比较两个数组并在 NumPy 中忽略 NaN 返回逐元素最大值
要比较两个数组并在忽略 NaN 的情况下返回逐元素最大值,请在 Python NumPy 中使用 **numpy.fmax()** 方法。返回值为 True 或 False。
比较两个数组并返回一个包含逐元素最大值的新数组。如果要比较的元素之一是 NaN,则返回非 NaN 元素。如果两个元素都是 NaN,则返回第一个元素。后者的区别对于复数 NaN 非常重要,复数 NaN 定义为实部或虚部至少有一个是 NaN。其净效应是,尽可能忽略 NaN。
步骤
首先,导入所需的库 -
import numpy as np
使用 array() 方法创建两个二维 NumPy 数组。我们插入了一些包含 NaN 值的元素 -
arr1 = np.array([[6, 9, np.NaN],[25, 11, 21]]) arr2 = np.array([[8, np.NaN, np.NaN],[22, 19, 26]])
显示数组 -
print("Array 1...
", arr1) print("
Array 2...
", arr2)
获取数组的类型 -
print("
Our Array 1 type...
", arr1.dtype) print("
Our Array 2 type...
", arr2.dtype)
获取数组的维度 -
print("
Our Array 1 Dimensions...
",arr1.ndim) print("
Our Array 2 Dimensions...
",arr2.ndim)
获取数组的形状 -
print("
Our Array 1 Shape...
",arr1.shape) print("
Our Array 2 Shape...
",arr2.shape)
要比较两个数组并在忽略 NaN 的情况下返回逐元素最大值,请在 Python NumPy 中使用 numpy.fmax() 方法。返回值为 True 或 False -
print("
Result (maximum ignoring NaNs)...
",np.fmax(arr1, arr2))
示例
import numpy as np # Creating two 2D numpy array using the array() method # We have inserted elements with some NaN values arr1 = np.array([[6, 9, np.NaN],[25, 11, 21]]) arr2 = np.array([[8, np.NaN, np.NaN],[22, 19, 26]]) # Display the arrays print("Array 1...", arr1) print("Array 2...", arr2) # Get the type of the arrays print("Our Array 1 type...", arr1.dtype) print("Our Array 2 type...", arr2.dtype) # Get the dimensions of the Arrays print("Our Array 1 Dimensions...",arr1.ndim) print("Our Array 2 Dimensions...",arr2.ndim) # Get the shape of the Arrays print("Our Array 1 Shape...",arr1.shape) print("Our Array 2 Shape...",arr2.shape) # To compare two arrays and return the element-wise maximum ignoring NaNs, use the numpy.fmax() method in Python Numpy # Return value is either True or False print("Result (maximum ignoring NaNs)...",np.fmax(arr1, arr2))
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输出
Array 1... [[ 6. 9. nan] [25. 11. 21.]] Array 2... [[ 8. nan nan] [22. 19. 26.]] Our Array 1 type... float64 Our Array 2 type... float64 Our Array 1 Dimensions... 2 Our Array 2 Dimensions... 2 Our Array 1 Shape... (2, 3) Our Array 2 Shape... (2, 3) Result (maximum ignoring NaNs)... [[ 8. 9. nan] [25. 19. 26.]]
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