﻿ Group by Multiple columns in Pandas

# Group by Multiple columns in Pandas

## Pandas Advanced Grouping and Aggregation: Exercise-1 with Solution

Grouping by Multiple columns:
Write a Pandas program to group data by multiple columns to perform complex data analysis and aggregations.

Sample Solution:

Python Code :

``````import pandas as pd
# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
'Type': ['X', 'Y', 'X', 'Y', 'X', 'Y'],
'Value': [1, 2, 3, 4, 5, 6]}

df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
# Group by 'Category' and 'Type'
print("\nGroup by 'Category' and 'Type':")
grouped = df.groupby(['Category', 'Type']).sum()
print(grouped)
``````

Output:

```Sample DataFrame:
Category Type  Value
0        A    X      1
1        A    Y      2
2        B    X      3
3        B    Y      4
4        C    X      5
5        C    Y      6

Group by 'Category' and 'Type':
Value
Category Type
A      X         1
Y         2
B      X         3
Y         4
C      X         5
Y         6
```

Explanation:

• Import pandas.
• Create a sample DataFrame.
• Group by 'Category' and 'Type' columns.
• Sum the grouped data.
• Print the result.

Python Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

What is the difficulty level of this exercise?

Test your Programming skills with w3resource's quiz.

﻿

Follow us on Facebook and Twitter for latest update.