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Lernen Challenge 1: List Comprehension | Python
Data Science Interview Challenge
course content

Kursinhalt

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
2. NumPy
3. Pandas
4. Matplotlib
5. Seaborn
6. Statistics
7. Scikit-learn

book
Challenge 1: List Comprehension

List comprehensions in Python provide an elegant and concise way to create lists. Their advantages are:

  • Readability and conciseness: List comprehensions allow you to reduce the amount of code, making it more readable and concise. Instead of using multiple lines of code with loops and conditional statements, you can get the same result in one line.
  • Performance: In many cases, list comprehensions are faster than traditional loops, especially when processing large amounts of data, which is key in Data Science.
  • Built-in filtering capabilities: These allow you to easily apply conditional expressions to filter data, which is especially useful when preprocessing and cleaning datasets.

Thus, list comprehensions are a powerful tool in the hands of a data scientist, allowing you to quickly and efficiently process and transform data.

Aufgabe

Swipe to start coding

Given a list of numbers, write a Python function to square all even numbers in the list. List [1, 2, 3, 4, 5] should result into [4, 16].

List Comprehention

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 1. Kapitel 2
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book
Challenge 1: List Comprehension

List comprehensions in Python provide an elegant and concise way to create lists. Their advantages are:

  • Readability and conciseness: List comprehensions allow you to reduce the amount of code, making it more readable and concise. Instead of using multiple lines of code with loops and conditional statements, you can get the same result in one line.
  • Performance: In many cases, list comprehensions are faster than traditional loops, especially when processing large amounts of data, which is key in Data Science.
  • Built-in filtering capabilities: These allow you to easily apply conditional expressions to filter data, which is especially useful when preprocessing and cleaning datasets.

Thus, list comprehensions are a powerful tool in the hands of a data scientist, allowing you to quickly and efficiently process and transform data.

Aufgabe

Swipe to start coding

Given a list of numbers, write a Python function to square all even numbers in the list. List [1, 2, 3, 4, 5] should result into [4, 16].

List Comprehention

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 1. Kapitel 2
Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
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