Question

a typeerror: only size-1 arrays can be converted to python scalars

Answer 1

In this case if you intend to employ a function that receive a single element to every element in an array, you can apply np.vectorize. Follw the below script.

Code:

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()

In some cases you can skip the definition of f(x) and only pass np.int to the vectorize function: f2 = np.vectorize(np.int).

Remember that np.vectorize is only a convenience function and originally a for loop.

x.astype(int)

import numpy as np
import matplotlib.pyplot as plt
import datetime

time_start = datetime.datetime.now()

# My original answer
def f(x):
    rebuilt_to_plot = []
    for num in x:
        rebuilt_to_plot.append(np.int(num))
    return rebuilt_to_plot

for t in range(10000):
    x = np.arange(1, 15.1, 0.1)
    plt.plot(x, f(x))

time_end = datetime.datetime.now()

# Answer by ayhan
def f_1(x):
    return np.int(x)

for t in range(10000):
    f2 = np.vectorize(f_1)
    x = np.arange(1, 15.1, 0.1)
    plt.plot(x, f2(x))

time_end_2 = datetime.datetime.now()

print time_end - time_start
print time_end_2 - time_end

Hopefully, now you are able to solve this problem.