Python Studies: Crash Course

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Crash Course Notes

Hope I can do it very quick, just to get similarities and deltas to other languages and catch the main idea of programming in Python.

2023-01-02

Data Types and Variables

  • case sensitive,
  • no number in the beginning
  • no code words like while, if …
  • using snake coding possible: Snake_Part1_Part2
  • chained assignment pissible: 
        >>> a = b = c = 300  
    >>> print(a, b, c)  
    300 300 300
  • Numeric
    • Integer, can be on anny length 
      user_salary = 5000
    • Float 15 digit precision to 15’th decimal place, indicated by “.”
      number_float = 2.0Attention: float has some quirks due limited precision. Will affect comparisions:

      >>> 0.3 + 0.1  
0.4  
>>> 0.1 + 3.8  
3.9  
>>> 0.1 + 0.2  
0.30000000000000004

      Solutions:

      1. Using decimal module, providing support for decimal floating-point arithmetic like fixed precision.
      2. Higher precision: Use NumPy, a scientific computing library for Python. NumPy module provides a data type numpy.float128
      3. Round the floats
    • Complex Numbers (same about precision like float).
      complex_number = 1 + 1j
    • Boolean (true / false) –> mention: False or True
      my_bool = False # or True
    • binary: “0b” –> 0b0101
    • ocatal: “0o” –> 0o01234567
    • hex: “0x” –> 0x00FFPrint binary / ocatal / hex) using built-in function
      Built-in function bin(), oct(), hex()
      Built-in function format(), string method str.format(), f-strings
      if not –> will be printed as integer

      print(bin(my_variable)) #--> binary  
print(oct(my_variable)) #--> octal  
print(hex(my_variable)) #--> hex
    • String (Unicode characters)
    • List – in order, not fixed, different types
    • Tuple – in order, fixed list, different types
    • Dictionary – not in order, different types, keys different types, but must be irreversible
      dict = {1:’cat’, 2:’dog’}
      dict = {‘place’: ‘New York’, 1:[3,6,9]}also with dict()
      dict = ({1:’cat’, 2:’dog’})
    • Set – not arranged in any particular sequence, can be modified

2023-01-03

IDLE and Python Shell

  • IDLE is good to learn python, but not for larger projects. Actual I prefer “Visual Studio Code”
  • installing idle (but should already)
  • IDLE has 2 main windows:
    • Shell Window (fire up when start)
    • Editor Winwdo (via edit new file in shell window)
      you can open as many file windows you want
    • in console just open [python] (or python3] and also sehll will start. Hint (so there like [python3] and [python] is most equal):$ sudo apt install python-is-python3
    • running python scripts (there also a lot of command line parameters):$ python3 $script.py
    • IDLE has also an debug mode and other features. See IDLE tutorial.

Operators in the Python language

Operators in Python are separated into following categories.
Operator quick view / Cheat Sheet:

Details in the Operator Cheat Sheet ore more detailed in:

2023-01-04

Numbers in Python:

Strings

Outlook:
Methods in Python. Methods are very similar to functions, but in Python a functionm, part of an object (like the data objects of a string, but also others). Is not restricted to classes instances.
In Python every data type comes with a collection methods

2023-01-05

Flow Control in Python

basically 3 types of control flows:

  • Sequencing – no flow control
  • Selection – typical ‘if’ statement
  • Repetion – ‘for’ and ‘while’

Decision control or branching statement. Python is following the off-side-rule

If one branche is not in use yet – using the “pass“ Statement as a placeholder – aka “how to do nothing in Python”. This also works for empty functions, classes…

‘if’ statement

  • if & if-else 
    if(condition):  
  # execution this block if condition is _True_  
else:  
  # execution of this block if condition _False_
  • if-else-elif, for making number of choices 
    if condition_1:  
  # condition_1 execution this block if condition_1 is true

elif condition_2:  
  # condition_2 execution this block if condition_2 is true

elif condition_3:  
  # condition_3 execution this block if condition_3 is true

...

else:  
  # execution of this block if non of the conditions are true

Wrapping conditions like “(< condition >)” are possible & don’t forget the “:“.

Nesting:
Each execution block itself can contain a new if statement. But should be avoided due high potential of confusion.

Repetition loops ‘while’ & ‘for’

‘while’ loop

  • indefinite iteration: Block executing as long as some condition is met.
  • definite iteration: Number of times the designated block will be executed is specified explicitly at the time the loop starts.
  • break & continue statements (break & continue are Python keywords).
  • of course, break or continue statement should be combined with an if statement… 
    while (expression) 
  # statement_1  
  # statement_2  
  # ...  
  # statement_n

  break # --> will leave the loop

  # statement_n+1  
  # statement_n+2

  continue # --> will go back to while (expression)

  # statement_n+3  
  # ...

# break will continue here

Attention: Python doesn’t have a ‘do-while’ construct. But can be emulated with the break statement.

‘for’ loop

Attention this is very different from other languages I know (e.g. C). Python is using iterable objects and Interger is not iterable. Need to be converted by ‘range()’ function but is therefore much more flexible and object orientated.

Iteralbles & Iteration, definitions:

Term Meaning
Iteration: looping process through the objects, collection
Iterable: Object that can be iterated
Iterator: Object that produces successive items
or values from its associated iterable
iter(): Built-in function used to obtain an
iterator from an iterable, go to next iterabel
Attention: can’t go back, no “prev() iterator!
  • Iterable with iter():
    • String
    • List
    • Tuple
    • Set
    • Dict
  • Not iterable with iter():
    • Integer
    • Float
    • Built in functions

Many objects can be designed to be iterable (e.g. open files or other I/O operations). Most objects in Python can be made iterable, including user defined ones.

The most basic code itself is very simple:

    for var in iterable:  
        # Execute this block  
    else:
        # Do this block

    # continue (e.g. after a finishing loop or break condition

An very simple Example:

    a = ['aaa', 'bbb', 'ccc']  
    for i in a:
       print(i)

will get this output:

    aaa
    bbb
    ccc

Using ‘break’ and ‘continue’ statements:

‘break’ and ‘continue’ work similar way like the ‘while’ loop. ‘break’ terminates the loop completely and proceeds to the first statement following the loop.

Attention:‘break’ will override the “else:” part.

Using range()

With ‘range()’ function I can iterate through Integer “ranges”.

    range(stop)                 #     0 ... stop  
    range(start, stop)          # start ... stop  
    range(start, stop, step)    # start ... stop by step increment
  • in case of integer start could also be negative
  • of course not only integers can be used, mostly(?) all Iterables too.

Key words

There are actual 35 Keywords in Python. See also Python Reference.
False class from or
None continue global pass
True def if raise
and del import return
as elif in try
assert else is while
async except lambda with
await finally nonlocal yield
break for not

2023-01-06

Functions – basic overview

  • calling a function is also known as invoking a function
  • Argument: value sent to the function when it is called
  • Two major types:
  • Inside the custom functions I can define local variables, outside defined variables are named global variables
  • Typecasting: Using the right types (depending on the return of the build in and custom functions).
    As example, the built in input() function will handle input as string:

    x = input("Number 1:")      # e.g.: 8  
y = input("Number 2:")      # e.g.: 9  
s = x + y  
print(s)                    # result will be 89

    so adding typecasting before print(s):

    x = input("Number 1:")      # e.g.: 8  
y = input("Number 2:")      # e.g.: 9  
s = int(x) + int(y)         # typecasting x and y
print(s)                    # result will be correct: 17

Functions – User defined

  • Syntax: 
        def _function_name_ ([_parameters_]):  
    _statement(s)_
Component Meaning
def keyword, function is being defined
function_name valid Python identifier, names the function
parameters optional, comma-separated list of parameters
: denotes the end of the Python function header
statement(s) Python statements doing the function
return a stating passing back data, be aware about type
  • parameter vs. argument
    parameters behave like local defined variables, like the ones defined locally inside the function, the argument is the content of this parameters.
    –> Real Python @ Argument Passing
  • return Statement @ Real Python
  • pre define the parameters and also return statement type: 
    def f(a: int = 12, b: str = 'baz') -> float:
print(a, b)
return(3.5)
  • starting with Python3 Python Function Annotations:
    Providing a way to attach metadata to function’s parameter.
    –> Real Python @ function annotation
  • do nothing with the function or execution of the function
    –> Annotionas are nothing more than dictionaries of metadata
  • good for documentation
  • good for type checking
  • good for automated tools parsing code
    • can be modified dynamically for counting how often function was called
  • add documentation with docstrings.
  • starting and ending with tripple-quote 
    ...

""" single line Docstring """  
...

""" Multi  
    line  
    Docstring"""
  • Docstrings are accessible inside Python with the expression:

 

_function_name_.__doc__.

Sets in Python – basic overview

  • A collection of things, not arranged in any particular sequence is a set.
  • Sets are unordered.
  • Set elements are unique. Duplicate elements are not allowed.
  • A set itself may be modified, but the elements contained in the set must be of an immutable type.
  • can be created with built-in function set(), this will also avoid duplicates, but still unordered and the original order is not preserved.
  • most basic: 
    x = set(_iter_)
  • example: 
    s = 'quux'  
list(s)

'>>> ['q', 'u', 'u', 'x']

set(s)  
{'x', 'u', 'q'}
  • Sets, more detailed @ Real Python:

Lists and Tuples and Dictionary in Python – basic overview

  • the most important, versatile und useful data types in Python
  • List
    • Lists are ordered.
    • Lists can contain any arbitrary objects.
    • List elements can be accessed by index.
    • Lists can be nested to arbitrary depth.
    • Lists are mutable.
    • Lists are dynamic.
  • Tuple are most similar to list, beside:
    • Are defined by enclosing the elements in parentheses (()) instead of square brackets ([]).
    • Tuples are immutable. They can’t be modified. So why use?
      • Program execution is faster when manipulating a tuple.
      • If data don’t want to be modified.
      • Data type Dictionary need one of its components a immutable value. List is not working for that, but tuple.
  • Dictionary
    • Dictionaries and lists share equal characteristics for:
      • mutable
      • dynamic, can grow and shrink as needed.
      • can be nested. A list can contain another list.
        • A dictionary can contain another dictionary.
        • A dictionary can also contain a list, and vice versa.
    • Dictionaries differ from lists by how elements are accessed:
      • List elements are accessed by indexing
      • Dictionary elements are accessed via keys.

Modules and Packages in Python – basic overview

Modules and Packages allow modular programming. Advantages:

  • Simplicity
  • Maintainability
  • Reusability
  • Scoping

A module can be written in

  • Python (of curse)
  • in C
  • there are built-in modules

Modules are imported by ‘import’ statement

        import 'module'

Package is ‘packaging’ modules itself.
– grouping and organizing Modules.
– allows hierarchical structuring of the module namespace, using dot notation.
– help avoid collisions between module names

Conclusion

Now I got a very basic idea about Python. No, I can’t code Python right now. This will be start in the next quests over the next month in 2023.

I can currently only hope that I have enough free time (less overtime @ work) and energy to follow this path.

But at least – now I have a basic idea of this programming language.

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Published by tgohle

Your host at "TGONets Den", I'm from Dresden, Germany