np.nan和np.inf什么意思

np.nan表示"not a number"，是一个特殊的浮点数值，表示不是数字的值。通常出现在数学计算中，例如0/0或者sqrt(-1)等非法计算的结果。

np.inf表示"无穷大"，也是一个特殊的浮点数值，表示一个数大于任何实数。在数学运算中，例如1/0或者log(0)等，会出现无穷大的情况。

相关问题

np.where(Self_Time < Hour[np.min(np.where(Hour == Country_Time[i])) + 1]), np.inf, np.nan)

As an AI language model, I cannot provide a definitive answer without additional context and information about the variables and the code block. However, I can provide some general information about the np.where() function and the code snippet you provided.

The np.where() function is a NumPy function that returns an array of elements from x or y, depending on the condition. The syntax of np.where() is as follows:

np.where(condition, x, y)

- condition: A boolean array or a condition that evaluates to a boolean array.
- x: An array-like object that has the same shape as the condition. It is used to select elements where the condition is True.
- y: An array-like object that has the same shape as the condition. It is used to select elements where the condition is False.

In the code snippet you provided, the np.where() function is used to return an array of values where the condition is True, or np.inf (positive infinity) where the condition is False. The condition is:

Self_Time < Hour[np.min(np.where(Hour == Country_Time[i])) + 1]

This condition compares the values in the Self_Time array with the value in the Hour array at the index returned by np.min(np.where(Hour == Country_Time[i])) + 1. The np.min() function returns the index of the minimum value in the array returned by np.where(). The +1 is added to the index to get the next value in the Hour array.

Overall, the code block you provided seems to be part of a larger program that involves time calculations and comparisons. Without additional context, it is difficult to provide a more specific explanation.

python中要对数据中出现的np.inf和np.NAN如何删除，代码如何

可以使用NumPy库中的`numpy.isfinite()`函数来判断哪些元素是有限的，然后再使用布尔索引来删除含有`np.inf`和`np.NAN`的元素。

下面是一个示例代码：

import numpy as np

# 创建一个包含np.inf和np.NAN的数组
arr = np.array([1, 2, np.inf, 4, np.NAN, 6])

# 判断哪些元素是有限的
mask = np.isfinite(arr)

# 使用布尔索引来删除含有np.inf和np.NAN的元素
filtered_arr = arr[mask]

print(filtered_arr)

输出结果为：

[1. 2. 4. 6.]

在上面的例子中，我们使用`numpy.isfinite()`函数来判断哪些元素是有限的，然后使用布尔索引来删除含有`np.inf`和`np.NAN`的元素，并将结果存储在`filtered_arr`中。