Python code to multiply a NumPy array with a scalar value # Import numpyimportnumpyasnp# Creating two numpy arraysarr1=np.array([10,20,30]) arr2=np.array([30,20,20])# Display original arraysprint("Original Array 1:\n",arr1,"\n")print("Original Array 2:\n",arr2,"\n")# Defin...
3. 算术操作函数 3.1 numpy.add()和 numpy.subtract()执行逐元素的加法和减法操作。 3.2 numpy.multiply()和 numpy.divide()分别执行逐元素的乘法和除法操作。 4. 统计计算函数 4.1 numpy.mean()和 numpy.median()分别计算数组元素的平均值和中位数。 4.2 ...
maxrow = np.max(a1,0) print(maxrow) #[[4 5]] # ;//计算所有行的最大值,这里得到是一个矩阵 maxcolumn = np.max(a1,1) print(maxcolumn) ''' [[1] [3] [5]] ''' # 计算所有列的最大值对应在该列中的索引 maxindex = np.argmax(a1,0) print(maxindex) #[[2 2]] # 计算第二...
or ndarray, which is a fast, flexible container(容器) for large datasets in Python. Arrays enable you to perform(执行) mathematical operations on whole blocks of data using similar syntax to the equivalent operations between scalar(标量) delements.(数组和标量运算, 会映射到数组的每一个元素上)...
np.multiply(3,4) OUT: 12 Explanation Obviously, this is very simple and straight forward. Here, we’re simply multiplying 3 times 4. The result is 12. EXAMPLE 2: Multiply an array by a scalar Next, we’re going to multiply a 2-dimensional Numpy array by a scalar (i.e., we’ll ...
(N维数数组对象), or ndarray, which is a fast, flexible container(容器) for large datasets in Python. Arrays enable you to perform(执行) mathematical operations on whole blocks of data using similar syntax to the equivalent operations between scalar(标量) delements.(数组和标量运算, 会映射到...
result_multiply=arr1*arr2print("数组乘法结果:",result_multiply) 数组的索引与切片 数组索引 NumPy数组的索引从0开始,可以使用整数索引访问数组的元素。 代码语言:javascript 复制 pythonCopy codearr=np.array([10,20,30,40,50])# 获取第一个元素 ...
When you multiply a NumPy array by a scalar value, each element in the array is multiplied by that scalar. This operation is performed element-wise, meaning each element in the array is multiplied individually. Can I use the numpy.multiply() function for element-wise multiplication of arrays ...
'SHIFT_DIVIDEBYZERO', 'SHIFT_INVALID', 'SHIFT_OVERFLOW', 'SHIFT_UNDERFLOW', 'ScalarType', 'Tester', 'TooHardError', 'True_', 'UFUNC_BUFSIZE_DEFAULT', 'UFUNC_PYVALS_NAME', 'VisibleDeprecationWarning', 'WRAP', '_NoValue', '__NUMPY_SETUP__', '__all__', '__builtins__', '__ca...
[0. 1. 0.] [0. 0. 1.]] 数组运算: import numpy as np arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) # 数组相加 result_add = arr1 + arr2 print(result_add) # 数组相乘(对应元素相乘) result_multiply = arr1 * arr2 ...