learn-co-students/dsc-learn-lessons-ds-canvas-test-batch

Working with Lessons on Learn

Introduction

In this lesson you will practice the workflow we will be using for the rest of the course and also get some hands-on practice with important tools that you will need to be familiar with as a professional Data Scientist: the command line, git, GitHub, and Jupyter Notebook.

Objectives

You will be able to:

• Use basic commands to navigate the command line
• Use bash commands to create new files and folders
• Use git clone to clone a repository
• Perform cell operations within Jupyter Notebooks

Setting the Stage

There are two types of lessons on Learn - READMEs, where you are mainly reading text or watching videos and perhaps executing a code block or two just to see what happens. For those lessons, you don't need to know any of what we're teaching below. All you need to do is to read them on Learn. Enjoy!

However, there are some lessons that require you to write code - Projects and Labs. Generally, labs are a little shorter, allowing you to practice a specific skill in isolation (like practicing throwing the ball up without hitting it to improve your tennis serve). Projects come towards the end of a section and are more open-ended, allowing you to bring together a range of different skills that you have learned (like playing a whole game of tennis - or at least practicing a complete serve).

It's easy to tell from a lesson's title if it is a Project, a Lab, or a README. If the title ends with "Lab", it's a lab. If the title starts with the word "Project", it's a project. If the title doesn't end with either a "Lab" or a "Project", it's a README!

The process we're teaching you below is only required for labs and projects - not READMEs

The Command Line

Before we get to Jupyter Notebooks, we'll need to know a tiny bit about command line. On Mac computers, this is the terminal application. On Windows computers, we'll use "Git Bash". The command line serves as a low-level interpreter through which you, the user, can send commands directly to the computer. As a computer user, you previously have probably sent commands to the computer through a graphical user interface (GUI) such as a web browser, text editor, photo editor, or any other of the myriad of computer programs now in existence. While the command line is initially daunting with its cryptic looking text, we will quickly see some of the many advantages it can have.

One of the many useful features of the command line will be using git to clone (download) a local copy of the curriculum hosted on learn.co. This will allow you to work offline and to save changes as you work through exercises and start programming!

To start, for Mac users, open the terminal application, for Windows, open “Git Bash”.

pwd

The first command to try out is pwd which stands for print working directory. This will tell you where you currently are in the computer's directory structure. Try it out.

cd

The next essential command is cd which stands for change directory. This allows you to navigate to different folders on your computer's hard drive. Typing cd by itself will automatically take you to your home directory. Typing cd and a folder name will take you to that folder. Typing cd .. will move you one folder up in the hierarchy. Play around and trying moving between folders for a minute or two.

Tab Completion

An extraordinarly useful feature when working with the command line is tab completion. This allows you to hit the tab button to autocomplete names once you have made a unique specification.

For example, if you navigate to your root directory by running the command cd, if you're on a Mac, you will probably have 2 folders within your root directory named "Downloads" and "Documents" (these are standard folder names created by default in most systems, although you may have renamed them, or your system may be different). With these, or longer folder names, it can sometimes become cumbersome to type the full folder name. instead, you can start typing the command and folder name such as "cd Dow" and then press tab to autocomplete. Like magic, the command line should complete the statement correctly to be cd Downloads. (Note: this will not work if you have another folder that begins with "Dow". Similarly, if you just typed cd D or cd Do followed by tab, the command line will not autocomplete, as the selection is not unique, because D or Do could both refer to either Documents or Downloads. Also note that these commands are case sensitive, and folder capitalization much be matched exactly.

ls

Continuing with navigating the computer's hard drive, it's useful to know how to list files. This is done with the ls command, short for list.

You can also pass optional parameters to ls such as ls -a which lists all files (including hidden files), ls -l gives a long listing of files (including file size and last edit times). You can also pass multiple parameters simultaneously such as ls -al to produce a detailed listing of all files.

* the wildcard parameter

Also very useful is the wildcard parameter. For example, if you wanted to list all files in the current working directory that begin with a, you could type ls a*. Here, the asterix (*) denotes anything is allowed following the a. Similarly, to list all pdf files in the current working directory you can use ls *.pdf, or to list all text files, you can use ls *.txt.

mkdir

Finally, as you continue to navigate the file directory from the command line it can be useful to be able to create new folders. To do this, use the mkdir command, which stands for make directory. Try it out with mkdir NewFolderName. Afterward, use the ls command to see that there is indeed a new folder, and if you wish, move into the new folder using the cd command.

Cloning GitHub repositories and Learn.co lessons

You have already read a little bit about GitHub and how it's used to share code. Since you can now navigate the file directory using the command line, you're ready to download some course materials from GitHub to your local environment. In practice, you will do the following:

• Fork the course material repository: Forking is the process of making a personal copy of the Learn course materials on your own GitHub account.

• Clone the forked course material repository: Cloning is the process of downloading a repository from Github onto your personal computer.

Don't worry if this process seems a little confusing. It will be explained in more detail soon. For now, you can just follow the steps below:

• Create a folder on your computer for your course materials and navigate into it
• Then create a subfolder titled "section01" and navigate into that
• Return to your web browser and navigate to the lesson you want to download
• Click the GitHub icon

You'll be redirected to the associated GitHub repository like this.

• Click the fork button, as shown in order to create a copy to your personal account which you can edit and update.

One of two things will happen. Either it'll pop up a modal (window) and if you scroll to the bottom of it you'll see it says that you already have a fork. That is quite possible - Learn auto-forks certain lessons for you. If that's the case, just click on the link to view your existing fork.

If you don't already have a fork, after a moment of this: You'll be redirected to your new personal copy of the repository:

Finally:

• Press cmd+L to highlight the url bar and cmd+c to copy the url (whenever we use cmd to refer to holding down the command key on a Mac computer, on a Windows computer, hold down the control key instead)
• Return to the terminal (you should be in your "section01" folder)
• Type: git clone and paste your repo url (cmd + v for Mac; for Windows, in git bash, shift + insert)
• Et Voila! The repository and all of its contents will be downloaded locally to your computer!
• Remember that you then need to cd into the new repository once you've downloaded it
• The next step is to open your Jupyter Notebook locally (not on Learn) using the command line

Jupyter Notebook

So, now that you understand a bit about how to use the terminal and how to clone GitHub repositories from our Learn lessons, we should talk about the Jupyter Notebooks that will run most of the content in this course.

Make sure to activate your conda virtual environment in your terminal first by typing source activate learn-env. Then type jupyter notebook in your command line and press enter. Next, your default browser will open a new window or tab and you will see the list of files that are in your current directory (remember you want to be in the GitHub repo directory that we just downloaded).

Note: To stop a Jupyter Notebook, go to your command line where the notebook is running and press the control key + the letter c (ctrl+c).

Then click on the index.ipynb file which is the Jupyter Notebook we will be using in this and future labs and lessons. This will open a new tab where you will see the same content from Learn!

Background on Jupyter

Jupyter is a web application that allows you to create and work with documents that have live code. It's a very popular tool among Data Scientists, as it allows for both explanations of thinking behind code as well as the code itself.

Introduction to cells

The notebook itself consists of cells. Double click on this content in Jupyter notebook to see what we mean. Once you double click on a cell, you are in insert mode. This means that you are able to edit the cells, just as you would if this were a word document. You can tell that you are in insert mode because of the green border around the cell.

After entering insert mode for this cell, change some content. Don't worry about what you change as you can always undo it. You can revert the changes to a cell by making sure that you are still in insert mode and by pressing command + z on a Mac or control + z on Windows.

To get out of insert mode and see the effect of your changes, press shift + enter.

Adding cells

If you wish to quickly add a new cell you can do so using the following steps:

• Make sure you are not in insert mode
  • Remember you can tell you are in insert mode when you have a green border around the cell
  • To get out of insert mode, press shift + enter. Another option is to press the escape key
  • You will no longer see a cell bordered in green
• Then press the letter b to create a new cell

Deleting cells

To delete a cell you once again should be in escape mode, and then press the x key.

Of course, you'll want a way to undo your deletion. From escape mode, you can press z to undo deletion of a cell. Note that this is different from cmd + z. Pressing cmd + z while in insert mode undoes any changes inside of a cell while, whether these changes be deletions or text insertions. Pressing z from escape mode undoes the deletion of a cell.

Go to escape mode and press x. This cell disappears!

Then bring it back with z.

Types of Cells

The current cell and every other cell in this lesson is a markdown cell, meaning that it allows us to write text and stylize that text. For example, if you surround some text with two asterisks (**) on both sides, the text becomes bold. That's markdown.

Cells can also contain code. If you are writing in a cell that is for Python code, everything in that cell must be valid Python code or you will see an error.

This is a python cell without valid Python code so you will see an error

So, a cell must either be of type markdown or of type code, in which case all of the contents must be valid Python. It cannot be both. We can quickly change a cell from markdown to code with some keyboard shortcuts.

• From escape mode, you can change a cell to type code by pressing the letter y
• From escape mode, you can change a cell to type markdown by pressing the letter m

Anytime you create a new cell, say with the shortcut key b, the new cell will default to code mode. You can switch to escape mode and press the letter m to change the cell from code to markdown.

View All Shortcuts

Press the key h while in escape mode to view the menu for all of Jupyter's shortcuts.

Working with Python in Jupyter

Ok, now that you know a little bit about adding and deleting cells, as well as changing cell types from markdown to code, let's focus on working with Python in Jupyter. We'll go into a large amount of detail about working with a Jupyter Notebook in Python, but the main takeaway is this: if you see a Python cell, you should press shift + enter to execute that cell.

The major gotcha in working with Python code is that we must execute the cells in order for Python to register the code in them. So for example, just seeing the cell where we define name to 'bob' below does not write that cell to memory.

name = 'bob'

If we try to reference that variable later on without having executed the cell, Python will tell us that it is not defined.

name

To execute or run a cell, we must press shift + enter on that cell (or when that cell is selected). Upon running a cell, Python will show the the last line of the cell's return value underneath. Let's run the cell below to see this:

age = 14
age

As you can see the variable age is set to 14, so when the cell is run 14 is displayed underneath.

One tricky thing to note is that assignment, the action of assigning a variable, does not have a return a value. So, even though the cell is run, if the last line of cell is the assigning of a variable, nothing is displayed underneath.

hometown = 'NYC'

Notice, even after pressing shift + enter on the code above, nothing is displayed below. But if we reference the variable hometown, we see that the cell was run as the variable was defined.

hometown

Yes, it's pretty confusing, but the important thing to take away is that we need to run our cells with Python code by pressing shift + enter if we want Python to read our variables and functions and remember them later on. Remember, in the case of assignment, the return value is None, which does not show an output. We can see this more concretely below by running the cell below:

None

Working through labs and readmes

As you read through labs we encourage you to press shift + enter on each of the Python cells that contains code. Often in labs, we will assign variables to data early on. Then we will ask you to work with data stored in those variables. To avoid going to the top of the lab to press shift + enter, it's best just to press that while reading along.

Summary

In this lesson, you learned about the command line, cloning GitHub repositorites, and working with Jupyter notebooks. You saw that in Jupyter notebooks, you can either be in insert mode or escape mode. While in insert mode, you can edit the cells and undo changes within that cell with cmd + z on a Mac or ctl + z on Windows. In escape mode, you can add cells with b, delete a cell with x, and undo deletion of a cell with z. You can also change the type of a cell to markdown with m and to Python code with y.

Then you saw how to work with Python code in Jupyter notebooks. You saw that to execute your code in the cell, you need to press shift + enter. If you do not do this, then the variables that you assigned in Python are not going to be recognized by Python later on in the notebook.