In this tutorial, I will explain how to use the Python array index -1 in detail with examples. In a recent Python webinar, array index -1 was the topic of the discussion. We will use index -1 to fetch the last element of the given collection without knowing the length of the collection. Let us learn more about this topic.
What is Negative Indexing in Python?
Negative indexing is useful in Python lists. It will allow you to access elements from the end of the list. While positive indices start from 0, negative indices start from -1, where -1 refers to the last element of the collection, -2 to the second last, and so on.
For example, consider the following list of popular US cities:
cities = ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"]Using negative indexing, you can access the elements as follows:
cities[-1]returns “Phoenix”cities[-2]returns “Houston”cities[-3]returns “Chicago”
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Python Array Index -1 Examples
We use negative indexing when we need to access the last few elements of a list without knowing its length.
Let us see some practical examples of how negative indexing can be applied in Python through some real scenarios.
Example 1: Accessing the Last Element
Consider you have a list of top universities in the USA, and you want to fetch the last university in the list:
universities = ["Harvard", "Stanford", "MIT", "Caltech", "Princeton"]
print(universities[-1])Output:
PrincetonYou can see the executed example code in the below screenshot.
In this example, universities[-1] return “Princeton”, the last element in the list.
Check out How to Get Values from a JSON Array in Python?
Example 2: Slicing with Negative Indices
Negative indexing can also be used in slicing operations to create sublists. If you want to get the last three universities from the list, you can use negative indices in the slice.
universities = ["Harvard", "Stanford", "MIT", "Caltech", "Princeton"]
last_three_universities = universities[-3:]
print(last_three_universities)Output:
['MIT', 'Caltech', 'Princeton']Let us have a look at the screenshot below to know more.
In this example, universities[-3:] return a sublist containing the last three universities.
Read How to Sort an Array in Python?
Example 3: Removing the Last Element
Sometimes, you may need to remove the last element from a Python list. For this, you can use negative indexing like the below:
states = ["California", "Texas", "Florida", "New York", "Illinois"]
removed_state = states.pop(-1)
print(removed_state)
print(states)Output:
Illinois
['California', 'Texas', 'Florida', 'New York']Go through the screenshot below for the reference.
Here, states.pop(-1) removes and returns the last element, “Illinois”, leaving the list with the remaining states.
Example 4: Reversing a List
Negative indexing can be leveraged to reverse a list using slicing. This is useful when you need to process elements in reverse order.
presidents = ["Washington", "Adams", "Jefferson", "Madison", "Monroe"]
reversed_presidents = presidents[::-1]
print(reversed_presidents)Output:
['Monroe', 'Madison', 'Jefferson', 'Adams', 'Washington']
In this example, presidents[::-1] create a new list with elements in reverse order.
Example 5: Processing Recent Data
Think that you are working with a list of recent stock prices for a company based in the USA, and you want to analyze last week’s data.
stock_prices = [150, 152, 153, 155, 157, 160, 162, 165, 167, 170]
last_week_prices = stock_prices[-7:]
print(last_week_prices)Output:
[155, 157, 160, 162, 165, 167, 170]Here, stock_prices[-7:] extracts the prices from the last seven days.
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Example 6: Handling User Input
Suppose you have a list of user inputs, and you want to validate the most recent input; then you can write the code like below:
user_inputs = ["John", "Doe", "john.doe@example.com", "New York", "NY", "10001"]
latest_input = user_inputs[-1]
print(latest_input)Output:
10001In this case, user_inputs[-1] retrieves the most recent input.
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Common Issues and How to Avoid Them
Now, let me show you some common issues that you might receive and how to fix them.
Issue 1: Index Out of Range
If you try to access an index that doesn’t exist, Python will raise an IndexError.
states = ["California", "Texas", "Florida"]
print(states[-4])Output:
IndexError: list index out of rangeTo avoid this, ensure your index is within the valid range of the list.
Issue 2: Modifying Lists While Iterating
Modifying a list while iterating it using negative indices can lead to unexpected behavior.
cities = ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"]
for i in range(len(cities)):
if cities[-1] == "Phoenix":
cities.pop(-1)
print(cities)Output:
['New York', 'Los Angeles', 'Chicago', 'Houston']Here, the loop may not behave as expected if the list size changes during iteration. To avoid this, consider creating a copy of the list or using list comprehensions.
Check out How to Print Duplicate Elements in Array in Python
Best Practices for Using Negative Indexing in Python
To use the negative indexing efficiency, consider the following best practices:
Use Clear and Descriptive Variable Names
The name of the variables should clearly describe their purpose so that it will be easier to understand.
recent_temps = temperatures[-7:]Combine Positive and Negative Indexing
Combining positive and negative indexing makes code understandable.
data = ["A", "B", "C", "D", "E"]
middle_elements = data[1:-1]
print(middle_elements)Output:
['B', 'C', 'D']Document Your Code
Add comments to explain why you’re using negative indexing.
# Extract the last three elements for analysis
last_three_elements = data[-3:]Conclusion
In this tutorial, I explained the concept of negative indexing in Python arrays, such as what negative indexing is and why we use negative indexing. We have seen many real-time examples, and I have also shown some common issues and how to avoid them. Best practices for using negative indexing are also discussed in this article.
Hope this article helped you gain some knowledge of Array Index -1 in Python.
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I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.
