Sorting Dataframes

Sorting

cereal.sort_values(by='rating')

	name	mfr	calories	shelf	weight	cups	rating
10	Cap'n'Crunch	Q	120	2	1.0	0.75	18.042851
12	Cinnamon Toast Crunch	G	120	2	1.0	0.75	19.823573
35	Honey Graham Ohs	Q	120	2	1.0	1.00	21.871292
...	...	...	...	...	...	...	...
65	Shredded Wheat spoon size	N	90	1	1.0	0.67	72.801787
64	Shredded Wheat 'n'Bran	N	90	1	1.0	0.67	74.472949
3	All-Bran with Extra Fiber	K	50	3	1.0	0.50	93.704912

77 rows × 7 columns

When we read in our data, it is generally ordered in the same way it is stored.

We can easily sort the rows of a dataframe based on the values within a column.

The verb that we use for that is .sort_values().

For example, if we wanted to order the cereals based on rating, we could do so by using the argument by within the .sort_values() verb.

This allows us to see the cereals with lower ratings on the top.

sorted_ratings = cereal.sort_values(by='rating', ascending=False)
sorted_ratings

	name	mfr	calories	shelf	weight	cups	rating
3	All-Bran with Extra Fiber	K	50	3	1.0	0.50	93.704912
64	Shredded Wheat 'n'Bran	N	90	1	1.0	0.67	74.472949
65	Shredded Wheat spoon size	N	90	1	1.0	0.67	72.801787
...	...	...	...	...	...	...	...
35	Honey Graham Ohs	Q	120	2	1.0	1.00	21.871292
12	Cinnamon Toast Crunch	G	120	2	1.0	0.75	19.823573
10	Cap'n'Crunch	Q	120	2	1.0	0.75	18.042851

77 rows × 7 columns

What if we wanted the cereals with higher ratings at the top?

Then we would order them in descending order by setting the argument ascending=False.

Perfect, now we have the highest rated cereals at the top of the dataframe.

Let’s apply what we learned!