GroupBy and Handle Missing data in Pandas

Last update on April 11 2025 12:50:52 (UTC/GMT +8 hours)

14. 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.

For more Practice: Solve these Related Problems:

• Write a Pandas program to group data with missing values and fill missing aggregated results with a default value.
• Write a Pandas program to group a DataFrame that contains NaNs and then apply aggregation functions while ignoring missing data.
• Write a Pandas program to group data and use transform to replace missing values in each group with the group’s median.
• Write a Pandas program to perform groupby operations on data with missing entries and then filter out groups where the aggregated value is NaN.

Python Code Editor:

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