Pandas groupby is used for grouping the data according to the categories and applying a function to the categories. It also helps to aggregate data efficiently. The Pandas groupby() is a very powerful function with a lot of variations. It makes the task of splitting the Dataframe over some criteria really easy and efficient.
Pandas dataframe.groupby()
Pandas dataframe.groupby() function is used to split the data into groups based on some criteria. Pandas objects can be split on any of their axes. The abstract definition of grouping is to provide a mapping of labels to group names.
Syntax: DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)
Parameters :
- by : mapping, function, str, or iterable
- axis : int, default 0
- level : If the axis is a MultiIndex (hierarchical), group by a particular level or levels
- as_index : For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output
- sort : Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. groupby preserves the order of rows within each group.
- group_keys : When calling apply, add group keys to index to identify pieces
- squeeze : Reduce the dimensionality of the return type if possible, otherwise return a consistent type
Returns : GroupBy object
Dataset Used: For a link to the CSV file Used in Code, click here
Example 1: Use groupby() function to group the data based on the “Team”.
Python3
# importing pandas as pdimport pandas as pd# Creating the dataframedf = pd.read_csv("nba.csv")# Print the dataframeprint(df.head()) |
Output:
Name Team Number Position Age Height Weight College Salary
0 Avery Bradley Boston Celtics 0.0 PG 25.0 6-2 180.0 Texas 7730337.0
1 Jae Crowder Boston Celtics 99.0 SF 25.0 6-6 235.0 Marquette 6796117.0
2 John Holland Boston Celtics 30.0 SG 27.0 6-5 205.0 Boston University NaN
3 R.J. Hunter Boston Celtics 28.0 SG 22.0 6-5 185.0 Georgia State 1148640.0
4 Jonas Jerebko Boston Celtics 8.0 PF 29.0 6-10 231.0 NaN 5000000.0
Now apply the groupby() function.
Python3
# applying groupby() function to# group the data on team value.gk = df.groupby('Team')# Let's print the first entries# in all the groups formed.gk.first() |
Output :
Name Number Position Age Height Weight College Salary
Team
Atlanta Hawks Kent Bazemore 24.0 SF 26.0 6-5 201.0 Old Dominion 2000000.0
Boston Celtics Avery Bradley 0.0 PG 25.0 6-2 180.0 Texas 7730337.0
Brooklyn Nets Bojan Bogdanovic 44.0 SG 27.0 6-8 216.0 Oklahoma State 3425510.0
Charlotte Hornets Nicolas Batum 5.0 SG 27.0 6-8 200.0 Virginia Commonwealth 13125306.0
Chicago Bulls Cameron Bairstow 41.0 PF 25.0 6-9 250.0 New Mexico 845059.0
Cleveland Cavaliers Matthew Dellavedova 8.0 PG 25.0 6-4 198.0 Saint Mary's 1147276.0
Dallas Mavericks Justin Anderson 1.0 SG 22.0 6-6 228.0 Virginia 1449000.0
Denver Nuggets Darrell Arthur 0.0 PF 28.0 6-9 235.0 Kansas 2814000.0
Detroit Pistons Joel Anthony 50.0 C 33.0 6-9 245.0 UNLV 2500000.0
Golden State Warriors Leandro Barbosa 19.0 SG 33.0 6-3 194.0 North Carolina 2500000.0
Houston Rockets Trevor Ariza 1.0 SF 30.0 6-8 215.0 UCLA 8193030.0
Indiana Pacers Lavoy Allen 5.0 PF 27.0 6-9 255.0 Temple 4050000.0
Los Angeles Clippers Cole Aldrich 45.0 C 27.0 6-11 250.0 Kansas 1100602.0
Los Angeles Lakers Brandon Bass 2.0 PF 31.0 6-8 250.0 LSU 3000000.0
Memphis Grizzlies Jordan Adams 3.0 SG 21.0 6-5 209.0 UCLA 1404600.0
Miami Heat Chris Bosh 1.0 PF 32.0 6-11 235.0 Georgia Tech 22192730.0
Milwaukee Bucks Giannis Antetokounmpo 34.0 SF 21.0 6-11 222.0 Arizona 1953960.0
Let’s print the value contained in any one of the groups. For that use the name of the team. We use the function get_group() to find the entries contained in any of the groups.
Python3
# Finding the values contained in the "Boston Celtics" groupgk.get_group('Boston Celtics') |
Output :
Name Team Number Position Age Height Weight College Salary
0 Avery Bradley Boston Celtics 0.0 PG 25.0 6-2 180.0 Texas 7730337.0
1 Jae Crowder Boston Celtics 99.0 SF 25.0 6-6 235.0 Marquette 6796117.0
2 John Holland Boston Celtics 30.0 SG 27.0 6-5 205.0 Boston University NaN
3 R.J. Hunter Boston Celtics 28.0 SG 22.0 6-5 185.0 Georgia State 1148640.0
4 Jonas Jerebko Boston Celtics 8.0 PF 29.0 6-10 231.0 NaN 5000000.0
5 Amir Johnson Boston Celtics 90.0 PF 29.0 6-9 240.0 NaN 12000000.0
6 Jordan Mickey Boston Celtics 55.0 PF 21.0 6-8 235.0 LSU 1170960.0
7 Kelly Olynyk Boston Celtics 41.0 C 25.0 7-0 238.0 Gonzaga 2165160.0
8 Terry Rozier Boston Celtics 12.0 PG 22.0 6-2 190.0 Louisville 1824360.0
9 Marcus Smart Boston Celtics 36.0 PG 22.0 6-4 220.0 Oklahoma State 3431040.0
10 Jared Sullinger Boston Celtics 7.0 C 24.0 6-9 260.0 Ohio State 2569260.0
11 Isaiah Thomas Boston Celtics 4.0 PG 27.0 5-9 185.0 Washington 6912869.0
12 Evan Turner Boston Celtics 11.0 SG 27.0 6-7 220.0 Ohio State 3425510.0
13 James Young Boston Celtics 13.0 SG 20.0 6-6 215.0 Kentucky 1749840.0
14 Tyler Zeller Boston Celtics 44.0 C 26.0 7-0 253.0 North Carolina 2616975.0
Example 2: Use groupby() function to form groups based on more than one category (i.e. Use more than one column to perform the splitting).
Python3
# importing pandas as pdimport pandas as pd# Creating the dataframedf = pd.read_csv("nba.csv")# First grouping based on "Team"# Within each team we are grouping based on "Position"gkk = df.groupby(['Team', 'Position'])# Print the first value in each groupgkk.first() |
Output :
Name Number Age Height Weight College Salary
Team Position
Atlanta Hawks C Al Horford 15.0 30.0 6-10 245.0 Florida 12000000.0
PF Kris Humphries 43.0 31.0 6-9 235.0 Minnesota 1000000.0
PG Dennis Schroder 17.0 22.0 6-1 172.0 Wake Forest 1763400.0
SF Kent Bazemore 24.0 26.0 6-5 201.0 Old Dominion 2000000.0
SG Tim Hardaway Jr. 10.0 24.0 6-6 205.0 Michigan 1304520.0
... ... ... ... ... ... ... ...
Washington Wizards C Marcin Gortat 13.0 32.0 6-11 240.0 North Carolina State 11217391.0
PF Drew Gooden 90.0 34.0 6-10 250.0 Kansas 3300000.0
PG Ramon Sessions 7.0 30.0 6-3 190.0 Nevada 2170465.0
SF Jared Dudley 1.0 30.0 6-7 225.0 Boston College 4375000.0
SG Alan Anderson 6.0 33.0 6-6 220.0 Michigan State 4000000.0
