add B_explore_python assignment

.gitignore

@@ -129,7 +129,5 @@ dmypy.json
 .pyre/
 
 
-
 # project-specific files
 README_init.md
-B_explore_python/

B_explore_python/README.md

+# Assignment B: Explore Python &nbsp; (<span style="color:red">XX Pts</span>)
+
+This assignment demonstrates Python's basic data structures.
+
+### Challenges
+1. [Challenge 1:](#1-challenge-1-indexing-fruits) Indexing Fruits
+2. [Challenge 2:](#2-challenge-2-packaging-fruits) Packaging Fruits
+3. [Challenge 3:](#3-challenge-3-sorting-fruits) Sorting Fruits
+4. [Challenge 4:](#4-challenge-4-income-analysis) Income Analysis
+5. [Challenge 5:](#5-challenge-5-code-income-analysis) Code Income Analysis
+6. [Challenge 6:](#6-challenge-6-python-built-in-functions)
+    Python built-in functions
+
+&nbsp;
+### 1.) Challenge 1: Indexing Fruits
+Explore Python. Review Python's basic
+[data structures](https://www.dataquest.io/blog/data-structures-in-python).
+
+Answer questions on a piece of paper.
+
+```py
+# Python is known for advanced list processing.
+>>> fruits = ['apple', 'pear', 'orange', 'banana']
+>>> print(fruits)
+>>> fruits
+['apple', 'pear', 'orange', 'banana']
+
+>>> len(fruits)
+4
+
+>>> 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: banana
+
+>>> print(f"the last two fruits are: {fruits[-2:]}")
+the last two fruits are: ['orange', 'banana']
+```
+
+
+&nbsp;
+### 2.) Challenge 2: Packaging Fruits
+
+Review Python's built-in
+[data structures](https://www.dataquest.io/blog/data-structures-in-python)
+and answer questions on a piece of paper.
+
+1. What are the differences between `fruits`, `fruitbag` and
+  `fruitbox`?
+
+    ```py
+    >>> fruits = ['apple', 'pear', 'orange', 'banana']
+    >>> fruitbag = {'apple', 'pear', 'orange', 'banana'}
+    >>> fruitbox = ('apple', 'pear', 'orange', 'banana')
+
+    >>> print(fruits)
+    ['apple', 'pear', 'orange', 'banana']
+
+    >>> print(fruitbox)
+    ('apple', 'pear', 'orange', 'banana')
+
+    >>> print(fruitbag)
+    {'orange', 'banana', 'apple', 'pear'}
+
+    >>> print(fruits[1])
+    pear
+    >>> print(fruitbox[1])
+    pear
+    >>> print(fruitbag[1])
+    TypeError: object is not subscriptable
+    >>>
+    ```
+
+1. How is the structure for Eric called?
+
+    ```py
+    eric = {"name": "Eric", "salary": 5000, "birthday": "Sep 25 2001"}
+
+    >>> print(eric)
+    {'name': 'Eric', 'salary': 5000, 'birthday': 'Sep 25 2001'}
+    >>> print(eric["salary"])
+    5000
+    ```
+
+
+&nbsp;
+### 3.) Challenge 3: Sorting Fruits
+
+1. What is the difference between *sort()* and built-in function *sorted()*,
+[link](https://www.python-engineer.com/posts/sort-vs-sorted) (2 Pts)?
+
+    ```py
+    >>> fruits = ['apple', 'pear', 'orange', 'banana']
+
+    >>> f1 = sorted(fruits)
+    >>> print(f"{f1},\n{fruits}")
+    ['apple', 'banana', 'orange', 'pear'],
+    ['apple', 'pear', 'orange', 'banana']
+
+    >>> f2 = fruits.sort()
+    >>> print(f"{f2},\n{fruits}")
+    None,
+    ['apple', 'banana', 'orange', 'pear']
+    ```
+
+1. Some people say that Arrays in other languages are
+Lists in Python. Other people argue that Tuples are Arrays.
+   - a) Which statement is (more) correct? (1 Pt)
+   - c) Why? (1 Pt)
+   - b) Name three differences between Arrays and Lists?
+        (3 Pt)
+
+1. Draw sketches to visualize Python data structures:
+*List*, *Set*, *Tuple*, *Dictionary* and *Array* (from other
+languages like C, C++). (1 Pt)
+
+
+&nbsp;
+### 4.) Challenge 4: Income Analysis
+The US tax Income Revenue Service (IRS) annually 
+publishes income statistics by ZIP codes
+([reports](https://www.irs.gov/statistics/soi-tax-stats-individual-income-tax-statistics-2020-zip-code-data-soi)).
+
+For example, California ZIP Code
+[93636](https://simplemaps.com/us-zips/93636)
+is a rural agricultural county of Madera, north of
+Fresno in the Central Valley.
+Income distribution for the tax year 2020 was:
+```
+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 < $50M>]          550
+```
+Numbers mean that 980 tax returns were filed in the
+bracket [$50,000 to under $75,000] taxable income.
+
+A common statistical analysis is to compute:
+
+- the *mean (average) income* per tax filer and the
+
+- the *median income* per tax filer.
+
+Assume $50 million as upper limit for *"more"* in the
+highest bracket.
+
+Answer questions:
+
+1. What is the difference between *mean (average)* and
+*median* calculations?  (1 Pt)
+
+1. Why are both indicators relevant? (1 Pt)
+
+1. Calculate manually the *average* income for Madera
+    county.
+
+1. Calculate manually the *median* income for Madera
+    county.
+
+
+&nbsp;
+### 5.) Challenge 5: Code Income Analysis
+
+Write Python code to perform this income analysis.
+
+<b>Use pure Python</b> (no *Pandas* nor *Numpy*) for this simple example.
+
+Think about following steps:
+
+1. Chose a suitable Python 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. Code data for one ZIP code into your structure (no need to read `.xlsx`-files).
+
+1. Define two functions `mean_income(...)` and `median_income(...)` that take
+    data for one ZIP code as input and return respective numbers.
+
+1. Define function `number_of_returns(...)`.
+
+1. Implement functions and demonstrate they return correct values.
+
+1. Demonstrate analysis for other ZIP codes:
+    - [94040](https://simplemaps.com/us-zips/94040) (Mountain View, CA),
+    - [94304](https://simplemaps.com/us-zips/94304) (Palo Alto, CA),
+    - [94027](https://simplemaps.com/us-zips/94027) (Atherton, CA),
+    - [50860](https://simplemaps.com/us-zips/93636) (Redding, IA) and
+    - [10023](https://simplemaps.com/us-zips/10023) (New York City, NY Upper West side).
+
+
+&nbsp;
+### 6.) Challenge 6: Python built-in functions
+Learn about Python's [built-in functions](https://docs.python.org/3/library/functions.html). Test the [*globals()*](https://docs.python.org/3/library/functions.html#globals) function.
+```py
+  >>> 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()*](https://docs.python.org/3/library/functions.html#input) function.
+```py
+  >>> s = input('--> ')
+  --> Monty Python's Flying Circus
+  >>> s
+  "Monty Python's Flying Circus"
+  exit()
+```
+(2 Pts)

README.md

@@ -4,8 +4,7 @@ This repository contains the assignments for class
 [Computer Science for Big Data](https://lms.bht-berlin.de/course/view.php?id=30704):
 
 - Assignment A - [Python setup](A_setup_python/README.md).
-- Assignment B - [Explore Python](B_explore_python/).
-- Assignment C - [Python cost model](C_python_cost_model/).
+- Assignment B - [Explore Python](B_explore_python/README.md).