Python Data Type Conversion Tutorial

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Description - a brief synopsis, abstract or summary of what the learning resource is about: 

In this Python tutorial, you'll tackle implicit and explicit data type conversion of primitive and non-primitive data structures with the help of code examples!

Every value in Python has a data type. Data types are a classification of data that tells the compiler or the interpreter how you want to use the data. The type defines the operations that can be done on the data and the structure in which you want the data to be stored. In data science, you will often need to change the type of data, so that it becomes easier to use and work with.
This tutorial will tackle some of the important and most frequently used data structures, and you will learn to change their types to suit your needs. More specifically, you will learn:
-Implicit and Explicit Data Type Conversion
-Primitive versus Non-primitive Data Structures
-Integer and Float Conversions
-Data Type Conversion with Strings
-Conversion to Tuples and Lists
-Binary, Octal, and Hexadecimal Integers in Python

Python has many data types. You must have already seen and worked with some of them. You have integers and float to deal with numerical values, boolean (bool) to deal with true/false values and strings to work with alphanumeric characters. You can make use of lists, tuples, dictionary, and sets that are data structures where you can store a collection of values.

Authoring Person(s) Name: 
Sejal Jaiswal
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
More info about
Keywords - short phrases describing what the learning resource is about: 
Data conversion
Data skills education
Data usage
Published / Broadcast: 
Thursday, November 7, 2019
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Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.
Target Audience - intended audience for which the learning resource was created: 
Data manager
Data professional
Software engineer
Technology expert group
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
Up to 1 hour