How to Efficiently Remove Duplicate Rows Based on Indices in Pandas?

Removing Pandas Rows with Duplicate Indices

In data analysis scenarios, duplicate indices can arise, leading to the need for efficient removal of such rows. This article explores solutions to this problem using the widely used Pandas library.

Pandas' Approach to Duplicate Removal

Pandas offers several methods for removing duplicate rows based on index values:

• reset_index().drop_duplicates(subset='index').set_index('index'): This approach involves resetting the DataFrame index, identifying duplicates using drop_duplicates(), and setting the original index back as the index column.
• groupby().first(): A more concise method involves grouping the DataFrame by its index and selecting the first occurrence using the first() function.
• [~df3.index.duplicated(keep='first')]: The duplicated method directly operates on the Pandas Index, enabling the removal of duplicates while preserving the first instance. You can use keep='last' to retain the last instance of duplicates.

Performance Comparison

The time complexity of each method varies based on the size and complexity of the DataFrame. Benchmarking these methods using a sample DataFrame:

• drop_duplicates(subset='index'): Least performant due to its underlying sort operation.
• groupby().first(): Slightly less performant than duplicated().
• [~df3.index.duplicated(keep='first')]: Most performant and readable.

Sample Demonstration

To illustrate the use of the duplicated method, consider the sample DataFrame df3 with duplicate index values:

import pandas as pd
import datetime

# Example DataFrame with duplicate indices
startdate = datetime.datetime(2001, 1, 1, 0, 0)
enddate = datetime.datetime(2001, 1, 1, 5, 0)
index = pd.date_range(start=startdate, end=enddate, freq='H')
data1 = {'A' : range(6), 'B' : range(6)}
data2 = {'A' : [20, -30, 40], 'B' : [-50, 60, -70]}
df1 = pd.DataFrame(data=data1, index=index)
df2 = pd.DataFrame(data=data2, index=index[:3])
df3 = df2.append(df1)

print(df3)

# Remove duplicate rows with duplicate indices
df3 = df3[~df3.index.duplicated(keep='first')]

print(df3)

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