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Hello and welcome to a video on Python. This assignment will ask you to create variables.

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Variables in Python are like a container for information. For example, we could use a variable called

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name to hold information about a name. So here I'm going to set a variable called

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name equal to Jane. Because we're using a text string, I'm going to enclose it in quotation marks.

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Next we'll print name to our results. I'll click this button here to run the

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code. When you first open a notebook, it

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may take a moment for it to actually run your commands. But here you see below,

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we've printed the name Jane. I could change this to something else,

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and now it prints Smith. We'll change it back to Jane. This is a

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statistics course, however. So we're usually more concerned with variables that contain numbers.

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Here I've created a variable called var1, and I've set it equal to 57. We'll

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print var1 below. In Python, two stars, two asterisks,

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means raised to the power of when using it as an operation. So if I say 23 asterisks

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asterisks 2, it will raise it to the power of 2 and we'll square it.

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We'll save that in a variable called var2 and we'll print it below. A division

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symbol means divide. So 23 forward slash 2 will

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divide 23 by 2. We'll save that in a variable called var3 and print it below.

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We can also use order of operations here too. So if we wanted to do them in a certain order, we could

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do something like var4 equals parentheses 23 divided by 2, close parentheses,

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all raised to the power of 2, and that will print below as var4. Let's go ahead and

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print everything out. So you can see we've set 57 to 57. There we go.

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23 raised to the power of 2. 23 squared is 529. 23 divided by 2

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is 11.5 and 23 divided by 2 raised to the power of 2. So 11.5

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times 11.5 is 132.25. If you've done a little

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programming before, you may have noticed I didn't have to tell Python that var1 is a number. It will figure out what

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kind of variable it is based on the information you put in it. That's good. Maybe we want to enter

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in a string of numbers. We call this string of numbers a list. A list doesn't have to contain numbers.

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In fact, we can make a list of just about anything. In this case, we'll make a list called

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var. And I'm just going to put in some random numbers here. Let's print that out.

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This might be pretty useful. What if we wanted to multiply every number in var by var 1? In other

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words, we want to multiply 57 times 2, then 57 times 5, then by 2,

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etc. If we try to simply multiply the two together, let's see what happens.

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So vars_mult, meaning I'm multiplying vars together. And you can name your variables, whatever

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you want, so long as they're sensible and make sense to you and whoever's reading your code, equals

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vars times var1. The asterisk is the multiplication symbol in Python.

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We'll print that out as vars_mult. Well, that's not what we wanted, right?

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What Python has done instead is take this list and repeat it 57 times. Maybe this

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would be useful in other circumstances, but not this time. This is where pandas, a package in Python,

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comes in. Let's try to use it to solve this problem. First, we'll need to import pandas.

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You may need to install pandas first. Do that with the following line. I'll take the hashtag off

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of it. It's exclamation point pip and that's what you use in front of any installation you're doing.

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Install pandas. Once you've installed pandas, you don't need to do it again.

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Now, this next line, letss Python know we want to use pandas and create an abbreviation for us to use so we aren't

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typing pandas over and over. We'll import pandas as PD. So we're

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telling Python import this library so that I can use it in my code.

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We'll create a new variable called DF to stand for data frame. When we enter this data,

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the A part of this code is for the name of the column. So DF equals

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PD.dataframe. So data frame here is a function from pandas that

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we're using. These words will make more sense the longer you code and get used to hearing

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them and seeing them. We're opening a set of parentheses and then inside that parentheses,

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we're opening a set of brackets. A is going to be the name of this column where

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we have these numbers attached to it. So let's print DF.

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Okay, so this first column you see here is our index. The index and Python always starts

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at zero. So we know that we have nine numbers here, not just eight.

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The A column here is exactly what we named it above and we could have named this anything. We could

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have changed this name to puppy or a var one or time in seconds.

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Whatever it is we were trying to measure or capture. You want this name to reflect

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what that item is so that it makes more sense in your code. In this case, we're just going to call it A.

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Let's create a new column called B by multiplying A by var one that we created earlier.

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So up here in our code, we've already run this section of code where we created var one. That

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means that it's available to us to use down here in this new section of code where we're calling

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var one. If we hadn't run that first section of code, we would get an error saying that

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that variable does not exist. So first, we're going to create our new data frame

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and we're going to set it um set the name as b. So df

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open braces b equals df open braces a

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and this is how we refer to different variables in a data frame. Multiply by var one and then

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let's print our new data frame and you can see we have a just as we did before but now

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we have a new column called b where all of those variables were multiplied by 57.

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So that's more like it. We've already kind of talked about what a data frame is in pandas but we

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can do all sorts of useful things with data frames. Now what if we have a data frame already that

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we want to import into colab to use with pandas? Let's go ahead and try that now. First, make

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sure you have the data you want to use saved in your Google Drive. We'll use the classdataS20 file that

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you'll use for your homework as an example. We've already mounted pandas above.

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So in order to do this, you would want to say import pandas as PD in

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addition to from google.colab import drive. You have to have both

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working at the same time for this to work.

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Next we have a line where we're mounting our Google Drive and this will be the same for everyone.

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It's just drive.mount/content/drive. Make sure to put

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your data in a folder called statlab all lowercase and move your copy of the class

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data into it. That way your file path will look the exact same as this.

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So you'll set your file path and it will be equal to open

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"/content/drive/MyDrive/statlab/classdataS20.xlsx".

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Next, we'll create a data frame called DF. You can name your data frame anything so if you wanted to name

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it class data or something else that just makes more sense to you, please feel free to do so.

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And we'll set that equal to PD, so pandas, we're using another pandas function. Read

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Excel, and then we're going to set that argument to file path right here. An argument is

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something that goes inside the set of parentheses after your function. We'll talk more about arguments in a later assignment.

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Next, we'll print DF.head, which is the top of

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our data frame. This first time that I'm running

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this, it's going to ask if if I want to give it permission to connect to Google Drive. I'm going to say connect to Google

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Drive. I'll select my email address or your UALR address is what you want to

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select and hit continue and then hit continue and this

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will take it just a moment to do.

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There we go. So we can see we have three variables, IV1, IV2, and DV.

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So that was a lot of steps. Basically, we told Python. We'd like to access a file on our Google Drive and

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we created a file path to the exact file we wanted to access. Finally, we used pandas to read

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the Excel file using that file path. Pretty good. Let's see if we can make some new variables now.

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You'll notice these already have names. So instead of referring to them as A or B, we'll call

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them IV1, IV2, and DV. What if we want to multiply IV1 and

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IV2 together? Let's call this variable IV3.

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So df['IV3] = df['IV1'] * df['IV2'].

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And we'll go ahead and print the first couple of results from that data frame.

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Okay, so as expected, these are some pretty big numbers. These were multiplying 80 times

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88.5 and getting 7,880. But you

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can see that it multiplies each one down the row. So 78 times 93, 7254, 80

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times 80, 6400. And you can just sort of manually check these if you feel like you want to to make sure

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that what you're doing is what you think you're doing. Usually if you're getting the wrong

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results here, it is a person error and not a Python error. So something

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you've done in the code is wrong or your calculations are off or something like that.

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We could also use that variable we created earlier, var1. So we've created that constant remember

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of 57. Let's divide IV3 by var1 and make a new variable IV4.

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So df IV4 equals df IV3 divided by var1 and we'll go ahead and

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print the first few results as that.

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So now you can see we've taken IV3 and divided it by 57 and we've ended up with this

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column of numbers here. Nice. That should be all you need to get started on your first

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homework assignment. Please let us know if you have any questions about this material and happy coding.