Python

What is Python - From python.org

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. 
Its high-level built in data structures, combined with dynamic typing and dynamic binding, 
make it very attractive for Rapid Application Development, as well as for use as a scripting 
or glue language to connect existing components together. 

Python's simple, easy to learn syntax emphasizes readability and 
therefore reduces the cost of program maintenance. Python supports modules and 
packages, which encourages program modularity and code reuse. The Python interpreter 
and the extensive standard library are available in source or binary form without charge 
for all major platforms, and can be freely distributed.

Navigation

Check each folder in the order of listing 
Inside each folder will contain its own resources

Important Python Reference - For details check python folders

Function:
def my_function():
  print("Hello from a function")


Constructor:
class MotorBike:
    def __init__(self, speed):
        self.speed = speed


Abstract Class:
from abc import ABC, abstractmethod 
class AbstractAnimal(ABC):
    @abstractmethod
    def bark(self): pass


Exception Handling:
Python exception handling:
try:
	<block of statements>
except: 
	<block of statements>
else: 
	<block of statements>
finally:
	<block of statements>


List Data Structure: 
marks = [23, 56, 67]


Set Data Structure: 
# Does not allow duplicates (its ignored) and does not maintain order. 
marks = {23, 56, 67}


Dictionary:
occurances = dict(a=5, b=6, c=8)

Tuples:
# Immutable sequence of values
person = ('Alice', 30, 'USA')

List Comprehension:
numbers = ['Zero', 'One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven', 'Eight', 'Nine']
numbers_length_four = []
numbers_length_four = [number for number in numbers if len(number) == 4]
print(numbers_length_four)  # ['Zero', 'Four', 'Five', 'Nine']

Important Pandas Reference - For details check pandas folders

Series:

# This will return Series with autogenerated index 
adjectives = pd.Series(["Smart", "Handsome", "Charming", "Brilliant", "Humble", "Smart"])
print(adjectives)

# This will create Series with our own index and data 
fruits = ["Apple", "Orange", "Plum", "Grape", "Blueberry", "Watermelon"]
weekdays = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Monday"]
print(pd.Series(index=weekdays, data=fruits))

# Create Series with Dictionary
attack_powers = dict(Grass= 10,Fire= 15,Water= 15,Fairy_Fighting= 20,Grass_Psychic= 50)
print(pd.Series(attack_powers))

# 0r the below way 
attack_powers = {
    "Grass": 10,
    "Fire": 15,
    "Water": 15,
    "Fairy, Fighting": 20,
    "Grass, Psychic": 50
}
print(pd.Series(attack_powers))

# or the below way
attack_powers = pd.Series({
    "Grass": 10,
    "Fire": 15,
    "Water": 15,
    "Fairy, Fighting": 20,
    "Grass, Psychic": 50
})
print(attack_powers)

# Import from CSV
# Squeeze converts DataFrame to Series 
google = pd.read_csv("google_stock_price.csv", usecols=["Price"]).squeeze("columns")

# Import csv file with index column from 'Name'
pokemon = pd.read_csv("pokemon.csv", index_col="Name").squeeze("columns")

# Extract value by Index Location
print(pokemon.iloc[700:1010])
# Two ways of extracting value by Index Label
print(pokemon.loc["Meowth"])
print(pokemon.get("Meowth"))
# Extract multiple values by Index Label
print(pokemon.loc[["Charizard", "Jolteon", "Meowth"]])
# Extract value by Index Label with fall back value if not present
print(pokemon.get("Digimon", "Nonexistent"))

Reference

https://www.python.org/doc/essays/blurb/