Welcome back.
Our data becomes 392 rows and eight columns.
This is after dropping not available values.
Now let's create a statistical summary for our data starting with printing statistical summary.
And we need to describe the data set and use transpose function run.
And here we go.
The transpose function reflects the dataframe over its main diagonal by writing rows as columns and
vice versa.
So here we have the columns.
They are now become the rows.
So mpg cylinders, displacement, horsepower, weight, acceleration, model year and origin.
And here the count.
So we have the counts of all the uh the values in the rows.
They are the same because we deleted the non-available things and mean the mean for mile per hour,
mile per gallon.
Here we have the mean, the standard deviation, the minimum 25% percentile of the mpg 50%, 50, 75%
and maximum.
So we use those statistical summary in order to determine the range later on when working with Android
to allow the user to put, for example, the cylinders number of cylinders between 3 and 8, and instead
of using, for example, ten cylinders or two cylinders.
So we use the statistical summary to determine the minimum and maximum to determine mean and standard
deviation and the count.
So those are very important.
Also we're going to use them in the standard scaler later on okay.
So we get the statistical summary for our date.