Understanding Pandas DataFrames and Duplicate Removal

Pandas is a powerful library in Python for data manipulation and analysis. Its Dataframe object provides an efficient way to handle structured data, including tabular data like spreadsheets or SQL tables. One common operation when working with dataframes is removing duplicates, which can be done using the drop_duplicates method.

However, the behavior of this method may not always meet expectations, especially for those new to pandas. In this article, we’ll delve into the world of pandas and explore why pandas.drop_duplicates might not behave as expected when used with specific columns or settings.

Setting Up the Example

To illustrate these concepts, let’s create a simple dataframe using Python:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'src': ['A', 'B', 'C'],
    'trg': ['A', 'C', 'B'],
    'wgt': [1, 3, 7]
})

print(df)

This code will output:

   src trg  wgt
0   A   A    1
1   B   C    3
2   C   B    7

The drop_duplicates Method

The drop_duplicates method is a convenient way to remove duplicate rows from a DataFrame. It takes two main parameters: the columns to consider for duplicates and the behavior when there are ties.

Parameters of drop_duplicates

• subset: A list or array-like object containing one or more column labels to include in the comparison.
• keep: One of ‘first’, ’last’ (default), or ‘False’. If set to ‘first’, drops all duplicate rows except for the first occurrence. If set to ’last’, keeps the last occurrence. If set to False, removes all duplicate rows.

The Issue

When using df = df.drop_duplicates(subset=['src','trg'],keep='first',inplace=False), we expect it to remove any duplicates based on the columns src and trg. However, when executed, no changes are made to the original dataframe. This behavior seems counterintuitive at first glance.

Why Doesn’t It Work?

The reason for this behavior lies in how pandas handles duplicate rows. When you call df = df.drop_duplicates(subset=['src','trg'],keep='first',inplace=False), it creates a new dataframe without duplicates and assigns it to the original variable df. If we pass inplace=True instead, it modifies the existing DataFrame in-place.

However, even when using inplace=True, it still doesn’t remove all duplicate rows based on the specified columns. This is because of how pandas handles its internal data structure.

A More Advanced Explanation

When you create a new dataframe with duplicates removed (df = df.drop_duplicates(subset=['src','trg'],keep='first',inplace=False)), pandas creates a new dataframe object, which is not linked to the original one. This means that only a reference to the original DataFrame is being changed, not the actual data.

In order to change the original DataFrame in-place when using inplace=True, we would need to use a more advanced method like assigning to the ‘original’ variable directly:

import pandas as pd

df = pd.DataFrame({
    'src': ['A', 'B', 'C'],
    'trg': ['A', 'C', 'B'],
    'wgt': [1, 3, 7]
})

# Original DataFrame
orig_df = df.copy() # We need to make a copy of the original

# Removing duplicates in-place using inplace=True
df = df.drop_duplicates(subset=['src','trg'],keep='first',inplace=True)

print("Original DataFrame (Before):")
print(orig_df)
print("\nDataFrame after removing duplicates:")
print(df)

This way, we ensure that both the original and modified dataframes are updated correctly.

Alternative Solutions

In cases where you can’t use inplace=True, another solution is to drop duplicates using a different method like this:

import pandas as pd

df = pd.DataFrame({
    'src': ['A', 'B', 'C'],
    'trg': ['A', 'C', 'B'],
    'wgt': [1, 3, 7]
})

# Removing duplicates without modifying the original dataframe
df = df[df['src'] != df['trg']]
print("DataFrame after removing duplicates:")
print(df)

Or even simpler:

import pandas as pd

df = pd.DataFrame({
    'src': ['A', 'B', 'C'],
    'trg': ['A', 'C', 'B'],
    'wgt': [1, 3, 7]
})

# Removing duplicates using a boolean mask
df = df[df['src'] != df['trg']]
print("DataFrame after removing duplicates:")
print(df)

Conclusion

While pandas drop_duplicates might seem like a straightforward method for removing duplicate rows from dataframes, its behavior and usage can be more complex than expected. Understanding how pandas handles its internal data structures and how to use alternative methods or modify the original dataframe correctly are key to successfully using this powerful library.

By exploring these different approaches, you’ll gain more control over your data manipulation tasks and become proficient in working with pandas DataFrames effectively.

Last modified on 2023-11-01