How to Set up VS Code for Data Science & AI

Summary

In this video, Dave, a data scientist, shares his experience with VS Code for data science projects, highlighting its advantages over Jupyter Notebooks in terms of workflow and productivity. He introduces VS Code, explains essential settings and extensions like Python, Pylance, and Jupyter, and demonstrates how to run Python code and Jupyter notebooks within VS Code. Dave emphasizes the efficiency of using VS Code for the entire data science project lifecycle, showcasing its interactivity and readiness for production.

Takeaways

• 👋 Introduction: Dave, a data scientist, introduces a tutorial on setting up VS Code for data science projects, which has improved his workflow and productivity.
• 🛠️ Tool Overview: VS Code is a free, integrated development environment (IDE) by Microsoft, compatible with Windows, Linux, and macOS, offering features like debugging, syntax highlighting, and intelligent code completion.
• 🌟 Productivity Boost: Dave emphasizes VS Code's impact on his efficiency as a data scientist, allowing for the entire project lifecycle management without the need to convert Jupyter notebooks to Python files.
• 📚 Extensions: The script highlights the importance of installing specific extensions for Python, such as the Python extension pack, Pylance, and Jupyter, to enhance VS Code's functionality for data science tasks.
• 🎨 Customization: Dave discusses customizing VS Code with themes and settings, including workspace-specific settings that can override user-level settings for project-specific configurations.
• 🔍 Interactive Coding: A key feature demonstrated is the ability to run Python code interactively within VS Code using the 'Jupyter send selection to interactive window' setting, combining the benefits of Jupyter notebooks with VS Code's features.
• 📝 Code Management: Dave explains how VS Code facilitates easier code management for data science projects, making the transition from development to production smoother without the need for code duplication.
• 📚 Workspace Setup: The script includes a step-by-step guide on setting up a workspace in VS Code, which helps in organizing and managing data science projects more effectively.
• 🔄 Workflow Efficiency: Dave demonstrates the efficiency of using VS Code for data science by showing how to interactively run and test code segments, making the debugging and development process faster.
• 🌱 Learning Resource: The video serves as an educational resource for those looking to enhance their data science workflow with VS Code, offering insights into extensions, settings, and best practices.
• 🔄 Transition from Jupyter: The script highlights the benefits of transitioning from Jupyter Lab/Notebooks to VS Code for data scientists seeking a more efficient and feature-rich coding environment.

Q & A

What is the main topic of Dave's video?

The main topic of Dave's video is about setting up Visual Studio Code (VS Code) for data science projects and how it has improved his workflow and productivity.

Why did Dave switch from Jupyter Notebooks to VS Code?

Dave switched from Jupyter Notebooks to VS Code because it has significantly improved his workflow and productivity, offering more features and better support for the entire data science project life cycle.

What does Dave consider as the biggest advantage of using VS Code over Jupyter Notebooks?

The biggest advantage of using VS Code over Jupyter Notebooks is that it allows managing the entire data science project life cycle more efficiently, eliminating the need to transform notebooks into Python files for production.

What is VS Code according to Dave's introduction?

VS Code is a free, integrated development environment (IDE) made by Microsoft, available for Windows, Linux, and macOS, with features like debugging, syntax highlighting, intelligent code completion, and embedded Git support.

What are some of the must-have extensions Dave recommends for Python development in VS Code?

Dave recommends the Python extension pack, which includes Python (essential for running Python code), Intellicode (an AI assistant for Python development), an environment manager, Python indent fixer, and Docstring.

What is the purpose of the 'Jupyter' extension in VS Code?

The 'Jupyter' extension allows users to work with Jupyter Notebooks within VS Code, providing the benefits of both Jupyter and VS Code environments.

How does Dave demonstrate the interactivity of running Python code in VS Code?

Dave demonstrates the interactivity by showing how to run selected lines of code in a Python file using the 'Jupyter: Send Selection to Interactive Window' feature, which allows for an interactive session similar to Jupyter Notebooks.

What is the significance of the 'Pylance' extension mentioned in the script?

The 'Pylance' extension is a feature-rich language server for Python in VS Code that enhances the autocompletion suggestions and IntelliSense capabilities, making Python development more efficient.

How does Dave use workspace settings in VS Code to customize his projects?

Dave uses workspace settings to override user settings specific to a project, allowing for project-specific configurations that are saved within the VS Code workspace file.

What is the 'Jupyter Send Selection to Interactive Window' setting in VS Code and how does it help in data science projects?

The 'Jupyter Send Selection to Interactive Window' setting allows users to send selected code from a Python file to a Jupyter interactive window instead of the Python terminal, enabling an interactive way of running and testing code blocks during data science projects.

How does Dave suggest using the interactive window feature in VS Code for data transformation?

Dave suggests using the interactive window to test and validate parts of the data transformation function by selecting and running specific lines of code to check the output, ensuring the correctness of the transformation before applying it to the entire dataset.

Outlines

00:00

😀 Introduction to VS Code for Data Science

Dave, a data scientist, introduces the video's focus on setting up VS Code for data science projects. He explains the benefits of using VS Code over Jupyter Notebooks and Labs, highlighting improved workflow and productivity. The video will cover an introduction to VS Code, personal themes and settings, essential extensions, running Python code, and using Jupyter Notebooks within VS Code. VS Code is praised for its features like multi-language support, debugging, syntax highlighting, intelligent code completion, snippets, code refactoring, and embedded Git.

05:01

🛠️ Setting Up VS Code with Extensions and Themes

The paragraph details the process of setting up VS Code for data science, starting with installing the Python extension pack which includes essential tools for Python development. Dave discusses the importance of Intellicode for AI-assisted code completion, improving speed and accuracy. He also mentions other extensions like Pylance for enhanced Intellisense, and the Jupyter extension for integrating Jupyter Notebooks within VS Code. Additionally, he covers changing themes and icon themes to personalize the VS Code environment.

10:04

📁 Organizing Data Science Projects in VS Code

Dave demonstrates how to organize a data science project in VS Code by creating a workspace, which allows for project-specific settings. He explains the difference between user-level and workspace-level settings and their implications for project organization. The paragraph emphasizes the convenience of workspace settings for maintaining a consistent project environment across different sessions.

15:04

🔄 Combining Jupyter Notebooks and Python Files in VS Code

The paragraph explains how to work with Jupyter Notebooks and Python files in VS Code. Dave shows how to open and run a Jupyter Notebook within VS Code using the Jupyter extension. He also discusses the advantage of running Python files interactively using the 'Send Selection to Interactive Window' setting, which allows for testing and validating code sections without rerunning the entire script, thus mimicking the interactivity of Jupyter Notebooks.

20:06

🚀 Enhancing Productivity with Interactive Python Script Execution

Dave illustrates the productivity benefits of using VS Code for data science by interactively running sections of a Python script. He demonstrates how to validate a data transformation function step by step, emphasizing the speed and efficiency gained from this method. The paragraph highlights the seamless integration of interactive execution with the robust features of VS Code, resulting in a workflow that is both productive and enjoyable.

🌟 Conclusion and Call to Action

In conclusion, Dave reflects on the significant improvement in his productivity and coding experience after transitioning from Jupyter Lab and Notebooks to VS Code. He encourages viewers to subscribe to his channel for more data science-related content and thanks them for watching. The paragraph serves as a summary of the benefits of using VS Code for data science and an invitation for viewers to follow for further insights.

Keywords

💡Data Science

Data Science is a field that involves extracting knowledge and insights from data, often large datasets, through various statistical and computational techniques. In the video, the theme revolves around setting up a development environment for data science projects, emphasizing the importance of a tool like VS Code in enhancing the workflow and productivity of a data scientist.

💡VS Code

VS Code, short for Visual Studio Code, is a free, open-source, and highly customizable integrated development environment (IDE) made by Microsoft. The video highlights VS Code's features and how it has transformed the presenter's data science workflow, offering a more efficient and productive coding experience compared to Jupyter Notebooks.

💡Extensions

In the context of VS Code, extensions are add-ons that enhance the functionality of the editor, allowing for additional features and support for different languages and tools. The script mentions several must-have extensions for Python and data science, like the Python extension pack and Jupyter, which are crucial for setting up VS Code for data science tasks.

💡Integrated Development Environment (IDE)

An IDE is a software application that provides comprehensive facilities to computer programmers for software development. The video introduces VS Code as an IDE and discusses its benefits for data scientists, such as supporting multiple languages, offering debugging tools, and having a marketplace for extensions.

💡Jupyter Notebooks

Jupyter Notebooks is an open-source web application that allows creation and sharing of documents containing live code, equations, visualizations, and narrative text. The video compares the traditional use of Jupyter Notebooks with the presenter's new workflow using VS Code, highlighting the productivity gains made by the switch.

💡Python

Python is a high-level, interpreted programming language widely used for general-purpose programming, including data science. The video emphasizes the importance of the Python extension in VS Code, which is essential for executing Python code within the IDE and managing Python environments.

💡Debugging

Debugging is the process of finding and resolving bugs or defects that prevent a computer program from working correctly. The video mentions VS Code's debugging features as one of the reasons it is favored for data science, allowing for more efficient problem-solving and code refinement.

💡Syntax Highlighting

Syntax highlighting is a feature of text editors that displays text, especially source code, in different colors and fonts depending on the category of terms. The video script mentions syntax highlighting as one of the features of VS Code that contributes to a better coding experience.

💡Intelligent Code Completion

Intelligent code completion, also known as IntelliSense, is a feature that suggests possible code completions as the programmer types. The video describes how this feature in VS Code, powered by extensions like Pylance, speeds up coding and reduces errors, which is particularly beneficial in data science projects.

💡Workspace

In the context of VS Code, a workspace is a set of settings and configurations associated with a specific project or folder. The video demonstrates how to save a workspace in VS Code, allowing for project-specific settings and a more organized workflow for data science projects.

💡Code Refactoring

Code refactoring is the process of restructuring existing computer code without changing its external behavior to improve nonfunctional attributes of the software. The video script briefly mentions code refactoring as one of the features of VS Code, which can be useful for maintaining and improving the quality of code in data science projects.

Highlights

Introduction to setting up VS Code for data science, emphasizing its impact on workflow and productivity.

Brief overview of VS Code as a free IDE by Microsoft, available on multiple platforms.

VS Code's features such as multi-language support, debugging, syntax highlighting, and intelligent code completion.

The extensibility and customizability of VS Code, with a vast marketplace for extensions.

Productivity benefits of VS Code over Jupyter Notebooks, including project lifecycle management.

Demonstration of creating a workspace in VS Code for project-specific settings.

Importance of the Python extension pack for running Python code within VS Code.

Explanation of Intellicode as an AI assistant for Python development in VS Code.

The convenience of extensions like Pylance for enhanced autocompletion and Intellisense.

Jupyter extension that allows running Jupyter Notebooks within VS Code.

Customization options for themes and icon themes in VS Code to suit personal preferences.

Workspace and user settings distinction in VS Code for project-specific and global configurations.

The 'Jupyter: Send Selection to Interactive Window' setting for interactive Python code execution.

Combining the interactivity of Jupyter Notebooks with the functionality of VS Code for efficient coding.

Example of using VS Code for data transformation and validation within a Python script.

The productivity gains from using VS Code's interactive window for step-by-step data science projects.

Final thoughts on the advantages of VS Code for data science, including code readiness for production.

Transcripts

welcome everyone today i have another

exciting new video for you guys uh first

of all my name is dave i'm a data

scientist and in this video i will show

you how to set up fies code for data

science this is something that has

completely changed the game for me

coming from working with jupiter

notebooks and jupiter lab which are

awesome tools by the way but vs code has

totally changed the game for me in terms

of my workflow and my productivity so in

this video i'll show you how i've set up

my fuse code and i use it for my data

science projects right so here's what

we'll cover in this video

i'll first give a brief introduction

about field code then i'll go over some

themes and settings that i personally

use then some must have extensions for

if you want to write biting code then

i'll show you how to run python code

using field code and lastly i will show

you how you can also run jupyter

notebooks within fields code all right

so what is fies code so for those of you

that don't know it's a free integrated

development environment or ide for short

made by microsoft

and it's available for windows linux

macos so doesn't matter what system

you're using you can install fies code

for free and get started now why do i

like vs code first of all because it has

so many features it supports many

languages it has a really good features

for debugging syntax highlighting

intelligent code completion this is a

big one

snippets code refactoring and uh

embedded git within it so that's really

convenient for version control what also

makes it really awesome is the

extensibility and the customizability so

there are many

many settings and there's a huge

marketplace with extensions to add new

languages teams debuggers etc and you

really have to know a few settings and

extensions to make it work for writing

python code and doing data science

projects and then productivity since

i've made the switch to fierce code

coming from jupiter lab jupiter

notebooks it has just made me so much

more efficient as a data scientist and

then this is probably the biggest

advantage of using vs code versus just

uh jupiter notebooks or jupiter lab it's

that you can manage the entire data

science project life cycle and this also

has to do with with just productivity as

i just mentioned when you first write

your code in a jupiter notebook and then

once it's ready for production you have

to transform that notebook into a python

file and that just takes a lot of time

basically have to write your code twice

and vs code makes this so much easier

and i will show you how to do that so

first of all if you don't have fierce

code you can just google it go to the

website and you can download it install

it for free alright let's now hop into

vs code and i'll show you how i've set

up mine to work on data science projects

so

i've opened up a blank field code file

we'll start off by going to top right

corner file and opening a folder so i've

made a demo folder for this video which

i will open it has a typical data

science project

layout so it has some data

we have some code and some python code

and we have some notebooks let me close

this out for now and the next thing that

i will do once i've imported the folder

is i will save it as a workspace so i go

to file save workspace as

and

i'll just save it here as demo which is

fine what i've just done is i've saved

this file with

the imported folder as a vs code

workspace file and within this workspace

you can save settings that are

particular to this project so that could

be really convenient and another nice

way of working like this is that now

once i want to work start working on a

project again i can just open up the

workspace file and it will open up vs

code within this workspace with all my

folders attached so now we've set up the

project so now how do you get started as

i mentioned when you just downloaded vs

code it's kind of blank it's kind of

empty so we need to add some extensions

and we to have to tweak some settings in

order to make it work for us so let's

hop into the extensions and you can do

this by clicking on this icon in the

left bar over here

and this will first show you a list of

all the extensions that you currently

have installed as you can see these

these are all my extensions that i'm i'm

running and there also is a search bar

where you can search for extensions so

first and foremost

what we'll start with is the python

extension back

this is basically a must-have pack that

installs uh

six different packages that i think are

just very convenient some of them are

necessary to run python code within vs

code so on the overview over here you

basically have a description of

what's in the in the extension and

i already have this extension pack

installed so

for me it says disable uninstall but for

you when you don't have it installed

there will be an install button over

here so you can just do that install it

it's free it will take a couple of

seconds and it will stall install the

following extensions

to fuse code so i'll quickly go over

what these are so first of all python

is essential to run python code so this

is a extension that is produced by

microsoft so this basically enables you

to run python code and here you can see

you can manage your

environments which version of python you

are using so this is a must-have so

what's uh what else is in it then we

have intellicode which

is very awesome this is and i just

explained how this is

not so good within jupiter lab and

jupiter notebooks but this is just

basically makes your life as a data

scientist easier it's an ai assistant

python development tool that

autocompletes suggest etc so you can see

an example here of what it does whenever

you start typing intellicode will prompt

some suggestions

and using the arrow keys you can toggle

through them and then just hit enter and

it will auto complete the code so this

will do two things

it will make you write code faster and

it will also make sure that you make

less errors because you don't have to

type all the

methods and the attributes

by hand and you can just auto complete

them so

that in turn will also

enable you to just work faster all right

so what else is in it there's an

environment manager

django comes uh with it if if you work

with django that's really uh convenient

uh this is the extension to fix python

indents uh automatically also very

convenient and then you have outer dock

string which is also a cool tool this uh

basically what this does if you type

three quotes it will insert a dog string

template which is very important to just

comment your functions and your codes

for yourself but also when you're

working with other people alright so

that's the python extension pack

let me take a look at my notes yes so

the next one that we're looking into is

by lance so let me just look it up

pylance same thing here i've already

have it installed

you can install it over here basically

what pylance is it's a feature rich

server language in python for vs code

and it basically it works together with

intellisense so as it says here pylance

has the ability to supercharge your

python intellisense basically this makes

the autocompletion suggestions etc even

better

so another really nice extension to have

and then lastly jupiter and

this is a wrong tab

this is

a really cool extension that basically

gives you the best of both worlds so i

will show you later how this works but

basically this allows us to use all the

uh the benefits from jupiter and jupiter

notebooks within fies code so jupiter

over here install it as well those are

like the the essentials and there's a

lot more

code snap is also a pretty

funny tool that you can use to

make pretty

code export so you can highlight a

section of code and then export it to to

a jpeg for example so i sometimes use

this for presentations pretty cool

path intelligence you can look into

basically makes working with bots

easier so when you type double quotes

you can look through your parts and

easily look up data data files for

example but yeah that's just about it

about the extensions oh no wait one more

thing you can also look for teams and

icon teams

within the

marketplace so if you want to tweak some

settings and adjust the look and feel of

this code you can do that here so for

example i know that atom has a has a

team for example so

yeah you can look that up check if it

fits fits your vibe

how you do that once you've installed

the theme you can go

to the settings in the

lower left corner here and by clicking

on the gear icon and then you can hit

color team and this will show you all

the themes so i can basically just

scroll through those and you can see how

it how it will change and when you

install a theme from the marketplace it

will show up here so let me just i use

the dark plus default dark this is the

one i like so this is how you switch up

your teams and another cool thing

is you can change your icon

theme if i click here file icon team i

think the

you can disable them and i think this is

like the default or maybe this one these

are from vs code itself so you have

minimal and set eye and you can see on

the left on the left over here what what

it does so

um i use the material icon team and why

i do that is uh what i really like about

this team is that it creates different

folders for

different

depending on the name of the folder so

here you can see data has a little

database icon docs as doc items

different color and the models has a

different

different color and a different icon so

and the source is in green with the code

icon next to it so basically this allows

me to really easily look up folders that

i want to check so not just by name but

also by color and it's i think it just

adds a little nice touch and now there's

one uh more thing i want to show you

about the settings um so if i go to

settings over here now first of all

there are a ton of settings that you can

tweak from font sizes to

whatever you can

look through all of these i have almost

everything just on default i've also

never really looked in

too much of it because i like

the way it comes out of the box but

there's one important thing

to note here and that is that you have a

user level and a workspace level and

basically how this works is

on the user level everything that you

tweak will be saved within ps code and

then once you open up a new file and you

open up a new project by default it will

look at the user level uh you can

basically compare this to just your your

settings in any uh program you just

change them and when you open up a new

instance of the program your settings

are still there but then a really

convenient thing is that you also have

workspace settings and workspace

settings override your usb user settings

and they are tied to the workspace so as

i showed in the beginning of this video

i imported this folder and then i saved

this this project to this workspace now

this workspace contains settings and for

example if i want to increase the font

size over here so let me just check i

can if i click here and here you won't

see any difference but here i will make

adjustments on a user level and here i

will make adjustment to just the

workspace level that is a distinction

that you have to be aware of all right

so and there's one setting

that i want to show you and

this is really cool and we'll check that

out in a bit and that is the jupiter

send selection to interactive window and

i will do that on the user level and i

will look for jupiter sent to

so jupyter send selection to interactive

window we want to check this button over

here and what this does as it describes

here when pressing shift enter send

selected code in a python file to the

jupyter interactive window as opposed to

python terminal and i will show you

later in this video what this will do

but this is awesome all right now that

we've set up fierce goat with the

settings and the extensions i'll now

show you why it's so awesome and give

you a few examples

so let me first start by

opening up a notebook and by the way

this is just a notebook some notebooks

that i downloaded from this github

repository over here it's just some

basic bandless exercises

and what i can do is let me just open it

up and and here you can see that we can

just open up a jupyter notebook and also

just run it just like we're used to so

this is really awesome and this works

because we have installed the jupiter

extension and now we can basically just

work like any other notebook in the top

right corner over here we can select our

environment so i'm using anaconda and my

base environment and this is really

awesome so we can just um

work on jupiter notebooks here and let

me just open this up in the finder it's

just a notebook file and what we can do

if i just open this up and open like a

regular jupiter session you can see this

this is basically just the same as you

would normally work but now we're doing

it in fields code so that is how you

work with jupyter notebooks within vs

code and now i'll show you how to work

with python files and something that has

completely changed the game for me in

terms of my productivity so let me close

this and i'll open up a quick python

file here very basic file we import

pandas we load a data frame and we print

the data so the main difference between

working with jupiter notebooks and

working with python files is that in

jupyter notebooks you run code sell by

cell which is very convenient you can

use breakpoints and you can basically

for for data science projects you can

very easily manage a project step by

step so you first load the data you

check it you do some explorations you

tweak some things and this is really

convenient because in a data science

project you constantly go back and forth

back and forth so you load the data you

do some tweaks visualize something

create a function back and forth and

iterate until you have your desired

output so that's what makes a jupyter

notebook very convenient for that and if

you compare this to running just python

file for example that we have over here

we can do run python file is it will run

everything

in one go so it will import the library

it will load the data and then it will

print the head of the data in this case

but the thing is say for example you're

working with quite a big data set you do

some transformations that maybe take a

couple of seconds each

and then you've noticed that all the way

at the end your

visualization doesn't really work you

mess it up

now if you were working in a python file

you would have to adjust your code and

then rerun that entire python file and

do all those transformations again so

load the data etc and this can take a

couple of seconds sometimes even minutes

every time that you run the file and a

jupyter notebook counters this by

running it in codeblocks but now by

using vs code we can get the best of

both worlds and i'll show you how to do

this so remember the setting

that we tweaked within the settings is

jupiter

sent

so it says here when pressing shift

enter

send the selected code in python file to

a jupyter interactive window as opposed

to the python terminal what this

basically means is that we can now

within the python file as you can see

this is not a notebook but this is a

python file we can select a line of code

and then hit shift and enter

and vs code will fire up a interactive

jupiter session so this is basically the

same thing that's running in the

background whenever you're running a

jupyter notebook and it will store the

lines of code that you've run within

memory so

you can see for example here the

variables no there's nothing over here

but then once we load the data

so i can either select the line

or i can just have my cursor on one of

the lines and hit shift enter

what you can see it will run this line

of code and then it will store it into

the variables so what this basically

means is that we can write our python

code but run it in an interactive

way just like we would do in jupyter in

a jupyter notebook all right and i'll

show you why this is so awesome and how

you can use this in like a regular data

science project so i have a little

script here that basically reads some

data then there is a function

to transform some of the data and then

to store it so this is like the the

typical first step within a data science

project i will set my cursor on the

first line and hit shift enter to fire

up an interactive window that's

completed so then i go to the second

line and import the data and what i can

then do i can just select the data and

then run it so this is so convenient for

working on a python script is i can just

use

my arrows and then select

certain variables or select whole lines

to run the code so if i have my cursor

just on this line or i select the whole

line i can run it and then what i can do

i can just move my cursor forward then

select just data and then run it and it

will show me the output and this just

makes it so fast to to work through

through a code and you also don't have

to

um for example in a notebook insert a

line below then type data there and then

check the output you can just run this

very interactively and it's yeah it's

just very a very natural way of writing

code

also you have all the

tools

in the toolbar here that you

also have in the jupyter notebook so for

example i can clear everything up let me

show you another cool thing so i have a

data transformation function here that

basically takes the data and let me just

show what data is

and it takes the item price and it

basically turns it into a float so as

you can see it has a it has a dollar

sign here and that makes it a an object

within panda so we can't do any uh

calculations with it so this would be

like a typical data transformation and

i've

defined this this function and then what

i can do here is i can

call this function put data variable we

didn't and then we have our data

transformed but what you would typically

do is you write this function

line by line and in between you test a

few things so what i can now do is

instead of just

running this whole function i can just

go to

this particular section highlight it

and then check whether the output is

correct so i can start off with the item

price

and as you can see this is just the item

price from

the data data frame and this is an

object and then i can use the string

that replace method so i'll select

this over here and i can check

all right this works the dollar sign is

now

gone but it's still an object and then i

can say okay but now we want this as a

float so we run

we add s-type float to the end run it

and boom now you can see dollar sign is

removed and pandas recognized it is

recognizes it as a float alright so now

i've basically validated

my function i'll select it i'll run it

the function is now defined so i can see

this is a function what i can then do is

i run the line over here and then now my

data transformed is a new data frame

which has the item price as a float so

yeah this has completely changed the

game for me in terms of my my

productivity and

once you get used to this way of working

it just becomes so fast you can just

hop through your code using the arrow

keys and then make selections by

using command or control and shift and

highlighting certain parts or by using

alt or option for example then basically

you have the best of both worlds so you

have the interactivity of going back and

forth

of jupiter notebooks but you also have

the added functionality of vs code in

the extensions to write in a python file

uh with all the added features that we

discussed like the auto completion uh

and the suggestions to basically speed

up your coding make less errors etc and

you combine those and it will just

really improve your workflow it has at

least done uh for me as i already

mentioned the best thing is that the

code that you're writing is also ready

for production because it's in in a

python file and not in a jupyter

notebook so you don't have to do that

transformation so that's what i wanted

to show you in today's video this is how

i use and set up vs code for my data

science project as i said it has

completely changed the game for me

coming from jupiter lab jupiter notebook

switching to the workflow that i just

explained over here i'm just way more

productive and can write code

way faster it's also way more fun i

think so

yeah um i hope this video helped you out

if it did i would really appreciate it

if you like this video subscribe to the

channel

i'll be sharing more videos related to

data science basically whenever

i encounter something in my work

that i think can help other people i

will try to create a video about it

so yeah if that's something you're

interested in definitely subscribe

thanks for watching see you next time