 Like I was saying pandas allows us to load in data and do analysis on there And you know we've been working with the CSV files the CSV and Technically speaking we could take our data from a CSV file and then load it into Do data analysis now you see I am providing two separate options here I have sort of the pandas variation and the numpy variation because we can convert it into a numpy array as Well, I do want to point out one little thing about a numpy array to start So if I came in numpy as in P now The thing I want to at least stress is one we're going to try and load in that iris.csv file. I've got it in my data folder that we've been working with in the past and so I'll go ahead and I'll say I'm gonna call it iris because that's what I did in the slides and mp load text Now the first thing it's asking for is the file name now remember We're dealing with the fact that all of our files are in different locations. So in my case, for example, I'm currently working out of the core Library or core folder now one of the things I'm gonna have to do to access the iris again Is I have to go up a library down into the data folder and there's where that iris.csv file is So again in Python, that's going up with two dashes Going down into a folder the data folder iris.csv Now one of the things that I will need to specify with numpy is I do need to Specify what the delimiter for those values are and so in this case they would be the comma So okay, I'll go ahead and run this and it airs So the entire idea and specifically here's the key reason that Sometimes numpy is not the best option for loading data Specifically numpy will not handle those strings that are inside that csv If you're dealing with a csv that does not have all of these things numpy will be great But we have them so we're running into a problem. That's where pandas can come into play now pandas has its own library and own approach to loading in a Csv file and in fact, it's simply called read and you can already see it's got a few options But the one we're looking for right now is csv Now it's got a number. Oh my goodness You can see just the list of things going on. Luckily speaking our csv file is pretty straightforward It still it needs to get specified by going up the directory down the directory and into that file But that's all I need to write. I actually don't need to write anything else So go run you see I get no errors, which is always great. That means code worked hopefully and I can print my iris and wouldn't you happen to know it? It's got all of my data Now some of the things that you should kind of take note of here is it is loading those data It is sort of having to there we are all resize a little bit to make things readable But you can start to see it's present presenting out each one of the records as is Lined up with their particular features very similar to what we saw when we built a data frame now One of the issues is well the iris data set has a lot of data in it not a lot lot But 150 lines is kind of a lot for my screen and so it is cutting it off and one thing to point out It's printing everything so printing everything by sort of providing these dots dots here It's basically saying. Oh, hey, there's more entries. We're just gonna skip over them to the very end And then it's at least telling me hey, here's how many rows and columns you have One of the things that you can do. This is actually one of my favorite techniques, especially when I'm trying to look at you know my data's and Some of the ways that I'm kind of doing analysis on there is I can use a function called Head now specifically the head function in a data frame Just prints the first five entries of your data frame So in this case instead of seeing everything and whatnot. I'm only seeing the top five There is the other one You know head versus tail tail will do the same thing with the bottom. I don't use it as often That's just more, you know, I like to use head But those are ways that we can load data into our pandas or load a CSV file into a pandas data frame