Pandas: How to make a deep/shallow copy of a DataFrame

Introduction

Pandas is a powerful, easy-to-use data analysis and manipulation tool built on top of the Python programming language. One common operation when working with DataFrames in pandas is copying them. This tutorial will guide you through making deep and shallow copies of DataFrames, highlight the differences, and showcase examples from basic to advanced use cases.

Understanding DataFrame Copies

Before diving into how to make copies of DataFrame in pandas, it’s crucial to understand what deep and shallow copies are.

• Shallow Copy: creates a new object, but doesn’t create copies of nested objects. Instead, it just references the original objects.
• Deep Copy: creates a new object and recursively adds the copies of nested objects found in the original, ensuring no shared objects between the copy and the original.

Creating a Shallow Copy of a DataFrame

To create a shallow copy of a DataFrame, you can use the .copy(deep=False) method.

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': ['x', 'y', 'z']})
shallow_copy_df = df.copy(deep=False)

Modifying the shallow copy won’t affect the original DataFrame’s data, but changes to objects within the DataFrame will reflect in both the original and the copy.

Creating a Deep Copy of a DataFrame

A deep copy can be created by simply using the .copy() method without passing any argument or by setting deep=True.

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': ['x', 'y', 'z']})
deep_copy_df = df.copy()

With a deep copy, changes made to the copy of the DataFrame do not affect the original DataFrame, providing complete isolation between the two.

Copy With Customizations

Beyond the basic copy commands, pandas allows for more nuanced copying, enabling you to tailor the copy process to your specific needs. This section explores some advanced use cases.

Copying Specific Columns

Sometimes, you might want to create a copy of a DataFrame but only select specific columns. This can be efficiently achieved using column indexing along with the .copy() method.

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': ['x', 'y', 'z'], 'C': [True, False, True]})
copy_columns_df = df[['A', 'C']].copy()

This approach creates a deep copy of the selected columns of the original DataFrame.

Copying and Filtering Rows

To copy a DataFrame while simultaneously filtering rows based on specific conditions, you can combine conditions with the .copy() method.

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': ['x', 'y', 'z'], 'C': [True, False, True]})
filtered_copy_df = df[df['C']].copy()

This example creates a deep copy of the DataFrame, including only rows where the value in column ‘C’ is True.

Performance Considerations

While copying DataFrames, it’s essential to consider the impact on performance, especially with large datasets. Deep copies are more resource-intensive than shallow copies, as they require creating entirely new objects. Shallow copies are faster and consume less memory, making them more suitable for situations where the nested objects within the DataFrame do not need to be duplicated.

Conclusion

Copying DataFrames in pandas is a versatile operation, offering both deep and shallow copy functionalities to fit various data manipulation needs. Understanding when to use each type of copy is crucial for effective data management and ensuring that your data analysis workflows are efficient and error-free. Mastering these techniques will greatly enhance your data science skills and enable you to handle DataFrames more proficiently.