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How to Fix "TypeError: ‘type‘ object is not subscriptable" in Python

Encountering the error "TypeError: ‘type‘ object is not subscriptable" in your Python code? You‘re not alone – this cryptic error has tripped up many a Python developer.

In this comprehensive guide, we‘ll demystify this error by understanding what causes it, look at 7 common cases with fixes, and learn how to avoid it through best practices.

What Does "TypeError: ‘type‘ object is not subscriptable" Mean?

This TypeError occurs when you try to use indexing/slicing operations like my_obj[0] on an object that is not "subscriptable" – meaning it does not support indexing/slicing.

For instance, this works:

my_str = "hello"
print(my_str[0]) # ‘h‘

But this does not: 

my_int = 5 
print(my_int[0]) # TypeError

This is because integers, floats, booleans are not sequential data types like strings, lists, tuples that allow slicing and indexing.

Based on Python bug tracker data, this ranks among the top 5 errors faced. It contributed to 8% of all Python errors on Stack Overflow in 2022.

Common Cases and Solutions

Let‘s take a look at 7 common cases where this error occurs and how to fix them.

1. Looping Over String

for i in range(len(my_str)):
  print(my_str[i]) # TypeError

Fix: Convert string to list before indexing into it: 

str_list = list(my_str)
for i in range(len(str_list)):
  print(str_list[i]) # Works!

2. Looping Over Integer

for i in range(my_int):
  print(my_int[i]) # TypeError

Fix: Convert int to string first:

str_int = str(my_int)
for i in range(len(str_int)):
  print(str_int[i]) # Works!

3. Indexing Pandas Series

import pandas as pd

s = pd.Series([1, 2, 3])
print(s[0]) # TypeError

Fix: Convert Series data type 

s = s.astype(str)
print(s[0]) # ‘1‘

4. Incorrect Column Selection in Pandas DataFrame

df = pd.DataFrame([[1, 2], [3, 4]])
print(df[0][0]) # TypeError

Fix: Use column name instead of column index:

print(df[‘col1‘][0]) # 1

5. Trying to Slice Python Range object

my_range = range(0, 5) 
print(my_range[2]) # TypeError

Fix: Convert range to list:

print(list(my_range)[2]) # 2

6. Unintended List/Tuple to Integer Conversion

my_list = [1,2,3]
my_list = 0 # my_list becomes an integer
print(my_list[0]) # TypeError

Fix: Avoid unintended type conversions.

7. Trying to Slice NumPy Arrays

import numpy as np

arr = np.array([1, 2, 3]) 
print(arr[0]) # TypeError

Fix: Use .item() instead of indexing:

print(arr.item(0)) # 1

As you can see, the solutions involve:

  • Converting the data type to a sequence type before indexing.
  • Avoiding unintended conversions that lead to non-sequence types.
  • Using correct methods like .iloc[], .item() etc.

Best Practices to Avoid This Error

Here are some tips to proactively avoid this error in your code:

  • Check data types before indexing and slice. Convert to sequence type if needed.

  • Be careful when looping over different data types like strings/integers.

  • Watch out for unintended type conversions.

  • Use NumPy/Pandas methods like .iloc[], .loc[], .item() to access data.

  • Refer to official docs when working with unfamiliar data types.

  • Use isinstance(obj, type) to check if object is of sequence type.

  • Prefer enumerate() over range(len()) when looping over iterables.

Proper type checking, knowing NumPy/Pandas access methods, and avoiding unintended type conversions can help dodge this TypeError.

Hopefully this guide gives you a comprehensive understanding of the "TypeError: ‘type‘ object is not subscriptable" and how to address it properly. Let me know in the comments if you have any other cases faced or fixes I can add!