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[Solved] TypeError: only length-1 arrays can be converted to Python scalars while plot showing

Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error TypeError: only length-1 arrays can be converted to Python scalars while plot showing in python. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

How TypeError: only length-1 arrays can be converted to Python scalars while plot showing Error Occurs?

Today I get the following error TypeError: only length-1 arrays can be converted to Python scalars while plot showing in python.

How To Solve TypeError: only length-1 arrays can be converted to Python scalars while plot showing Error ?

  1. How To Solve TypeError: only length-1 arrays can be converted to Python scalars while plot showing Error ?

    To Solve TypeError: only length-1 arrays can be converted to Python scalars while plot showing Error dataframe['column'].squeeze() should solve this. It basically changes the dataframe column to a list.

  2. TypeError: only length-1 arrays can be converted to Python scalars while plot showing

    To Solve TypeError: only length-1 arrays can be converted to Python scalars while plot showing Error dataframe['column'].squeeze() should solve this. It basically changes the dataframe column to a list.

Solution 1

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.

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()

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

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.

Solution 2

dataframe['column'].squeeze() should solve this. It basically changes the dataframe column to a list.

Summery

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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