Pandas: How to Drop Duplicates Based on Complex Conditions in Python

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What will you learn?

In this tutorial, you will master the art of dropping duplicate rows from a Pandas DataFrame based on intricate conditions. You will learn how to apply custom functions or lambda functions to define and eliminate duplicates efficiently.

Introduction to the Problem and Solution

Encountering duplicate rows in a Pandas DataFrame is a common scenario while working with data. However, dealing with duplicates solely based on specific conditions can be challenging. This involves scenarios where duplicates need to be dropped only if multiple columns meet certain criteria or if a combination of columns matches another row.

To tackle this challenge, the pandas.DataFrame.drop_duplicates() method comes to the rescue. By leveraging custom functions or lambda functions, we can precisely define our complex conditions for identifying and removing duplicates.

Code

# Import necessary library
import pandas as pd

# Sample DataFrame
data = {'A': [1, 1, 2, 2],
        'B': [3, 4, 4, 6],
        'C': ['foo', 'bar', 'foo', 'bar']}
df = pd.DataFrame(data)

# Define a function that returns True for rows that should be considered duplicates
def custom_condition(row):
    return (row['A'] == 1) and (row['B'] == 4)

# Drop duplicates based on the custom condition function
filtered_df = df[~df.apply(custom_condition, axis=1)]

filtered_df

# Copyright PHD

Explanation

  • Importing Libraries: The pandas library is imported as pd.
  • Creating Sample Data: A sample DataFrame (df) is created with columns A, B, and C.
  • Custom Condition Function: The function custom_condition is defined to identify rows considered as duplicates.
  • Applying Custom Condition: The custom condition is applied using .apply() along the specified axis.
  • Dropping Duplicates: Rows satisfying the condition are removed from the DataFrame using boolean indexing.
  • Output: The resulting filtered DataFrame without duplicate rows based on complex conditions is displayed.
    How do I drop exact duplicates in Pandas?

    To drop exact duplicates in Pandas, utilize the drop_duplicates() method without specifying any arguments. This removes rows where all column values are identical.

    Can I drop duplicates based on a single column value?

    Yes, you can drop duplicates based on a single column value by passing just that column name within square brackets when calling drop_duplicates().

    Is it possible to keep only the last occurrence of duplicated rows?

    Certainly! Setting keep=’last’ inside drop_duplicates() retains only the last occurrence of duplicated rows while discarding others.

    How can I specify multiple columns for identifying duplicate values?

    You can pass a list of column names within square brackets when calling drop_duplicates(subset=[‘col1’, ‘col2’]) to identify duplicate values across multiple columns.

    Can I drop some but not all identical rows in Pandas?

    Absolutely. By setting specific criteria or conditions while using drop_duplicates(), you can choose which identical rows should be dropped from your DataFrame.

    What happens if my custom condition returns False instead of True?

    If your defined condition returns False for certain row(s), those row(s) will not be treated as duplications and hence won’t be dropped from your DataFrame during duplicate filtering process.

    Is there an alternative method besides apply() for dropping conditional duplicates?

    Another efficient approach involves utilizing vectorized operations combined with boolean indexing rather than applying functions row-wise; this might offer better performance depending on your dataset size and complexity.

    Conclusion

    Mastering how to drop duplicate rows in a Pandas DataFrame based on complex conditions empowers you to handle datasets efficiently. These techniques provide flexibility in data manipulation tasks. For further exploration and advanced functionalities related to working with DataFrames check out resources like PythonHelpDesk.com!

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