类型错误:只有长度为 1 的数组可以在绘图显示时转换为 Python 标量 [英] TypeError: only length-1 arrays can be converted to Python scalars while plot showing

问题描述

我有这样的 Python 代码:

I have such Python code:

import numpy as np
import matplotlib.pyplot as plt

def f(x):
    return np.int(x)

x = np.arange(1, 15.1, 0.1)
plt.plot(x, f(x))
plt.show()

还有这样的错误:

TypeError: only length-1 arrays can be converted to Python scalars

我该如何解决?

推荐答案

only length-1 arrays can convert to Python scalars"错误当函数需要单个值但您传递一个数组时引发.

The error "only length-1 arrays can be converted to Python scalars" is raised when the function expects a single value but you pass an array instead.

如果您查看 np.int 的调用签名，您会发现它接受单个值，而不是数组.一般来说，如果要将接受单个元素的函数应用于数组中的每个元素，可以使用 np.vectorize:

If you look at the call signature of np.int, you'll see that it accepts a single value, not an array. In general, if you want to apply a function that accepts a single element to every element in an array, you can use np.vectorize:

import numpy as np
import matplotlib.pyplot as plt

def f(x):
    return np.int(x)
f2 = np.vectorize(f)
x = np.arange(1, 15.1, 0.1)
plt.plot(x, f2(x))
plt.show()

您可以跳过 f(x) 的定义，只需将 np.int 传递给 vectorize 函数:f2 = np.vectorize(np.int).

You can skip the definition of f(x) and just pass np.int to the vectorize function: f2 = np.vectorize(np.int).

请注意，np.vectorize 只是一个方便的函数，基本上是一个 for 循环.这在大型阵列上效率低下.只要有可能，就使用真正矢量化的函数或方法(例如 astype(int) 作为 @FFT 建议).

Note that np.vectorize is just a convenience function and basically a for loop. That will be inefficient over large arrays. Whenever you have the possibility, use truly vectorized functions or methods (like astype(int) as @FFT suggests).

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