Everything About Python — Beginner To Advanced

Everything About Python — Beginner To Advanced

Everything You Need To Know In One Article. This article aims to outline all of the key points of Python programming language.

Everything You Need To Know In One Article. This article aims to outline all of the key points of Python programming language.

My target is to keep the information short, relevant and focus on the most important topics which are absolutely required to be understood.

After reading this blog, you will be able to use any Python library or implement your own Python packages.

You are not expected to have any prior programming knowledge and it will be very quick to grasp all of the required concepts.

I will also highlight top discussion questions that people usually query regarding Python programming language.

Lets build the knowledge gradually and by the end of the article, you will have a thorough understanding of Python.
Please let me know whether you want me to post exercises and their solutions to help you practice Python.
Lets build the knowledge gradually and by the end of the article, you will have a thorough understanding of Python.### 1. Introducing Python

What Is Python?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Why Python?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Why Not Python?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

How Does Python Work?

This image illustrates how python runs on our machines:

The key here is the Interpreter that is responsible for translating high level Python language to low level machine language.

2. Variables — Object Types And Scope

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Python supports numbers, strings, sets, lists , tuples and dictionaries. These are the standard data types.

Declare And Assign Value To Variable

Assignment sets a value to a variable:

myFirstVariable = 1
mySecondVariable = 2
myFirstVariable = "Hello You"

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

*Note how I assigned an integer value of 1 and then a string value of “Hello You” to the myFirstVariable variable. *This is possible due to the fact that the data types are dynamically typed in python.

Numeric

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
value = 1 #integer
value = 1.2 #float with a floating point

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Strings

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
name = 'farhad'

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
a = 'me'
a[1]='y'
It will throw a Type Error

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Variables can have local or global scope.

Local Scope

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
def some_funcion():
  TestMode = False
print(TestMode) <- Breaks as the variable doesn't exist outside

Global Scope

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
TestMode = True
def some_function():
  global TestMode
  TestMode = False
some_function()
print(TestMode) <--Returns False

Removing the line “global TestMode” will only set the variable to False within the some_function() function.

Note: Although I will write more on the concept of modules later, but if you want to share a global variable across a number of modules then you can create a shared module file e.g. configuration.py and locate your variable there. Finally, import the shared module in your consumer modules.

Finding Variable Type

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
type('farhad')
--> Returns <type 'str'>

Comma In Integer Variables

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
9,8,7 is three numeric variables

3. Operations

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Numeric Operations

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
1//3  #returns 0.333...
1/3 #returns 0 as both of the operands are integers

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
2**3 = 2 * 2 * 2 = 8

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
7%2 = 1

String Operations

Concat Strings:

'A' + 'B' = 'AB'

Repeat String:

‘A’*3 will repeat A three times:  AAA

Slicing:

y = 'Abc'
y[:2] = ab
y[1:] = c
y[:-2] = a
y[-2:] = bc


Reversing:

x = 'abc'
x = x[::-1]

Negative Index:

If you want to start from the last character then use negative index.

y = 'abc'
print(y[:-1]) # will return c

Also used to remove any new line carriages/spaces.

Finding Index

name = 'farhad'
index = name.find('r')
#returns 2
name = 'farhad'
index = name.find('a', 2) # finds index of second a
#returns 4

For Regex, use:

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Casting

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Set Operations

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
a = {1,2,3}

Intersect Sets

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
a = {1,2,3}
b = {3,4,5}
c = a.intersection(b)

Difference In Sets

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
a = {1,2,3}
b = {3,4,5}
c = a.difference(b)

Union Of Collections

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
a = {1,2,3}
b = {3,4,5}
c = a.union(b)

Ternary Operator

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Syntax:

[If True] if [Expression] Else [If False]

For example:

Received = True if x = 'Yes' else False

4. Comments

Single Line Comments

#this is a single line comment

Multiple Line Comments

One can use:

```this is a multi
line
comment```

5. Expressions

Expressions can perform boolean operations such as:

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

6. Pickling

Converting an object into a string and dumping the string into a file is known as pickling. The reverse is known as unpickling.

7. Functions

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Define New Function

def my_new_function():
  print('this is my new function')

Calling Function

my_new_function()

Finding Length Of String

Call the len(x) function

len('hello')
prints 5

Arguments

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
def my_new_function(my_value):
  print('this is my new function with ' + my_value)

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

We can pass in optional arguments by providing a default value to an argument:

def my_new_function(my_value='hello'):
  print(my_value)
#Calling
my_new_function() => prints hello
my_new_function('test') => prints test

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

If your function can take in any number of arguments then add a * in front of the parameter name:

def myfunc(*arguments):
  return a
myfunc(a)
myfunc(a,b)
myfunc(a,b,c)

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

It allows you to pass varying number of keyword arguments to a function.

You can also pass in dictionary values as keyword arguments.

Return

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
def my_function(input):
  return input + 2

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
resultA, resultB = get_result()
get_result() can return ('a', 1) which is a tuple

Lambda

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
my_lambda = lambda x,y,z : x - 100 + y - z
my_lambda(100, 100, 100) # returns 0

Syntax:

variable = lambda arguments: expression

Lambda functions can be passed as arguments to other functions.

dir() and help()

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Photo by Noah Silliman on Unsplash

8. Modules

What is a module?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

PYTHONPATH

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

How To Import Modules?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
import MyFirstPythonFile

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
import my_module

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
print(my_module.my_object)

Note: If you do not want the interpreter to execute the module when it is loaded then you can check whether the name == ‘main

2. From

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
from my_module import my_object

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
print(my_object)

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
from my_module import *

Note: Modules are only imported on the first import.

If you want to use C module then you can use PyImport_ImportModule

Use import over from if we want to use the same name defined in two different modules.

9. Packages

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
from packageroot.packagefolder.mod import my_object

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
from data_service.database_data_service.microsoft_sql.mod

Note: Ensure each directory within your package import contains a file init.py.

Feel free to leave the files blank. As init.py files are imported before the modules are imported, you can add custom logic such as start service status checks or to open database connections etc.

PIP

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
pip install package_name

10. Conditions

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
if a = b:
  print 'a is b'
elif a < b:
  print 'a is less than b'
elif a > b:
  print 'a  is greater than b'
else:
  print 'a is different'

Note how colons and indentations are used to express the conditional logic.

Checking Types

if not isinstance(input, int):
  print 'Expected int'
  return None

You can also add conditional logic in the else part. This is known as nested condition.

#let's write conditions within else
else:
 if a = 2:
    print 'within if of else'
 else:
     print 'within else of else'

11. Loops

While

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
while (input < 0):
 do_something(input)
 input = input-1

For

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
for  i in range(0,10)

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
for letter in 'hello'
  print letter

One-Liner For

Syntax:

[Variable] AggregateFunction([Value] for [item] in [collection])

Yielding

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Combine For with If

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
name = 'onename'
anothername = 'onenameonename'
for character in name:
  if character in anothername
     print character

Break

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
for i in range(0,10):
 if (i==5):
   break

while True:
  x = get_value()
  if (x==1):
     break

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
def fib(input):
 if (input <=1):
   print(str(input))
 else:
   first = 0
   second = 1
   count = 0
   for count in range(input):
     result = first + second
     print(first)
     first = second
     second = result
     count += 1
     
fib(7)

12. Recursion

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Let’s implement a factorial recursive function:

Rules:

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Steps:

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
def factorial(n):
  if n==0:
    return 1
  else:
    return n * factorial(n-1)

Another Example: Let’s write Fibonacci recursive function:

Rules:

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

0, 1, 1, 2, 3, 5, 8…

Steps:

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
def fibonacci(n):
 if (n<=1):
   return n
 else:
   return fibonacci(n-1)+fibonacci(n-2)
print(fibonacci(6))

It is important to have an exit check otherwise the function will end up in infinite loop.

13. Frames And Call Stack

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

You can use traceback to find the list of functions if an error is encountered.

14. Collections

Lists

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
my_list = ['A', 'B']

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
my_list.append('C') #adds at the end
my_list[1] = 'D' #update
my_list.pop(1) # removes
or
del my_list[1:2] # removes
my_list.extend(another_list) # adds second list at end

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
my_list.sort()

Tuples

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
my_tuple = tuple()
or
my_tuple = 'f','m'
or
my_tuple = ('f', 'm')

Note: If a tuple contains a list of items then we can modify the list. Also if you assign a value to an object and you store the object in a list and then change the object then the object within the list will get updated.

Dictionaries

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
my_dictionary = dict()
my_dictionary['my_key'] = 1
my_dictionary['another_key'] = 2

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
my_dictionary = {'my_key':1, 'another_key':2}

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
for key in dictionary:
  print key, dictionary[key]

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
dictionary.items() # returns items
#checking if a key exists in a dictionary
if ('some key' in dictionary):
  #do something

Note: If you want to perform vectorised/matrix operations on a list then use NumPy Python package

15. Compilation And Linking

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Compilation:

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Linking:

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

16. Iterators

Iterators

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Filter

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Map

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Reduce

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Zip

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
name = 'farhad'
suffix = [1,2,3,4,5,6]
zip(name, suffix)
--> returns (f,1),(a,2),(r,3),(h,4),(a,5),(d,6)

17. Object Oriented Design — Classes

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
class MyClass:
  def MyClassFunction(self):  #self = reference to the object
    return 5
#Create new instance of MyClass and then call the function
m = MyClass()
returned_value = m.MyClassFunction()

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Note: If a tuple (immutable collection) contains a list (mutable collection) of items then we can modify the list. Also if you assign a value to an object and you store the object in a list and then change the object then the object within the list will get updated.

init

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
class MyClass:
   def __init__(self, first_property):
       self.first_property = first_property
   def MyClassFunction(self):
      return self.first_property

#Create an instance
m = MyClass(123)
r = m.MyClassFunction()
r will be 123

Note: self parameter will contain the reference of the object, also referred to as “this” in other programming languages such as C#

str

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
m = MyClass(123)
print m #Calls __str__

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

cmp

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Overloading

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Shallow Vs Deep Copy Of Objects

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
import copy
m = MyClass(123)
mm = copy.copy(m)

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
import copy
m = MyClass(123)
mm = copy.deepcopy(m)

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

18. Object Oriented Design — Inheritance

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
class ParentClass:
 def my_function(self):
   print 'I am here'

class SubClass1(ParentClass):
class SubClass2(ParentClass):

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Note: Python supports multiple inheritance unlike C#

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Multi-Inheritance:

class A(B,C):  #A implments B and C


  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
super(A, self).function_name()

19. Garbage Collection — Memory Management

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

As multiple objects can share memory references, python employs two mechanisms:

  1. Reference counting: Count the number of items an object is referenced, deallocate an object if its count is 0.
  2. The second mechanism takes care of circular references, also known as cyclic references, by only de-allocating the objects where the allocation — deallocation number is greater than threshold.
  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
import gc
collected_objects = gc.collect()

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

20. I/O

From Keyboard

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
user_says = raw_input()
print(user_says)

Files

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Open files

with open(file path, 'r') as my_file:
  for line in my_file
#File is closed due to with/as

Note: readline() can also be executed to read a line of a file.

To open two files

with open(file path) as my_file, open(another path) as second_file:
  for (line number, (line1, line2)) in enumerate(zip(my_file, second_file):

Writing files

with open(file path, 'w') as my_file:
  my_file.write('test')

Note: Use os and shutil modules for files.

Note: rw — read-write mode and a — append mode.

SQL

Open a connection

import MySQLdb
database = MySQLdb.connect(“host”=”server”, “database-user”=”my username”, “password”=”my password”, “database-name”=”my database”)
cursor = database.cursor()

Execute a SQL statement

cursor.fetch("Select * From MyTable")
database.close()

Web Services

To query a rest service

import requests
url = 'http://myblog.com'
response = requests.get(url).text

To Serialise and Deserialise JSON

Deserialise:

import json
my_json = {"A":"1","B":"2"}
json_object = json.loads(my_json)
value_of_B = json_object["B"]

Serialise:

import json
a = "1"
json_a = json.dumps(a)

21. Error Handling

Raise Exceptions

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
try:
  raise TypError
except:
  print('exception')

Catching Exceptions

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
try:
   do_something()
except:
   print('exception')

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
try:
   do_something()
except TypeError:
   print('exception')

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
try:
   do_something()
except TypeError:
   print('exception')
finally:
   close_connections()

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Try/Except/Else

try:
  do something
except IfTryRaisedException1:
  do something else
except (IfTryRaisedException2, IfTryRaisedException3)
  if exception 2 or 3 is raised then do something
else:
  no exceptions were raised

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
try:
  do something
except Exception1 as my_exception:
  do something about my_exception

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.
assert <bool>, 'error to throw'

Note: Python supports inheritance in exceptions

You can create your own exception class by:

class MyException(Exception): pass

22. Multi-Threading And GIL

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Note: GIL adds overheads to the execution. Therefore, be sure that you want to run multiple threads.

23. Decorators

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

24. Unit Testing In Python

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

1.Assume your function simply decrements an input by 1

def my_function(input):
  return input - 1

  1. You can unit test it by:
import unittest
class TestClass(unittest.TestCase):
 def my_test(self):
    self.assertEqual(my_function(1), 0)) #checking 1 becomes 0

We can also use doctest to test code written in docstrings.

25. Top Python Discussion Questions

Why Should I Use Python?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

How To Make Python Run Fast?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Which IDEs Do People Use?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

What Are The Top Python Frameworks And Packages?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

PyUnit (unit testing), PyDoc (documentation), SciPy (algebera and numerical), Pandas (data management), Sci-Kit learn (ML and data science), Tensorflow (AI), Numpy (array and numerical), BeautifulSoap (web pages scrapping), Flask (microframework), Pyramid (enterprise applications), Django (UI MVVM), urllib (web pages scraping), Tkinter (GUI), mock (mocking library), PyChecker(bug detector), Pylint (module code analysis)

How To Host Python Packages?

  1. Reference counting: Count the number of items an object is referenced, deallocate an object if its count is 0.
  2. The second mechanism takes care of circular references, also known as cyclic references, by only de-allocating the objects where the allocation — deallocation number is greater than threshold.
#(#!/my account/local/bin/python)

  1. You can use command line tool and execute it

  2. Use PyPRI or PyPI server

Can Python and R be combined?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Is there a way to catch errors before running Python?

  • Interpreted high-level object-oriented dynamically-typed scriptinglanguage.
  • Python **interpreter **reads one line of code at a time, interprets it into low level machine language (byte code) and then executes it.
  • As a result, run time errors are usually encountered.

Photo by Curtis MacNewton on Unsplash

Summary

This article outlined the most important 25 concepts of Python in a short, relevant and focused manner. I genuinely hope it has helped someone get better understanding of Python.

I believe I have concentrated on the must know topics which are absolutely required to be understood.This knowledge is sufficient to write your own python packages in the future or using existing Python packages.

Rest, just practice as much as possible and you can implement your own library in Python because this article contains all the knowledge you need.

If you want me to post Python exercises and solutions then please let me know.

Hope it helps.

Thanks for reading ❤

If you liked this post, share it with all of your programming buddies!

What's Python IDLE? How to use Python IDLE to interact with Python?

What's Python IDLE? How to use Python IDLE to interact with Python?

In this tutorial, you’ll learn all the basics of using **IDLE** to write Python programs. You'll know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.

In this tutorial, you'll learn how to use the development environment included with your Python installation. Python IDLE is a small program that packs a big punch! You'll learn how to use Python IDLE to interact with Python directly, work with Python files, and improve your development workflow.

If you’ve recently downloaded Python onto your computer, then you may have noticed a new program on your machine called IDLE. You might be wondering, “What is this program doing on my computer? I didn’t download that!” While you may not have downloaded this program on your own, IDLE comes bundled with every Python installation. It’s there to help you get started with the language right out of the box. In this tutorial, you’ll learn how to work in Python IDLE and a few cool tricks you can use on your Python journey!

In this tutorial, you’ll learn:

  • What Python IDLE is
  • How to interact with Python directly using IDLE
  • How to edit, execute, and debug Python files with IDLE
  • How to customize Python IDLE to your liking

Table of Contents

What Is Python IDLE?

Every Python installation comes with an Integrated Development and Learning Environment, which you’ll see shortened to IDLE or even IDE. These are a class of applications that help you write code more efficiently. While there are many IDEs for you to choose from, Python IDLE is very bare-bones, which makes it the perfect tool for a beginning programmer.

Python IDLE comes included in Python installations on Windows and Mac. If you’re a Linux user, then you should be able to find and download Python IDLE using your package manager. Once you’ve installed it, you can then use Python IDLE as an interactive interpreter or as a file editor.

An Interactive Interpreter

The best place to experiment with Python code is in the interactive interpreter, otherwise known as a shell. The shell is a basic Read-Eval-Print Loop (REPL). It reads a Python statement, evaluates the result of that statement, and then prints the result on the screen. Then, it loops back to read the next statement.

The Python shell is an excellent place to experiment with small code snippets. You can access it through the terminal or command line app on your machine. You can simplify your workflow with Python IDLE, which will immediately start a Python shell when you open it.

A File Editor

Every programmer needs to be able to edit and save text files. Python programs are files with the .py extension that contain lines of Python code. Python IDLE gives you the ability to create and edit these files with ease.

Python IDLE also provides several useful features that you’ll see in professional IDEs, like basic syntax highlighting, code completion, and auto-indentation. Professional IDEs are more robust pieces of software and they have a steep learning curve. If you’re just beginning your Python programming journey, then Python IDLE is a great alternative!

How to Use the Python IDLE Shell

The shell is the default mode of operation for Python IDLE. When you click on the icon to open the program, the shell is the first thing that you see:

This is a blank Python interpreter window. You can use it to start interacting with Python immediately. You can test it out with a short line of code:

Here, you used print() to output the string "Hello, from IDLE!" to your screen. This is the most basic way to interact with Python IDLE. You type in commands one at a time and Python responds with the result of each command.

Next, take a look at the menu bar. You’ll see a few options for using the shell:

You can restart the shell from this menu. If you select that option, then you’ll clear the state of the shell. It will act as though you’ve started a fresh instance of Python IDLE. The shell will forget about everything from its previous state:

In the image above, you first declare a variable, x = 5. When you call print(x), the shell shows the correct output, which is the number 5. However, when you restart the shell and try to call print(x) again, you can see that the shell prints a traceback. This is an error message that says the variable x is not defined. The shell has forgotten about everything that came before it was restarted.

You can also interrupt the execution of the shell from this menu. This will stop any program or statement that’s running in the shell at the time of interruption. Take a look at what happens when you send a keyboard interrupt to the shell:

A KeyboardInterrupt error message is displayed in red text at the bottom of your window. The program received the interrupt and has stopped executing.

How to Work With Python Files

Python IDLE offers a full-fledged file editor, which gives you the ability to write and execute Python programs from within this program. The built-in file editor also includes several features, like code completion and automatic indentation, that will speed up your coding workflow. First, let’s take a look at how to write and execute programs in Python IDLE.

Opening a File

To start a new Python file, select File → New File from the menu bar. This will open a blank file in the editor, like this:

From this window, you can write a brand new Python file. You can also open an existing Python file by selecting File → Open… in the menu bar. This will bring up your operating system’s file browser. Then, you can find the Python file you want to open.

If you’re interested in reading the source code for a Python module, then you can select File → Path Browser. This will let you view the modules that Python IDLE can see. When you double click on one, the file editor will open up and you’ll be able to read it.

The content of this window will be the same as the paths that are returned when you call sys.path. If you know the name of a specific module you want to view, then you can select File → Module Browser and type in the name of the module in the box that appears.

Editing a File

Once you’ve opened a file in Python IDLE, you can then make changes to it. When you’re ready to edit a file, you’ll see something like this:

The contents of your file are displayed in the open window. The bar along the top of the window contains three pieces of important information:

  1. The name of the file that you’re editing
  2. The full path to the folder where you can find this file on your computer
  3. The version of Python that IDLE is using

In the image above, you’re editing the file myFile.py, which is located in the Documents folder. The Python version is 3.7.1, which you can see in parentheses.

There are also two numbers in the bottom right corner of the window:

  1. Ln: shows the line number that your cursor is on.
  2. Col: shows the column number that your cursor is on.

It’s useful to see these numbers so that you can find errors more quickly. They also help you make sure that you’re staying within a certain line width.

There are a few visual cues in this window that will help you remember to save your work. If you look closely, then you’ll see that Python IDLE uses asterisks to let you know that your file has unsaved changes:

The file name shown in the top of the IDLE window is surrounded by asterisks. This means that there are unsaved changes in your editor. You can save these changes with your system’s standard keyboard shortcut, or you can select File → Save from the menu bar. Make sure that you save your file with the .py extension so that syntax highlighting will be enabled.

Executing a File

When you want to execute a file that you’ve created in IDLE, you should first make sure that it’s saved. Remember, you can see if your file is properly saved by looking for asterisks around the filename at the top of the file editor window. Don’t worry if you forget, though! Python IDLE will remind you to save whenever you attempt to execute an unsaved file.

To execute a file in IDLE, simply press the F5 key on your keyboard. You can also select Run → Run Module from the menu bar. Either option will restart the Python interpreter and then run the code that you’ve written with a fresh interpreter. The process is the same as when you run python3 -i [filename] in your terminal.

When your code is done executing, the interpreter will know everything about your code, including any global variables, functions, and classes. This makes Python IDLE a great place to inspect your data if something goes wrong. If you ever need to interrupt the execution of your program, then you can press Ctrl+C in the interpreter that’s running your code.

How to Improve Your Workflow

Now that you’ve seen how to write, edit, and execute files in Python IDLE, it’s time to speed up your workflow! The Python IDLE editor offers a few features that you’ll see in most professional IDEs to help you code faster. These features include automatic indentation, code completion and call tips, and code context.

Automatic Indentation

IDLE will automatically indent your code when it needs to start a new block. This usually happens after you type a colon (:). When you hit the enter key after the colon, your cursor will automatically move over a certain number of spaces and begin a new code block.

You can configure how many spaces the cursor will move in the settings, but the default is the standard four spaces. The developers of Python agreed on a standard style for well-written Python code, and this includes rules on indentation, whitespace, and more. This standard style was formalized and is now known as PEP 8. To learn more about it, check out How to Write Beautiful Python Code With PEP 8.

Code Completion and Call Tips

When you’re writing code for a large project or a complicated problem, you can spend a lot of time just typing out all of the code you need. Code completion helps you save typing time by trying to finish your code for you. Python IDLE has basic code completion functionality. It can only autocomplete the names of functions and classes. To use autocompletion in the editor, just press the tab key after a sequence of text.

Python IDLE will also provide call tips. A call tip is like a hint for a certain part of your code to help you remember what that element needs. After you type the left parenthesis to begin a function call, a call tip will appear if you don’t type anything for a few seconds. For example, if you can’t quite remember how to append to a list, then you can pause after the opening parenthesis to bring up the call tip:

The call tip will display as a popup note, reminding you how to append to a list. Call tips like these provide useful information as you’re writing code.

Code Context

The code context functionality is a neat feature of the Python IDLE file editor. It will show you the scope of a function, class, loop, or other construct. This is particularly useful when you’re scrolling through a lengthy file and need to keep track of where you are while reviewing code in the editor.

To turn it on, select Options → Code Context in the menu bar. You’ll see a gray bar appear at the top of the editor window:

As you scroll down through your code, the context that contains each line of code will stay inside of this gray bar. This means that the print() functions you see in the image above are a part of a main function. When you reach a line that’s outside the scope of this function, the bar will disappear.

How to Debug in IDLE

A bug is an unexpected problem in your program. They can appear in many forms, and some are more difficult to fix than others. Some bugs are tricky enough that you won’t be able to catch them by just reading through your program. Luckily, Python IDLE provides some basic tools that will help you debug your programs with ease!

Interpreter DEBUG Mode

If you want to run your code with the built-in debugger, then you’ll need to turn this feature on. To do so, select Debug → Debugger from the Python IDLE menu bar. In the interpreter, you should see [DEBUG ON] appear just before the prompt (>>>), which means the interpreter is ready and waiting.

When you execute your Python file, the debugger window will appear:

In this window, you can inspect the values of your local and global variables as your code executes. This gives you insight into how your data is being manipulated as your code runs.

You can also click the following buttons to move through your code:

  • Go: Press this to advance execution to the next breakpoint. You’ll learn about these in the next section.
  • Step: Press this to execute the current line and go to the next one.
  • Over: If the current line of code contains a function call, then press this to step over that function. In other words, execute that function and go to the next line, but don’t pause while executing the function (unless there is a breakpoint).
  • Out: If the current line of code is in a function, then press this to step out of this function. In other words, continue the execution of this function until you return from it.

Be careful, because there is no reverse button! You can only step forward in time through your program’s execution.

You’ll also see four checkboxes in the debug window:

  1. Globals: your program’s global information
  2. Locals: your program’s local information during execution
  3. Stack: the functions that run during execution
  4. Source: your file in the IDLE editor

When you select one of these, you’ll see the relevant information in your debug window.

Breakpoints

A breakpoint is a line of code that you’ve identified as a place where the interpreter should pause while running your code. They will only work when DEBUG mode is turned on, so make sure that you’ve done that first.

To set a breakpoint, right-click on the line of code that you wish to pause. This will highlight the line of code in yellow as a visual indication of a set breakpoint. You can set as many breakpoints in your code as you like. To undo a breakpoint, right-click the same line again and select Clear Breakpoint.

Once you’ve set your breakpoints and turned on DEBUG mode, you can run your code as you would normally. The debugger window will pop up, and you can start stepping through your code manually.

Errors and Exceptions

When you see an error reported to you in the interpreter, Python IDLE lets you jump right to the offending file or line from the menu bar. All you have to do is highlight the reported line number or file name with your cursor and select Debug → Go to file/line from the menu bar. This is will open up the offending file and take you to the line that contains the error. This feature works regardless of whether or not DEBUG mode is turned on.

Python IDLE also provides a tool called a stack viewer. You can access it under the Debug option in the menu bar. This tool will show you the traceback of an error as it appears on the stack of the last error or exception that Python IDLE encountered while running your code. When an unexpected or interesting error occurs, you might find it helpful to take a look at the stack. Otherwise, this feature can be difficult to parse and likely won’t be useful to you unless you’re writing very complicated code.

How to Customize Python IDLE

There are many ways that you can give Python IDLE a visual style that suits you. The default look and feel is based on the colors in the Python logo. If you don’t like how anything looks, then you can almost always change it.

To access the customization window, select Options → Configure IDLE from the menu bar. To preview the result of a change you want to make, press Apply. When you’re done customizing Python IDLE, press OK to save all of your changes. If you don’t want to save your changes, then simply press Cancel.

There are 5 areas of Python IDLE that you can customize:

  1. Fonts/Tabs
  2. Highlights
  3. Keys
  4. General
  5. Extensions

Let’s take a look at each of them now.

Fonts/Tabs

The first tab allows you to change things like font color, font size, and font style. You can change the font to almost any style you like, depending on what’s available for your operating system. The font settings window looks like this:

You can use the scrolling window to select which font you prefer. (I recommend you select a fixed-width font like Courier New.) Pick a font size that’s large enough for you to see well. You can also click the checkbox next to Bold to toggle whether or not all text appears in bold.

This window will also let you change how many spaces are used for each indentation level. By default, this will be set to the PEP 8 standard of four spaces. You can change this to make the width of your code more or less spread out to your liking.

Highlights

The second customization tab will let you change highlights. Syntax highlighting is an important feature of any IDE that highlights the syntax of the language that you’re working in. This helps you visually distinguish between the different Python constructs and the data used in your code.

Python IDLE allows you to fully customize the appearance of your Python code. It comes pre-installed with three different highlight themes:

  1. IDLE Day
  2. IDLE Night
  3. IDLE New

You can select from these pre-installed themes or create your own custom theme right in this window:

Unfortunately, IDLE does not allow you to install custom themes from a file. You have to create customs theme from this window. To do so, you can simply start changing the colors for different items. Select an item, and then press Choose color for. You’ll be brought to a color picker, where you can select the exact color that you want to use.

You’ll then be prompted to save this theme as a new custom theme, and you can enter a name of your choosing. You can then continue changing the colors of different items if you’d like. Remember to press Apply to see your changes in action!

Keys

The third customization tab lets you map different key presses to actions, also known as keyboard shortcuts. These are a vital component of your productivity whenever you use an IDE. You can either come up with your own keyboard shortcuts, or you can use the ones that come with IDLE. The pre-installed shortcuts are a good place to start:

The keyboard shortcuts are listed in alphabetical order by action. They’re listed in the format Action - Shortcut, where Action is what will happen when you press the key combination in Shortcut. If you want to use a built-in key set, then select a mapping that matches your operating system. Pay close attention to the different keys and make sure your keyboard has them!

Creating Your Own Shortcuts

The customization of the keyboard shortcuts is very similar to the customization of syntax highlighting colors. Unfortunately, IDLE does not allow you to install custom keyboard shortcuts from a file. You must create a custom set of shortcuts from the Keys tab.

Select one pair from the list and press Get New Keys for Selection. A new window will pop up:

Here, you can use the checkboxes and scrolling menu to select the combination of keys that you want to use for this shortcut. You can select Advanced Key Binding Entry >> to manually type in a command. Note that this cannot pick up the keys you press. You have to literally type in the command as you see it displayed to you in the list of shortcuts.

General

The fourth tab of the customization window is a place for small, general changes. The general settings tab looks like this:

Here, you can customize things like the window size and whether the shell or the file editor opens first when you start Python IDLE. Most of the things in this window are not that exciting to change, so you probably won’t need to fiddle with them much.

Extensions

The fifth tab of the customization window lets you add extensions to Python IDLE. Extensions allow you to add new, awesome features to the editor and the interpreter window. You can download them from the internet and install them to right into Python IDLE.

To view what extensions are installed, select Options → Configure IDLE -> Extensions. There are many extensions available on the internet for you to read more about. Find the ones you like and add them to Python IDLE!

Conclusion

In this tutorial, you’ve learned all the basics of using IDLE to write Python programs. You know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.

You’ve learned how to:

  • Work with the Python IDLE shell
  • Use Python IDLE as a file editor
  • Improve your workflow with features to help you code faster
  • Debug your code and view errors and exceptions
  • Customize Python IDLE to your liking

Now you’re armed with a new tool that will let you productively write Pythonic code and save you countless hours down the road. Happy programming!

Importance of Python Programming skills

Importance of Python Programming skills

Python is one among the most easiest and user friendly programming languages when it comes to the field of software engineering. The codes and syntaxes of python is so simple and easy to use that it can be deployed in any problem solving...

Python is one among the most easiest and user friendly programming languages when it comes to the field of software engineering. The codes and syntaxes of python is so simple and easy to use that it can be deployed in any problem solving challenges. The codes of Python can easily be deployed in Data Science and Machine Learning. Due to this ease of deployment and easier syntaxes, this platform has a lot of real world problem solving applications. According to the sources the companies are eagerly hunting for the professionals with python skills along with SQL. An average python developer in the united states makes around 1 lakh U.S Dollars per annum. In some of the top IT hubs in our country like Bangalore, the demand for professionals in the domains of Data Science and Python Programming has surpassed over the past few years. As a result of which a lot of various python certification courses are available right now.

Array in Python: An array is defined as a data structure that can hold a fixed number of elements that are of the same python data type. The following are some of the basic functions of array in python:

  1. To find the transverse
  2. For insertion of the elements
  3. For deletion of the elements
  4. For searching the elements

Along with this one can easily crack any python interview by means of python interview questions

Tkinter Python Tutorial | Python GUI Programming Using Tkinter Tutorial | Python Training

This video on Tkinter tutorial covers all the basic aspects of creating and making use of your own simple Graphical User Interface (GUI) using Python. It establishes all of the concepts needed to get started with building your own user interfaces while coding in Python.

This video on Tkinter tutorial covers all the basic aspects of creating and making use of your own simple Graphical User Interface (GUI) using Python. It establishes all of the concepts needed to get started with building your own user interfaces while coding in Python.

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Original video source: https://www.youtube.com/watch?v=VMP1oQOxfM0