In this example, I’ll explain how to calculate the mean value of a NumPy array by row.To accomplish this, we have to set the axis argument of the mean function to 1:print(np.mean(my_array, axis = 1)) # Get mean of array rows # [2. 5.]...
1. mean() 函数定义: numpy.mean(a, axis=None, dtype=None, out=None, keepdims=<class numpy._globals._NoValue at 0x40b6a26c>)[source] 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...
importnumpyasnp# create an arrayarray1 = np.array([0,1,2,3,4,5,6,7]) # calculate the mean of the arrayavg = np.mean(array1) print(avg)# Output: 3.5 mean() Syntax The syntax ofmean()is: numpy.mean(array, axis=None, dtype=None, out=None, keepdims=<no value>, where=<no ...
Thenumpy.nanmean()method returns the arithmetic mean of the array, ignoringNaNs. Example 1: Find the Mean of a ndArray importnumpyasnp# create an arrayarray1 = np.array([[[0,1], [2, np.NaN]], [[4,5], [6,7]]]) # find the mean of entire arraymean1 = np.nanmean(array1)...
python的numpy库中的mean（）函数⽤法介绍1. mean() 函数定义：numpy.mean(a, axis=None, dtype=None, out=None, keepdims=<class numpy._globals._NoValue at 0x40b6a26c>)[]Compute the arithmetic mean along the specified axis.Returns the average of the array elements. The average is taken over...
如果dataFrame是用numpy.array()得到的对象(ndarray)来初始化,那么可能使得dataFrame.mean()方法无法正常工作: the error result by numpy.array() the normal result: the doc of the dataFrame.mean() ...
Axis or axes along which the means are computed. The default is to compute the mean of the flattened array. 来自<https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean> 计算均值所依据的一个或多个轴。默认值是计算平坦数组的平均值。
关于numpy mean函数的axis参数 importnumpy as np X= np.array([[1, 2], [4, 5], [7, 8]])printnp.mean(X, axis=0, keepdims=True)printnp.mean(X, axis=1, keepdims=True) 1. 2. 3. 4. 结果是分别是 [[ 1.5] [[4. 5.]] [ 4.5]...
array print出来像一个表格,可以按行按列来观察。 原来是一个list相当于一行 np.where用于寻找一个condition下的坐标,返回的是一个2个元素的tuple,第一个元素是一个array,第二个是数据类型 left_index = np.where(a2[0] < zmin - Y)[0][-1] ...
使用NumPy的mean函数非常简单,首先你需要安装NumPy库,然后导入它: import numpy as np 接下来,你可以创建一个NumPy数组并使用mean函数来计算平均值: data = np.array([1, 2, 3, 4, 5]) average = np.mean(data) print("Mean of the data set:", average) ...