﻿ Aggregating data in Pandas: Multiple functions example

# Aggregating data in Pandas: Multiple functions example

## Python Pandas Numpy: Exercise-33 with Solution

Aggregate data in a DataFrame by multiple functions.

Sample Solution:

Python Code:

``````import pandas as pd

# Create a sample DataFrame
data = {'Department': ['HR', 'IT', 'Finance', 'IT', 'HR', 'Finance'],
'Salary': [50000, 60000, 45000, 70000, 55000, 60000],
'Experience': [2, 5, 1, 7, 3, 4]}

df = pd.DataFrame(data)

# Group by 'Department' and aggregate data with multiple functions
aggregated_df = df.groupby('Department').agg({
'Salary': ['mean', 'sum'],
'Experience': 'max'
}).reset_index()

# Display the aggregated DataFrame
print(aggregated_df)
```
```

Output:

```  Department   Salary         Experience
mean     sum        max
0    Finance  52500.0  105000          4
1         HR  52500.0  105000          3
2         IT  65000.0  130000          7
```

Explanation:

Here's a breakdown of the above code:

• We create a sample DataFrame (df) with columns 'Department', 'Salary', and 'Experience'.
• The df.groupby('Department') groups the DataFrame by the 'Department' column.
• The agg() function is used to apply multiple aggregation functions to different columns. We calculate the mean and sum of 'Salary' and the maximum value of 'Experience'.
• The result is stored in the "aggregated_df" DataFrame, and "reset_index()" is used to make the 'Department' column a regular column instead of an index.
• The aggregated DataFrame is then printed.

Flowchart:

Python Code Editor:

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