Python | Pandas dataframe.aggregate()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Dataframe.aggregate() function is used to apply some aggregation across one or more columns. Aggregate using callable, string, dict, or list of string/callables. The most frequently used aggregations are:

sum: Return the sum of the values for the requested axis
min: Return the minimum of the values for the requested axis
max: Return the maximum of the values for the requested axis

Pandas dataframe.aggregate() Syntax in Python

Syntax: DataFrame.aggregate(func, axis=0, *args, **kwargs)

Parameters:

func : callable, string, dictionary, or list of string/callables. Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. For a DataFrame, can pass a dict, if the keys are DataFrame column names.
axis : (default 0) {0 or ‘index’, 1 or ‘columns’} 0 or ‘index’: apply function to each column. 1 or ‘columns’: apply function to each row.
Returns: Aggregated DataFrame

Python dataframe.aggregate() Example

Below, we are discussing how to add values of Excel in Python using Pandas , we will see step-by-step how to add values of Excel in Python using Pandas are follows:

For link to CSV file Used in Code, click

Step 1: Importing Pandas and Reading CSV File

Aggregate 'sum' and 'min' function across all the columns in data frame.

Python3


                    # importing pandas package
import pandas as pd

# making data frame from csv file
df = pd.read_csv(&quot;nba.csv&quot;)

# printing the first 10 rows of the dataframe
df[:10]

Output :

Step 2: Aggregating Data Across All Columns

Aggregation works with only numeric type columns.

Python3


                    # Applying aggregation across all the columns 
df.aggregate(['sum', 'min'])

Output:

For each column which are having numeric values, minimum and sum of all values has been found. For Pandas Dataframe df , we have four such columns Number, Age, Weight, Salary.

Step 3: Aggregating Specific Columns

In Pandas, we can also apply different aggregation functions across different columns. For that, we need to pass a dictionary with key containing the column names and values containing the list of aggregation functions for any specific column.

Python3


                    # importing pandas package 
import pandas as pd 
  
# making data frame from csv file 
df = pd.read_csv("nba.csv") 
  
# We are going to find aggregation for these columns 
df.aggregate({"Number":['sum', 'min'], 
              "Age":['max', 'min'], 
              "Weight":['min', 'sum'],  
              "Salary":['sum']})

Output:

Separate aggregation has been applied to each column, if any specific aggregation is not applied on a column then it has NaN value corresponding to it.

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