PySpark vs Pandas DataFrame

Two of the most popular tools in the Python ecosystem for data manipulation are PySpark and Pandas. Both PySpark and Pandas provide data structures for handling data and offer a wide range of functionalities for data manipulation. However, there are significant differences between the two tools, and choosing the right one for your task can be crucial.

PySpark is a distributed computing framework for processing large datasets. PySpark allows you to distribute your data across a cluster of machines and process it in parallel. PySpark offers a DataFrame data structure that is similar to Pandas DataFrame. However, PySpark DataFrames are designed for large datasets that cannot fit into memory. PySpark provides a wide range of functions for data manipulation, such as filtering, merging, and sorting. PySpark also provides a powerful machine-learning library that makes it easy to build predictive models from your data.

Pandas is a Python library for data manipulation and analysis. It offers a DataFrame data structure that is similar to a spreadsheet in Excel. Pandas DataFrames are designed for small to medium-sized datasets that can fit into memory. Pandas offer a wide range of functions for data manipulation, such as filtering, merging, and sorting. Pandas also provides a powerful visualization library that makes it easy to create graphs and charts from your data.

Differences

The main difference between PySpark and Pandas DataFrame is in their scalability. Pandas DataFrame is designed for small to medium-sized datasets that can fit into memory, while PySpark DataFrame is designed for large datasets that cannot fit into memory. PySpark allows you to distribute your data across a cluster of machines and process it in parallel, making it possible to process massive datasets that would be impossible with Pandas.

Here is a comparison between pandas and pySpark reading CSV files.

Another significant difference is in the programming model. Pandas DataFrame is designed for a single machine, and all data manipulation functions are applied in memory. PySpark DataFrame, on the other hand, is designed for distributed computing, and all data manipulation functions are applied in parallel across multiple machines.

Conclusion

Choosing the right tool for your data analysis and manipulation task is crucial. If you are working with small to medium-sized datasets that can fit into memory, Pandas DataFrame is an excellent choice. However, if you are working with large datasets that cannot fit into memory, PySpark DataFrame is the way to go. PySpark allows you to distribute your data across a cluster of machines and process it in parallel, making it possible to process massive datasets that would be impossible with Pandas.

As the data increase pandas take a significant amount of time whereas PySpakr distributes the data across clusters and process and takes less time.

Comparison of Pyspark and Pandas reading NYC_trip dataset(9.36GB , 101246797 rows) https://www.kaggle.com/datasets/microize/newyork-yellow-taxi-trip-data-2020-2019.

All the code used for writing this article can be found in my Colab notebook: https://colab.research.google.com/drive/1MZ8wa51ERTpDihduzubyR91y64Tv0czK?usp=sharing

Note: To process the 9.36GB File I connected the Colab with a custom GCE VM machine with n1-highmem-4 machine type.