Grouping in Pandas
Grouping is used to group data using some criteria from our dataset. It is used as split-apply-combine strategy.
- Splitting the data into groups based on some criteria.
- Applying a function to each group independently.
- Combining the results into a data structure.
Examples:
We use groupby() function to group the data on “Maths” value. It returns the object as result.
Python
df.groupby(by=['Maths']) |
Output:
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000012581821388>
Applying groupby() function to group the data on “Maths” value. To view result of formed groups use first() function.
Python
a = df.groupby('Maths') a.first() |
Output:
First grouping based on “Maths” within each team we are grouping based on “Science”
Python
b = df.groupby(['Maths', 'Science']) b.first() |
Output:
Grouping and Aggregating with Pandas
In this article, we are going to see grouping and aggregating using pandas. Grouping and aggregating will help to achieve data analysis easily using various functions. These methods will help us to the group and summarize our data and make complex analysis comparatively easy.
Creating a sample dataset of marks of various subjects.
Python
# import module import pandas as pd # Creating our dataset df = pd.DataFrame([[9, 4, 8, 9], [8, 10, 7, 6], [7, 6, 8, 5]], columns=['Maths', 'English', 'Science', 'History']) # display dataset print(df) |
Output: