Exploring Deep and Shallow Copy in Pandas DataFrames

Pandas is a vital tool in the data scientist’s toolkit, a powerful Python library for data analysis and manipulation. One important aspect of data manipulation is understanding how to properly copy DataFrames to avoid unintentional changes to your original data. This blog post explores the copy() function in Pandas and its significance in creating both deep and shallow copies of DataFrames.

Introduction to DataFrame Copy in Pandas

In Pandas, the copy() function is used to create a copy of a DataFrame. The syntax of the function is as follows:

Example in pandas

DataFrame.copy(deep=True)

deep : This is a boolean parameter. If set to True (default), a deep copy of the DataFrame is made. If set to False , a shallow copy of the DataFrame is made.

What is a Deep Copy?

A deep copy creates a new object with a new memory address, copying all the elements from the original object. Modifications to the original object do not affect the copied object, and vice versa.

Example of Deep Copy

Example in pandas

import pandas as pd 

df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) 

deep_copied_df = df.copy(deep=True) 
deep_copied_df.iloc[0, 0] = 100 

print("Original DataFrame:\n", df) 
print("Deep Copied DataFrame:\n", deep_copied_df)

What is a Shallow Copy?

A shallow copy creates a new object, but does not create copies of nested objects found in the original. Instead, it stores references to the same objects. Modifying elements in a shallow copy may also modify elements in the original object.

Example of Shallow Copy

Example in pandas

shallow_copied_df = df.copy(deep=False) 
shallow_copied_df.iloc[0, 0] = 100 

print("Original DataFrame:\n", df) 
print("Shallow Copied DataFrame:\n", shallow_copied_df)

When to Use Deep and Shallow Copies?

Deep Copy:

Use a deep copy when you need to create a completely independent copy of the original DataFrame.
Ideal for scenarios where modifications to the copied DataFrame should not affect the original DataFrame.

Shallow Copy:

A shallow copy is quicker to create and uses less memory since it doesn’t create a copy of the objects in the DataFrame.
Useful when you want to create a quick copy of the DataFrame, but don’t intend to modify any of the objects inside it.

Conclusion

Understanding the difference between deep and shallow copies in Pandas is crucial for effective data manipulation. A deep copy creates a completely independent copy of the original DataFrame, while a shallow copy creates a new object with references to the same inner objects. Depending on your use case, choosing the right type of copy can prevent unintended changes to your data, ensuring the integrity and accuracy of your data analysis processes. With this knowledge, you can confidently manipulate your Pandas DataFrames, fully aware of how changes to one DataFrame might affect another.