Interact

Sorting Rows

“The NBA is the highest paying professional sports league in the world,” reported CNN in March 2016. The table nba_salaries contains the salaries of all National Basketball Association players in 2015-2016.

Each row represents one player. The columns are:

Column Label Description
PLAYER Player’s name
POSITION Player’s position on team
TEAM Team name
'15-'16 SALARY Player’s salary in 2015-2016, in millions of dollars

The code for the positions is PG (Point Guard), SG (Shooting Guard), PF (Power Forward), SF (Small Forward), and C (Center). But what follows doesn’t involve details about how basketball is played.

The first row shows that Paul Millsap, Power Forward for the Atlanta Hawks, had a salary of almost $$18.7$ million in 2015-2016.

# This table can be found online: https://www.statcrunch.com/app/index.php?dataid=1843341
nba_salaries = Table.read_table(path_data + 'nba_salaries.csv')
nba_salaries
PLAYER POSITION TEAM '15-'16 SALARY
Paul Millsap PF Atlanta Hawks 18.6717
Al Horford C Atlanta Hawks 12
Tiago Splitter C Atlanta Hawks 9.75625
Jeff Teague PG Atlanta Hawks 8
Kyle Korver SG Atlanta Hawks 5.74648
Thabo Sefolosha SF Atlanta Hawks 4
Mike Scott PF Atlanta Hawks 3.33333
Kent Bazemore SF Atlanta Hawks 2
Dennis Schroder PG Atlanta Hawks 1.7634
Tim Hardaway Jr. SG Atlanta Hawks 1.30452

... (407 rows omitted)

The table contains 417 rows, one for each player. Only 10 of the rows are displayed. The show method allows us to specify the number of rows, with the default (no specification) being all the rows of the table.

nba_salaries.show(3)
PLAYER POSITION TEAM '15-'16 SALARY
Paul Millsap PF Atlanta Hawks 18.6717
Al Horford C Atlanta Hawks 12
Tiago Splitter C Atlanta Hawks 9.75625

... (414 rows omitted)

Glance through about 20 rows or so, and you will see that the rows are in alphabetical order by team name. It’s also possible to list the same rows in alphabetical order by player name using the sort method. The argument to sort is a column label or index.

nba_salaries.sort('PLAYER').show(5)
PLAYER POSITION TEAM '15-'16 SALARY
Aaron Brooks PG Chicago Bulls 2.25
Aaron Gordon PF Orlando Magic 4.17168
Aaron Harrison SG Charlotte Hornets 0.525093
Adreian Payne PF Minnesota Timberwolves 1.93884
Al Horford C Atlanta Hawks 12

... (412 rows omitted)

To examine the players’ salaries, it would be much more helpful if the data were ordered by salary.

To do this, we will first simplify the label of the column of salaries (just for convenience), and then sort by the new label SALARY.

This arranges all the rows of the table in increasing order of salary, with the lowest salary appearing first. The output is a new table with the same columns as the original but with the rows rearranged.

nba = nba_salaries.relabeled("'15-'16 SALARY", 'SALARY')
nba.sort('SALARY')
PLAYER POSITION TEAM SALARY
Thanasis Antetokounmpo SF New York Knicks 0.030888
Jordan McRae SG Phoenix Suns 0.049709
Cory Jefferson PF Phoenix Suns 0.049709
Elliot Williams SG Memphis Grizzlies 0.055722
Orlando Johnson SG Phoenix Suns 0.055722
Phil Pressey PG Phoenix Suns 0.055722
Keith Appling PG Orlando Magic 0.061776
Sean Kilpatrick SG Denver Nuggets 0.099418
Erick Green PG Utah Jazz 0.099418
Jeff Ayres PF Los Angeles Clippers 0.111444

... (407 rows omitted)

These figures are somewhat difficult to compare as some of these players changed teams during the season and received salaries from more than one team; only the salary from the last team appears in the table. Point Guard Phil Pressey, for example, moved from Philadelphia to Phoenix during the year, and might be moving yet again to the Golden State Warriors.

The CNN report is about the other end of the salary scale – the players who are among the highest paid in the world.

To order the rows of the table in decreasing order of salary, we must use sort with the option descending=True.

nba.sort('SALARY', descending=True)
PLAYER POSITION TEAM SALARY
Kobe Bryant SF Los Angeles Lakers 25
Joe Johnson SF Brooklyn Nets 24.8949
LeBron James SF Cleveland Cavaliers 22.9705
Carmelo Anthony SF New York Knicks 22.875
Dwight Howard C Houston Rockets 22.3594
Chris Bosh PF Miami Heat 22.1927
Chris Paul PG Los Angeles Clippers 21.4687
Kevin Durant SF Oklahoma City Thunder 20.1586
Derrick Rose PG Chicago Bulls 20.0931
Dwyane Wade SG Miami Heat 20

... (407 rows omitted)

Kobe Bryant, in his final season with the Lakers, was the highest paid at a salary of $$25$ million. Notice that the MVP Stephen Curry doesn’t appear among the top 10. He is quite a bit further down the list, as we will see later.

Named Arguments

The descending=True portion of this call expression is called a named argument. When a function or method is called, each argument has both a position and a name. Both are evident from the help text of a function or method.

help(nba.sort)
Help on method sort in module datascience.tables:

sort(column_or_label, descending=False, distinct=False) method of datascience.tables.Table instance
    Return a Table of rows sorted according to the values in a column.
    
    Args:
        ``column_or_label``: the column whose values are used for sorting.
    
        ``descending``: if True, sorting will be in descending, rather than
            ascending order.
    
        ``distinct``: if True, repeated values in ``column_or_label`` will
            be omitted.
    
    Returns:
        An instance of ``Table`` containing rows sorted based on the values
        in ``column_or_label``.
    
    >>> marbles = Table().with_columns(
    ...    "Color", make_array("Red", "Green", "Blue", "Red", "Green", "Green"),
    ...    "Shape", make_array("Round", "Rectangular", "Rectangular", "Round", "Rectangular", "Round"),
    ...    "Amount", make_array(4, 6, 12, 7, 9, 2),
    ...    "Price", make_array(1.30, 1.30, 2.00, 1.75, 1.40, 1.00))
    >>> marbles
    Color | Shape       | Amount | Price
    Red   | Round       | 4      | 1.3
    Green | Rectangular | 6      | 1.3
    Blue  | Rectangular | 12     | 2
    Red   | Round       | 7      | 1.75
    Green | Rectangular | 9      | 1.4
    Green | Round       | 2      | 1
    >>> marbles.sort("Amount")
    Color | Shape       | Amount | Price
    Green | Round       | 2      | 1
    Red   | Round       | 4      | 1.3
    Green | Rectangular | 6      | 1.3
    Red   | Round       | 7      | 1.75
    Green | Rectangular | 9      | 1.4
    Blue  | Rectangular | 12     | 2
    >>> marbles.sort("Amount", descending = True)
    Color | Shape       | Amount | Price
    Blue  | Rectangular | 12     | 2
    Green | Rectangular | 9      | 1.4
    Red   | Round       | 7      | 1.75
    Green | Rectangular | 6      | 1.3
    Red   | Round       | 4      | 1.3
    Green | Round       | 2      | 1
    >>> marbles.sort(3) # the Price column
    Color | Shape       | Amount | Price
    Green | Round       | 2      | 1
    Red   | Round       | 4      | 1.3
    Green | Rectangular | 6      | 1.3
    Green | Rectangular | 9      | 1.4
    Red   | Round       | 7      | 1.75
    Blue  | Rectangular | 12     | 2
    >>> marbles.sort(3, distinct = True)
    Color | Shape       | Amount | Price
    Green | Round       | 2      | 1
    Red   | Round       | 4      | 1.3
    Green | Rectangular | 9      | 1.4
    Red   | Round       | 7      | 1.75
    Blue  | Rectangular | 12     | 2


At the very top of this help text, the signature of the sort method appears:

sort(column_or_label, descending=False, distinct=False)

This describes the positions, names, and default values of the three arguments to sort. When calling this method, you can use either positional arguments or named arguments, so the following three calls do exactly the same thing.

sort('SALARY', True)
sort('SALARY', descending=True)
sort(column_or_label='SALARY', descending=True)

When an argument is simply True or False, it’s a useful convention to include the argument name so that it’s more obvious what the argument value means.