1AKIFT POS Test (Group A)

2025-01-13

Max. 100 points

Name:

Task	Max.	Achieved
1	50
2	50
Sum	100

Grading: >= 88: 1, >=76: 2, >=64 : 3, >50: 4, <=50: 5

Write a function count_words(text: str) -> dict in Python that takes a string and returns a dictionary containing how often each word occurs in the string. Words are mainly delimited by whitespace, but they may also be delimited by '.', ':', ',', '!', '?'. Hint: you have to split the input at any of these characters as well as on whitespace.
You may not use any imports for your solution. To make things easier, the result has to be case-insensitive by transforming the input to lowercase. Use type annotations for the function and provide a docstring to receive full points. The expected result is a dictionary containing the words in text as keys and the number of times each word occurs as values.
For example
```
count_words('Hello')  # -> {'hello': 1}
count_words('Hello world!')  # -> {'hello': 1, 'world': 1}
count_words('Hello world! Hello hello')  # -> {'hello': 3, 'world': 1}
count_words('Peace. Peace, peace. PEACE! Plz.')  # -> {'peace': 4, 'plz': 1}
```
(50 points)
5 points for correct type annotations
5 points for a proper docstring
5 points for correctly dealing with non-whitespace delimiters
5 points for converting the text to lowercase
5 points for splitting the text
5 points for initializing an empty dict
5 points for a correct loop
5 points for correctly dealing with the first occurrance of a word
5 points for correctly incrementing additional occurances
5 points for returning the result
```
def count_words(text: str) -> dict:
    """
    Return how often each word occurs in `text`.
    """
    for delimiter in {'.', ':', ',', '!', '?'}:
        text = text.replace(delimiter, ' ')
    words = text.lower().split()
    counts = {}
    for word in words:
        counts[word] = counts.get(word, 0) + 1
    return counts
```
You're in charge of analyzing CO2 data measured by multiple sensors in your company's building. You will get the sensor data weekly, one file per sensor. The first analysis you're planning to do is to calculate the average CO2 pollution per week for each sensor. The data in each file is straightforward. One measurement per line, starting with a timestamp, then a comma ",", and then the CO2 value in PPM. A sample input file looks like
```
2024-01-02T19:49,1100
2024-01-02T19:59,1100
2024-01-02T20:09,1200
...
2024-01-02T22:09,700
2024-01-02T22:19,800
```
Implement a function average_co2(filename) that returns the average CO2 in the given file. As example, execute the function on a file named "sensor1_2024-01-02.csv". Assume the file contains the data above (only the 5 visible data lines). The average then is (1100 + 1100 + 1200 + 700 + 800) / 5 = 4900 / 5 = 980.0. In that case, running the function looks like
```
average_co2('sensor1_2024-01-02.csv')  # returns 980.0
```
Add a proper docstring, a sample doctest, and type annotations to the function to receive full points. (50 points)
5 points for correct type annotations
5 points for a proper docstring
5 points for single doctest
5 points for initializing counter and total
5 points for reading the file
5 points for correctly splitting the input lines
5 points for converting the PPM to int
5 points for correct counter variable
5 points for correctly summing up the values
5 points for returning the correct result
```
def average_co2(filename: str) -> float:
    """
    Return average CO2 from the given file.

    >>> average_co2('sensor1_2024-01-02.csv')
    980.0
    """
    count = 0
    total = 0
    with open(filename) as f:
        for line in f:
            total += int(line.split(',')[-1])
            count += 1
    return total / count
```