Introduction to Jupyter Notebooks - Interface | Ipython Kernel | Sharing | GitHub

Key Takeaways

hello everyone welcome to data science with her my name is Hershey TV I'm a data science instructor and mentor so in the previous video we discussed how to create an ideal Python environment for data science and we looked at how we fired up stupid a notebook and to start off with our project so in this video I'm going to cover what are the basics of Jupiter notebook what is that what are the different elements and how should you get started with Jupiter notebooks to start your journey on any data science project so getting started with Jupiter notebooks coming right up [Music] so we all know path can be done in many ways and common methods include running Python scripts using a terminal or using the path shell by simply typing in Python and equals five let's say you want to look at a is five so you learn Python using the potential but we did analysis or data science making the news these days we have a person based Jupiter notebooks that are being used by beginners and experts alike so I paithan if I talk about I pattern pattern provides ripple read evaluate print loop a shell for interactive Python development it enables us to visualize the charts and plots using the geoid toolkits and provides kernel for Jupiter as well so if you want to look at ipython you can simply type in a pattern and your tunnel head okay come on not found so let's first think that you need to do is in the previous video we discussed about creating in a corner environment so we created an environment called corner D HWH underscore env and let's put activate the environment so if you want to know about how to create an environment I have put down the link to my previous video and the description so you can take a look so we have our environment activated now so let's type I by T alright so this is going to start and I buy thin shell for us so this is the interactive Python development environment that I paths and basically provides and so here also you can run your Python so let's say I want to see if I plus six okay and let's say I want to import my library let's say mat plot Lib dot PI plot as PLD ok let's see if the same boots yep so this has imported the library and now if I want to plot a simple chart let's say so let's pass in the data one two three four and so I've got this matplotlib object and when I do PLT dot show let's see what we get so basically we have this line drawn for us basically using the ipython shell I can do a bunch of stuff provides a kernel on which the chipra notebook is based so so projectable has succeeded ipython notebook and is based on ipython as it makes use of its kernel to do all the computations and then serves the output to the front-end interface so the kernel provides the multiple language support to jupiter notebooks beat our python julia or any other language and it extends the ipython stow to output features to build a super intuitive and interactive browser-based GOI for our series data science with her shell we'll be focusing on learning Python using different notebooks so under the title we have the menu bar which has a lot of options so we have five edit view insert so the most common ones are the file in case you want to create a new notebook open an already-existing notebook or if you want to download your notebook as an HTML there different ways to download your notebook then you have insert in case you want to insert a cell above or below then there are so different ways to run the cells if you want to run all the cell to run all if you want to run on you if you are on a particular cell and if you want to run all the cells above that particular cell then you hit run all above and similarly to run all below and then we have different kernel settings interrupt to basically if let's say if your cell has stuck and it's not yielding and your output so you can click on this interrupt option and they're different other options that you can take a look at they have edit cut cells copy cells paste and these icons let them go through this icon so we have + to add a new cell then if you want to delete the cell you can simply cut the cell using the scissor and then let's say you have two cells which is let's say this one is one this one's two and in case you want to move your number one cell to the top you can click on you can move around yourselves like using these up and down arrow keys so in case you want to run this cell click on run or you what you can do is you know simply press Shift + Enter on your keyboard and that will run the cell for you then we have this coder that so the kernel is basically a program that runs and interprets the user's code so there are different kernels for different languages and Duprey notebooks uses ipython kernel to execute the Python code now the kernel executes the code in the cell and then it returns the output if any to the front-end interface and the state of kernel basically pertains to the entire document and not just an individual cell so if I click if let's say I type equals five and run the cell so a would be available for me in the next so the colonel state does not pertains to a single cell it pertains to the entire document so we have four different types of cell in algebra notebook we have cool which is where we write a Python code and markdown row and we convert and heading so I'm on this particular cell the selected cell is a code cell as I can see in the drop-down so let's say if I write some code here let's try to import matplotlib dot PI plot as PLT and again you can either use the Run button or hit Shift + Enter on your keyboard then let's plot the same data 1 comma 2 comma 3 comma 4 and then enter PLT dot show and when I run this ok great so we have this plot which is plotting 1 2 3 4 this data that I provided so I the code cell can execute my Python code now when I click on this let's say I go to the cell ahead markdown so this is basically a markdown cell which is the text so whenever we want to add documentation by putting in some text formatted using markdown so I can add some mark down here so let's say I go to this cell I convert this into a markdown and I had hash tag okay map plot lib plot I hit enter and this is basically my heading matplotlib plot so there different ways to write markdown and I'll add the link to the cheat sheet to learn markdown if you want to take a look at that then Rowen beacon word is basically just another tool to convert your trip eternal book into HTML PDF or any other file format and heading is simply your this is the level 2 heading so this is simply you can achieve this heading cell by adding two hash tags in your mug down cell so this is same as your mug down simply hitting virgin okay beat any type of cell you'll have to run all of them using hi the Run button or Shift + Enter on your keyboard once you're done with your analysis your work your notebook is ready you might want to share it with your boss with your potential employer or your collaborators so there may be different reasons for you to share your notebooks up with some people you might want to share your code for which you can use github and there are some others you might just want to share a pre-rendered static version of your notebook so there are several ways the first one is you can use the download as option under the file menu so you can download your notebook as a PDF HTML or any other option that that's available to you then we have github you can share the link of your github public repository or add collaborators to your private project now get up has over 2 million notebooks hosted and it's a common place for all the notebook levels and also github has an integrated support for iPad and B files both on its suggests on its website and and the repositories as well so the other tool is you can use env viewer notebook viewer so MVP is one of the most popular notebook renderers on the web and if you already have your notebook somewhere hosted online be it on github or bitbucket or any other place envy viewer basically will render your notebook and it will provide a shareable URL link for you as well so that was all about Jubran notebooks so now we are at a place where we can get started where the any of our data analysis or the science project using tupola notebooks in the next video I'm going to be talking about the basics of the fundamentals of Python that you are going to need in order to move forward on your path to learning data science and so if you found this video useful don't forget to like the video and comment down below if you have any suggestions anything to add to the material they just taught or how you found this video useful and don't forget to subscribe to the channel so that you don't miss out on all the upcoming videos on there science so till then stay tuned and keep learning data science with her shed