Cheat Sheets #3: Python Essentials, Object Oriented, Data Structures, Complexities, Flask

1. Python Basics Cheat Sheet



Basic rules to write Python syntax:

Rule #1: Python is white-space dependent; code blocks are indented using spaces.
Rule #2: Python language is case sensitive. It matters for variables, functions and any keyword in general.

Data Types

  • Numbers: a=2(Integer), b=2.0(Float), c=1+2j(Complex)
  • List: a=[1,2,3,’Word’]
  • Tuple: a= (1,2,4)
  • String: a=“New String”
  • Sets: a= {2,3,4,5}
  • Dictionary: x= {‘a’: [1,2],‘b’: [4,6]


Numeric Operator (Say, a holds 5, b holds 10)

  • a + b = 15
  • a — b = -5
  • a * b = 50
  • b/a = 2
  • b % a = 0
  • a**b =510
  • 0//2.0 = 3.0, -11//3 = -4

Comparison Operator

  • (a == b): not true
  • (a!= b): true
  • (a > b): not true.
  • (a > b): not true
  • (a >= b): not true
  • (a <= b) is true

Boolean Operator

  • a and b
  • a or b
  • not a


List Operations

  • List=[]:Defines an empty list
  • list[i]=a: Stores a at the ith position
  • list[i]: Retrieves the character at the ith position
  • list[i:j]: Retrieves characters in the range i to j
  • append(val): Adds item at the end
  • pop([i]): Removes and returns item at index i

String Operations

  • String[i]: Retrieves the character at the ith position
  • String[i:j]: Retrieves characters in the rangei to j

Dictionary Operations

  • dict={} : Defines an empty dictionary
  • dict[i]=a: stores “a” to the key “i”
  • dict[i]: Retrieves the item with the key “i”
  • key: Gives all the key items
  • values: Gives all the values

Flow Control Method:

if-else (Conditional Statement):

For loop (Iterative Loop Statement):

While loop (Conditional Loop Statement):


def new_function():
print(“Hello World”)

Lambda Function

lambda a,b: a+b
lambda a,b: a*b

Generic Operations

  • range(5): 0,1,2,3,4
  • S=input(“Enter:”)
  • Len(a): Gives item count in a
  • min(a): Gives minimum value in a
  • max(a): Gives minimum value in a
  • sum(a): Adds up items of an iterable and returns sum
  • sorted(a): Sorted list copy of a
  • importing modules: import random

File Operations:

  • “r” — Read — Default value. Opens a file for reading, error if the file does not exist
  • “a” — Append — Opens a file for appending, creates the file if it does not exist
  • “w” — Write — Opens a file for writing, creates the file if it does not exist
  • “x” — Create — Creates the specified file, returns an error if the file exists

Try & Except Block:

[Statement body block]
raise Exception()
except Exception as e:
[Error processing block]

Oops Concepts

  • Inheritance: A process of using details from a new class without modifying existing class.
  • Polymorphism: A concept of using common operation in different ways for different data input.
  • Encapsulation: Hiding the private details of a class from other objects.
Source :-

Class/object Example:

Class: class Pen:

Data Structures

The areas in which Data Structures are applied:

  • Compiler design
  • Operating system
  • Database Management System
  • Statistical Analysis Package
  • Numerical Analysis
  • Graphics
  • Artificial Intelligence
  • Simulations

Data structures used in the following areas:

  • RDBMS: Array (Array of structure)
  • Network data model: Graph
  • Hierarchical Data model: Trees

Types of Data Structures:

Primitive Data Structures:

  • We can use * to repeat the string for a specific number of times
  • String can be sliced, that is to select parts of the string
z1 = x[2:]
# Slicing
z2 = y[0] + y[1]
Output: ke
  • To capitalize the strings
  • To retrieve the length of the strings
str1 = "Cake 4 U"
str2 = "404"
  • To replace parts of a string with another string
str1.replace('4 U', str2)
  • Boolean: It is a built-in data type that can take the values TRUE or FALSE

Non- Primitive Data Structures:

  • Array: It is a compact way of collecting data types where all entries must be of the same data type.
import array as arr
a = arr.array("I",[3,6,9])
  • Linked list: List in Python is used to store collection of heterogeneous items. It is described using the square brackets [] and hold elements separated by comma
x = [] # Empty list
  • Stack: It is a container of objects that can be inserted or removed according to LIFO (Last in First Out) pop() method is used during disposal in Python
stack.pop() # Bottom -> 1 -> 2 -> 3 -> 4 -> 5 (Top)
stack.pop() # Bottom -> 1 -> 2 -> 3 -> 4 (Top)
  • Queue: It is a container of objects that can be inserted or removed according to FIFO (First in First Out)
  • Graph: It is a data structure that consists of a finite set of vertices called nodes, and a finite set of ordered pair (u,v) called edges. It can be classified as direction and weight
  • Binary Tree: Tree is a hierarchical data structure. Here each node has at most two children
  • Binary Search Tree: It provides moderate access/ search and moderate insertion/ deletion
  • Heap: It is a complete tree and is suitable to be stored in an array, it is either MIN or Max
  • Hashing: Collection of items that are stored in a way that it becomes easy to find them is hashing

Lists and tuples (In Python):

  • To initialize empty list /tuple:
Lists: myList = []
Tuples: myTuple = ()
  • To specify size of tuple/list:
  • To get an element in position x in list/tuple:
"x" in myList­OrT­uple
  • Index of element ‘X’ of list/tuple
myLis­tOr­Tup­le.i­nd­ex(­"­x") -- If not found, throws a Value­Error exception
  • Number of occurrences of X in list/tuple:
  • Update an item of List/tuple:
Lists: myList[x] = "x"
Tuples: tuples are immutable!
  • Remove element in position X of list/tuple:
Lists: del myList[x]
Tuples: tuples are immutable!
  • Concatenate two lists/tuples:
Lists: myList1 + myList2
Tuples: myTuple1 + myTuple2
Concatenating a List and a Tuple will produce a TypeE­rror exception
  • Insert element in position x of a list/t­uple
Lists: myLis­t.i­nse­rt(x, "value")
Tuples: tuples are immutable!
  • Append “­x” to a list/t­uple:
Syntax: Lists: myList.append("x")
Tuples: tuples are immutable!
  • Convert a list/tuple to tuple/list:
Syntax: List to Tuple: tuple(myList)
Tuple to List: list(­myT­uple)


  • To initialize an empty set:
Syntax: mySet = set()
  • Initialize a non-empty set
Syntax: mySet = set(el­ement1, elemen­t2...)
  • To add element X to the set
  • Remove element “­x” from a set:
Method 1:­mov­e("x­") -- If "­x" is not present, raises a KeyErorr
Method 2: mySet.di­sca­rd(­"­x") -- Removes the element, if present
  • Remove every element from the set
  • Check if “­x” is in the set
Syntax: "x" in mySet
  • Union of two sets
Method 1: mySet1.union(mySet2)
Method 2: mySet1 | mySet2
  • Inters­ection of two sets
Method 1: mySet1.intersect(mySet2)
Method 2: mySet1 & mySet2
  • Difference of two sets
Method 1: mySet1.difference(mySet2)
Method 2: mySet1 - mySet2
  • Symmetric difference of two sets
Method 1: mySet1.symmetric_difference(mySet2)
Method 2: mySet1 ^ mySet2
  • Size of the sets:


  • Initialize an empty Dict
myDict = {}
  • Add an element with key “­k” to the Dict
myDic­t["k­"] = value
  • Update the element with key “­k”
myDic­t["k­"] = newValue
  • Get element with key “­k”
myDic­t["k­"] -- If the key is not present, a KeyError is raised
  • Check if the dictionary has key “­k”
"k" in myDict
  • Get the list of keys
  • Get the size of the dictionary
  • Delete element with key “­k” from the dictionary
del myDict­["k"]
  • Delete all the elements in the dictionary

Algorithms and the complexities:

Symbol Table:

Flask — Web app development Framework


Flask for Django users comparison




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Speedster Saurabh

Speedster Saurabh

Don’t be intimidated by jargon. For example, a model is just a fancy word for “recipe.”

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