Explore feature relationships

Let's do some exploratory analysis. You are already familiar with the retinol dataset. It stores the information on the relationship between different factors (background information, clinical data) and the levels of retinol (plasma retinol) and beta-carotene (plasma B-carotene) in blood plasma. Low levels of these compounds were reported to be associated with higher risk of cancer.

Which numeric features may have strong linear relationship to the plasma retinol feature?

The matplotlib.pyplot module is already imported as plt.

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Practicing Coding Interview Questions in Python

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Practicing Coding Interview Questions in Python

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In this chapter, we'll refresh our knowledge of the main data structures used in Python. We'll cover how to deal with lists, tuples, sets, and dictionaries. We'll also consider strings and how to write regular expressions to retrieve specific character sequences from a given text.

Exercise 1: What are the main data structures in Python?Exercise 2: List methods Exercise 3: Operations on sets Exercise 4: Storing data in a dictionary Exercise 5: What are common ways to manipulate strings?Exercise 6: String indexing and concatenation Exercise 7: Operations on strings Exercise 8: Fixing string errors in a DataFrame Exercise 9: How to write regular expressions in Python?Exercise 10: Write a regular expression Exercise 11: Find the correct pattern Exercise 12: Splitting by a pattern

This chapter focuses on iterable objects. We'll refresh the definition of iterable objects and explain, how to identify one. Next, we'll cover list comprehensions, which is a very special feature of Python programming language to define lists. Then, we'll recall how to combine several iterable objects into one. Finally, we'll cover how to create custom iterable objects using generators.

Exercise 1: What are iterable objects?Exercise 2: enumerate()Exercise 3: Iterators Exercise 4: Traversing a DataFrame Exercise 5: What is a list comprehension?Exercise 6: Basic list comprehensions Exercise 7: Prime number sequence Exercise 8: Coprime number sequence Exercise 9: What is a zip object?Exercise 10: Combining iterable objects Exercise 11: Extracting tuples Exercise 12: Creating a DataFrame Exercise 13: What is a generator and how to create one?Exercise 14: Shift a string Exercise 15: Throw a dice Exercise 16: Generator comprehensions

This chapter will focus on the functional aspects of Python. We'll start by defining functions with a variable amount of positional as well as keyword arguments. Next, we'll cover lambda functions and in which cases they can be helpful. Especially, we'll see how to use them with such functions as map(), filter(), and reduce(). Finally, we'll recall what is recursion and how to correctly implement one.

Exercise 1: How to pass a variable number of arguments to a function?Exercise 2: Positional arguments of variable size Exercise 3: Keyword arguments of variable size Exercise 4: Combining argument types Exercise 5: What is a lambda expression?Exercise 6: Define lambda expressions Exercise 7: Converting functions to lambda expressions Exercise 8: Using a lambda expression as an argument Exercise 9: What are the functions map(), filter(), reduce()?Exercise 10: The map() function Exercise 11: The filter() function Exercise 12: The reduce() function Exercise 13: What is recursion?Exercise 14: Calculate the number of function calls Exercise 15: Calculate an average value Exercise 16: Approximate Pi with recursion

This chapter will cover topics on scientific computing in Python. We'll start by explaining the difference between NumPy arrays and lists. We'll define why the former ones suit better for complex calculations. Next, we'll cover some useful techniques to manipulate with pandas DataFrames. Finally, we'll do some data visualization using scatterplots, histograms, and boxplots.

Exercise 1: What is the difference between a NumPy array and a list?Exercise 2: Incorrect array initialization Exercise 3: Accessing subarrays Exercise 4: Operations with NumPy arrays Exercise 5: How to use the .apply() method on a DataFrame?Exercise 6: Simple use of .apply()Exercise 7: Additional arguments Exercise 8: Functions with additional arguments Exercise 9: How to use the .groupby() method on a DataFrame?Exercise 10: Standard DataFrame methods Exercise 11: BMI of villains Exercise 12: NaN value imputation Exercise 13: How to visualize data in Python?Exercise 14: Explore feature relationships

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Exercise 15: Plot a histogram Exercise 16: Creating boxplots Exercise 17: Final thoughts