Python Tutorial : Data Types for Data Science in Python
Key Takeaways
Explains IF functions in spreadsheets
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Hello, I'm Jason Myers and welcome to the Python data types for data science course. I'm an author, open-source maintainer, and an avid Python user.
In most programming languages, the data type system set the stage for the capabilities of the language. Understanding how to use the fundamental data types of a language greatly empowers you as a data scientist. You've already encountered integers and strings, so let's start with container sequences.
A container sequence gets its name because it holds a sequence of elements of other data types. In the data science world, we'll use these containers to store our data for aggregation, order, sorting, and more. Python provides several container sequences such as lists, sets, and tuples to name a few. They can be mutable meaning that they can have elements added and removed from them. Immutability, not able to be altered, allows us to protect our reference data, and replace individual data points with sums, avgs, derivations, etc. We can iterate, also known as looping, over the data contained within these containers. Being able to iterate over these sequences allows us to group data, aggregate it, and process it over time. Let's start with learning about container types by looking at lists.
Often we need to hold an ordered collection of items, and lists allow us to do just that. Lists are mutable so we can add or remove data from them. Lists also allow us to access an individual element within them using an index. Let's see this in action. If I wanted to store a list of cookies I've eaten this week. I would begin by creating that list of cookies. When I eat another cookie, I'll want to add that to my list as well, which I can do with the append method. Then I can print the whole list of cookies
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