Pandas vs PySpark..!

Key differences, when to use either, free resources for learning

There have been a lot of details on what Pandas or PySpark is, but in this article, I will briefly describe the main differences between these two packages. Along with when to use either one of these. Also, there are links to some free resources and datasets at the end of the article.

Image Source

1. Definitions

1.1 What is PySpark?

PySpark is the Python library for Spark programming. It allows you to use the powerful and efficient data processing capabilities of Apache Spark from within the Python programming language. PySpark provides a high-level API for distributed data processing that can be used to perform common data analysis tasks, such as filtering, aggregation, and transformation of large datasets.

1.2 What is Pandas?

Pandas is a Python library for data manipulation and analysis. It provides powerful data structures, such as the DataFrame and Series, that are designed to make it easy to work with structured data in Python. With pandas, you can perform a wide range of data analysis tasks, such as filtering, aggregation, and transformation of data, as well as data cleaning and preparation.

Both definitions look more or less the same, but there is a difference in their execution and processing architecture. Let’s go over some major differences between these two.

2. Key Differences between PySpark and Pandas

1. PySpark is a library for working with large datasets in a distributed computing environment, while pandas is a library for working with smaller, tabular datasets on a single machine.
2. PySpark is built on top of the Apache Spark framework and uses the Resilient Distributed Datasets (RDD) data structure, while pandas uses the DataFrame data structure.
3. PySpark is designed to handle data processing tasks that are not feasible with pandas due to memory constraints, such as iterative algorithms and machine learning on large datasets.
4. PySpark allows for parallel processing of data, while pandas does not.
5. PySpark can read data from a variety of sources, including Hadoop Distributed File System (HDFS), Amazon S3, and local file systems, while pandas is limited to reading data from local file systems.
6. PySpark can be integrated with other big data tools like Hadoop and Hive, while pandas is not.
7. PySpark is written in Scala, and runs on the Java Virtual Machine (JVM), while pandas is written in Python.
8. PySpark has a steeper learning curve than pandas, due to the additional concepts and technologies involved (e.g. distributed computing, RDDs, Spark SQL, Spark Streaming, etc.).

How to decide which library to use — PySpark vs Pandas

The decision of whether to use PySpark or pandas depends on the size and complexity of the dataset and the specific task you want to perform.

1. Size of the dataset: PySpark is designed to handle large datasets that are not feasible to work with on a single machine using pandas. If you have a dataset that is too large to fit in memory, or if you need to perform iterative or distributed computations, PySpark is the better choice.
2. Complexity of the task: PySpark is a powerful tool for big data processing and allows you to perform a wide range of data processing tasks, such as machine learning, graph processing, and stream processing. If you need to perform any of these tasks, PySpark is the better choice.
3. Learning Curve: PySpark has a steeper learning curve than pandas, as it requires knowledge of distributed computing, RDDs, and Spark SQL. If you are new to big data processing and want to get started quickly, pandas may be the better choice.
4. Resources available: PySpark requires a cluster or distributed system to run, so you will need access to the appropriate infrastructure and resources. If you do not have access to these resources, then pandas is a good choice.

In summary, use PySpark for large datasets and complex tasks that are not feasible with pandas, and use pandas for small datasets and simple tasks that can be handled on a single machine.

List of Resources

Free resources for learning

PySpark:

• The PySpark documentation (https://spark.apache.org/docs/latest/api/python/index.html) is a great resource for learning PySpark, as it provides detailed information on the library’s API and includes examples of common use cases.
• The PySpark tutorials on the Databricks website (https://databricks.com/learn/learn-spark/pyspark-tutorials) are a good resource for learning PySpark, as they provide hands-on examples and explanations of how to use the library.
• “Learning PySpark” by Tomasz Drabas and Denny Lee is a free ebook that provides an introduction to PySpark, including examples and explanations of how to use the library for data processing tasks.

Pandas:

• The pandas documentation (https://pandas.pydata.org/docs/) is a great resource for learning pandas, as it provides detailed information on the library’s API and includes examples of common use cases.
• The pandas tutorials on the DataCamp website (https://www.datacamp.com/courses/pandas-foundations) are a good resource for learning pandas, as they provide hands-on examples and explanations of how to use the library.
• “Python for Data Analysis” by Wes McKinney is a free ebook that provides an introduction to pandas, including examples and explanations of how to use the library for data processing tasks.

Free datasets for practicing

PySpark:

• The UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/index.php) contains a wide variety of datasets that can be used to practice PySpark, including datasets for classification, regression, and clustering tasks.
• The Kaggle Datasets (https://www.kaggle.com/datasets) provides access to a large collection of datasets that can be used to practice PySpark, including datasets for machine learning, natural language processing, and computer vision tasks.
• The Amazon Web Services (AWS) Public Datasets (https://aws.amazon.com/public-datasets/) provides access to a variety of datasets that can be used to practice PySpark, including datasets for finance, astronomy, and genomics.

Pandas:

• The UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/index.php) contains a wide variety of datasets that can be used to practice pandas, including datasets for classification, regression, and clustering tasks.
• The Kaggle Datasets (https://www.kaggle.com/datasets) provides access to a large collection of datasets that can be used to practice pandas, including datasets for machine learning, natural language processing, and computer vision tasks.
• Data.gov (https://www.data.gov/) provides access to a wide variety of datasets that can be used to practice pandas, including datasets for finance, transportation, and education.

Note that some dataset providers may require you to signup and agree to terms of use before you can download the datasets.