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Move from Excel to Python with Pandas Transcripts

Chapter: Data wrangling with Pandas

Lecture: Pandas' dt, the date time accessor

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Now we're going to read in our sample sales data into our Jupiter notebook.

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So we'll do the imports. I went ahead and put those in here,

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and now you can see the data frame of that represents the Excel file.

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And if we do DF info,

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it tells us that the purchase date is a date time 64 data type,

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which is good, which is what we had expected.

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Quantity, price, extended amount and shipping costs are numeric values.

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So everything appears to be in order here.

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Here's how we might think about actually accessing the purchase date.

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So if we know that we have a purchase state,

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maybe we could try typing month after that.

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And we get an attribute error so Pandas doesn't know how to get at the month

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And so what penance has done is it has introduced a concept of an excess

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er and D T stands for daytime.

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So now it knows that this is a daytime data type,

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and there is an excess er called D T,

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which enables us to get at the underlying data in that column.

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And here we want to pull out the month we can do a similar sort of

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so year works as expected. And there are some that you may not think of

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what's try like Day of Week Pandas goes in and Comptel,

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what day of the week each of those days is and assigns a numerical value to

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it. So remember the example we had of trying to get the quarter and how

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we had to do a fairly,

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maybe non intuitive calculation for Excel?

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Let's take a look at what if we just use quarter?

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Ah, so that tells us that Pamela's knows the concept of quarter and can automatically

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calculate that force, which is really helpful.

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And the recent one highlight This is there are a lot of options available once you

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have the correct data type to make your data manipulation just a little bit easier.

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For instance, what if you want to know whether a current month has 30 or

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31? Or maybe it's a leap year.

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We can look at days and month so we can see that it calculates a 31

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and 30. We can also see if something is the end of the month.

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So none of these examples that are showing just the head and the tail.

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But it is a helpful thing to keep in mind as you doom or data manipulation

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Now, one of the things that you really need to keep in mind is

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that I did all of this.

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But there's been no underlying change to the data frame.

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If we want to actually add some of these new columns to data frame,

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we need to make sure that we explicitly do so.

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So what I've done here is I've created two new columns purchase month and purchase year

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and assigned the month and year to that.

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You can see the data frame now has the purchase month and year.

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So we are, um, replicating what we had in our Excel spreadsheet and if

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we wanted to add one more to the purchase corner.

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Now we have our purchase quarter,

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and you can see that this is March.

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The first quarter in this November,