Detailed overview of the df.memory_usage() Pandas concept.
1Understanding df.memory_usage()
Welcome to this deep dive into df.memory_usage().
When building data pipelines, Pandas is a powerful tool.
### Concept Overview
Return the memory usage of each column in bytes.
Let's explore its syntax and behavior.
📌
Pandas relies heavily on NumPy under the hood.
editor.html
# Example of df.memory_usage()
print(df.memory_usage(deep=True))localhost:3000
2Example: Advanced Scenarios
Now let's examine a practical implementation. In the following example, we demonstrate how to apply df.memory_usage() effectively.
editor.html
# Advanced use case for df.memory_usage()
def advanced_example():
print(df.memory_usage(deep=True))localhost:3000
3Best Practices
To achieve true mastery over df.memory_usage(), follow community best practices.
- →Use vectorized operations over iterations (e.g.
iterrows()) for performance. - →Always verify memory usage when loading large files.
By following these guidelines, you make your code production-ready.
💡
Vectorized operations are preferred over apply().
editor.html
# Best practices applied
# Example of df.memory_usage()
print(df.memory_usage(deep=True))localhost:3000