Discovering attention-grabbing bits of information in a DataFrame is usually simpler in the event you change the rows’ order. You’ll be able to type the rows by passing a column title to .sort_values().

In circumstances the place rows have the identical worth (that is frequent in the event you type on a categorical variable), you might want to break the ties by sorting on one other column. You’ll be able to type on a number of columns on this manner by passing a listing of column names.

Modifying the Order of Columns

You’ll be able to change the rows’ order by sorting them in order that essentially the most attention-grabbing information is on the prime of the dataframe.

For instance, once we apply sort_values() on the weight_kg column of the canine dataframe, we get the lightest canine on the prime, Stella the Chihuahua, and the heaviest canine on the backside, Bernie the Saint Bernard.

canine.sort_values("weight_kg")

      title        breed  shade  height_cm  weight_kg date_of_birth
5   Stella    Chihuahua    Tan         18          2    2015-04-20
3   Cooper    Schnauzer   Grey         49         17    2011-12-11
0    Bella     Labrador  Brown         56         24    2013-07-01
1  Charlie       Poodle  Black         43         24    2016-09-16
2     Lucy    Chow Chow  Brown         46         24    2014-08-25
4      Max     Labrador  Black         59         29    2017-01-20
6   Bernie  St. Bernard  White         77         74    2018-02-27

Setting the ascending argument to False will type the information the opposite manner spherical, from heaviest to lightest canine.

canine.sort_values("weight_kg", ascending=False)

      title        breed  shade  height_cm  weight_kg date_of_birth
6   Bernie  St. Bernard  White         77         74    2018-02-27
4      Max     Labrador  Black         59         29    2017-01-20
0    Bella     Labrador  Brown         56         24    2013-07-01
1  Charlie       Poodle  Black         43         24    2016-09-16
2     Lucy    Chow Chow  Brown         46         24    2014-08-25
3   Cooper    Schnauzer   Grey         49         17    2011-12-11
5   Stella    Chihuahua    Tan         18          2    2015-04-20

Sorting by A number of Variables

We are able to type by a number of variables by passing a listing of column names to sort_values. Right here, we type first by weight, then by peak. Now, Charlie, Lucy, and Bella are ordered from shortest to tallest, although all of them weigh the identical.

canine.sort_values(["weight_kg", "height_cm"])

      title        breed  shade  height_cm  weight_kg date_of_birth
5   Stella    Chihuahua    Tan         18          2    2015-04-20
3   Cooper    Schnauzer   Grey         49         17    2011-12-11
1  Charlie       Poodle  Black         43         24    2016-09-16
2     Lucy    Chow Chow  Brown         46         24    2014-08-25
0    Bella     Labrador  Brown         56         24    2013-07-01
4      Max     Labrador  Black         59         29    2017-01-20
6   Bernie  St. Bernard  White         77         74    2018-02-27

To alter the course values are sorted in, move a listing to the ascending argument to specify which course sorting needs to be accomplished for every variable. Now, Charlie, Lucy, and Bella are ordered from tallest to shortest.

canine.sort_values(["weight_kg", "height_cm"], ascending=[True, False])

      title        breed  shade  height_cm  weight_kg date_of_birth
5   Stella    Chihuahua    Tan         18          2    2015-04-20
3   Cooper    Schnauzer   Grey         49         17    2011-12-11
0    Bella     Labrador  Brown         56         24    2013-07-01
2     Lucy    Chow Chow  Brown         46         24    2014-08-25
1  Charlie       Poodle  Black         43         24    2016-09-16
4      Max     Labrador  Black         59         29    2017-01-20
6   Bernie  St. Bernard  White         77         74    2018-02-27

Interactive Instance

Within the following instance, you’ll type homelessness by the variety of homeless people, from smallest to largest, and save this as homelessness_ind. Lastly, you’ll print the pinnacle of the sorted DataFrame.

# Kind homelessness by people
homelessness_ind = homelessness.sort_values("people")

# Print the highest few rows
print(homelessness_ind.head())

Once we run the above code, it produces the next outcome:

                area         state  people  family_members  state_pop
50            Mountain       Wyoming        434.0           205.0     577601
34  West North Central  North Dakota        467.0            75.0     758080
7       South Atlantic      Delaware        708.0           374.0     965479
39         New England  Rhode Island        747.0           354.0    1058287
45         New England       Vermont        780.0           511.0     624358

Try it for yourself.

To study extra about sorting and subsetting the information, please see this video from our course Data Manipulation with pandas.

This content material is taken from DataCamp’s Data Manipulation with pandas course by Maggie Matsui and Richie Cotton.