﻿ GroupBy and Handle Missing data in Pandas

# 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:

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