How can you analyze data with functional programming?

1 What is a pure function?

A pure function is a function that always returns the same output for the same input, and does not modify any external state or cause any side effects. For example, a function that calculates the sum of two numbers is a pure function, as it only depends on the input parameters and returns a single value. A pure function is easy to test, debug, and reason about, as it does not depend on any hidden variables or conditions.

2 How to use map, filter, and reduce?

Map, filter, and reduce are three common higher-order functions that take another function as an argument and apply it to a collection of data, such as an array or a list. Map transforms each element of the collection according to the function, filter selects only the elements that satisfy a condition given by the function, and reduce combines all the elements into a single value using the function. For example, you can use map to convert an array of temperatures from Celsius to Fahrenheit, filter to remove any outliers from a dataset, and reduce to calculate the average of an array of numbers.

3 How to use recursion and memoization?

Recursion is a technique that involves calling a function from within itself until a base case is reached. It can be useful for solving problems that have a recursive structure, such as traversing a tree or a graph, or performing mathematical operations like factorial or Fibonacci. However, recursion can also be inefficient and cause stack overflow if the function calls itself too many times. Memoization is a technique that involves caching the results of previous function calls and returning them if the same input is encountered again. It can improve the performance and avoid repeated computation of recursive functions. For example, you can use memoization to store the Fibonacci numbers that have already been calculated and return them quickly if they are requested again.

4 How to use currying and partial application?

Currying and partial application are two techniques that involve creating new functions from existing ones by fixing some of the arguments. Currying is a process of transforming a function that takes multiple arguments into a sequence of functions that each take one argument. Partial application is a process of creating a new function that has some of the arguments of the original function already filled in. Both techniques can help you create more modular and reusable code that can be easily composed and customized. For example, you can use currying to create a function that adds a fixed number to any given number, and use partial application to create a function that greets a person with a specific salutation.

5 How to use functional composition and pipelining?

Functional composition and pipelining are two techniques that involve combining multiple functions into a single function that performs a complex operation. Functional composition is a process of applying the output of one function as the input of another function, creating a new function that represents the combination of both functions. Pipelining is a process of chaining multiple functions together in a sequence, creating a new function that passes the input through each function and returns the final output. Both techniques can help you write more concise and readable code that avoids intermediate variables and nested function calls. For example, you can use functional composition to create a function that converts a string to uppercase and reverses it, and use pipelining to create a function that takes an array of strings, filters out the empty ones, maps them to their lengths, and reduces them to the sum.

6 How to use functional programming libraries and frameworks?

Functional programming libraries and frameworks are collections of functions, data structures, and utilities that implement functional programming concepts and features in different languages and environments. They can help you write more functional and elegant code, as well as provide you with tools and abstractions for data analysis and manipulation. Some examples of functional programming libraries and frameworks are Lodash and Ramda for JavaScript, Pandas and PyFunctional for Python, Spark and Scala for big data, and Clojure and Elixir for concurrency and distributed systems.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?