Pandas Groupby and Computing Mean

Pandas is an open-source library that is built on top of NumPy library. It is a Python package that offers various data structures and operations for manipulating numerical data and time series. It is mainly popular for importing and analyzing data much easier. Pandas is fast and it has high-performance & productivity for users. 

Groupby is a pretty simple concept. We can create a grouping of categories and apply a function to the categories. It’s a simple concept but it’s an extremely valuable technique that’s widely used in data science. It is helpful in the sense that we can :

• Compute summary statistics for every group
• Perform group-specific transformations
• Do the filtration of data

The groupby() involves a combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Example 1:

Python3

# import required module
import pandas as pd

# create dataframe
df = pd.DataFrame({'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],

                   'Max Speed': [380., 370., 24., 26.]})

# use groupby() to compute mean
df.groupby(['Animal']).mean()

Output

Example 2:

Python3

# import required module
import pandas as pd

# assign list
l = [[100, 200, 300], [10, None, 40], [20, 10, 30], [100, 200, 200]]

# create dataframe
df = pd.DataFrame(l, columns=["a", "b", "c", ])

# use groupby() to generate mean
df.groupby(by=["b"]).mean()

Output:

Example 3:

Python3

# import required module
import pandas as pd

# assign data
ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',  'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],

            'Rank': [1, 2, 2, 3, 3, 4, 1, 1, 2, 4, 1, 2],

            'Year': [2014, 2015, 2014, 2015, 2014, 2015, 2016, 2017, 2016, 2014, 2015, 2017],

            'Points': [876, 789, 863, 673, 741, 812, 756, 788, 694, 701, 804, 690]}

# create dataframe
df = pd.DataFrame(ipl_data)

# use groupby() to generate mean
df.groupby(['Team']).mean()

Output: