Python Exercise - Operators and Data Structures

Introduction

This unit covers Python’s data structures, including vectors (lists), data frames (using pandas), matrices, arrays, lists, and factors. You will work through various tasks to build familiarity with these concepts.

Task 1: Lists

Description:

Work with Python lists. You will define lists and perform various operations on them.

Instructions:

  1. Define two lists list1 = [1, 2, 3] and list2 = [4, 5, 6].
  2. Perform the following operations:
    • Concatenate the two list and name it my_list.
    • Calculate the sum.
    • Find the length of list1.
    • Access the second element of list2.
  3. Now append the list:
    • Append a new element 'apple' to the list.
    • Remove 2 from the list.
    • Append a nested list ['tree', 'leave', 'root']
    • Access the nested list and retrieve the second element.
    • Find the length of my_list.

Task 2: Data Frames

Description:

Learn how to create and manipulate data frames using pandas.

Instructions:

  1. Create a data frame using pandas with the following data:
    • Name = ['Alice', 'Bob', 'Charlie']
    • Age = [25, 30, 35]
    • Salary = [50000, 60000, 70000]
  2. Perform the following operations:
    • Display the first two rows of the data frame.
    • Add a new column called Department with values ['Human Resources', 'Engineering', 'Marketing'].
    • Select the Name and Salary columns.
    • Filter the data frame to only show rows where Age > 28.

Task 3: Matrices

Description:

Learn how to create and manipulate matrices using numpy.

Instructions:

  1. Create a 2x3 matrix with the following values: 
     [[1, 2, 3],
  [4, 5, 6]]
  2. Perform the following operations:
    • Transpose the matrix.
    • Calculate the sum of all elements.
    • Multiply each element of the matrix by 2.
    • Access the element at row 1, column 2.

Task 4: Arrays

Description:

Explore arrays in Python using numpy, which can represent multi-dimensional data structures.

Instructions:

  1. Create a 3-dimensional array with the shape (2, 2, 2) and values ranging from 1 to 8.
  2. Perform the following operations:
    • Access the element at position (1, 1, 0).
    • Slice the array to get the first 2D matrix.
    • Reshape the array into a 2x4 array.

Task 5: Factors

Description:

In Python, you can represent categorical variables using pandas Categorical type, similar to R’s factors.

Instructions:

  1. Create a pandas Categorical object with the following categories: ['low', 'medium', 'high'].
  2. Perform the following operations:
    • Assign the values ['low', 'high', 'medium', 'medium', 'low'] to a variable categories.
    • Print the categories and their frequency.
    • Convert categories to an ordered categorical type with the order ['low', 'medium', 'high'].
    • Sort the categories based on their levels.

Happy coding!

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