Pandas Aggregate Function

Aggregate function in Pandas performs summary computations on data, often on grouped data. But it can also be used on Series objects.

This can be really useful for tasks such as calculating mean, sum, count, and other statistics for different groups within our data.

Syntax

Here's the basic syntax of the aggregate function,

df.aggregate(func, axis=0, *args, **kwargs)

Here,

1. func - an aggregate function like sum, mean, etc.
2. axis - specifies whether to apply the aggregation operation along rows or columns.
3. *args and **kwargs - additional arguments that can be passed to the aggregation functions.

Apply Single Aggregate Function

Here's how we can apply a single aggregate function in Pandas.

import pandas as pd

data = {
    'Category': ['A', 'A', 'B', 'B', 'A', 'B'],
    'Value': [10, 15, 20, 25, 30, 35]
}

df = pd.DataFrame(data)

# calculate total sum of the Value column
total_sum = df['Value'].aggregate('sum')
print("Total Sum:", total_sum)

# calculate the mean of the Value column
average_value = df['Value'].aggregate('mean')
print("Average Value:", average_value)

# calculate the maximum value in the Value column
max_value = df['Value'].aggregate('max')
print("Maximum Value:", max_value)

Output

Total Sum: 135
Average Value: 22.5
Maximum Value: 35

Here,

• df['Value'].aggregate('sum') - calculates the total sum of the Value column in the data DataFrame
• df['Value'].aggregate('mean') - calculates the mean (average) of the Value column in the data DataFrame
• df['Value'].aggregate('max') - computes the maximum value in the Value column.

Apply Multiple Aggregate Functions in Pandas

We can also apply multiple aggregation functions to one or more columns using the aggregate() function in Pandas. For example,

import pandas as pd

data = {
    'Category': ['A', 'A', 'B', 'B', 'A', 'B'],
    'Value': [10, 15, 20, 25, 30, 35]
}

df = pd.DataFrame(data)

# applying multiple aggregation functions to a single column
result = df.groupby('Category')['Value'].agg(['sum', 'mean', 'max', 'min'])
print(result)

Output

          sum       mean  max  min
Category                           
A          55  18.333333   30   10
B          80  26.666667   35   20

In the above example, we're using the aggregate() function to apply multiple aggregation functions (sum, mean, max, and min) to the Value column after grouping by the Category column.

The resulting DataFrame shows the calculated values for each category.

Apply Different Aggregation Functions

In Pandas, we can apply different aggregation functions to different columns using a dictionary with the aggregate() function. For example,

import pandas as pd

data = {
    'Category': ['A', 'A', 'B', 'B', 'A', 'B'],
    'Value1': [10, 15, 20, 25, 30, 35],
    'Value2': [5, 8, 12, 15, 18, 21]
}

df = pd.DataFrame(data)

agg_funcs = {

 # applying 'sum' to Value1 column
    'Value1': 'sum',   
       
# applying 'mean' and 'max' to Value2 column 
    'Value2': ['mean', 'max']   
}

result = df.groupby('Category').aggregate(agg_funcs)
print(result)

Output

         Value1 Value2    
            sum   mean max
Category                  
A            55  17.00  18
B            80  16.00  21

Here, we're using the aggregate() function to apply different aggregation functions to different columns after grouping by the Category column.

The resulting DataFrame shows the calculated values for each category and each specified aggregation function.