Pandas Remove Duplicates

When dealing with duplicate rows in data analysis, the steps to identify and handle them depend on your specific needs. Here’s a general guide to address duplicate rows in a dataset using Python with pandas:

  1. Load your dataset
import pandas as pd

# Assuming your data is in a CSV file
df = pd.read_csv('your_data_file.csv')

  1. Check for duplicates:
# Find all duplicate rows
duplicates = df[df.duplicated()]
print(duplicates

  1. Check for duplicates:
# Find all duplicate rows
duplicates = df[df.duplicated()]
print(duplicates)

# Keep Last occurrence of each duplicate row
df_cleaned = df.drop_duplicates(keep='last')

# Keep the first occurrence of each duplicate row
data = data.drop_duplicates(keep='first')

# Resetting the index after removing duplicates:
df_cleaned.reset_index(drop=True, inplace=True)


# Export the cleaned data if needed:
df_cleaned.to_csv('cleaned_data.csv', index=False)

These examples offer greater flexibility for identifying and removing duplicate rows based on your unique needs. Effectively managing duplicates ensures your data stays accurate and dependable for analysis and modeling. Therefore, the next time you face unwanted duplicates in your dataset, you’ll be well-equipped to handle them efficiently.

Vijay Gokarn

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