GroupBy and Handle Missing data in Pandas

Pandas Advanced Grouping and Aggregation: Exercise-14 with Solution

GroupBy and Handling Missing data:
Write a Pandas program to handle missing data in GroupBy operations to ensure accurate and reliable data analysis.

Sample Solution:

Python Code :

import pandas as pd

# Sample DataFrame with missing values
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Value': [10, None, 30, 40, None, 60]}

df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
      
# Fill missing values with 0 and then group by 'Category' and sum
print("\nFill missing values with 0 and then group by 'Category' and sum:")
grouped = df.fillna(0).groupby('Category').sum()

print(grouped)

Output:

Sample DataFrame:
  Category  Value
0        A   10.0
1        A    NaN
2        B   30.0
3        B   40.0
4        C    NaN
5        C   60.0

Fill missing values with 0 and then group by 'Category' and sum:
          Value
Category       
A          10.0
B          70.0
C          60.0

Explanation:

• Import pandas.
• Create a sample DataFrame with missing values.
• Fill missing values with 0.
• Group by 'Category' and sum the data.
• Print the result.

Python Code Editor:

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

Previous: GroupBy and create a new column with Aggregated data in Pandas.
Next: GroupBy and Apply multiple Aggregations with named functions in Pandas.