Practice Tools and Libraries for Data Wrangling and Feature Engineering - 2.8 | 2. Data Wrangling and Feature Engineering | Data Science Advance
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2.8 - Tools and Libraries for Data Wrangling and Feature Engineering

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What Python library is ideal for data manipulation?

💡 Hint: Think of the most popular data manipulation tool in Python.

Question 2

Easy

Which library can help with numerical computations?

💡 Hint: Look for the foundational library for array operations.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the primary function of the Pandas library?

  • Web Development
  • Data Manipulation
  • Image Processing

💡 Hint: Consider what tasks you generally perform while working with datasets.

Question 2

True or False: Featuretools is used for data visualization.

  • True
  • False

💡 Hint: Think about the primary capabilities of this library.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a dataset with varying data types including categorical, numerical, and text fields. Discuss how you would use Pandas and scikit-learn together for preprocessing and building a machine learning model. Include specific functions you might call.

💡 Hint: Think of the sequence of data wrangling and how each library addresses different aspects.

Question 2

Compare the feature engineering capabilities of Featuretools with manual feature creation. Under what circumstances would you choose one over the other?

💡 Hint: Consider the trade-off between speed and domain knowledge.

Challenge and get performance evaluation