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  • 1

    Python Data Structures and String Manipulation

    Gratuito

    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.

  • 2

    Iterable objects and representatives

    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.

  • 3

    Functions and lambda expressions

    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.

  • 4

    Python for scientific computing

    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.

Initializing

Exercicio

Additional arguments

Let's use additional arguments in the .apply() method!

Your task is to create two new columns in scores:

  • mean is the row-wise mean value of the math score, reading score and writing score
  • rank defines how high the mean score is:
    • 'high' if the mean value \(> 90\)
    • 'medium' if the mean value \(> 60\) but \(\leq\) 90
    • 'low' if the mean value \(\leq 60\)

To accomplish this task, you'll need to define the function rank that, given a series, returns a list with two values: the mean of the series and a string defined by the aforementioned rule.

The module numpy is already imported for you as np.

Instruções

100 XP
  • Calculate the mean of the input series.
  • Return the mean and its rank as a list.
  • Insert the output of rank() into new columns of scores.