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:
