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The pandas.DataFrame.aggregate() function DataFrameaggregates columns or rows of a . The most commonly used aggregation functions are min, , maxand sum. The result of these aggregation functions is a reduced DataFramesize of .

pandas.DataFrame.aggregate()grammar

DataFrame.aggregate(func, axis, *args, **kwargs)

parameter

Return Value

This function returns a scalar, Seriesor DataFrame.

• If Series.aggressive()a function is called with , it returns a scalar.
• If DataFrame.agg()a function is called with , it returns a Series.
• If multiple functions are called DataFrame.agg(), it returns one DataFrame.

Example Code: PandasDataFrame.aggregate()

DataFrame.agg()is DataFrame.aggregate()an alias for . It is better to use the alias for brevity. So we will use it in the example code DataFrame.agg().

import pandas as pd

dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
                    'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
                    'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print(dataframe)

Below is DataFramean example of .

   Attendance    Name Obtained Marks
0          60  Olivia            90
1         100    John            75
2          80   Laura            82
3          78     Ben            64
4          95   Kevin            45

Let's first examine DataFrame.agg()the function with just one aggregate function.

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 78, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 90, 1: 75, 2: 82, 3: 64, 4: 45},
    }
)

dataframe1 = dataframe.agg("sum")
print(dataframe1)

Output:

Attendance                            413
Name              OliviaJohnLauraBenKevin
Obtained Marks                        356
dtype: object

Aggregate functions sumare applied to individual columns.

For integer columns, it generates the sum; for string columns, it concatenates the strings. dtype: objectThe function returns Series.

Example code: DataFrame.aggregate()Relationship with multiple functions

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 78, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 90, 1: 75, 2: 82, 3: 64, 4: 45},
    }
)

dataframe1 = dataframe.agg(["sum", "min"])
print(dataframe1)

Output:

     Attendance                     Name  Obtained Marks
sum         413  OliviaJohnLauraBenKevin             356
min          60                      Ben              45

Aggregate functions sumand minare applied to individual columns.

For columns of integer type, minthe function generates the minimum value, and for columns of string type, it displays the string with the minimum length.

Example code: DataFrame.aggregate()Aggregation with specified columns

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 78, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 90, 1: 75, 2: 82, 3: 64, 4: 45},
    }
)

dataframe1 = dataframe.agg({"Obtained Marks": "sum"})
print(dataframe1)

Output:

Obtained Marks    356
dtype: int64

Returns the sum of a single column. dtype: int64Indicates that the function returns one Series.

We can also apply multiple functions on a column.

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 78, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 90, 1: 75, 2: 82, 3: 64, 4: 45},
    }
)
dataframe1 = dataframe.agg({"Obtained Marks": ["sum", "max"]})
print(dataframe1)

Output:

     Obtained Marks
sum             356
max              90