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Instructor: In the last lesson,

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we got started using pandas.

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We installed the library,

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and we imported it to read a CSV file.

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And then, we used that file to get hold of a column,

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which has been automatically identified as soon as it read

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this data CSV.

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So, what exactly are we dealing with here?

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Well, one of the most useful things I find

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is to do type checks on any of the objects

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that I'm working with from a new novel library.

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For example, we can use the type method to check

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what exactly is the data type of this data

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that we're getting back from pandas.

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So if I go ahead and print this one, and comment out

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this second line, then you can see what we're getting

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is what's called a pandas DataFrame object.

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In the package overview, they talk about

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the two primary data structures of pandas.

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Series and a DataFrame.

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A DataFrame is kind of

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the equivalent of your whole table here.

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So, every single sheet inside an Excel file

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or inside a Google Sheet file

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would be considered a DataFrame in pandas.

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Now, what about the second part here,

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where we've gotten hold of one of the columns

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in our DataFrame?

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If I do a type check on this object, and I hit run,

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then you can see, this is a pandas Series object.

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The Series is the other super important concept in pandas.

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And the Series is basically equivalent to a list.

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It's kind of like a single column in your table.

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So, the temperature column would be a Series,

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the condition column would be a Series,

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and the day column would also be a Series.

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So, once you've groked this idea

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that the whole table is basically a DataFrame in pandas,

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and every single column is a series

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kind of like a list in pandas,

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then you're pretty much half of the way there

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to understanding how this library works.

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Now if we go over to the API reference,

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you can see that

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this is basically a list of all of the things

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that you can do with pandas.

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And it is a long list of things.

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But let's take a look at those two core classes,

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the DataFrame and the Series.

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So if we go to the DataFrame, you can see

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that it has things on how to construct a new DataFrame,

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how to get hold of the index,

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how to get hold of the column labels,

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and there's a whole bunch of attributes

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that you can tap into as well as many methods.

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For example, if we take a look at this section

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on serialization IO conversion,

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you can see that you can actually convert a DataFrame

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to various different file types.

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You can convert it to an Excel file,

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you can convert it to HTML.

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You can also convert it to a dictionary.

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So if we click on this,

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this takes us to the actual documentation

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on how you would use this method.

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And if you look at the basic Python documentation

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versus panda's documentation,

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you'll see why this is so much better.

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It lists out all of the possible parameters.

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It gives you examples on how you can use

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each of the methods, and it's also got some related methods

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that it thinks that you might want to take a look at.

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So, let's use this method.

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And what we're gonna do is, I'm gonna get my data,

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and I'm going to call to dict,

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which is gonna convert it into a dictionary.

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So, let's call that data_dict = data.to_dict().

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And then if we print out our new data dictionary,

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you can see that pandas has taken our table,

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and taken each column of the table

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to create a separate dictionary for each of the columns.

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So, we've got day, temperature, and condition,

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and we can now work with this

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as if it were a real dictionary.

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Now, if we take a look at the Series data type.

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And you take a look at the conversion section

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for this type of data,

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then you can see that you can actually convert

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each of the Series to a list, if you wanna be able

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to work with it just as you would with any other list.

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So, we saw that we can get our data,

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and then get the temperature column.

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And this, when we printed out the type, was a Series.

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So, we can get the Series, and then call .to_list().

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And this will turn this data Series into a Python list.

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So, let's call that temp_list.

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And let's go ahead and print it out.

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This would be a list of all the temperatures,

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and this is now converted into the raw Python data type.

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So, we can do all of the things

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that we can do to a Python list,

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like for example, we could check its length.

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So, here comes another challenge for you.

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Given what we've spoken about so far,

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can you figure out if you can work out

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the average temperature in our column of temperatures?

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So remember, you can always Google if you don't remember

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how to calculate the average of something.

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So, pause the video and give that a go.

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All right, so we know that we can get

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a list of all of the temperatures in that list,

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and we know that Python has a built-in method called sum.

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So, then we can get the sum of all of the temperatures

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in our list of temperatures,

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and then we can divide it by the length of the temp list.

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The sum of all the values divided

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by the number of values gives us the average,

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which is also known as the mean.

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And so if we print this out, then you can see,

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the average temperature of the week was 17.4.

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Now, an alternative way of solving this challenge is,

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maybe you took a look through this list of methods,

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and you might have come across some of the computations

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and statistics that you can do with your Series in pandas.

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Now, one of those methods is the mean.

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So, you can actually get rid of all of this excess work,

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and take our data Series,

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which is basically the column of data

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under the heading temp, and simply call .mean on it.

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And now if I print this out, you'll see

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it's the same result as before

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without a lot of the extra work.

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In addition to the mean, you can get other things

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like the median or the mode,

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and a whole bunch of other things just by calling

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the right method on the data Series.

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So, here's the challenge for you.

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I want you to get hold of the maximum value

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from this column of temperatures

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by using one of the data Series methods.

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Pause the video, have a look at the documentation,

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and see if you can complete this challenge.

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All right, to get the maximum value,

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we're probably gonna need this max method.

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So, we call this method in the same way as we did before,

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which is get hold of the data Series.

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So, our entire table is stored in data,

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and then we can get the column

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under the heading temperature.

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So, this is now a data Series,

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and then we can call that method max on it.

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And if we print it out,

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then you can see what we're getting is 24.

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So, the highest temperature was 24.

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So, you've seen that when we're working with pandas,

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it's really easy to get hold of data in a particular column.

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All we have to do is just take the DataFrame,

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use a set of square brackets,

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and then specify the name of the column,

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which it takes by default as the first row of the data.

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So, the day column, the temp, the condition.

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So if I want to get hold of all of the conditions,

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then I would say data["condition"].

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And if I go ahead and print this out,

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you can see it gets hold of all of the weather conditions,

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and selects that column to print out.

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Now, an alternative way to using

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the square bracket notation, where you have to be careful

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about the string you use here,

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it has to match, the name of the column exactly.

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Another way that you can work with the columns

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is simply by calling data.condition.

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And the fact that this code is valid at all means that

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pandas behind the scenes, has taken each of these columns

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and each of the headings,

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and converted those headings into attributes.

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So, we can say data.condition or data.day.

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And if I print that out, you can see

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it's actually gonna be

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exactly the same as doing it like this.

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So, it's up to you which method you wanna use

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to select the columns.

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But be aware that if your column name has a capital C,

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for example here, then your key has to be a capital C.

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And also, your attribute has to be a capital C.

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So effectively, when you're using a DataFrame like this,

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it's almost like you're treating it as a dictionary.

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And you're pulling out each column by the key.

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Now, when you are using the DataFrame like this,

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then you're kind of treating it more like an object.

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You're saying data attribute,

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and you get hold of the data in that column.

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So, I'm gonna restore everything to lowercase

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because I find it easier to read the code.

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But the next thing I wanna show you is a little bit harder,

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which is how do you get data,

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which are in the rows of our DataFrame.

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If I wanted to get hold of the entire row of data

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for where the day is equal to Monday,

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then the way that I would do that in pandas, is firstly,

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get a hold of my entire data table,

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and then, inside that data table, get hold of the column

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that I want to search through.

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So, I'm going to search through the day column,

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so I can use data.day or data["day"],

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both will work the same.

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But once I've got the column, then I can say, well,

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where inside that column, I wanna check

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for the row where the value is equal to Monday.

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This is basically gonna return my row that I want.

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So, I'm gonna print this out, and I'm going to

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comment out all of the previous code other than the place

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where we created our DataFrame.

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And then, I'm gonna run my code.

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And you can see it's pulled out that correct row

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where the day is equal to Monday.

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And it's given me all of the rest of the data for that row.

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So, here's a challenge for you.

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Can you figure out how to pull out the row of data

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from our weather data where the temperature

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was at the maximum?

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So, which row of data had the highest temperature

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in the week?

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Pause the video and give that a go.

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So, we know that we can get the maximum temperature

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in the temperature column just by using this code.

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Now, you can either use this method

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where you say data["temp"].

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Or, you can use the attribute .temp,

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which is the code that I prefer.

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I don't like writing a lot of strings if I can avoid it.

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In this case, we're checking to see which row inside

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our column of temperatures

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is equal to the maximum temperature.

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We would say data.temp == data.temp.max.

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And then, we're going to get our data,

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and access the row that fits that criteria.

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Now if I print out this row,

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then you can see it was the row for Sunday,

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where the temperature was 24 and the condition was sunny.

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Essentially, when we get our DataFrame

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and then we use some square brackets,

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and inside those square brackets, if we only put

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the name of our column, day, temp, or condition,

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then we would get the entire column.

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But if we filter that column by a condition,

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say when a particular column is equal to a particular value,

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then we actually get hold of the row instead.

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Now, once you've gotten hold of the data in the row,

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you can actually go one step further.

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Because we know that the row contains lots of data, right?

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What if we wanted that particular row's temperature

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or that particular row's condition?

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Well, let's say that we create a variable

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called monday, which is equal to our DataFrame,

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and then searching through that DataFrame,

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where the [data.day == "Monday"].

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So, now with this row, monday,

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we can then tap into the values under different columns

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by using the same kind of way that we got data

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in the entire column over here.

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So, we can say monday.condition.

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And if I print this out and just comment these other bits,

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then you can see that I get the actual condition

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for that particular day, which happens to be sunny.

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Now, here's a challenge.

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I want you to get Monday's temperature,

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but because my temperatures are in Celsius,

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I want you to convert it into Fahrenheit.

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So, pause the video,

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and see if you can complete that challenge.

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All right, we know we can get hold of Monday's temperature

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by saying monday., and then the name of that column

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in the row, which happens to be temp, T-E-M-P.

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And then, we get the first value in the series

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at index zero.

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And then if we wanna convert Celsius to Fahrenheit,

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all we have to do is to multiply the Celsius

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by 9 over 5, and then add 32.

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So, it'll be Monday temperature

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multiplied by 9 divided by 5,

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and then add 32.

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So, monday_temp_F, and then we can print this value out.

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So now when I run it, we get 53.6.

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And if I put Monday's temperature, 12,

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into this Google converter, I get the same value, 53.6.

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Now, the final thing I wanna show you is how you create

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a DataFrame from scratch.

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So, in our case, we created our DataFrame

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by reading from our CSV file.

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But what if you wanted to create a DataFrame

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just from some data that you're generating in Python?

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Let's say that I have this dictionary of values,

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I've got some students, and these are their names

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held in a list, and then each of them has a score.

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And the scores correspond to the students,

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so 76 is Amy's score, James scored 56,

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and Angela scored 65.

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Now, how would we create a DataFrame from this dictionary?

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It's really simple.

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We call our pandas library,

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and we get hold of the DataFrame class.

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And then, we initialize that class with some data.

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And the data, in our case, is just going to be

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our data dictionary.

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And now, if I go ahead and save this as our data,

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so I'm gonna comment out what we had previously,

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and then print out this data,

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then you can see, I've now created a table

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using the values from that dictionary.

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Now, we can go even further than this.

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When we've created a DataFrame, we can actually get

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that DataFrame to be converted to a CSV file.

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And this to_csv method takes only one required input,

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which is the path that you wanna save this file.

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So, let's just create a new file,

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which we'll call ("new_data.csv").

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And when I run this code, then watch over here,

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you see a new CSV file being created from thin air,

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and all of our data has been added to that CSV file.

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So, we're just getting a glimpse

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into how powerful this panda library can be.

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And we're only really using it to read CSV data

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and write CSV data.

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Which is a common format

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that you'll see being manipulated using Python.

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In later lessons,

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we're gonna dive deeper into data analysis,

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and we're gonna be looking not only at pandas,

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but we're also gonna be looking at NumPy,

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Matplotlib, and other libraries that make it easier for us

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to work with large chunks of data.

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In the next lesson,

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we're gonna be putting what we've learned to use

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by analyzing some squirrel data from Central Park.

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So for all of that and more, I'll see on the next lesson.