A Assignment

Lists, Arrays and DataFrames without Loops

Please save your solutions for Exercises 1 to 9 in a single Python script named unit05__ex(1-9)code.py.
For Bonus Exercise 10, use a separate script named unit05__ex10code.py.

Save both scripts in the same unit05 folder, compress the folder into a .zip file, and upload it to ILIAS.

For more information, please visit the following link:
https://geomoer.github.io/moer-base-python/unit00/unit00-04_submission_guidelines.html

Make sure your code is clearly structured and includes comments where helpful.

Exercise 1 – Theory: List vs Array vs DataFrame

Briefly explain the following (in your own words):

A) What is the main difference between a list and a NumPy array?
B) When would you use a DataFrame instead of a list or array?
C) What does “vectorized operation” mean in NumPy?

Exercise 2 – List Basics

Given the list my_list = [4, 7, 1, 9],
print the second value, the length of the list, and the sum of all elements.

Exercise 3 – List Condition (if-else)

Use the list numbers = [12, 8, 22, 5].
Use an if-else statement to compare the first and last values and print which one is greater.

Exercise 4 – List Extension

Given the list colors = ["red", "green"],
append "blue" to the list.
If the length is now 3, print "List updated.", else print "Update failed."

Exercise 5 – List Value Check (if)

Given the list grades = [85, 60, 78],
check if the second element is below 65.
If yes, print "Needs improvement".

Exercise 6 – NumPy Array Comparison (if)

Use the arrays:

import numpy as np
a = np.array([3, 8, 5])
b = np.array([4, 7, 6])

Compare the first element of each array using an if statement and print which is larger.

Exercise 7 – Array Calculation with if-elif-else

Given the array values = np.array([12, 18, 25]),
check the second element and print:

  • "Low" if < 10
  • "Medium" if between 10–20
  • "High" if > 20

Exercise 8 – DataFrame Basics

Use this DataFrame:

import pandas as pd
df = pd.DataFrame({
    "Name": ["Ali", "Nina", "Tom"],
    "Age": [28, 35, 22],
    "City": ["Berlin", "Leipzig", "Hamburg"]
})

Print the "Age" column.
If the second person is older than 30, print "Senior", else "Junior".

Exercise 9 – DataFrame Column Check (if-else)

Use this DataFrame:

df2 = pd.DataFrame({
    "Score": [75, 90, 66]
})

If the score in row 0 is >= 80, print "Excellent", otherwise "Needs improvement".

Exercise 10 – Bonus: Voting Statistics (DataFrame Analysis with Conditions)

Use the following DataFrame:

import pandas as pd

df = pd.DataFrame({
    "Name": ["Sara", "Leo", "Mia", "Jonas", "Ella"],
    "Age": [22, 19, 35, 42, 17],
    "Voted": [True, False, True, False, False]
})

Check whether the oldest person in the DataFrame has voted.

Print: “Oldest participant (Name) voted. Thank you!” or “Oldest participant (Name) did not vote.”

Updated: