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Welcome back to the class of our course about the complete introduction to that science with Python.

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So in this class, we are still going to talk about pandas and some basic operations that you guys can

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perform with this amazing plug in to Python.

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So basically today we are going to learn how to merge some data frames together and what exactly means

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merge.

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It's pretty simple.

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It's let's say, for example, you guys have more than one that frame.

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So let's say two, three or four, dereference you can put them together in only one data frame.

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So basically, if each of those that are frames are the same, so there is the same thing written on

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them, you can just put them all together and only one data frame.

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And even if there are some things that are not pretty much the same, you can keep some columns coming

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by, adding something to the to this operation.

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So we are going to see how it works.

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A bit more in depth right now.

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All right.

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So to be able to make everything to to run it pretty much well, to to understand how this works, what

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will do is pretty simple.

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We are going to create a data frame.

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So pretty simple.

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We are going to do what we have learned to in the past two classes.

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So first of all, we are going to import our pandas.

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So basically here we go as speedy.

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And from now on, what will do?

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We will create two databases for two basic databases.

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Let's say it's going to be food.

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So basically we'll have the number of the food, the name of the food.

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And finally, let's say the rate or the price of the food.

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So let's say we'll have here database.

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That will be food one.

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And from this moment, we are going to create or thereabouts, so what we'll have at first is pretty

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simple.

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We'll have the number of the food.

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So basically you can just give a number to your food.

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And in this case, the number will be from one to five since we are working with food.

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So one, two, three, four and five.

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All right.

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Then the next thing that we will have will be the name of the food.

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So basically here will give a name to our food.

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So let's call it name and let's do the exact same thing.

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So let's say we'll have apples.

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Who have on us,

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who have.

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I don't know, we can have pizza or anything that you want, you can just write down letters because

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it's just for the purpose of understanding how this works.

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So let's say apple, bananas, chips,

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popcorn,

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and let's say, I don't know, beans for pizza.

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Pizza is better here.

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All right.

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So we have our five foods, and the last thing will be price.

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So basically, we'll have a variable that will be price to have a list of all the prices.

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So here we go.

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We have, let's say, two dollars.

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Three dollars, four dollars.

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Eight dollars and six dollars.

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All right, so we have our first database right here, so basically our first dictionary or list of

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databases that we will use to create our database.

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Then next thing will create a second one a bit lower.

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So we have found one and the exact same thing.

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So we'll create another one that will be called food number two.

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And what what is going to be very interesting is that will keep everything the same.

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So to understand how it works and emerging, we are going to keep everything the same.

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And what we'll do right now, we will create data frames.

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So basically a little right here.

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So we'll have our table one or you can call it food one, table one, how you want it.

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And from this moment, we'll create or the so how exactly we create are the different.

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Like always we're going to use pendas pandas to create or the difference.

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We will use the extension that you guys can see right here.

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So that data frame here, you can find it right there.

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And from this moment, we are going to use food one, so food one for our benefit and we'll do the exact

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same thing for our table number two.

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Here we go.

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So don't forget, just change the name when you copy and paste everything.

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So right now what we have, we have our list of everything that we are going to use every dictionary.

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So for food one and food number two, it should be pretty much the same thing.

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So what I want to do right now, so we have our friends that are created right here so they can see

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table one will be the data from a food one and the table two will be the delivery of your food number

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two.

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So very important, number two as well.

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And from this moment, we want to fusun everything together or merge everything together.

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So basically what we'll do, we will create travel that will be named Fusun.

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So we'll call it Fusion.

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And here we are going to merge everything together.

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So basically, we are going to write down once a year and we are going to use our PDA extension to that

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refers to pendas.

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And here we'll use the formula merge.

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So basically the function merge to merge table one into two together.

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So table one as well as table two together.

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So right now here, what we done.

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What have we done?

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It's pretty simple here.

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We have merged the two tables together, so this is the formula to merge the two tables together.

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So as you can see, it's pretty simple.

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And we want to do right now is simply print the variable fusion.

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You're going to print fusion.

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If we run the.

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As you can see what happened, it simply printed everything together, so basically you have merged

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the two the two databases together.

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So let's say, for example, right now, instead of right here, we write down corn and we run once

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again are up.

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What's going to happen is that the apple will disappear because it's not the same thing.

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So what we'll do, it will merge the two the two databases together.

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But if it's not the same thing, well, it's just not going to merge it and it's not going to appear

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right here.

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So basically, it will just merge everything.

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That is the same in the two columns right here in the two databases that are being number one in the

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database.

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Number two, right now, let's say that you guys want to keep some comments coming and basically make

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it work, even if here we have corn and here we have apple.

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So basically, let's say here we have corn, here we have apple.

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The price of the corn is not two dollars.

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It's going to be I don't know.

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Eight dollars, for example, in here, we don't have chips, we have, let's say, beans.

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All right, so let's say we want to keep the column a name so common.

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What we'll do, it's pretty simple right here after our table to we will simply add a comma and write

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down on so on.

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Equal to what exactly?

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Want to keep coming, so we want to keep the names coming, so basically we'll just write down a name

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because we want to keep the name coming.

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And if we on the up, as you can see what's going to happen, we'll have our names.

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So right here, we'll have our Barona and we'll have everything that one.

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So basically, we keep the name coming right here.

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If you want to do the exact same thing for numbers, we can do it so we can just put everything we can

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just take it and write down the numbers and we run everything together.

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And as you can see, it's going to do the exact same thing.

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So the numbers will be coming in this case.

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So you can see the court appears and we have our corn for eight dollars and we have our apple for two

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dollars as well.

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So you can see it's pretty simple to understand.

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So basically, the first thing that you guys want to do to be able to make sure well, to be able to

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measure, not function, is to be able to measure database's.

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It's pretty simple what you want to do at first.

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You want to create your database.

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So basically here you will have your list that will be used for databases.

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Then you simply put those you create dereference with those data bases that you have.

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Well, with your list, you put all your lists in form of data frames right here with this function

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right there, and then you simply merge everything with the function right here.

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So I hope you guys understood this operation.

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So it's pretty simple what it does.

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It simply put together two databases that are not together.

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And when I see together, it's it will simply put together everything that is the same in the two databases.

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So everything that is not the same will just not be put it inside of this database.

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But it's possible to keep some Collins common as well with the UN function inside of our merge function.

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So that's a first class guys, all in our next class.