Course Content

Advanced Techniques in pandas

Let's examine our dataset a little bit. We have numerical columns, for instance `'Engine_volume'`

. Imagine you want information about all cars with an `'Engine_volume'`

less than `3`

, but greater than `2`

. Using the `.loc[]`

statement, we can easily do this.

However, knowing that Python provides a special function that can extract data between two values without using two conditions will be useful. This function is titled `.between(left_bound, right_bound)`

. You can apply it to numerical columns specifying the numbers' left and right bounds. Look at the example and learn how we can combine `.between()`

and `.loc[]`

statements.

The code below extracts data where `'Engine_volume' >= 2 and 'Engine_volume' <= 3`

, but what should we do to make one or even two boundaries exclusive? Let's find out using the same example. You can add an additional argument to the `.between()`

function.

`.between(2, 3, inclusive = 'right')`

- extracts data where`'Engine_volume' > 2 and 'Engine_volume' <= 3`

`.between(2, 3, inclusive = 'left')`

- extracts data where`'Engine_volume' >= 2 and 'Engine_volume' < 3`

`.between(2, 3, inclusive = 'both')`

- extracts data where`'Engine_volume' >= 2 and 'Engine_volume' <= 3`

. The result will be the same as without using`inclusive = 'both'`

.

`.between(2, 3, inclusive = 'neither')`

- extracts data where`'Engine_volume' > 2 and 'Engine_volume' < 3`

Section 3.

Chapter 3