Executing Python – Programs, Algorithms, and Functions | Part 1
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In this chapter, you were introduced to a few of the fundamental tools in Python for formalizing your knowledge. You learned how to write scripts and modules instead
of using the interactive shell. You were introduced to functions and several different popular ways of writing functions. Additionally, common algorithms that are discussed in basic computer science were presented, including bubble sort and binary search. You also learned about the importance of the DRY principle. You learned how functions help us to adhere to this principle, and how helper functions allow us to express the logical components of our code succinctly.
In the next chapter, you will turn to the practical tools that you will need in your Python toolkit, such as how to read and write files, and how to plot visual graphs of data.
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sort and binary search; write functions utilizing iterative, recursive, and dynamic programming algorithms; modularize code to make it structured and readable and use helper functions and lambda functions.
Add a function that prints the result of the computation in Exercise 34, Writing and Executing Our First Script.
The help function can be used to obtain a summary of any available information regarding a module, function, or class in Python.
104 | Executing Python – Programs, Algorithms, and Functions For example, if you had a list of positive numbers and wanted to find the maximum number of positive numbers in that list, an algorithm expressed in pseudocode could be as follows: 112 | Executing Python – Programs, Algorithms, and Functions 116 | Executing Python – Programs, Algorithms, and Functions For example, using a Python shell, you can define the following function that takes two inputs and returns the sum: Exercise 43: Defining and Calling the Function in Python Script In this exercise, you will define and call a function in a multiply.py Python script and execute it from Command Prompt: In this exercise, you worked on defining and calling a function within a Python script.
You created the multiply.py file in Exercise 43, Defining and Calling the Function in Python Script, and imported and used this function in this exercise.
When a function calls itself, it is known as a Recursive Function.
You can imagine that a function that calls itself might end up in an infinite loop; it is true that you can write a recursive function that will keep running indefinitely: In this exercise, you will calculate the time taken to execute the function in the previous exercise: In this exercise, you will take a function that computes the total USD for a transaction and use a helper function to apply the DRY principle.
However, it is defined within the function as 5, and when accessed within the function, you can see it takes the value of 5.
However, this variable is only local to the function, and outside the function the global score variable is still equal to 0.
This function can equivalently be written using the lambda function syntax, as follows: Lambda functions can be particularly useful in passing custom functions to a map, as you can quickly define a function on the fly without assigning it to a variable name.
map is a special function in Python that applies a given function to all items in a list.
In this exercise, you use map with a lambda function to apply the logistic function to a list of values.
The filter is another special function that, like map, takes a function and iterables (for example, a list) as inputs.
This function takes an iterable, such as a list, and sorts them according to a function.
You learned how functions help us to adhere to this principle, and how helper functions allow us to express the logical components of our code succinctly.
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