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Transform vs. aggregate in Pandas

Learn, difference between transform and aggregate while using Pandas groupby? By Pranit Sharma Last updated : October 05, 2023

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and data.

Transform vs. aggregate

Suppose we are given a dataframe with multiple columns and we apply groupby() method to some columns, if we want some values to broadcast across the whole group and return something with the same index, we always use the transform function and if for different columns are more than one thing, we want specific things to be returned on the same column, we use an aggregate() method.

The groupby() is a simple but very useful concept in pandas. By using groupby, we can create a grouping of certain values and perform some operations on those values.

The groupby() method split the object, apply some operations, and then combines them to create a group hence large amounts of data and computations can be performed on these groups.

Let us understand with the help of an example,

Python program to demonstrate the difference between transform and aggregate while using Pandas groupby

# Importing pandas package
import pandas as pd

# Importing numpy package
import numpy as np

# Creating dataframe
df = pd.DataFrame(dict(A = list('1122'), B=[1, 2, 3, 4], C=[5, 6, 7, 8]))

# Display original DataFrame
print("Original DataFrame:\n",df,"\n")

# Applying transform
tr = df.groupby('A').transform('mean')

# Display transform result
print("Transform Result:\n",tr,"\n")

# Using aggregate
ag = df.groupby('A').agg(['mean', 'std'])

# Display aggregate result
print("Aggregate Result:\n",ag)

Output

The output of the above program is:

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