Unlock the Power of Data Duplication: A Deep Dive into Pandas’ Copy Method

Understanding the Syntax

The copy method’s syntax is straightforward: copy(). However, it does take an optional argument: deep. This parameter determines whether to create a deep copy or a shallow copy of your data.

Deep Copies: Independent Duplicates

A deep copy creates a completely independent duplicate of your data. Changes made to the copied DataFrame or Series do not affect the original. This is particularly useful when you want to test different scenarios without risking alterations to your primary dataset.

Example 1: A Deep Copy in Action

import pandas as pd

# Create a sample DataFrame
original_df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Create a deep copy of the DataFrame
deep_copy_df = original_df.copy(deep=True)

# Modify the deep copy
deep_copy_df['A'] = [10, 20, 30]

print("Original DataFrame:")
print(original_df)
print("\nDeep Copy DataFrame:")
print(deep_copy_df)

As expected, the changes do not affect the original DataFrame.

Shallow Copies: Shared Data

On the other hand, a shallow copy shares the data with the original DataFrame or Series. Any changes made to the copied data will also reflect in the original. This can be useful when you want to create a duplicate that still references the same data source.

Example 2: A Shallow Copy in Action

import pandas as pd

# Create a sample DataFrame
original_df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Create a shallow copy of the DataFrame
shallow_copy_df = original_df.copy(deep=False)

# Modify the shallow copy
shallow_copy_df['A'] = [10, 20, 30]

print("Original DataFrame:")
print(original_df)
print("\nShallow Copy DataFrame:")
print(shallow_copy_df)

As expected, the changes are reflected in the original DataFrame as well.

By understanding the differences between deep and shallow copies, you can harness the full potential of Pandas’ copy method to work with your data more efficiently.