How to Solve Python AttributeError: ‘list’ object has no attribute ‘astype’

by | Programming, Python, Tips

This error occurs when you try to call the astype() method on a list as if it were a NumPy ndarray. You can solve this error by converting the list to an array using the numpy.array() method then call the astype() method. For example,

import numpy as np
lst = [1, 2, 3]
arr = np.array(lst)
arr = arr.astype('float32')

Otherwise, you can cast an array to a specific dtype using the dtype parameter in the numpy.array() method. For example,

import numpy as np
lst = [1, 2, 3]
arr = np.array(lst,dtype=np.float32)

This tutorial will go through the error and how to solve it with code examples.


AttributeError: ‘list’ object has no attribute ‘astype’

AttributeError occurs in a Python program when we try to access an attribute (method or property) that does not exist for a particular object. The part “‘list’ object has no attribute ‘astype’” tells us that the list object we are handling does not have the astype attribute. We will raise this error if we call the astype() method on a list object. 

astype() is a ndarray method that returns a copy of an array cast to a specific type.

Example

Let’s look at an example of using the astype() method. First, we will define a function which calculates the standard deviation of an array.

import numpy as np 

def get_std(data):

    data = data.astype('float32')

    std_val = np.std(data)

    return std_val

The first line in the function uses the astype() method to cast the data variable to the dtype float32.

Next, we will define a list of numeric strings, pass the list to the get_std() function and print the result to the console.

numbers = ['1', '2', '70', '13', '4', '91']

std = get_std(numbers)

print(f'Standard Deviation of list is {std}')

Let’s run the code to see what happens:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [7], in <cell line: 3>()
      1 numbers = ['1', '2', '70', '13', '4', '91']
----> 3 std = get_std(numbers)
      5 print(f'Standard Deviation of list is {std}')

Input In [6], in get_std(data)
      3 def get_std(data):
----> 4     data = data.astype('float32')
      5     std_val = np.std(data)
      6     return std_val

AttributeError: 'list' object has no attribute 'astype'

The error occurs because we tried to call astype() on the numbers variable, which is a list object. The astype() method is not an attribute of the list data type. We can check what attributes the list data type has by using the dir() method. For example,

dir(list)
['__add__',
 '__class__',
 '__contains__',
 '__delattr__',
 '__delitem__',
 '__dir__',
 '__doc__',
 '__eq__',
 '__format__',
 '__ge__',
 '__getattribute__',
 '__getitem__',
 '__gt__',
 '__hash__',
 '__iadd__',
 '__imul__',
 '__init__',
 '__init_subclass__',
 '__iter__',
 '__le__',
 '__len__',
 '__lt__',
 '__mul__',
 '__ne__',
 '__new__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__reversed__',
 '__rmul__',
 '__setattr__',
 '__setitem__',
 '__sizeof__',
 '__str__',
 '__subclasshook__',
 'append',
 'clear',
 'copy',
 'count',
 'extend',
 'index',
 'insert',
 'pop',
 'remove',
 'reverse',
 'sort']

When we call the dir() method it returns a list containing the attributes of the specified objects, without the values.

We can check for membership of a specific attribute using the in operator. If the in operator evaluates to True then the attribute exists in the list returned by dir(). If the in operator evaluates to values then the attribute does not exist in the list returned by dir().

print('astype' in dir(list))
False

The membership check returns False, verifying that astype() is not an attribute of the list data type.

Solution #1: Convert List to Ndarray

We can solve the error by converting the list to a NumPy ndarray using the numpy.array() method. Let’s look at the revised code:

import numpy as np 

def get_std(data):

    data = data.astype('float32')

    std_val = np.std(data)

    return std_val


numbers = np.array(['1', '2', '70', '13', '4', '91'])

std = get_std(numbers)

print(f'Standard Deviation of list is {std}')

Let’s run the code to see the result:

Standard Deviation of list is 36.31077194213867

The get_std() function successfully casts the array to float32 then calculates and returns the standard deviation of the array elements.

Solution #2: Convert List to Ndarray and Use dtype

We can simplify the solution by using the dtype parameter of the array method. The dtype parameter sets the desired data type for the array. In this case, we want the array to be float32. With this change, we can remove the asarray() call in the get_std() function. Let’s look at the revised code:

import numpy as np 

def get_std(data):

    std_val = np.std(data)

    return std_val

numbers = np.array(['1', '2', '70', '13', '4', '91'], dtype=np.float32)

std = get_std(numbers)

print(f'Standard Deviation of list is {std}')

Let’s run the code to see the result:

Standard Deviation of list is 36.31077194213867

We successfully calculated the standard deviation of the array and printed the result to the console.

Summary

Congratulations on reading to the end of this tutorial!

For further reading on AttributeErrors, go to the article:

How to Solve Python AttributeError: ‘list’ object has no attribute ‘keys’

To learn more about Python for data science and machine learning, go to the online courses page on Python for the most comprehensive courses available.

Have fun and happy researching!

Research Scientist at Moogsoft | + posts

Suf is a research scientist at Moogsoft, specializing in Natural Language Processing and Complex Networks. Previously he was a Postdoctoral Research Fellow in Data Science working on adaptations of cutting-edge physics analysis techniques to data-intensive problems in industry. In another life, he was an experimental particle physicist working on the ATLAS Experiment of the Large Hadron Collider. His passion is to share his experience as an academic moving into industry while continuing to pursue research. Find out more about the creator of the Research Scientist Pod here and sign up to the mailing list here!

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