When you're converting data with the R API,

it's almost the same.

You're going to need this comma there and the comma there.

And the as.factor is a function.

It's a global function, I should say.

And the data column you want to convert is the argument to it.

Stick with R, we'll just run this line,

trying to get the mean of the airtime column.

I already know it from the summary, of course.

It is, rumble please, airtime.

There we go, 114.3.

But there are 16,649 NA's, missing data.

That's why we got not a number.

To get around that, we say we want to ignore the NA's.

This is the mean of the remaining,

whatever it is, 35,000 columns.

We can use the function mean,

it's a synonym for H20 mean.

So, this function does exactly the same.

We've got the range function.

I should say these calculations are happening on your H2O server in the cluster.

If your data is really big,

it's not being downloaded as the R client and the calculations done there.

They're all happening remotely.

Let's just jump back to Python and see those commands.

More object oriented, so we select our column and run the mean function on it.

And I believe that's identical.

I couldn't find the range function,

but you can use summary and get the min and the max.