Fixing the “assignment to expression with array type” Error in Python

Let’s take a look at some examples to understand when this error occurs and how to properly fix it.

Understanding Array Slices

First, let’s quickly review how array and list slices work in Python. Slicing allows you to access a subset range of elements in an array or list.

import numpy as np

arr = np.array([1, 2, 3, 4, 5]) 

# Get subarray containing elements at indices 1, 2
subarr = arr[1:3]  
print(subarr)

# Output: [2 3]

The slice arr[1:3] extracts the 2nd and 3rd elements from the array arr. This gives us back a new subarray containing just those elements.

Similarly for lists:

my_list = [10, 20, 30, 40, 50]

sublist = my_list[1:3] 
print(sublist)

# Output: [20, 30]

The slice [1:3] extracts the 2nd and 3rd elements from the list my_list.

So far so good. But what happens if we try to assign a value to this slice?

arr[1:3] = 100

This gives us an error:

TypeError: 'numpy.ndarray' object does not support item assignment

We get this error because arr[1:3] evaluates to a subarray, not a variable we can assign to. Python doesn’t allow directly assigning to the result of an expression in this way.

The same thing happens with lists:

my_list[1:3] = 100

# TypeError: 'list' object does not support item assignment

Attempting to assign directly to the slice my_list[1:3] results in an error.

So how do we properly fix this?

Fix 1: Assign Slices to Temporary Variables

The correct way is to assign the slice to a temporary variable first, then modify that variable.

For example:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

# Assign slice to temp variable 
temp = arr[1:3]  

# Modify temp
temp = 100

print(arr) 

# Output: [1 100 100 4 5]

By assigning the slice arr[1:3] to temp first, we can then assign a new value to temp which will modify the original array.

Similarly, for lists:

my_list = [10, 20, 30, 40, 50]

temp = my_list[1:3]

temp = [100, 200] 

print(my_list)

# Output: [10, 100, 200, 40, 50]

So the key is not to assign directly to the slice arr[1:3] or my_list[1:3], but to create a temporary variable first that can be modified.

Fix 2: Use Array Indexing Instead of Slices

Another option is to avoid using slices entirely, and instead index individual elements in the array or list:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])  

# Index element at index 1
arr[1] = 100  

# Index element at index 2
arr[2] = 200   

print(arr)

# Output: [1, 100, 200, 4, 5]

By indexing individual elements instead of a slice range, we can assign new values for those elements in the array.

And similarly with lists:

my_list = [10, 20, 30, 40, 50]

my_list[1] = 100
my_list[2] = 200

print(my_list) 

# Output: [10, 100, 200, 40, 50]

Indexing individual elements avoids the “assignment to expression” error since it is no longer assigning to a slice expression.

Fix 3: Reassign the Entire Slice

You can also fix the error by reassigning the entire slice, instead of modifying it in-place:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

arr[1:3] = [100, 200] # Reassign entire slice 

print(arr)

# Output: [1, 100, 200, 4, 5]

Here we are replacing the entire slice arr[1:3] with a new array [100, 200].

This works because we are assigning to the slice arr[1:3] as a whole, not assigning directly to the result of the slice expression.

And similarly for lists:

my_list = [10, 20, 30, 40, 50]

my_list[1:3] = [100, 200] # Replace entire slice

print(my_list) 

# Output: [10, 100, 200, 40, 50]

By reassigning the entire slice, we avoid the “assignment to expression” error.

Why Does This Error Happen?

In Python, an array or list slice returns a view or copy of a subset of the original data. It does not return a reference to the original data structure itself.

When you try to assign directly to a slice, Python interprets that as trying to assign to the result of the slice expression:

arr[1:3] = 100

This is essentially saying:

temp = arr[1:3] 
temp = 100

But temp here is just a temporary array containing the slice data, not a variable. Hence the assignment fails.

Python protects its array and list data structures by preventing direct in-place modification of slices. Instead, you need to create a variable that can be modified, or reassign the slice completely.

So in summary, the “assignment to expression” error occurs when trying to modify a slice in-place instead of through a separate variable. Python does not allow directly modifying the result of an expression in this way.

Examples of the Error

Here are some more examples of how this error may occur:

1. Assigning to an array slice

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

# Error 
arr[1:3] = 100 

# TypeError: 'numpy.ndarray' object does not support item assignment

Attempting to directly assign a value to the slice arr[1:3] results in a type error.

2. Assigning to a 2D array slice

arr_2d = np.array([[1, 2, 3], [4, 5, 6]]) 

# Error
arr_2d[0, 1:3] = 200 

# TypeError: 'numpy.ndarray' object does not support item assignment

Multidimensional arrays exhibit the same behavior – you cannot assign directly to slices.

3. Assigning to a list slice

my_list = [10, 20, 30, 40, 50]

# Error  
my_list[1:3] = 100 

# TypeError: 'list' object does not support item assignment

The same issue arises when trying to modify a slice of a list.

4. Modifying a list element in-place

my_list = [10, 20, 30, 40, 50]

# Error
my_list[1].append(100)

# AttributeError: 'int' object has no attribute 'append'

This causes an attribute error because my_list[1] returns an int, not a list we can append to.

So in all cases, we cannot directly assign or modify a slice in-place, but instead need to use a temporary variable, index individual elements, or reassign the entire slice.

Proper Way to Modify Array Slices

To properly modify an array or list slice in Python, you should:

1. Assign the slice to a temporary variable

Here is the continuation of the article:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

# Assign slice to temporary variable
temp = arr[1:3] 

# Modify temp 
temp = [100, 200]

1. Index individual elements instead of slicing

arr = np.array([1, 2, 3, 4, 5])

# Index elements directly  
arr[1] = 100
arr[2] = 200

1. Reassign the entire slice

arr = np.array([1, 2, 3, 4, 5])

# Reassign entire slice
arr[1:3] = [100, 200]

1. Use array views instead of copies

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

# Create view into first 3 elements
view = arr[:3]  

# Modify view in-place 
view[1] = 100

Now view is a view referencing the original array data, not a new copy. So modifying view will modify arr.

1. Use numpy.copyto() to modify slices

import numpy as np

arr = np.array([1, 2, 3, 4, 5]) 

# Create array to assign
new_data = np.array([100, 200])  

np.copyto(arr[1:3], new_data)

numpy.copyto() assigns values from one array directly into a slice of another array.

1. Use list comprehensions to modify slices

my_list = [1, 2, 3, 4, 5]

# List comprehension to modify slice
my_list[1:3] = [x+100 for x in my_list[1:3]]

List comprehensions provide a concise way to transform a list slice inplace.

Following these methods will ensure you modify array and list slices correctly without any “assignment to expression” errors. The key points to remember are:

• Don’t assign directly to slice expressions
• Use temporary variables, indexing, or reassignment instead
• Array and list slices return copies, not references
• numpy.copyto() and views can be used to modify arrays in-place

Hopefully this gives you a better understanding of how to fix this common Python array and list modification error. Let me know if you have any other questions!