numpy ndarray object has no attribute append

Question

I am struggling with a program I am making on the part where I have to store values that I get from my loop in an array.

What I tried to do is to make an empty array called M. Then for every new value "a" calculated in a loop I use the command M.append(a) to add the value a to the array M.

The thing is, python says this error : 'numpy.ndarray' object has no attribute 'append'

and I don't know how to fix it.

Here is my code :

import numpy as np
from matplotlib import pyplot as plt
with open('expFcn0.txt') as f:
    M = np.array([])
    print(M)
    lines = f.readlines()
    x = [float(line.split()[0]) for line in lines]
    y = [float(line.split()[1]) for line in lines]
    for i in range(0,181):
        a=np.log(y[i])/x[i]
        print(a)
        i=i+1
        M.append(a)
    print(M)

    
    plt.plot(x, y, 'r--')
    plt.xlabel('Time')
    plt.ylabel('Biomass')
    plt.title('Exponential Function')
    plt.show()

Thank you very much!

Answers suggest using the numpy.append method, but appending to a numpy array is not good practice. You are probably better off creating a python list and appending to that. If you want a numpy array of the results, you can create a numpy array from that list. — jkr
– jkr, Commented Jan 14, 2021 at 18:41
BTW, if it's not a typo, modify index, i=i+1, doesn't work and should be avoided inside for loop. — Ke Zhang
– Ke Zhang, Commented Jan 15, 2021 at 0:26

Yehuda · Accepted Answer · 2021-01-14 18:38:49Z

3

Numpy arrays don't have a method append(). You need to use np.append(array, values) as per the documentation, or for your case, np.append(M, a).

answered Jan 14, 2021 at 18:38

Yehuda

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jkr · Accepted Answer · 2021-01-15 02:13:37Z

Other answers explain that numpy arrays do not have an .append() method and point to numpy.append. Using numpy.append, however, is bad practice because it creates a new array each time. A better solution is to create one numpy and fill it during the for loop (see end of answer).

An even better solution would make use of numpy's broadcasting. That's a core feature of numpy, and it's what helps make numpy fast.

import numpy as np

with open('expFcn0.txt') as f:
    lines = f.readlines()
    x = np.array([float(line.split()[0]) for line in lines])
    y = np.array([float(line.split()[1]) for line in lines])

M = np.log(y) / x

You can also look into numpy.loadtxt to read the file into a numpy array directly.

How to fill a numpy array in a for loop:

import numpy as np

with open('expFcn0.txt') as f:
    lines = f.readlines()
    x = [float(line.split()[0]) for line in lines]
    y = [float(line.split()[1]) for line in lines]

M = np.zeros(181)
for i in range(181):
    a = np.log(y[i])/x[i]
    print(a)
    M[i] = a

Shradha · Accepted Answer · 2021-01-14 18:39:43Z

0

Numpy arrays do not have an append method. Use the Numpy append function instead:

M = np.append(M, a)

answered Jan 14, 2021 at 18:39

Shradha

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