Building a command line data app

You are building a command line tool that lets a user interactively explore a dataset. We've defined four functions: mean(), std(), minimum(), and maximum() that users can call to analyze their data. Help finish this section of the code so that your users can call any of these functions by typing the function name at the input prompt.

Note: The function get_user_input() in this exercise is a mock version of asking the user to enter a command. It randomly returns one of the four function names. In real life, you would ask for input and wait until the user entered a value.

Deze oefening maakt deel uit van de cursus

Writing Functions in Python

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Add the functions std(), minimum(), and maximum() to the function_map dictionary, like we did with mean().
The name of the function the user wants to call is stored in func_name. Use the dictionary of functions, function_map, to call the chosen function and pass data as an argument.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Add the missing function references to the function map
function_map = {
  'mean': mean,
  'std': ____,
  'minimum': ____,
  'maximum': ____
}

data = load_data()
print(data)

func_name = get_user_input()

# Call the chosen function and pass "data" as an argument
____[____](data)

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Deze oefening maakt deel uit van de cursus

Writing Functions in Python

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The goal of this course is to transform you into a Python expert, and so the first chapter starts off with best practices when writing functions. You'll cover docstrings and why they matter and how to know when you need to turn a chunk of code into a function. You will also learn the details of how Python passes arguments to functions, as well as some common gotchas that can cause debugging headaches when calling functions.

Exercise 1: Docstrings Exercise 2: Crafting a docstring Exercise 3: Retrieving docstrings Exercise 4: Docstrings to the rescue!Exercise 5: DRY and "Do One Thing"Exercise 6: Extract a function Exercise 7: Split up a function Exercise 8: Pass by assignment Exercise 9: Mutable or immutable?Exercise 10: Best practice for default arguments

If you've ever seen the "with" keyword in Python and wondered what its deal was, then this is the chapter for you! Context managers are a convenient way to provide connections in Python and guarantee that those connections get cleaned up when you are done using them. This chapter will show you how to use context managers, as well as how to write your own.

Exercise 1: Using context managers Exercise 2: The number of cats Exercise 3: The speed of cats Exercise 4: Writing context managers Exercise 5: The timer() context manager Exercise 6: A read-only open() context manager Exercise 7: Advanced topics Exercise 8: Context manager use cases Exercise 9: Scraping the NASDAQ Exercise 10: Changing the working directory

Decorators are an extremely powerful concept in Python. They allow you to modify the behavior of a function without changing the code of the function itself. This chapter will lay the foundational concepts needed to thoroughly understand decorators (functions as objects, scope, and closures), and give you a good introduction into how decorators are used and defined. This deep dive into Python internals will set you up to be a superstar Pythonista.

Exercise 1: Functions are objects Exercise 2: Building a command line data app

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Exercise 3: Reviewing your co-worker's code Exercise 4: Returning functions for a math game Exercise 5: Scope Exercise 6: Understanding scope Exercise 7: Modifying variables outside local scope Exercise 8: Closures Exercise 9: Checking for closure Exercise 10: Closures keep your values safe Exercise 11: Decorators Exercise 12: Using decorator syntax Exercise 13: Defining a decorator

Now that you understand how decorators work under the hood, this chapter gives you a bunch of real-world examples of when and how you would write decorators in your own code. You will also learn advanced decorator concepts like how to preserve the metadata of your decorated functions and how to write decorators that take arguments.

Exercise 1: Real-world examples Exercise 2: Print the return type Exercise 3: Counter Exercise 4: Decorators and metadata Exercise 5: Preserving docstrings when decorating functions Exercise 6: Measuring decorator overhead Exercise 7: Decorators that take arguments Exercise 8: Run_n_times()Exercise 9: HTML Generator Exercise 10: Timeout(): a real world example Exercise 11: Tag your functions Exercise 12: Check the return type Exercise 13: Great job!