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Pandas groupby.apply() method duplicates first group

Given a pandas dataframe, we have to avoid duplicates after using groupby.apply(). Submitted by Pranit Sharma, on October 21, 2022

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.

Problem statement

Suppose, we are given a DataFrame with some columns and we need to apply groupby for two columns, now the output shows the first row twice and we need to avoid this duplicate result.

Avoiding duplicates after using groupby.apply()

However this problem has been solved with the new updates, versions older than v0.25 usually hold this problem of resulting in duplicate rows but the new versions have all the issues fixed and hence we can directly use apply() method to groupby() and print the groupby object.

Let us understand with the help of an example,

Python program to avoid duplicates after using groupby.apply()

# Importing pandas package
import pandas as pd

# Creating a dictionary
d = {
    'A': ['x', 'y'],
    'B': [1, 2]
}

# Creating DataFrame
df = pd.DataFrame(d)

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

# defining a function
def func(group):
     print("Group:\n",group.name,"\n")
     return group

# applying groupby
res = df.groupby('A').apply(func)

# Display result
print("Result:\n",res)

Output

The output of the above program is:

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