Assignment B: Explore Python   (20 Pts)

This assignment demonstrates Python's basic data structures.

Challenges

1. Challenge 1: Indexing Fruits
2. Challenge 2: Packaging Fruits
3. Challenge 3: Sorting Fruits
4. Challenge 4: Income Analysis
5. Challenge 5: Code Income Analysis
6. Challenge 6: Explore Python built-in functions

1.) Challenge 1: Indexing Fruits

Review Python's basic data structures.

# Python is known for advanced list processing.
>>> fruits = ['apple', 'pear', 'orange', 'banana', 'apple']
>>> print(fruits)
>>> fruits
['apple', 'pear', 'orange', 'banana', 'apple']

>>> print(len(fruits))
5

>>> print(f"the third fruit is: {fruits[2]}")
the third fruit is: orange

>>> print(f"the second and third fruits are: {fruits[1:3]}")
the second and third fruits are: ['pear', 'orange']

>>> print(f"the last fruit is: {fruits[-1]}")
the last fruit is: apple

>>> print(f"the second-last fruit is: {fruits[-2]}")
the second-last fruit is: banana

>>> print(f"the last three fruits are: {fruits[-3:]}")
the last three fruits are: ['orange', 'banana', 'apple']

Perform examples on your laptop. (1 Pt)

2.) Challenge 2: Packaging Fruits

Review Python's built-in data structures. Perform examples and answer questions on a piece of paper.

1. What are data types for fruits, fruitbag and fruitbox called? (1 Pt)

2. Name three properties that characterize each data type. (1 Pts)

3. Why does output for fruitbag differ from input? (1 Pt)

   >>> fruits = ['apple', 'pear', 'orange', 'banana', 'apple']
   >>> fruitbag = {'apple', 'pear', 'orange', 'banana', 'apple'}
   >>> fruitbox = ('apple', 'pear', 'orange', 'banana', 'apple')
   
   >>> print(fruits)
   ['apple', 'pear', 'orange', 'banana']
   
   >>> print(fruitbox)
   ('apple', 'pear', 'orange', 'banana', 'apple')
   
   >>> print(fruitbag)
   {'banana', 'apple', 'pear', 'orange'}
   
   >>> print(fruits[1])
   pear
   >>> print(fruitbox[1])
   pear
   >>> print(fruitbag[1])
   TypeError: object is not subscriptable
   >>>

4. How is the structure for Eric called? (1 Pt)

   eric = {"name": "Eric", "salary": 5000, "birthday": "Sep 25 2001"}
   
   >>> print(eric)
   {'name': 'Eric', 'salary': 5000, 'birthday': 'Sep 25 2001'}
   >>> print(eric["salary"])
   5000

5. Some people say that Arrays in other languages are Lists in Python. Other people argue that Tuples are closer to Arrays.

   • a) Which statement is (more) correct?
   • b) Name two differences between Arrays and Lists? (1 Pt)
   • c) What are differences between a Python 2-dimensional List (List with Lists) and a NumPy Array ndarray (read the first three paragraphs)? (1 Pt)

6. Draw sketches to visualize Python data structures: List, Set, Tuple, Dictionary and NumPy Array. (1 Pt)

3.) Challenge 3: Sorting Fruits

>>> fruits = ['apple', 'pear', 'orange', 'banana', 'apple']

>>> f1 = sorted(fruits)
>>> print(f"{f1},\n{fruits}")
['apple', 'apple', 'banana', 'orange', 'pear'],
['apple', 'pear', 'orange', 'banana', 'apple']


>>> f2 = fruits.sort()
>>> print(f"{f2},\n{fruits}")
None,
['apple', 'apple', 'banana', 'orange', 'pear']

What is the difference between List-function sort() and built-in function sorted(), see link (2 Pts)?

4.) Challenge 4: Income Analysis

The US tax Income Revenue Service (IRS) annually publishes income statistics by ZIP codes (postal codes) (reports).

For example, California ZIP Code 93636 is for Madera county, an agricultural region north of Fresno in the Central Valley. The income distribution in this county was for the tax year 2020:

income bracket:         number of tax returns
                        filed in bracket
[$1 to under $25,000]            1,800
[$25,000 to under $50,000]       1,380
[$50,000 to under $75,000]         980
[$75,000 to under $100,000]        830
[$100,000 to under $200,000]     1,660
[$200,000 or more, up to $10M]     550

In the bracket [$50,000 to under $75,000] of taxable income (exact to 74,999), a total of 980 tax returns were filed, which means 980 tax payers reported taxable income in this band.

We assume $10 million ($10M) as upper limit in the highest bracket.

A common statistical analysis is to compute:

• the mean (average) income and the

• the median income across all tax payers in Madera county.

For calculating the mean income, average the income within each bracket.

For calculating the median income, consider a linear rising income from the lower bound to the upper bound in the bracket holding the median income.

Answer questions:

1. What is the difference between mean (average) and median calculations?

   • Why are both indicators relevant?
   • When should one over the other be preferred?

2. Calculate manually the mean or average income for Madera county (result: $453,073 ).

3. Calculate manually the median income for Madera county (result: $60,714 ).

(3 Pts)

5.) Challenge 5: Code Income Analysis

Write Python code to perform this income analysis for arbitray ZIP regions.

Use file income_tax_analysis.py as template.

Use pure Python, no libraries such as Pandas or Numpy or built-in library functions.

Think about following steps:

1. Chose a suitable Python data structure to represent tax data for a ZIP code.

   • Which data is relevant for the analysis?
   • How can data be structured?
   • Use only use Python structures: list, set, tuple, dictionary. (1 Pt)

2. Implement that data structure for the Madera data (fill data in code, no need to read .xlsx-files). (1 Pt)

3. Define two functions mean_income(...) and median_income(...) that take a data structure for a ZIP code as input and return respective numbers as results.

4. Define a function number_of_returns(...) that returns what the name suggests.

5. Implement these functions and demonstrate they return correct values.

6. Demonstrate analysis for other ZIP codes using IRS tax reports. Select a state (CA: California, IA: Iowa, NY: New York City), download the .xlsx and navigate to ZIP codes for:

   • 94040 (Mountain View, CA),
   • 94304 (Palo Alto, CA),
   • 94027 (Atherton, CA),
   • 50860 (Redding, IA) and
   • 10023 (New York City, NY Upper West side). (1 Pt)

Develop (test, debug) code in income_tax_analysis.py in your IDE (VS Code, PyCharm, Jupyter, etc.).

Run the final result in a terminal (Jupyter: export Python file):

$ python income_tax_analysis.py

Results:

mean_income in Madera County, CA          is:    453,073 - median_income is:   60,714
mean_income in Mountain View, CA          is:  1,740,371 - median_income is:  114,820
mean_income in Palo Alto, CA              is:  2,077,038 - median_income is:  153,658
mean_income in Atherton, CA               is:  2,623,881 - median_income is:  354,087
mean_income in Redding, IA                is:     33,333 - median_income is:   31,249
mean_income in New York City, NY U West   is:  1,544,990 - median_income is:  104,774

(4 Pts)

6.) Challenge 6: Explore Python built-in functions

Learn about Python's built-in functions. Test the globals() function.

  >>> globals()
  {'__name__': '__main__', '__doc__': None, '__package__': None, '__loader__': <class '_frozen_
  importlib.BuiltinImporter'>, '__spec__': None, '__annotations__': {}, '__builtins__': <module
  'builtins' (built-in)>, 'fruits': ['apple', 'pear', 'orange', 'banana']}

Test the input() function.

  >>> s = input('--> ')
  --> Monty Python's Flying Circus
  >>> s
  "Monty Python's Flying Circus"
  exit()

(1 Pt)