python list slicing assignment

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In Python, list slicing is a common practice and it is the most used technique for programmers to solve efficient problems. Consider a Python list, in order to access a range of elements in a list, you need to slice a list. One way to do this is to use the simple slicing operator i.e. colon(:). With this operator, one can specify where to start the slicing, where to end, and specify the step. List slicing returns a new list from the existing list.

Python List Slicing Syntax

The format for list slicing is of Python List Slicing is as follows:

If Lst is a list, then the above expression returns the portion of the list from index Initial to index End , at a step size IndexJump .

Indexing in Python List

Indexing is a technique for accessing the elements of a Python List . There are various ways by which we can access an element of a list.

Positive Indexes

In the case of Positive Indexing, the first element of the list has the index number 0, and the last element of the list has the index number N-1, where N is the total number of elements in the list (size of the list).

Positive Indexing of a Python List

In this example, we will display a whole list using positive index slicing.

Negative Indexes

The below diagram illustrates a list along with its negative indexes. Index -1 represents the last element and -N represents the first element of the list, where N is the length of the list.

Negative Indexing of a Python List

In this example, we will access the elements of a list using negative indexing.

As mentioned earlier list slicing in Python is a common practice and can be used both with positive indexes as well as negative indexes. The below diagram illustrates the technique of list slicing:

In this example, we will transform the above illustration into Python code.

Examples of List Slicing in Python

Let us see some examples which depict the use of list slicing in Python.

Example 1: Leaving any argument like Initial, End, or IndexJump blank will lead to the use of default values i.e. 0 as Initial, length of the list as End, and 1 as IndexJump.

Example 2: A reversed list can be generated by using a negative integer as the IndexJump argument. Leaving the Initial and End as blank. We need to choose the Initial and End values according to a reversed list if the IndexJump value is negative. 

Example 3: If some slicing expressions are made that do not make sense or are incomputable then empty lists are generated.

Example 4: List slicing can be used to modify lists or even delete elements from a list.

Example 5: By concatenating sliced lists, a new list can be created or even a pre-existing list can be modified. 

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Understanding Python's slice assignment

Python slice notation

Understanding python's slice notation.

• Understanding Python's slice assignment (this blog post)

Basic syntax

In order to understand Python's slice assignment, you should at least have a decent grasp of how slicing works. Here's a quick recap:

Where start_at is the index of the first item to be returned (included), stop_before is the index of the element before which to stop (not included) and step is the stride between any two items.

Slice assignment has the same syntax as slicing a list with the only exception that it's used on the left-hand side of an expression instead of the right-hand side. Since slicing returns a list, slice assignment requires a list (or other iterable). And, as the name implies, the right-hand side should be the value to assign to the slice on the left-hand side of the expression. For example:

Changing length

The part of the list returned by the slice on the left-hand side of the expression is the part of the list that's going to be changed by slice assignment. This means that you can use slice assignment to replace part of the list with a different list whose length is also different from the returned slice. For example:

If you take empty slices into account, you can also insert elements into a list without replacing anything in it. For example:

Using steps

Last but not least, step is also applicable in slice assignment and you can use it to replace elements that match the iteration after each stride. The only difference is that if step is not 1 , the inserted list must have the exact same length as that of the returned list slice. For example:

More like this

Learn everything you need to know about Python's slice notation with this handy guide.

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Python List Slicing

To access a range of items in a list , you need to slice a list. One way to do this is to use the simple slicing operator :

With this operator you can specify where to start the slicing, where to end and specify the step.

Slicing a List

If L is a list, the expression L [ start : stop : step ] returns the portion of the list from index start to index stop , at a step size step .

Basic Example

Here is a basic example of list slicing.

Note that the item at index 7 'h' is not included.

Slice with Negative Indices

You can also specify negative indices while slicing a list.

Slice with Positive & Negative Indices

You can specify both positive and negative indices at the same time.

Specify Step of the Slicing

You can specify the step of the slicing using step parameter. The step parameter is optional and by default 1.

Negative Step Size

You can even specify a negative step size.

Slice at Beginning & End

Omitting the start index starts the slice from the index 0. Meaning,  L[:stop]  is equivalent to  L[0:stop]

Whereas, omitting the stop index extends the slice to the end of the list. Meaning, L[start:]  is equivalent to  L[start:len(L)]

Reverse a List

You can reverse a list by omitting both start and stop indices and specifying a step as -1.

Modify Multiple List Values

You can modify multiple list items at once with slice assignment . This assignment replaces the specified slice of a list with the items of assigned iterable .

Insert Multiple List Items

You can insert items into a list without replacing anything. Simply specify a zero-length slice.

You can insert items into the middle of list by keeping both the start and stop indices of the slice same.

Delete Multiple List Items

You can delete multiple items out of the middle of a list by assigning the appropriate slice to an empty list.

You can also use the del statement with the same slice.

Clone or Copy a List

When you execute new_List = old_List , you don’t actually have two lists. The assignment just copies the reference to the list, not the actual list. So, both new_List and old_List refer to the same list after the assignment.

You can use slicing operator to actually copy the list (also known as a shallow copy ).

Slicing Lists in Python: A Comprehensive How-To Guide

Slicing is a very useful technique in Python that allows you to extract subsets or slices from sequences like lists, tuples, and strings. Slicing provides an efficient and convenient way to get a specific subset range from a list without having to loop through and index each item individually.

In this comprehensive guide, you will learn what list slicing is, how it works under the hood, and how to leverage slicing operations to extract, modify or analyze subsets of list data in Python.

We will cover the basics of slicing syntax, slice object internals, and practical applications with code examples. You will also learn advanced techniques like multidimensional slicing on nested lists and gotchas to avoid when slicing mutables.

By the end, you will have a deep understanding of slicing in Python and be able to use it proficiently in your own programs for efficient data extraction and processing. The knowledge is useful for Python developers, data scientists, and anyone working with sequential data structures.

Table of Contents

What is list slicing in python, omitting start and end indices, negative indices, stepping or stride value, empty slices, multidimensional slicing, practical examples of list slicing in python, slicing gotchas and best practices.

List slicing in Python is an operation that allows you to retrieve a range of items from a list by specifying start and end indices. It returns a new list containing the extracted elements.

The basic syntax for slicing a list is:

• original_list is the list you want to slice.
• start is the index of the first element you want included in the sliced list.
• end is the index of the first element you want excluded from the sliced list.
• new_list contains the sliced elements from index start up to but not including end.

For example:

This slices fruits from index 1 (inclusive) to index 3 (exclusive) and assigns the new sliced list to fruits_subset .

Some key properties of slicing lists in Python:

• Slicing extracts a new list with the specified items. It does not modify the original list.
• The start index is inclusive and the end index is exclusive.
• You can omit either start or end index to slice from start/end of list by default.
• Slicing handles negative indices to refer to positions from end of list.
• Returns empty list if start >= end indices.
• Supports stepping with a stride value.

We’ll explore these in more detail with examples next.

How List Slicing Works in Python

Slicing notation in Python is implemented through slice objects. A slice object represents the start, stop, and step values given to slice a sequence, and contains metadata about the slicing operation.

The slice object is created by calling slice() built-in and passed to the sequence indexing operator [] .

Understanding what happens internally helps explain the behavior and flexibility of slicing notation in Python.

When a slice object is passed to the indexing operator, the sequence looks up its _ _getitem_ _ method to handle the slice and extract the subset items accordingly.

Now let’s look at different ways to slice lists using this slice object notation.

If you omit start index, 0 is taken by default:

Similarly, if you omit end, it slices all the way to the end by default:

You can also omit both to make a full slice copy of the entire list:

Python allows negative indices for slicing, which refer to positions counted backwards from the end of the list.

Here, -3 refers to index 2 from start and -1 refers to index 4 from start when counted backwards from the end.

Negative indices provide an easy way to slice items from the end of a list without needing to know the exact length.

You can also specify a step or stride value to skip items while slicing:

The third argument 2 specifies a step of 2. So it extracts every alternate item from index 0 to end.

Stepping works with negative stride too:

Here, step -1 reverses the list by going backwards from end to start.

If start index >= end index, slicing returns an empty list since no items exist in that range:

The slice is essentially invalid in these cases.

Slicing also works on nested lists (lists within lists) to extract subsets from sublists:

You can slice both the outer and inner lists separately.

This multidimensional slicing provides a very flexible way to extract sections from matrix or grid-like data structures in Python.

Now let’s look at some practical examples of how list slicing can be used in real code.

1. Getting a sublist with specific indices

Use simple slicing to extract part of a list:

2. Omitting indices for start/end defaults

Utilize default start and end values:

3. Negative index for slicing from end

Get the last 3 items by negative index:

4. Skipping items with step value

Extract every 3rd item:

5. Reversing list with negative step

Reverse the entire list:

6. Copying a list

Make a full slice copy:

7. Removing list section in-place

Delete section by assigning an empty slice:

8. Multidimensional slice on matrix

Extract subsets from nested lists:

These are some common use cases for slicing lists in Python. The key strengths are efficiently extracting sections, reversing, stepping through items, and working with multidimensional data.

While slicing syntax is very flexible, there are some best practices to keep in mind:

Slicing does not modify the original list - It extracts a new list with a copy of the sliced data. To modify in-place, delete slice or assign to slice.

Avoid slicing mutable objects - Slicing mutable objects like dicts or sets returns a shallow copy that still references the original. Changes to sliced copy affect original.

Handle out of bound errors - Slicing indices outside of the valid range will raise an IndexError . Catch exceptions or check bounds first.

Use negative indices wisely - It can make code harder to understand vs positive indices. Best for slicing relative to end.

Prefer start:end over start:len(list) - Knowing the length to slice from start to end is inefficient. Omit end instead.

Use stepped slices for regular skips - Stepping is useful for skipping fixed intervals instead of manual loops.

Test edge cases on empty lists - Be careful slicing empty lists and watch for off-by-one errors.

Following best practices will help you avoid subtle bugs and write clean, efficient list slicing code.

This guide covered the fundamentals of slicing in Python - from basic syntax, to internals, practical examples, advanced techniques, and common gotchas.

Key takeaways:

• Slicing extracts subsequences from lists, tuples, strings without modifying originals
• Slice object passed to __getitem__ represents start, stop, and step
• Omit indices to slice from defaults of start/end
• Use negative indices to refer to positions from end
• Step provides skip intervals for slicing
• Multidimensional slicing works on nested collections
• Avoid slicing mutable objects and watch edge cases

You should now be able to leverage slicing for efficient data extraction, processing, and analysis in your Python code. Slicing is a universally useful technique for Python developers.

Some next topics to explore further are NumPy array slicing, Pandas dataframe slicing, and generator expressions for lazy slicing.

I hope you found this guide helpful in deepening your understanding of this core Python language feature. Let me know if you have any other questions!

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Python List Slicing: How to Use It [With Simple Examples]

When writing a Python program you might want to access multiple elements in a list. In this scenario, Python list slicing can be very useful.

With Python’s list slicing notation you can select a subset of a list, for example, the beginning of a list up to a specific element or the end of a list starting from a given element. The slicing notation allows specifying the start index, stop index, and interval between elements (step) to select.

Slicing is a topic that can be a bit confusing for Python beginners and in this article, I will help you understand slicing based on my personal experience of using it in several Python applications.

Let’s see some examples of list slicing!

What Is List Slicing in Python?

In Python, you can use the colon character( : ) within square brackets to print part of a list (this is called slicing).

The first step to using slicing with a Python list is to understand the syntax for slicing:

The first important concept to know is that when you apply the slicing operator to a list you get back another list.

The syntax of slicing in Python supports the following arguments:

• start : the start index (inclusive)
• stop : the stop index (exclusive)
• step : the interval between elements returned in the slice

Based on my experience, one of the confusing aspects of slicing can be the fact that the start index is inclusive and the stop index is exclusive.

This is something you will have to remember and get used to to make sure you select the correct subset of a list.

6 Examples of List Slicing in Python

Let’s go through a few examples to explain how you can use the start , stop , and step arguments in the slicing notation .

Let’s take a list that contains the first 10 numbers of the Fibonacci sequence:

In the following sections, you will see the lists returned by the slicing operator when using different combinations of the start, stop, and step arguments.

1. Slice with Start and Stop Arguments

In this example, we specify where the slice starts and stops.

Start is equal to 2 and stop is equal to 5. The result is a slice of the original list that goes from index 2 (inclusive) to index 5 (exclusive).

2. Slice with Start Argument Only

When you only specify the start argument, the slice goes from the start index to the end of the list.

Considering that start is equal to 3 the slice goes from index 3 to the end of the original list.

3. Slice with Stop Argument Only

When you only specify the stop argument the slice goes from the beginning of the list to the stop index (exclusive).

Given that stop is equal to 4 the slice goes from the beginning of the original list to index 4 exclusive.

4. Slice with Start, Stop, and Step Arguments

You can combine the start, stop, and step arguments to have more control over the slice of the original list.

This is the first time we use the step argument in this tutorial, here is what it does:

In this example, start is equal to 1, stop is equal to 8 and step is equal to 2. This means that the slice goes from index 1 (inclusive) to index 8 (exclusive).

The fact that step is 2 means that the slice includes every other element of the list within the limits defined by start and stop.

5. Slicing with Positive Step Argument Only (without Start and Stop)

Let’s see what happens if you only use a positive step value with the list slicing operator.

Notice that the slice notation contains two colons followed by the value 2. That’s because there are no values for start/stop and the step is equal to 2.

The result is a slice that contains every other element in the original list.

6. Slice with Negative Step Argument Only

You can use an empty start, stop, and a negative value for the step argument (-1) to reverse the elements of a list.

This is similar to the result you can get using Python’s built-in reversed() function .

In this tutorial, you have learned what list slicing is in Python and how you can use different combinations of start, stop, and step values to obtain different slices.

How are you using slicing in your Python programs? Let me know in the comments below!

Related article : go through the following CodeFatherTech tutorial to learn about methods provided by Python lists .

Claudio Sabato is an IT expert with over 15 years of professional experience in Python programming, Linux Systems Administration, Bash programming, and IT Systems Design. He is a professional certified by the Linux Professional Institute .

With a Master’s degree in Computer Science, he has a strong foundation in Software Engineering and a passion for robotics with Raspberry Pi.

Related posts:

• 5 Ways to Copy a List in Python: Let’s Discover Them
• How Do You Get Every Other Element From a Python List?
• Unpack List in Python: A Beginner-Friendly Tutorial
• How to Create a List of Random Numbers in Python

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Python Slice Assignment

Slice assignment is a little-used, beautiful Python feature to replace a slice with another sequence. Simply select the slice you want to replace on the left and the values to replace it on the right side of the equation. For example, the slice assignment list[2:4] = [42, 42] replaces the list elements with index 2 and 3 with the value 42 .

I’ve recorded a quick video that shows you how the slice assignment feature works in a Python One-Liner :

Play With Slice Assignment in Your Interactive Shell

Before I’ll explain it to you, feel free to play with this feature yourself:

One of my Finxter users, Mike , asked the following great question:

“I was going through a lot of slicing puzzles on the Finxter site, I came across this:

I originally answered [‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’] but got it wrong.”

This is the point where I want to open your knowledge gap: what’s the output of this puzzle instead?

** For your convenience, you can also solve this specific puzzle on the Finxter app here. **

“As the answer is [‘a’], I immediately became curious because that’s [not] the answer if we use this code

Why do these work differently? Thanks, I really appreciate your time and all of the content you provide each day.”

Again, great question. Mike did most of the heavy lifting himself. The answer is simple (if you have read my slicing booklet already):

• The first version is slice assignment .
• The second version is basic slicing .

They are not the same. You should not confuse slicing and slice assignment. Here is the difference:

1) S licing creates a new subsequence of the original sequence. You can assign this new sequence to a variable y. In essence, you overwrite any previous value of variable y:

2) Slice assignment replaces the selected slice in the original sequence y with the value specified on the right-hand side of the equation:

Note that the two code snippets also demonstrate how you can convert a string to a list and convert a list back to a string.

This Python lesson is based on my free “Coffee Break Python” Email Series. Join us. It’s fun! ?

Where to Go From Here?

Enough theory. Let’s get some practice!

Coders get paid six figures and more because they can solve problems more effectively using machine intelligence and automation.

To become more successful in coding, solve more real problems for real people. That’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

You build high-value coding skills by working on practical coding projects!

Do you want to stop learning with toy projects and focus on practical code projects that earn you money and solve real problems for people?

🚀 If your answer is YES! , consider becoming a Python freelance developer ! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

If you just want to learn about the freelancing opportunity, feel free to watch my free webinar “How to Build Your High-Income Skill Python” and learn how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.

To help students reach higher levels of Python success, he founded the programming education website Finxter.com that has taught exponential skills to millions of coders worldwide. He’s the author of the best-selling programming books Python One-Liners (NoStarch 2020), The Art of Clean Code (NoStarch 2022), and The Book of Dash (NoStarch 2022). Chris also coauthored the Coffee Break Python series of self-published books. He’s a computer science enthusiast, freelancer , and owner of one of the top 10 largest Python blogs worldwide.

His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.

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Mastering list slicing in python: a comprehensive guide, introduction to lists in python.

If you’re new to programming, you may have heard the term “list” thrown around. In Python, a list is a datatype that allows you to store a collection of values.

So, what makes a list different from other datatypes? For starters, lists are ordered, which means that the elements in the list follow a specific order.

Additionally, lists are mutable, which means that you can change the elements in the list. This makes lists incredibly versatile and practical for a wide range of programming applications.

Example of a List

Before diving into the different aspects of lists in Python, let’s take a look at an example. A list is defined by placing a sequence of values within square brackets, separated by commas.

For example, if you wanted to create a list of integers, you can do so like this:

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

This list contains the values 1 through 5. You can also create a list of other datatypes, such as strings or floating-point numbers.

As long as the sequence of values is enclosed in square brackets and separated by commas, you have a list.

Accessing Elements of a List

Once you have created a list, you can access its elements by using an index. In Python, indexing starts at 0, which means that the first element of the list is at index 0, the second element is at index 1, and so on.

To access a specific element of a list, you can use the index of the element within square brackets. For example:

print(my_list[0]) # Output: 1

In this example, we print the first element of the list, which is at index 0.

You can also access elements using negative indexing. Negative indexing starts from the end of the list, with -1 being the last element of the list.

For example:

print(my_list[-1]) # Output: 5

This prints the last element of the list, which is at index -1.

List Slicing in Python

Now that you’re familiar with accessing individual elements of a list, let’s take a look at list slicing. List slicing allows you to access a subset of elements from a list, rather than just a single element.

Using the Slicing Operator

The syntax for list slicing in Python is [start:stop:step]. The start parameter indicates the index where the slice should begin, the stop parameter indicates the index where the slice should end (excluding the element at the stop index), and the step parameter indicates the number of positions to move between each element in the slice.

For example, consider the following list:

If we wanted to slice the list to include only the first three elements, we would use the following syntax:

sliced_list = my_list[0:3]

print(sliced_list) # Output: [1, 2, 3]

In this example, the start parameter is 0, the stop parameter is 3 (which excludes the element at index 3), and the step parameter is not specified, which defaults to 1.

Default Values of Slicing

If you don’t specify any of the parameters in the slicing syntax, Python will use default values. The default value for the start parameter is 0, the default value for the stop parameter is the length of the list, and the default value for the step parameter is 1.

If we wanted to slice the entire list using the default values, we would use the following syntax:

sliced_list = my_list[:]

print(sliced_list) # Output: [1, 2, 3, 4, 5]

In this example, the start parameter is not specified, so it defaults to 0. The stop parameter is also not specified, so it defaults to the length of the list, which is 5 in this case.

Finally, the step parameter is not specified, so it defaults to 1.

Example of Slicing a List

To better understand how list slicing works, let’s take a look at another example. Consider the following list:

my_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

If we wanted to slice this list to include only the odd numbers between 1 and 9, we could use the following syntax:

sliced_list = my_list[1:10:2]

print(sliced_list) # Output: [1, 3, 5, 7, 9]

In this example, the start parameter is 1 (the index of the first odd number), the stop parameter is 10 (the index of the number after the last odd number), and the step parameter is 2 (to only include the odd numbers).

Iterating over a Sliced List

Once you have sliced a list, you can iterate over the sliced portion using a for loop or list comprehension.

Consider the following sliced list from the previous example:

sliced_list = [1, 3, 5, 7, 9]

If we wanted to iterate over this sliced list using a for loop, we could use the following syntax:

for num in sliced_list:

This would output the following:.

Alternatively, we could use a list comprehension to iterate over the sliced list and perform an operation on each element:

squared_list = [num**2 for num in sliced_list]

print(squared_list) # Output: [1, 9, 25, 49, 81]

In this example, we square each element in the sliced list using a list comprehension. The resulting list contains the square of each odd number from the original list.

Lists and list slicing are fundamental concepts in Python, and they play essential roles in a wide range of programming applications. By understanding the basics of creating and accessing elements of a list, as well as slicing a list to include only certain elements, you can perform a variety of operations with ease.

With this knowledge in mind, you’ll be able to implement lists and list slicing in your own programs with confidence. 3) Explaining the Code “for x in A[1:]”

If you’ve worked with Python for a while, you may have come across code that looks something like this:

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

for x in A[1:]:

In this code, we have a list called “A” containing the integers 1 through 5.

The line “for x in A[1:]” is where things get interesting. Let’s break it down and understand what’s going on.

Understanding the Slicing in the Code

First, we have the list “A”, as previously mentioned. We then use slicing to create a new list that starts from index 1 and goes to the end of the original list (excluding the element at index 0).

This is indicated by the syntax [1:]. So, essentially, we are slicing the original list “A” to create a new list that excludes the first element.

Omitting Stop and Step

Notice that we don’t specify a stop or step value in the slice. When we omit the stop value, Python assumes we want to slice to the end of the list.

When we omit the step value, Python defaults to a step of 1, meaning that we include every element in the sliced list.

Example of Code “for x in A[1:]”

To better understand the code above, let’s take a look at what it prints.

When we run this code, we get the following output:

As you can see, the for loop is iterating over each element in the sliced list, skipping the first element and printing the remaining elements.

4) Slicing a List in Reverse Order

So far, we’ve looked at slicing a list from the beginning to the end. But what if we want to slice a list in reverse order?

Fortunately, Python makes this easy with negative indexing.

Using Negative Indexing

Negative indexing means that we start counting from the end of the list, with -1 representing the last element of the list, -2 representing the second-to-last element, and so on. So, if we wanted to slice a list in reverse order, we would use negative indexing to specify the start and stop indices.

Example of Slicing a List in Reverse Order

Consider the following list:.

If we wanted to slice this list to exclude the last two elements, we could use the following syntax:

sliced_list = A[:-2]

In this example, we use negative indexing to specify that we want to slice from the beginning of the list up to (but not including) the last two elements. The result is a sliced list that excludes the last two elements, as desired.

When working with negative indexing, it’s important to keep in mind that the slicing syntax still follows the [start:stop:step] format. So, in the example above, the start index is not specified, which defaults to the beginning of the list.

The stop index is -2, which excludes the last two elements of the list. And the step value is not specified, which defaults to 1.

In summary, list slicing is a powerful feature in Python that allows you to work with subsets of lists. By using slicing syntax to specify the start, stop, and step values, you can create new lists that include only the elements you need.

Additionally, negative indexing allows you to easily slice a list in reverse order, opening up even more possibilities for data manipulation. By understanding these concepts, you’ll have more tools at your disposal for working with lists in Python.

In summary, this article has covered the essentials of list slicing in Python. We began by introducing lists and explaining how they are different from other datatypes.

From there, we looked at accessing elements of a list and how to slice a list to include only a specific subset of elements. We also examined the code “for x in A[1:]”, explaining how it iterates over a sliced list.

Finally, we explored slicing a list in reverse order using negative indexing. Overall, understanding list slicing is crucial to manipulating data in Python more efficiently.

By mastering list slicing techniques, you have a powerful tool at your disposal for creating more efficient and effective code in Python.

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A Complete Guide on List Slicing and slice() Method of Python

• by pickupbr
• June 11, 2020 August 3, 2020

Slicing is extraction of a part of a sequence like list, tuple, string. It can fetch multiple elements with a single statement. It does not remove the values from the iterable; instead makes a new object with the desired elements.

start  – Starting integer where the slicing of the object starts. Default to  None  if not provided.

  end – Integer until which the slicing takes place. The slicing ends at index  end – 1 (last element) .

s tep – Integer value which determines the increment between each index for slicing. Defaults to  None  if not provided.

• for zero or positive value of start/end, elements are counted from the start of the iterable.
• for negative value of start/end, elements are counted from the end of the iterable (for e.g.-1 is the last element, -2nd to last etc).
• If step value is 0, then it is set as 1.
• If step value is negative, then the elements are selected in reverse order.

When start and end values are not provided colon must be used instead.

Default value of start and end

• When start value is not provided – Default value of start depends on step size. When step value is positive or omitted, it defaults to the start of the iterable and if step value is negative, start defaults to the end of the iterable.
• When end value is not provided – Similar to start, default value of end depends on step size. When step value is positive or omitted, it defaults to the end of the iterable and if step value is negative it defaults to the start of the iterable.

Let’s create a list to show slicing and Slice at position 3 to 6.

Basic usage of slicing

Python supports slice notation for any sequential data like lists, strings, tuples and ranges. Slicing is a flexible tool to build new sequential from the existing one. It takes start, end and step as an argument. Each of them has a default value as described above. Because of its ease and simplicity it is also popular in data science libraries like NumPy and Pandas.

When given values are out of range

If output returns, no elements.

When provided values of start and end returns no element, then it will return as an empty list.

Using negative indexing

Use negative index to refer element position with respect to end of the list. For e.g. -1 index refers to the last element of the list.

Here -1 refers to the last element and as the end element is not include, so it selects the elements till the second last element.

Getting a list in reverse order

If step value is negative then the elements will selected in reverse order.

Get substring using slice object

The slice object is used to slice a sequence (i.e. string, list, tuple, set or range). This is used for extended slicing. If only one value is passed then start and step value is considered as none i.e. start will default to starting of the iterable and step will default to 1.

Substitute part of a list

Slicing notation can also be used to assign elements.

Replace and Resize part of the list

In this case we extend the original list.

It’s also possible to replace bigger sub-list with a smaller sub-list:

Replace Every n-th Element

Following code will replace every 2nd element with a ‘*’:

Using slice assignment with step sets a limitation on the list we provide fo r assignment. The provided list should exactly match the number of elements to replace. If the length does not match, Python throws the error ValueError : attempt to assign sequence of size 4 to extended slice of size

We can use negative step too

By providing start and stop values .

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A Guide to Python List Slicing

Python lists can be manipulated to create sub-lists using list slice notation or the slice object. This tutorial will go over slicing and provide a few examples to illustrate the various concepts covered. 

List Slicing Syntax

A sub-list can be quickly and easily extracted from a list without the need to loop through the items in the list. There are two ways to slice a list in Python; using what we call slice notation  [start:stop:step]  or using the slice(start,stop,step)  method. Both methods function in much the same way and are equivalent.

• start  refers to the index at which to start the slicing. The item at the start index is included in the sub-list .
• stop  refers to the index at which to stop slicing. The item at the stop index is NOT included in the sub-list .
• step  refers to the number of indices to jump during slicing. This defaults to 1.
• start ,  stop  and  step  are all  optional  arguments and can be omitted as we'll show in the examples to follow. The first item in a list is located at index  0  which you should bear in mind when creating sub-lists.
• start ,  stop  and  step  can be positive or negative numbers. A negative value indicates that the count will start from the end of the list rather than the beginning.

Let's look at some examples now.

List Slicing Examples

We'll start by defining a list with 15 items, starting with item 1 at index 0 and ending with item 15 at index 14 .

Basic List Slicing

List slicing in reverse.

Remember that the last item in a list can be accessed by the index  -1  so there is no need to calculate the length of the list if you need to find the last item.

You will recall that  stop  refers to the index at which to stop slicing but  is not included in the sub-list.

List Slicing with a Step

The  step  defaults to 1 but can be modified to step through the list at any rate.

To step in reverse you must indicate a negative step.

Summary of List Notation

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Python list slice

last modified January 29, 2024

In this article we show how to create list slices in Python.

A list is an mutable, ordered collection of values. The list elements can be accessed by zero-based indexes.

A list slice is a portion of elements of a list.

Python slice syntax

List slicing is an operation that extracts certain elements from a list and forms them into another list. Possibly with different number of indices and different index ranges.

The indexes are zero-based. They can be negative.

The syntax for list slicing is as follows:

The start, end, step parts of the syntax are integers. Each of them is optional. They can be both positive and negative. The value having the end index is not included in the slice.

We include all elements up to the end index, not including the element with index end.

Python list slice start/end

The start index is the beginning index; the end index is the ending index of a slice.

The program creates two slices.

With the len function, we get the size of the list. Since the end index of a slice is not included, it can be used in the slice syntax.

We create a list slice with start=0 and end=5. The elements with indexes 0, 1, 2, 3 and 4 are included in the slice.

The second slice has elements with indexex 2..last-1.

Python list slice omit indexes

All three indexes of the slice syntax can be ommitted. If we omit the start index, the slice is created from the first element. If we omit the end index, the slice is generated up to the last element. If we omit the step, than the default step is 1.

In the program, we omit the indexes.

We create a slice from the beginning up to the third element.

A slice from the fourth element to the last element is created.

Here we create a copy of the list.

Here we also create a copy of the list.

Python slice negative indexes

Indexes can be negative numbers. Negative indexes refer to values from the end of the list. The last element has index -1, the last but one has index -2 etc. Indexes with lower negative numbers must come first in the syntax. This means that we write [-6, -2] instead of [-2, -6]. The latter returns an empty list.

The program uses negative start/end indexes.

We create a slice from the fourt index from the end to the penultimate index.

Python list slice step

The third index in a slice syntax is the step. It allows us to take every n-th value from a list.

We form four new lists using the step value.

Here we create a slice having every second element from the n list, starting from the second element, ending in the eighth element. The new list has the following elements: [-1, 1, 3, 5].

Here we build a slice by taking every second value from the beginning to the end of the list.

This creates a copy of a list.

The slice has every third element, starting from the second element to the end of the list.

Python list slice negative step

With a negative step, we take every nth element from the end. The start index must be greater than the end index.

The program uses negative step.

We create a slice from the sixth element to the first element, skipping every second element.

This creates a slice of every second element from the end to the beginning.

This line creates a copy of the list with reversed elements.

Python list slice assignments

The slice syntax can be used with assignments.

We have a list of eight integers. We use the slice syntax to replace the elements with new values.

Python datastructures - language reference

In this article we have covered list slice operations in Python.

My name is Jan Bodnar and I am a passionate programmer with many years of programming experience. I have been writing programming articles since 2007. So far, I have written over 1400 articles and 8 e-books. I have over eight years of experience in teaching programming.

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Python Course #8: Lists, List Slicing, and all List Functions (incl. Free Cheat Sheet)

In this article, you will learn about the sequential data type list . In the first article on data types you have seen the primitive data types bool , int , float and str . While str is also a sequential data type and behaves similar to a list in many ways with one key difference. A str can only store values of one kind, and those are characters such as letters and numbers. list s however, can store other types and each element can be of a different data type.

Free Python Lists Cheat Sheet

Pick up you free Python lists cheat sheet from my Gumroad shop :

Declaring a Python List

To declare a list you put all the values of a list between two brackets [] and separate them with commas , . In the following example, the list containing a bool , int , float , and str is assigned to the variable l :

Accessing Python List Elements

You can either access the whole list by stating the variable l or you can access individual elements with the [] -operator (also called bracket-operator). Each element of a list has an index starting from the left with 0, adding 1 in every step going to the right.

To access the first element, you need to enter a 0 as an index between the brackets directly added after the variable name. For the l declared above, you would get True as a value:

To access the second element, you replace 0 with a 1 and so on. But how would you access the last element or the second last element? One solution is to use len(...) . len(...) returns the length of a list as an int :

The last element of a list is always at index len(...)-1 . However, Python has a trick up its sleeve, so you don’t have to use len(...) ; instead, you can use negative indices. The index -1 will always return the last element of a list, and the index -2 returns the second last element and so on.

Python List Slicing

With the bracket-operator it is also possible to get a sub list that represents only a part of the original list. This is called list slicing . To make the examples a bit easier to comprehend, declare a list ` i’ where the elements have the same value as the index:

If now want to slice out the list [4, 5, 6, 7, 8] you enter the following:

The first index (here 4 ) is the start index. From this index on, all elements are returned one position before the second index (here 9 ). If you want a list starting from a certain index until the end of the list , you don’t have to enter a second index:

And if you want a list from the beginning up to a certain index, don’t enter the first index:

It is possible to only get every n-th element of a list with list slicing by adding another colon : after the second index indicating the step width. If you only want every 3rd element of the sub list i[5:18] use the following:

And as before, you can leave out the first and second index or both if the end of your sub list should be the start or end of the original list:

Variables in Python Lists

You can also use variables to add elements to a list. However, the values stored in the variable will be copied over into the list element, which means when you change the value of a variable after it has been added to a list, the change won’t appear in the list:

A list can also contain other lists:

This can be helpful if you want to represent your data in a table, for example. To access the 1st element of the 2nd list , you stack the [] -operator:

The first bracket-operator [1] accesses the outer list and returns the element stored and this index 1 which is the inner list [6, 7, 8] . The second bracket-operator now acts on the list returned by the first bracket-operator and returns the element at index 0 .

You are also able to use variables as index with in the brackets:

Changing Python List Elements

The [] -operator can also be used to assign new values to elements in a list :

And you can even bulk-assign new values to list elements using list slicing:

But you have to be careful here because the selected slice and the list containing the new values have to be the same size to make this bulk-assignment work.

Python Mutable Data Types

Changing the values stored in list elements is a property of a mutable data type. A mutable data types also allow you to add and remove elements after it is declared. In contrast to mutable data, types are immutable ones where you aren’t able to add or remove elements after their declaration. You will see an example of an immutable data type in an upcoming article on Python tuples .

Now let’s take a close look at how to add, remove, and manipulate list s in Python

Python List Manipulation Functions

A list manipulation function is always called with a dot . after the list variable name and then stating the function’s name followed by parenthesis with the function’s parameters.

To add (or append) a single element to the end of a list use the append() function:

.extend() or +

If you want to append several elements (a list of elements) to the end of a list use extend() or + :

To add a new element to a list at a certain index of a list while moving all current elements one index further back use insert() where the first parameter of index() is the index at which you would like to insert the new element and the second parameter is the element to insert itself. And don’t forget that the first index of a Python list is 0 :

To remove an element from a list use the remove() function which will remove the first matching element in your list going from left to right:

If you want to remove all elements from a list and end up with an empty list use clear() :

To figure out the index of a list element use the index() function which tells you the index of the first matching element in your list going from left to right:

Removing a list element from an arbitrary index can be done using the pop() function. pop() also returns the the element that should be removed. When passing no index to pop() the last list element will be removed and returned:

You don’t even have to implement a sorting algorithm when using Python list s. list s come with their own sorting function sort() . The list is sorted alphanumerically when no parameters are passed to sort() . You can also specify that your list should be sorted in the reversed order by passing reverse=True to sort() . And if you rather want to sort a list by the str length of the elements, you can pass key=len as an argument:

To simply reverse your list use reverse() :

Python List Property Functions

The following functions do not manipulate the list content. However, they are pretty helpful when you want to figure out specific properties of your list s

As you have already seen above when learning about the [] -operator len() will return the length of a list:

If you want to know how often an element is included in a list use count() :

To check if an element is inside a list you can also use Python’s in operator:

max(list) and min(list)

To find the maximum and minimum element of a list use max() and min() . When not passing any parameters to max() and min() they will return the maximum/minimum element of the alphanumeric order, however, you can set a key such as key=len to find the maximum/minimum element according to that key:

When you want to duplicate a list you have to sure if you want a reference or a copy. You can reference a list by assigning the original variable to another variable, than both variables point to the same list (you could also say share a list ). When referencing a list a change using one variable will also be reflected when accessing the other variable.

If you need want to keep the original list and only make changes in the list assigned to another variable use the .copy() function:

The copy() function concludes this video. Make sure to get the free Python Lists Cheat Sheet in my Gumroad shop . If you have any questions about this article, feel free to join our Discord community to ask them over there.

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Mastering Python’s List Assignment: Resolving Index Out of Range Errors

Resolve "index out of range" errors

As a Python developer, working with lists is an essential part of your daily coding routine. However, even experienced programmers can stumble upon the dreaded “index out of range” error when dealing with list assignments. This error occurs when you attempt to access or modify an index that doesn’t exist within the list’s bounds. Fear not, as this comprehensive tutorial will equip you with the knowledge and techniques to conquer this challenge and unlock the full potential of Python’s list assignment.

Understanding the “Index Out of Range” Error

Before diving into the solutions, let’s first understand the root cause of the “index out of range” error. In Python, lists are zero-indexed, meaning the first element has an index of 0, the second element has an index of 1, and so on. When you try to access or modify an index that falls outside the list’s valid range, Python raises an IndexError with the “index out of range” message.

Here’s an example that illustrates the error:

In this case, we’re attempting to access the fourth element ( my_list[3] ) of a list that only contains three elements (indices 0, 1, and 2).

Solution 1: Validating Indices Before Assignment

One effective solution to prevent “index out of range” errors is to validate the index before attempting to assign a value to it. You can achieve this by checking if the index falls within the list’s valid range using the len() function and conditional statements.

In this solution, we first declare a list my_list with three elements. We then define two variables: index and new_value .

Next, we use an if statement to check if the index is less than the length of my_list . The len(my_list) function returns the number of elements in the list, which is 3 in this case.

If the condition index < len(my_list) is True, it means that the index is a valid index within the list’s bounds. In this case, we assign the new_value (4) to the element at the specified index (2) using the list assignment my_list[index] = new_value . Finally, we print the updated list, which now has the value 4 at index 2.

However, if the condition index < len(my_list) is False, it means that the index is out of range for the given list. In this case, we execute the else block and print the message “Index out of range!” to inform the user that the provided index is invalid.

This solution is effective when you need to ensure that the index you’re trying to access or modify is within the valid range of the list. By checking the index against the list’s length , you can prevent “index out of range” errors and handle invalid indices appropriately.

It’s important to note that this solution assumes that the index variable is provided or calculated elsewhere in the code. In a real-world scenario, you may need to handle user input or perform additional validation on the index variable to ensure it’s an integer and within the expected range.

Solution 2: Using Python’s List Slicing

Python’s list slicing feature allows you to access and modify a subset of elements within a list. This is a powerful technique that can help you avoid “index out of range” errors when working with list assignments .

In the first example, my_list[:2] = [10, 20] , we’re using list slicing to access and modify the elements from the start of the list up to (but not including) index 2. The slice [:2] represents the range from the beginning of the list to index 2 (0 and 1). We then assign the new values [10, 20] to this slice, effectively replacing the original values at indices 0 and 1 with 10 and 20, respectively.

In the second example, my_list[2:] = [30, 40] , we’re using list slicing to access and modify the elements from index 2 to the end of the list. The slice [2:] represents the range from index 2 to the end of the list. We then assign the new values [30, 40] to this slice. Since the original list only had three elements, Python automatically extends the list by adding a new element at index 3 to accommodate the second value (40).

List slicing is a powerful technique because it allows you to modify multiple elements within a list without worrying about “index out of range” errors. Python automatically handles the indices for you, ensuring that the assignment operation is performed within the valid range of the list.

Here are a few key points about list slicing:

• Inclusive start, exclusive end : The slice [start:end] includes the elements from start up to, but not including, end .
• Omitting start or end : If you omit the start index, Python assumes the beginning of the list. If you omit the end index, Python assumes the end of the list.
• Negative indices : You can use negative indices to start or end the slice from the end of the list. For example, my_list[-1] accesses the last element of the list.
• Step size : You can optionally specify a step size in the slice notation, e.g., my_list[::2] to access every other element of the list.

List slicing is a powerful and Pythonic way to work with lists, and it can help you avoid “index out of range” errors when assigning values to multiple elements within a list.

Solution 3: Using Python’s List Append Method

The append() method in Python is a built-in list method that allows you to add a new element to the end of an existing list. This method is particularly useful when you want to avoid “index out of range” errors that can occur when trying to assign a value to an index that doesn’t exist in the list.

In this example, we start with a list my_list containing three elements: [1, 2, 3] . We then use the append() method to add a new element 4 to the end of the list: my_list.append(4) . Finally, we print the updated list, which now contains four elements: [1, 2, 3, 4] .

Here’s how the append() method works:

• Python finds the current length of the list using len(my_list) .
• It assigns the new value ( 4 in this case) to the index len(my_list) , which is the next available index after the last element in the list.
• Since the new index is always valid (it’s one greater than the last index), there’s no risk of an “index out of range” error.

The append() method is a safe and convenient way to add new elements to the end of a list because it automatically handles the index assignment for you. You don’t need to worry about calculating the correct index or checking if the index is within the list’s bounds.

It’s important to note that the append() method modifies the original list in-place. If you want to create a new list instead of modifying the existing one, you can use the + operator or the list.copy() method to create a copy of the list first, and then append the new element to the copy.

Another advantage of using append() is that it allows you to add multiple elements to the list in a loop or by iterating over another sequence. For example:

In this example, we use a for loop to iterate over the new_elements list, and for each element, we call my_list.append(element) to add it to the end of my_list .

Overall, the append() method is a simple and effective way to add new elements to the end of a list, ensuring that you avoid “index out of range” errors while maintaining the integrity and order of your list.

Solution 4: Handling Exceptions with Try/Except Blocks

Python provides a robust exception handling mechanism using try/except blocks, which can be used to gracefully handle “index out of range” errors and other exceptions that may occur during program execution.

In this example, we first define a list my_list with three elements and an index variable with the value 4 .

The try block contains the code that might raise an exception. In this case, we attempt to access the element at my_list[index] , which is my_list[4] . Since the list only has indices from 0 to 2, this operation will raise an IndexError with the message “list index out of range” .

The except block specifies the type of exception to catch, which is IndexError in this case. If an IndexError is raised within the try block, the code inside the except block will be executed. Here, we print the message “Index out of range! Please provide a valid index.” to inform the user that the provided index is invalid.

If no exception is raised within the try block, the except block is skipped, and the program continues executing the code after the try/except block.

By using try/except blocks, you can handle exceptions gracefully and provide appropriate error messages or take alternative actions, rather than allowing the program to crash with an unhandled exception.

Here are a few key points about using try/except blocks for handling exceptions:

• Multiple except blocks : You can have multiple except blocks to handle different types of exceptions. This allows you to provide specific error handling for each exception type.
• Exception objects : The except block can optionally include a variable to hold the exception object, which can provide additional information about the exception.
• else clause : You can include an else clause after the except blocks. The else block executes if no exceptions are raised in the try block.
• finally clause : The finally clause is executed regardless of whether an exception was raised or not. It’s typically used for cleanup operations, such as closing files or releasing resources.
• Exception hierarchy : Python has a built-in exception hierarchy, where some exceptions are derived from others. You can catch a base exception to handle multiple related exceptions or catch specific exceptions for more granular control.

By using try/except blocks and handling exceptions properly, you can write more robust and resilient code that gracefully handles errors, making it easier to debug and maintain your Python programs.

Best Practices and Coding Standards

To ensure your code is not only functional but also maintainable and scalable, it’s essential to follow best practices and coding standards. Here are some recommendations:

• Validate user input : When working with user-provided indices, always validate the input to ensure it falls within the list’s valid range.
• Use descriptive variable and function names : Choose meaningful names that clearly convey the purpose and functionality of your code elements.
• Write clear and concise comments : Document your code with comments that explain the purpose, logic, and any non-obvious implementation details.
• Follow PEP 8 style guide : Adhere to Python’s official style guide , PEP 8, to ensure consistency and readability across your codebase.
• Test your code thoroughly : Implement unit tests and integrate testing into your development workflow to catch bugs and regressions early.

By following these best practices and coding standards, you’ll not only avoid “index out of range” errors but also produce high-quality, maintainable, and scalable Python code.

Mastering Python’s list assignment is crucial for efficient data manipulation and programming success. By understanding the root cause of “index out of range” errors and implementing the solutions outlined in this tutorial, you’ll be well-equipped to handle these challenges confidently. Whether you validate indices, leverage list slicing, use the append() method, or handle exceptions, you now have a comprehensive toolkit to tackle list assignment challenges head-on. Embrace these techniques, follow best practices, and continue honing your Python skills to unlock new levels of coding excellence.

• Index Out of Range
• List Assignment

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