make presentations in python

5 Best Ways to Create PowerPoint Files Using Python

💡 Problem Formulation: Automating the creation of PowerPoint presentations is a common task for those who need to generate reports or summaries regularly. For instance, a user may wish to create a presentation summarizing sales data from a CSV file or visualize a project’s progress in a structured format. The desired output is a fully formatted PowerPoint file (.pptx) with various elements like titles, texts, images, and charts, as specified by the input data or customization requirements.

Method 1: Using python-pptx

The python-pptx library provides a comprehensive set of features for creating PowerPoint files (.pptx) in Python. It allows for adding slides, text, images, charts, and more, with a high level of customization. Manipulate slides at a granular level by accessing placeholders, creating bulleted lists, and setting properties like font size or color programmatically.

Here’s an example:

The code snippet above creates a PowerPoint file named python-pptx-presentation.pptx with one slide that includes a title and a subtitle.

In this overview, we create a presentation object, add a new slide with a predefined layout, set text for the title and subtitle placeholders, and then save the presentation. This method gives users the ability to create detailed, professional presentations through code.

Method 2: Using Pandas with python-pptx

This method combines the data manipulation power of Pandas with the presentation capabilities of python-pptx to create PowerPoint files from DataFrame contents. It’s particularly useful for automating the inclusion of tabular data or creating charts based on the DataFrame’s data.

The output is a PowerPoint file named pandas-python-pptx.pptx containing a bar chart representing the quantity of fruits.

This snippet demonstrates using a Pandas DataFrame to generate chart data, which is then used to create a chart in a PowerPoint slide. It showcases the synergy between Pandas for data handling and python-pptx for presentation creation.

Method 3: Using ReportLab with python-pptx

Those seeking to include complex graphics or generate custom visuals can harness the graphic-drawing capabilities of ReportLab with python-pptx. This method leverages ReportLab to create an image, which can then be inserted into a PowerPoint slide.

The output would be a PowerPoint file named reportlab-pptx.pptx containing a slide with a custom bar chart image.

The code above creates a bar chart using ReportLab, saves the chart as an image, and then inserts the image into a PowerPoint slide. This approach is ideal if you need to include bespoke graphics that are not directly supported by python-pptx itself.

Method 4: Using Matplotlib with python-pptx

For those familiar with Matplotlib, this method involves creating a visual plot or chart with Matplotlib, saving it as an image, and then embedding the image into a PowerPoint slide using python-pptx.

The outcome is a PowerPoint file matplotlib-pptx.pptx , with a plot on a slide created by Matplotlib.

In this case, we graph a quadratic function using Matplotlib, save it as an image, and then add that image to a slide in our PowerPoint presentation. This method offers a blend of Matplotlib’s sophisticated plotting tools with the simplicity of python-pptx.

Bonus One-Liner Method 5: Using Officegen

The Officegen package allows for rapid PowerPoint creation with simpler syntax, although with less flexibility compared to python-pptx. It provides functions to add slides, titles, and bullet points.

The outcome is a PowerPoint file officegen-presentation.pptx with a single slide containing a large title.

This snippet uses Officegen to initiate a new presentation, adds a text title to a slide, and saves the presentation. While not as detailed as python-pptx, Officegen is quick for simple presentations.

Summary/Discussion

• Method 1: python-pptx. Full-featured control over presentations. Can be verbose for simple tasks.
• Method 2: Pandas with python-pptx. Ideal for data-driven presentations. Setup can be complex if unfamiliar with data libraries.
• Method 3: ReportLab with python-pptx. Powerful combo for custom graphics. Requires separate handling of graphics and presentation stages.
• Method 4: Matplotlib with python-pptx. Best for users comfortable with Matplotlib. Less direct than using python-pptx alone.
• Bonus Method 5: Officegen. Quick and easy for simple presentations. Limited customization options.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.

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Creating Powerpoint Presentations with Python

Posted by Chris Moffitt in articles   

Introduction

Love it or loathe it, PowerPoint is widely used in most business settings. This article will not debate the merits of PowerPoint but will show you how to use python to remove some of the drudgery of PowerPoint by automating the creation of PowerPoint slides using python.

Fortunately for us, there is an excellent python library for creating and updating PowerPoint files: python-pptx . The API is very well documented so it is pretty easy to use. The only tricky part is understanding the PowerPoint document structure including the various master layouts and elements. Once you understand the basics, it is relatively simple to automate the creation of your own PowerPoint slides. This article will walk through an example of reading in and analyzing some Excel data with pandas, creating tables and building a graph that can be embedded in a PowerPoint file.

PowerPoint File Basics

Python-pptx can create blank PowerPoint files but most people are going to prefer working with a predefined template that you can customize with your own content. Python-pptx’s API supports this process quite simply as long as you know a few things about your template.

Before diving into some code samples, there are two key components you need to understand: Slide Layouts and Placeholders . In the images below you can see an example of two different layouts as well as the template’s placeholders where you can populate your content.

In the image below, you can see that we are using Layout 0 and there is one placeholder on the slide at index 1.

In this image, we use Layout 1 for a completely different look.

In order to make your life easier with your own templates, I created a simple standalone script that takes a template and marks it up with the various elements.

I won’t explain all the code line by line but you can see analyze_ppt.py on github. Here is the function that does the bulk of the work:

The basic flow of this function is to loop through and create an example of every layout included in the source PowerPoint file. Then on each slide, it will populate the title (if it exists). Finally, it will iterate through all of the placeholders included in the template and show the index of the placeholder as well as the type.

If you want to try it yourself:

Refer to the input and output files to see what you get.

Creating your own PowerPoint

For the dataset and analysis, I will be replicating the analysis in Generating Excel Reports from a Pandas Pivot Table . The article explains the pandas data manipulation in more detail so it will be helpful to make sure you are comfortable with it before going too much deeper into the code.

Let’s get things started with the inputs and basic shell of the program:

After we create our command line args, we read the source Excel file into a pandas DataFrame. Next, we use that DataFrame as an input to create the Pivot_table summary of the data:

Consult the Generating Excel Reports from a Pandas Pivot Table if this does not make sense to you.

The next piece of the analysis is creating a simple bar chart of sales performance by account:

Here is a scaled down version of the image:

We have a chart and a pivot table completed. Now we are going to embed that information into a new PowerPoint file based on a given PowerPoint template file.

Before I go any farther, there are a couple of things to note. You need to know what layout you would like to use as well as where you want to populate your content. In looking at the output of analyze_ppt.py we know that the title slide is layout 0 and that it has a title attribute and a subtitle at placeholder 1.

Here is the start of the function that we use to create our output PowerPoint:

This code creates a new presentation based on our input file, adds a single slide and populates the title and subtitle on the slide. It looks like this:

Pretty cool huh?

The next step is to embed our picture into a slide.

From our previous analysis, we know that the graph slide we want to use is layout index 8, so we create a new slide, add a title then add a picture into placeholder 1. The final step adds a subtitle at placeholder 2.

Here is our masterpiece:

For the final portion of the presentation, we will create a table for each manager with their sales performance.

Here is an image of what we’re going to achieve:

Creating tables in PowerPoint is a good news / bad news story. The good news is that there is an API to create one. The bad news is that you can’t easily convert a pandas DataFrame to a table using the built in API . However, we are very fortunate that someone has already done all the hard work for us and created PandasToPowerPoint .

This excellent piece of code takes a DataFrame and converts it to a PowerPoint compatible table. I have taken the liberty of including a portion of it in my script. The original has more functionality that I am not using so I encourage you to check out the repo and use it in your own code.

The code takes each manager out of the pivot table and builds a simple DataFrame that contains the summary data. Then uses the df_to_table to convert the DataFrame into a PowerPoint compatible table.

If you want to run this on your own, the full code would look something like this:

All of the relevant files are available in the github repository .

One of the things I really enjoy about using python to solve real world business problems is that I am frequently pleasantly surprised at the rich ecosystem of very well thought out python tools already available to help with my problems. In this specific case, PowerPoint is rarely a joy to use but it is a necessity in many environments.

After reading this article, you should know that there is some hope for you next time you are asked to create a bunch of reports in PowerPoint. Keep this article in mind and see if you can find a way to automate away some of the tedium!

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Create interactive slides with Python in 8 Jupyter Notebook cells

Creating presentations in Jupyter Notebook is a great alternative to manually updating slides in other presentation creation software. If your data changes, you just re-execute the cell and slide chart is updated.

Jupyter Notebook is using Reveal.js (opens in a new tab) for creating slides from cells. The standard approach is to write slides code and Markdown in the Jupyter Notebook. When notebook is ready, it can be exported to standalone HTML file with presentation.

What if, you would like to update slides during the slide show? What is more, it would be fantastic to have interactive widgets in the presentation. You can do this in Mercury framework.

In this tutorial, we will create an interactive presentation in Jupyter Notebook and serve it with Mercury.

Create presentation in notebook

Please enable Slideshow toolbar in Jupyter Notebook. It can be done by clicking View -> Cell Toolbar -> Slideshow . It is presented in the screenshot below:

We will need following packages to create presentation in Python notebook:

Please make sure that they are installed in your environment.

1. Import packages and App setup

The first step is to import packages and setup Mercury App :

We setup title and description for App object.

Please note that we set Slide Type to Skip . This cell will not appear in the presentation.

2. Add title

The second cell is a Markdown with title:

The Slide Type is set to Slide . It is our first slide!

3. Add slide with Markdown

Add new Markdown cell with the following cell.

Please set Slide Type to Slide . It will be a second slide. I'm using ## as slide title ( # will produce too large title in my opinion).

4. Add Mercury Widget

Please add code cell with Text widget. We will use it, to ask users about their name.

We set Slide Type as Skip , so this cell will not appear in the presentation.

5. Display name

Let's use the name.value in the slide. Please add a code cell. We will display a Markdown text with Python variables by using Markdown function from Mercury package.

Please set the Slide Type to Slide .

You can display Markdown with Python variables by calling mr.Markdown() or mr.Md() functions. Both do the same.

The first five cells of the notebook:

You can enter your name in the widget during the notebook development. There will be no change in other cells. If you want to update the cell with new widget value, please execute it manually.

6. More widgets

We can add more widgets to the presentation. They will be used to control chart in the next slide.

We have used Slider and Select widgets. They are displayed in the notebook. This cell will not be displayed in the presentation, so set Slide Type to Skip .

7. Scatter plot

We will add a new code cell. It will have Slide Type set to Slide .

We used widgets values by accessing them with samples.value and color.value .

Screenshot of the notebook with scatter plot:

8. Final slide

Please add a last Markdown cell. Its Slide Type will be set to Slide :

Please notice that link is added with HTML syntax. There is a target="_blank" used to open link in a new tab.

Run presentation in Mercury

Please run Mercury local server in the same directory as notebook:

The above command will open a web browser at http://127.0.0.1:8000 . Please click on a card with presentation.

You can navigate between slides with arrows in the bottom right corner. You can enter the full screen mode by pressing F on the keyboard. Please use Esc to exit full screen mode.

You can change widgets values in the sidebar and presentation slides will be automatically recomputed:

You can export your slides as PDF or HTML by clicking Download button in the sidebar.

Creating Presentations with Jupyter Notebook

Jupyter Notebook can be turned into a slide presentation that is kind of like using Microsoft Powerpoint, except that you can run the slide’s code live! It’s really neat how well it works. The only con in my book is that there isn’t a lot of theming that can be applied to your slides, so they do end up looking a bit plain.

In this article, we will look at two methods of creating a slideshow out of your Jupyter Notebook. The first method is by using Jupyter Notebook’s built-in slideshow capabilities. The second is by using a plug-in called RISE .

Let’s get started!

Note: This article assumes that you already have Jupyter Notebook installed. If you don’t, then you might want to go to their website and learn how to do so.

The first thing we need to do is to create a new Notebook. Once you have that done and running, let’s create three cells so that we can have three slides. Your Notebook should now look like the following:

Now let’s turn on the “slideshow” tools. Go to the View menu and then click on the Cell Toolbar menu option. You will find a sub-menu in there that is called Slideshow . Choose that. Now your Notebook’s cell should look like this:

There are now little comboboxes on the top right of each cell. These widgets give you the following options:

You can just create a series of Slides if you like, but you can make the slideshow a bit more interesting by adding Sub-Slides and Fragments. Sub-slides are just slides that are below the previous one while Fragments are basically fragments within the previous slide. As an aside, I have actually never used Fragments myself. Anyway you can also set a slide to Skip, which just allows you to skip a slide or Notes, which are just speaker notes.

Let’s add some text to our first cell. We will add the text “# Hello Slideshow” to it and set the cell type to Markdown. Note the pound sign at the beginning of the text. This will cause the text to be a heading.

In cell two, we can add a simple function. Let’s use the following code:

For the last cell, we will add the following text:

Make sure you set that to be a Markdown cell as well. This is what my cells ended up looking like when I was done:

To make things simple, just set each of the cell’s individual comboboxes to Slide .

Now we just need to turn it into an actual slideshow. To do that, you will need save your Notebook and shut down the Jupyter Notebook server. Next you will need to run the following command:

To navigate your slideshow, you can use your left and right arrow keys or you can use spacebar to go forward and shift_spacebar to go back. This creates a pretty nice and simple slideshow, but it doesn’t allow you to run the cells. For that, we will need to use the RISE plugin!

Getting Started with RISE

Reveal.js – Jupyter/IPython Slideshow Extension (RISE) is a plugin that uses *reveal.js* to make the slideshow run live. What that means is that you will now be able to run your code in the slideshow without exiting the slideshow. The first item that we need to learn about is how to get RISE installed.

Installing rise with conda

If you happen to be an Anaconda user, then this is the method you would use to install RISE:

This is the easiest method of installing RISE. However most people still use regular CPython, so next we will learn how to use pip!

Installing rise with pip

You can use Python’s pip installer tool to install RISE like this:

You can also do `python -m pip install RISE` is you want to. Once the package is installed, you have a second step of installing the JS and CSS in the proper places, which requires you to run the following command:

If you somehow get a version of RISE that is older than 5.3.0, then you would also need to enable the RISE extension in Jupyter. However, I recommend just using the latest version so you don’t have to worry about that.

Using RISE for a SlideShow

Now that we have RISE installed and enabled, let’s re-open the Jupyter Notebook we created earlier. Your Notebook should now look like this:

You will notice that I circled a new button that was added by RISE to your Notebook. If you mouse over that button you will see that it has a tooltip that appears that says “Enter/Exit RISE Slideshow”. Click it and you should see a slideshow that looks a lot like the previous one. The difference here is that you can actually edit and run all the cells while in the slideshow. Just double-click on the first slide and you should see it transform to the following:

After you are done editing, press SHIFT+ENTER to run the cell. Here are the primary shortcuts you will need to run the slideshow effectively:

• SPACEBAR – Goes forward a slide in the slideshow
• SHIFT+SPACEBAR – Goes back a slide in the slideshow
• SHIFT+ENTER – Runs the cell on the current slide
• DOUBLE-CLICK – To edit a Markdown cell

You can view all the Keyboard shortcuts by going to the Help menu when not in Slideshow mode and clicking the Keyboard Shortcuts option. Most if not all of these shortcuts should work inside of a RISE slideshow.

If you want to start the slideshow on a specific cell, just select that cell and then press the Enter Slideshow button.

RISE also works with Notebook widgets. Try creating a new cell with the following code:

Now start the slideshow on that cell and try running the cell (SHIFT+ENTER). You should see something like this:

You can use RISE to add neat widgets, graphs and other interactive elements to your slideshow that you can edit live to demonstrate concepts to your attendees. It’s really quite fun and I have used RISE personally for presenting intermediate level material in Python to engineers.

RISE also has several different themes that you can apply as will as minimal support for slide transitions. See the documentation for full information.

Wrapping Up

In this chapter we learned about two good methods for creating presentations out of our Jupyter Notebooks. You can use Jupyter directly via their nbconvert tooling to generate a slideshow from the cells in your Notebook. This is nice to have, but I personally like RISE better. It makes the presentations so much more interactive and fun. I highly recommend it. You will find that using Jupyter Notebook for your presentations will make the slides that much more engaging and it is so nice to be able to fix slides during the presentation too!

Related Reading

• Presenting code using Jupyter Notebook
• The RISE Github page
• Jupyter nbconvert Usage
• How to build interactive presentations with Jupyter Notebook and Reveal JS

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How to Automate PowerPoint Presentations using Python

• אפריל 14, 2024

30 minutes free Consultation

Creating PowerPoint presentations can be a time-consuming task, especially when you have to update data or make repetitive changes. Fortunately, Python offers a way to automate PowerPoint presentations, saving you time and effort. In this article, we’ll explore how to use Python to automate PowerPoint presentations.

What is Python?

Python is a versatile and powerful programming language known for its simplicity and readability. It has a wide range of applications, including web development, data analysis, artificial intelligence, and automation. Python’s extensive library ecosystem makes it easy to interact with various file formats and applications, including Microsoft PowerPoint.

What is Microsoft PowerPoint?

Microsoft PowerPoint is a popular presentation software that allows users to create engaging and professional-looking slideshows. It offers a variety of features, such as templates, animations, and multimedia integration, to enhance the visual appeal and effectiveness of presentations.

Now, let’s dive into the steps to automate PowerPoint presentations using Python.

Step 1: Install the Python-pptx Library

To work with PowerPoint files in Python, we’ll use the Python-pptx library. You can install it by running the following command in your terminal or command prompt:

Step 2: Import the Required Libraries

In your Python script, import the necessary libraries:

Step 3: Create a New Presentation

To create a new PowerPoint presentation, use the Presentation() function:

Step 4: Add a New Slide

To add a new slide to the presentation, use the add_slide() method:

Step 5: Add Content to the Slide

You can now add various elements to the slide, such as text, images, and shapes. Here’s an example of adding a title and subtitle to the slide:

Step 6: Save the Presentation

Finally, save the presentation using the save() method:

Python provides a convenient way to automate PowerPoint presentations, allowing you to create and modify slides programmatically. By leveraging the Python-pptx library, you can save time and effort in creating and updating presentations. Whether you need to generate reports, create data-driven presentations, or automate repetitive tasks, Python and PowerPoint automation can greatly streamline your workflow.

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Automate PowerPoint Slides Creation with Python

Boost your Productivity with an Automated Tool for the Creation of Supply Chain Operational PowerPoint Reports with Python.

Boost your Productivity with an Automated Tool for the Creation of Supply Chain Operational PowerPoint Reports with Python

Article originally published on Medium .

You are a distribution planning manager in the logistics department of a fashion retail company.

To perform analysis, you connect to the warehouse management system to extract and process data using an automated tool built with Python.

However, you have to spend time manually putting these visuals in slides for your weekly operational reviews.

In this article, we will build a solution to automatically create PowerPoint slides with visuals and comments.

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Have a look at the video version of this article,

Problem Statement

You are part of the distribution planning team of an international clothing retailer with stores on all continents.

Your distribution network includes several local warehouses that replenish the stores.

Monthly Operational Reviews

At the end of the month, you organize an operational review with the store managers to assess the performance of the distribution network.

To animate the discussion you prepare some slides,

• Extract data from the Warehouse Management System (WMS)
• Process data and built visuals with Python
• Prepare a PowerPoint presentation

To be efficient, you would like to automate the process of PowerPoint deck creation.

Your solution will be fully automated

• Extract order lines of the last month from the WMS SQL database
• Process the data and compute KPIs with key insights by week
• Automatically put the visuals and insights in a PowerPoint presentation

The final deck will have slides like the ones below:

• 5 slides with visuals of the daily workload (left) and 1 slide for the monthly analysis of the order profile (right)
• A visual generated with Python
• A comment area will provide insights based on the visual

You can find the source code with dummy data here: Github

Let us explore all the steps to generate your final report with python.

Data Extraction

Connect to your WMS and extract shipment records

• Create your SQL Query to extract shipment records
• Use pandas.read_sql_query to do the query
• Results will be a pandas data frame

If you don’t have access to a WMS database , you can use the dataset shared in the GitHub repo.

Process the data

Add a column that calculates the number of lines per order using pandas.

Create the visuals

Create a simple bar plot chart that shows the number of Lines and Orders prepared per day.

Save the charts

In order to be added to the PowerPoint, you need to save it locally.

Add comments and insights

You can add comments based on the chart you share that will summarize the performance of each week.

Include these comments under the visuals for more clarity.

Create the PowerPoint Decks

We will use the open-source library python-pptx to build our PowerPoint decks.

For more details, have a look at the documentation .

Introduction Slide

We will start with a special introduction slide at the beginning of the presentation.

Daily Analysis Slide by WEEK

The structure of your slide will be always the same

• A title on top (e.g: Warehouse Workload (WEEK-5))
• A picture at the centre of the slide
• A text box for the comment area

💡 TIPS You can change the position of the objects by modifying the parameters of Inches() functions.

Weekly Analysis of Order Profile

In this slide, you will use a stacked bar plot chart and the comments will be based on the full month scope.

💡 TIPS You can change the font size by modifying the parameter of the functions Pt().

Finally, you have a PowerPoint file with 7 slides ready to be used for your meetings.

Conclusion & Next Steps

With this very simple example, you have a template to build your own PowerPoint automation solution.

You can now,

• Add visuals, tables or smart visuals of PowerPoint (check the documentation)
• Bring more insights or enrich the text with conditions

This python script can be launched locally on your computer with one click.

You can also automate the report distribution by email using the SMTP library of python.

For more details, you can have a look at this article I published a few weeks ago,

Let’s connect on Linkedin and Twitter , I am a Supply Chain Engineer that is using data analytics to improve logistics operations and reduce costs.

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Python Lists are just like dynamically sized arrays, declared in other languages (vector in C++ and ArrayList in Java). In simple language, a Python list is a collection of things, enclosed in [ ] and separated by commas. 

The list is a sequence data type which is used to store the collection of data. Tuples and String are other types of sequence data types.

Example of list in Python

Here we are creating Python List using [].

Lists are the simplest containers that are an integral part of the Python language. Lists need not be homogeneous always which makes it the most powerful tool in Python . A single list may contain DataTypes like Integers, Strings, as well as Objects. Lists are mutable, and hence, they can be altered even after their creation.

Creating a List in Python

Lists in Python can be created by just placing the sequence inside the square brackets[]. Unlike Sets , a list doesn’t need a built-in function for its creation of a list. 

Note: Unlike Sets, the list may contain mutable elements.  

Example 1: Creating a list in Python

Complexities for creating lists.

Time Complexity: O(1)

Space Complexity: O(n)

Example 2:  Creating a list with multiple distinct or duplicate elements

A list may contain duplicate values with their distinct positions and hence, multiple distinct or duplicate values can be passed as a sequence at the time of list creation.

Accessing elements from the List

In order to access the list items refer to the index number. Use the index operator [ ] to access an item in a list. The index must be an integer. Nested lists are accessed using nested indexing. 

Example 1: Accessing elements from list

Example 2: Accessing elements from a multi-dimensional list

Negative indexing

In Python, negative sequence indexes represent positions from the end of the array. Instead of having to compute the offset as in List[len(List)-3], it is enough to just write List[-3]. Negative indexing means beginning from the end, -1 refers to the last item, -2 refers to the second-last item, etc.

Complexities for Accessing elements in a Lists:

Space Complexity: O(1)

Getting the size of Python list

Python len() is used to get the length of the list.

Taking Input of a Python List

We can take the input of a list of elements as string, integer, float, etc. But the default one is a string.

Example 1: 

To know more see this .

Adding Elements to a Python List

Method 1: using append() method.

Elements can be added to the List by using the built-in append() function. Only one element at a time can be added to the list by using the append() method, for the addition of multiple elements with the append() method, loops are used. Tuples can also be added to the list with the use of the append method because tuples are immutable. Unlike Sets, Lists can also be added to the existing list with the use of the append() method.

Complexities for Adding elements in a Lists(append() method):

S pace Complexity: O(1)

Method 2: Using insert() method

append() method only works for the addition of elements at the end of the List, for the addition of elements at the desired position, insert() method is used. Unlike append() which takes only one argument, the insert() method requires two arguments(position, value). 

Complexities for Adding elements in a Lists(insert() method):

Time Complexity: O(n)

Method 3: Using extend() method

Other than append() and insert() methods, there’s one more method for the Addition of elements, extend() , this method is used to add multiple elements at the same time at the end of the list.

Note: append() and extend() methods can only add elements at the end.

Complexities for Adding elements in a Lists(extend() method):

Reversing a list, method 1:  a list can be reversed by using the reverse() method in python ., method 2: using the reversed() function:.

The reversed() function returns a reverse iterator, which can be converted to a list using the list() function.

Removing Elements from the List

Method 1: using remove() method.

Elements can be removed from the List by using the built-in remove() function but an Error arises if the element doesn’t exist in the list. Remove() method only removes one element at a time, to remove a range of elements, the iterator is used. The remove() method removes the specified item.

Note: Remove method in List will only remove the first occurrence of the searched element.

Complexities for Deleting elements in a Lists(remove() method):

Method 2: using pop() method.

pop() function can also be used to remove and return an element from the list, but by default it removes only the last element of the list, to remove an element from a specific position of the List, the index of the element is passed as an argument to the pop() method.

Complexities for Deleting elements in a Lists(pop() method):

Time Complexity: O(1)/O(n) (O(1) for removing the last element, O(n) for removing the first and middle elements)

Slicing of a List

We can get substrings and sublists using a slice. In Python List, there are multiple ways to print the whole list with all the elements, but to print a specific range of elements from the list, we use the Slice operation . 

Slice operation is performed on Lists with the use of a colon(:). 

To print elements from beginning to a range use:

To print elements from end-use:

To print elements from a specific Index till the end use 

To print the whole list in reverse order, use 

Note – To print elements of List from rear-end, use Negative Indexes. 

UNDERSTANDING SLICING OF LISTS:

• pr[0] accesses the first item, 2.
• pr[-4] accesses the fourth item from the end, 5.
• pr[2:] accesses [5, 7, 11, 13], a list of items from third to last.
• pr[:4] accesses [2, 3, 5, 7], a list of items from first to fourth.
• pr[2:4] accesses [5, 7], a list of items from third to fifth.
• pr[1::2] accesses [3, 7, 13], alternate items, starting from the second item.

Negative index List slicing

List comprehension.

Python List comprehensions are used for creating new lists from other iterables like tuples, strings, arrays, lists, etc. A list comprehension consists of brackets containing the expression, which is executed for each element along with the for loop to iterate over each element. 

newList = [ expression(element) for element in oldList if condition ]

Example:  

For better understanding, the above code is similar to as follows: 

Refer to the below articles to get detailed information about List Comprehension.

• List comprehension and ord() in Python

Basic Example on Python List

• Python program to interchange first and last elements in a list
• Python program to swap two elements in a list
• Python – Swap elements in String list
• Python | Ways to find length of list
• Maximum of two numbers in Python
• Minimum of two numbers in Python

To Practice the basic list operation, please read this article – Python List of program

List Methods

To know more refer to this article – Python List methods

The operations mentioned above modify the list Itself.

Built-in functions with List

Do go through recent articles on Lists

Useful Links:  

• Recent Articles on Python List
• Python Tutorials
• Multiple Choice Questions
• All articles in Python Category

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