Katzen zählen

Du arbeitest an einem Projekt zur Verarbeitung natürlicher Sprache, um herauszufinden, was großartige Autoren so großartig macht. Deine aktuelle Hypothese: Großartige Autoren schreiben sehr viel über Katzen. Um das zu belegen, möchtest du zählen, wie oft das Wort „cat“ in „Alice's Adventures in Wonderland“ von Lewis Carroll vorkommt. Du hast bereits eine Textdatei alice.txt mit dem vollständigen Inhalt dieses großartigen Buchs heruntergeladen.

Diese Übung ist Teil des Kurses

<Kurs>Funktionen in Python schreiben</Kurs>

Übungsanweisungen

Verwende den Context Manager open(), um alice.txt zu öffnen, und weise die Datei der Variablen file zu.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Open "alice.txt" and assign the file to "file"
____ ____('alice.txt') ____ file:
  text = file.read()

n = 0
for word in text.split():
  if word.lower() in ['cat', 'cats']:
    n += 1

print('Lewis Carroll uses the word "cat" {} times'.format(n))

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Diese Übung ist Teil des Kurses

<Kurs>Funktionen in Python schreiben</Kurs>

Mittlere SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

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: Context Manager verwenden Exercise 2: Katzen zählen

Aktuelle Übung

Exercise 3: Katzen schnell erkennen Exercise 4: Context Manager schreiben Exercise 5: Der Context Manager timer()Exercise 6: Ein schreibgeschützter open()-Kontextmanager Exercise 7: Fortgeschrittene Themen Exercise 8: Anwendungsfälle für Context Manager Exercise 9: NASDAQ-Analyse Exercise 10: Das Arbeitsverzeichnis ändern

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 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!