating point inputs, it is the same as theinput dtype. out : ndarray, optionalAlternate output array in which to place the result. The defaultis ``None``; if provided, it must have the same shape as theexpected output, but the type will be cast if necessary.See `doc.ufuncs` for details.keepdims : bool, optionalIf this is set to True, the axes which are reduced are leftin the result as dimensions with size one. With this option,the result will broadcast correctly against the input array.If the default value is passed, then `keepdims` will not bepassed through to the `mean` method of sub-classes of`ndarray`, however any non-default value will be. If thesub-class' method does not implement `keepdims` anyexceptions will be raised.Returns ------- m : ndarray, see dtype parameter aboveIf `out=None`, returns a new array containing the mean values,otherwise a reference to the output array is returned.See Also -------- average : Weighted average std, var, nanmean, nanstd, nanvarNotes ----- The arithmetic mean is the sum of the elements along the axis divided by the number of elements.Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for `float32` (see example below). Specifying a higher-precision accumulator using the `dtype` keyword can alleviate this issue.By default, `float16` results are computed using `float32` intermediates for extra precision.Examples -------- >>> a = np.array([[1, 2], [3, 4]]) >>> np.mean(a) 2.5 >>> np.mean(a, axis=0) array([ 2., 3.]) >>> np.mean(a, axis=1) array([ 1.5, 3.5])In single precision, `mean` can be inaccurate:>>> a = np.zeros((2, 512*512), dtype=np.float32) >>> a[0, :] = 1.0 >>> a[1, :] = 0.1 >>> np.mean(a) 0.54999924Computing the mean in float64 is more accurate:>>> np.mean(a, dtype=np.float64) 0.55000000074505806 File: c:\users\huawei\appdata\local\programs\python\python36\lib\site-packages\numpy\core\fromnumeric.py Type: function

• 總的來說, doc中所描述的是指 a.mean() 是 np.mean(a) 的簡單版, 使用方法基本是相同的, 唯一的不同就是前者是numpy數組對象的方法, 后者作為numpy的函數使用.
• 函數的作用就是: 返回數組元素沿給定軸的算數平均值。(算術平均值是沿坐標軸上的元素的和除以元素的個數。)
• 沿坐標軸是什么意思, 參考:

參考文章1: python numpy.mean() axis參數使用方法【sum(axis=)是求和，mean(axis=)是求平均值】
https://blog.csdn.net/Dontla/article/details/96466644

參考文章2: python 如何理解 numpy 數組操作中的 axis 參數?
https://blog.csdn.net/Dontla/article/details/99751690

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