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Python is one of the most popular languages for software development and data science in the world. Earlier this year, Stack Overflow ranked Python as the most ‘wanted’ language. The time to learn Python has never been better. And if you’re well-versed in the language, continuing to expand your skills is paramount. In this post, we look at some of the best tutorials on DZone for using Python for doing data science. 

Best of Python for Data Science on DZone

1. Python: Reading a JSON File by Mark Needham. While playing around with some code to spin up AWS instances using Fabric and Boto to define a bunch of default properties in a JSON file, and then load this into a script, this developer ran into some issues. So he cooked up a tutorial on it! 

2. Python CSV Files: Reading and Writing by Mike Driscoll. The mind behind Mouse vs. Python gives a tutorial on how to parse CSV data using the Python language. You’ll learn how to import the necessary libraries and use the right functions to both read and write CSV files. 

3. Pandas: Find Rows Where Column/Field Is Null by Mark Needham. A quick look at the code necessary to use the Pandas library for Python to run through rows and columns of data to find null values. This is a great code along for those getting started with Pandas and Python for data science. 

4. PySpark DataFrame Tutorial: Introduction to DataFrames by Kislay Keshari. A quick, high-level look at how PySpark works under the hood, followed by coding exercises that demonstrate how to run analyses on big data sets using the PySpark framwork. If you’re getting started with Python as a language for data science, this is a great way to learn to query, sort, filter, and group data. 

5. Upload Files With Python by David Liedle. In order to perform data analysis, you need to be able to upload data. In this tutorial, a sofware engineer walks us through how to use the Python language to upload files and data from an API. 

DZone Publications on Python and Big Data

1. DZone’s Guide to Big Data: Stream Processing, Statistics, and Scalability featuring articles by Jonas Bonér, Arjuna Chala, Wolf Ruzicka, Liz Bennett, Sunil Kappal, and Tom Smith. Big Data is the new competitive advantage and it is necessary for businesses. With Blockchain tech, Cloud, and IoT adding new dimensions to Big Data, we see the creation and growth of new Big Data Storage and Analytics applications to pull value from the data. The 2018 Guide to Big Data will explore the evolution of Big Data, provide case studies on Big Data reference architectures, and leave you with the knowledge to scale your Big Data architecture.

2. Core Python: Creating Beautiful Code With an Interpreted, Dynamically Typed Langauge by Ivan Mushketyk, Naomi Ceder, and Mike Driscoll. Python is an interpreted, dynamically typed language. Python uses indentation to create readable, even beautiful code. With Python’s vast array of built-in libraries, it can handle many jobs without the need for further libraries, allowing you to write useful code almost immediately. But Python’s extensive network of external libraries makes it easy to add functionality based on your needs.

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