Name: 複数のデータセットを組み合わせる - PythonとPandasによるデータ分析 p.5 (Combining multiple datasets - Data Analysis with Python and Pandas p.5)
Uploaded: 2021-01-14T10:44:04.000Z
Duration: 32 min 13 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

未来進行形

And welcome back to another data science with python and pandas tutorial video In this video, we're gonna be building on everything up to this point.

We're gonna continue with the minimum wage data set for one more video.

This one, uh, and kind of combined to other data sets just to Seymour about this whole relationships thing and learn a fume or cool features of pandas before I finalize this little miniseries with doing machine learning.

Ah, basically a machine learning workflow with pandas in the next video.

So I'm gonna be using to new data sets here.

We're gonna be using the 2016 presidential election vote by county as well as unemployment by county.

So it be nice to combine all of these data sets into one major data set and see if voting Proclivities, the unemployment rate and the minimum wage have any relationships with with each other, Right, Cool.

So that's the idea on really we're just going to see if there's any relationship.

It's pretty hard to based on that data loan, draw any riel conclusions, we're just doing it to c t learn more about pandas were really So if you find yourself offended by the results of this video um, not my problem.

So anyways, here we go import, import, import pandas as p d.

And then we're gonna bring in unemployment county as p dot Read under scorer Sissi And that is data sets slash.

And this one is unemployment by county dash us slash output that CSP.

So this one came with his own little directory, basically.

And then, um, we could do on an county dot head.

And there we get a rate that's our unemployment rate on.

Then it's by county, and then you're so cool.

So now we want to do is combine this plausibly with our minimum wage.

So to do that, we're just gonna come over here and basically take all of this data Copy.

Okay, so now we want to do is ah, we saw that missing data.

So let's go ahead and fix that real quick.

So instead we'll say Atzmon wage equals act min wage, uh, dropping in and then act me had fixed that capital w Okay, great.

So we want to take the minimum wage and we want to kind of add that to a new column in our unemployment county data set.

So the way I think I want to do that is by creating a new column and then mapping the values.

There's probably a better way than this, but it gives me the opportunity to show you guys at the most basic level a method that will always work for creating new column values and pandas.

This was not the most efficient, and in fact, it's going to probably take a moment, probably a minute or two to run.

But I'm gonna show it anyways because it always works.

And then we'll talk about it, will show a couple other examples that are going to be, like, way faster.

Let's do define, get, get you can't wage on.

This is a function is going to take two parameters is gonna take a year and by state, so recall minimum wages by state.

But our unemployment data's by county are voting data is also by county.

The minimum wage actually does kind of differ.

So right away, we've got a slight violation here.

And I'm really just trying to show you guys an example anyways, so whatever, but sure, that's that's a problem.

We just I don't have a minimum wage data by county, so I'm sure there's a file out there that exists.

If you're If you're trying to make an actual report here, just take note that this isn't quite fair.

Uh, anyway, what we want to do, we'll do a try and then, uh, accept.

And if we hit an exception, it we're gonna turn n p dot nan also have we?

Where when did we We never imported numb pie.

Men wait, because we don't wanna get the value.

So we're trying to get a minimum wage by year in state, so return Ackman wage.

Um, and we can't just say your state because that action Ackman wage is indexed by year.

So actually, Aaron do dot Lok, And then we grab the year, so dot Loke, what's that look year, Okay.

And then we'll actually, so it would be year, But no, we're gonna say that Lok year, Okay, as a reference to the index column, and then we want to get that specific.

We can quickly test that function by doing something like this.

I get men wage 2012 and, uh, Colorado and, in fact, 2012 should be a string Cool.

So that's 8.33 Probably impossible to see, but hopefully on your screen.

Now we're gonna map this function to a new column in pandas, and the way that we can do that is just like regular mapping function in python in general, so we can map a function to a list or array in python using map.

And this is like I said before, not going to be the most efficient way.

Especially when you have things like functions with multiple parameters or you have a fun like you want it.

You're not just mapping like one value to another place.

Maybe you want to run some sort of true function or calculation or, um, algorithm.

So first of all, I'm gonna throw in time.