Do you encounter the following error message?

TypeError: 'dict_keys' object is not subscriptable

You’re not alone! This short tutorial will show you why this error occurs, how to fix it, and how to never make the same mistake again.

So, let’s get started!

Solution

Python raises the “TypeError: 'dict_keys' object is not subscriptable” if you use indexing or slicing on the dict_keys object obtained with dict.keys(). To solve the error, convert the dict_keys object to a list such as in list(my_dict.keys())[0].

print(list(my_dict.keys())[0])

Example

The following minimal example that leads to the error:

d = {1:'a', 2:'b', 3:'c'}

print(d.keys()[0])

Output:

Traceback (most recent call last): File "C:\Users\...\code.py", line 2, in <module> print(d.keys()[0])

TypeError: 'dict_keys' object is not subscriptable

Note that the same error message occurs if you use slicing instead of indexing:

d = {1:'a', 2:'b', 3:'c'}

print(d.keys()[:-1]) # <== same error

Fixes

The reason this error occurs is that the dictionary.keys() method returns a dict_keys object that is not subscriptable.

You can use the type() function to check it for yourself:

print(type(d.keys()))

# <class 'dict_keys'>

Note: You cannot expect dictionary keys to be ordered, so using indexing on a non-ordered type wouldn’t make too much sense, would it?

You can fix the non-subscriptable TypeError by converting the non-indexable dict_keys object to an indexable container type such as a list in Python using the list() or tuple() function.

Here’s an example fix:

d = {1:'a', 2:'b', 3:'c'}

print(list(d.keys())[0])

# 1

Here’s an other example fix:

d = {1:'a', 2:'b', 3:'c'}

print(tuple(d.keys())[:-1])

# (1, 2)

Both lists and tuples are subscriptable so you can use indexing and slicing after converting the dict_keys object to a list or a tuple.

Full Guide: Python Fixing This Subsctiptable Error (General)

Summary

Python raises the TypeError: 'dict_keys' object is not subscriptable if you try to index x[i] or slice x[i:j] a dict_keys object.

The dict_keys type is not indexable, i.e., it doesn’t define the __getitem__() method. You can fix it by converting the dictionary keys to a list using the list() built-in function.

Alternatively, you can also fix this by removing the indexing or slicing call, or defining the __getitem__ method. Although the previous approach is often better.

What’s Next?

I hope you’d be able to fix the bug in your code! Before you go, check out our free Python cheat sheets that’ll teach you the basics in Python in minimal time:

If you struggle with indexing in Python, have a look at the following articles on the Finxter blog—especially the third!

Related Articles:

• Indexing in Python
• Slicing in Python
• Accessing the Index of Iterables in Python

Do you need to create a function that returns a NumPy array but you don’t know how? No worries, in sixty seconds, you’ll know! Go!

A Python function can return any object such as a NumPy Array. To return an array, first create the array object within the function body, assign it to a variable arr, and return it to the caller of the function using the keyword operation “return arr“.

Recommended Tutorial: How to Initialize a NumPy Array? 6 Easy Ways

Create and Return 1D Array

For example, the following code creates a function create_array() of numbers 0, 1, 2, …, 9 using the np.arange() function and returns the array to the caller of the function:

import numpy as np def create_array(): ''' Function to return array ''' return np.arange(10) numbers = create_array()

print(numbers)

# [0 1 2 3 4 5 6 7 8 9]

The np.arange([start,] stop[, step]) function creates a new NumPy array with evenly-spaced integers between start (inclusive) and stop (exclusive).

The step size defines the difference between subsequent values. For example, np.arange(1, 6, 2) creates the NumPy array [1, 3, 5].

To better understand the function, have a look at this video:

I also created this figure to demonstrate how NumPy’s arange() function works on three examples:

In the code example, we used np.arange(10) with default start=0 and step=1 only specifying the stop=10 argument.

If you need an even deeper understanding, I’d recommend you check out our full guide on the Finxter blog.

Recommended Tutorial: NumPy Arange Function — A Helpful Illustrated Guide

Create and Return 2D NumPy Array

You can also create a 2D (or multi-dimensional) array in a Python function by first creating a 2D or (xD) nested list and converting the nested list to a NumPy array by passing it into the np.array() function.

The following code snippet uses nested list comprehension to create a 2D NumPy array following a more complicated creation pattern:

import numpy as np def create_array(a,b): ''' Function to return array ''' lst = [[(i+j)**2 for i in range(a)] for j in range(b)] return np.array(lst) arr = create_array(4,3)

print(arr)

Output:

[[ 0 1 4 9] [ 1 4 9 16] [ 4 9 16 25]]

I definitely recommend reading the following tutorial to understand nested list comprehension in Python:

Recommended Tutorial: Nested List Comprehension in Python

More Ways

There are many other ways to return an array in Python.

For example, you can use either of those methods inside the function body to create and initialize a NumPy array:

• Method 1: Use np.array()
• Method 2: Use np.zeros()
• Method 3: Use np.ones()
• Method 4: Use np.full()
• Method 5: Use np.empty()
• Method 6: Use np.arange()
• Bonus: Initialize a NumPy array with CSV data

To get a quick overview what to put into the function and how these methods work, I’d recommend you check out our full tutorial.

Recommended Tutorial: How to Initialize a NumPy Array? 6 Easy Ways

Related Tutorials

• Python Return String From Function
• Python Return Dict From Function
• Python Return Set From Function

Programmer Humor

Q: How do you tell an introverted computer scientist from an extroverted computer scientist? A: An extroverted computer scientist looks at your shoes when he talks to you.