Key Concepts

Review core concepts you need to learn to master this subject

Functional Programming is Declarative Review

Declarative Programming Review Card

Functions Should Have no Side Effects

Using namedtuple

Lazy Iteration

Using Higher-order Functions Together

Working With Large Data Sets

Storing Data From CSV Files in a namedtuple

Functional Programming is Declarative Review

Functional programming is a programming paradigm that adheres to the declarative style of programming.

Functional Programming
Lesson 1 of 1
  1. 1
    Introduction
    Get ready to become a master of functional programming! This content branches off some content you may have run into from our Intermediate Python. We will do a quick review of: * tuples * lambda fu…
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  2. 2
    Immutable Data types
    Immutable data types are important to use in functional programming as they offer advantages, such as: * thread-safe data manipulation * preventing programmers from accidentally changing a value …
    Start
  3. 3
    Review of Lambda Functions
    Lambda functions are crucial in functional programming as they allow the production of neat and concise functions that require other functions as an argument. You should be familiar with lambda fun…
    Start
  4. 4
    Review of filter(), map(), and reduce()
    In this exercise, we will review the map(), filter(), and reduce(), higher-order functions provided by Python. These functions accept an iterable and a processing function as arguments and return a…
    Start
  5. 5
    Mapping a Filtered Collection
    In this exercise, we will see how we can combine the map() and filter() functions. Conceptually, if you’re working with a collection of items and find yourself saying, “I need to map only values …
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  6. 6
    Reducing a Filtered Collection
    In this exercise, we will explore how to use reduce() and filter() together. Let’s consider the following example. We have a tuple representing the menu items for a fictitious restaurant: The Codec…
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  7. 7
    Reducing a Mapped Collection
    In this exercise, we will be focussing on the benefits of using reduce() and map() together. Consider the example of having a dictionary representing a cost of an item sale called costs which map…
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  8. 8
    Combining all Three Higher-Order Functions
    Now that you’ve learned how to combine any two functions, let’s see how (and why) we can combine all three! A reason for doing this would be when you need to “filter” a collection before you “map” …
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  9. 9
    Importing Data From a CSV File
    Functional programming is widely applicable in the data science domain as higher-order functions can be used to process data files efficiently. One of the most common formats for a data file is a C…
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  10. 10
    Processing Data From a CSV File
    In this exercise, we will see how we can process the data that is stored in a CSV file. When working with a file that contains a large amount of data, generating every possible record using tuple(…
    Start
  11. 11
    Processing Data From a JSON File
    We may find ourselves in situations where we are working with JSON Data. The JSON format is the preferred way to structure data such that it may be transmitted over the internet. An API call usuall…
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  12. 12
    Review
    Congratulations on completing this module on functional programming in Python. We’ve covered many topics on how to write neat and efficient solutions using functional programming. Let’s briefly rec…
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