Pandas Groupby - Sort within groups

Pandas Groupby is used in situations where we want to split data and set into groups so that we can do various operations on those groups like - Aggregation of data, Transformation through some group computations or Filtration according to specific conditions applied on the groups. In similar ways, we can perform sorting within these groups. Example 1: Let's take an example of a dataframe: Python3 1==

df = pd.DataFrame({'X': ['B', 'B', 'A', 'A'],
                   'Y': [1, 2, 3, 4]})

# using groupby function
df.groupby('X').sum()

Output: Let's pass the sort parameter as False. Python3 1==

# using groupby function 
# with sort
df.groupby('X', sort = False).sum()

Output: Here, we see a dataframe with sorted values within the groups. Example 2: Now, let's take an example of a dataframe with ages of different people. Using sort along with groupby function will arrange the transformed dataframe on the basis of keys passes, for potential speedups. Python3 1==

data = {'Name':['Elle', 'Chloe', 'Noah', 'Marco',  
                 'Lee', 'Elle', 'Rachel', 'Noah'],  
        'Age':[17, 19, 18, 17,  
               22, 18, 21, 20]}   

df = pd.DataFrame(data) 
df

Output: Let's group the above dataframe according to the name Python3 1==

# using groupby without sort
df.groupby(['Name']).sum()

Output: Passing the sort parameter as False Python3 1==

# using groupby function 
# with sort
df.groupby(['Name'], sort = False).sum()

Output: Example 3: Let's take another example of a dataframe that consists top speeds of various cars and bikes. We'll try to get the top speeds sorted within the groups of vehicle type. Python3 1==

import pandas as pd


df = pd.DataFrame([('Bike', 'Kawasaki', 186),
                   ('Bike', 'Ducati Panigale', 202),
                   ('Car', 'Bugatti Chiron', 304), 
                   ('Car', 'Jaguar XJ220', 210),
                   ('Bike', 'Lightning LS-218', 218), 
                   ('Car', 'Hennessey Venom GT', 270),
                   ('Bike', 'BMW S1000RR', 188)], 
                  columns =('Type', 'Name', 'top_speed(mph)'))

df

Output: After Using the groupby function Python3 1==

# Using groupby function
grouped = df.groupby(['Type'])['top_speed(mph)'].nlargest()

# using nlargest() function will get the 
# largest values of top_speed(mph) within
# groups created
print(grouped)

Output: