class doubleHashTable:
    # initialize hash Table
    def _init_(self):
        self.size = int(input("Enter the Size of the hash table : "))
        
        # initialize table with all elements 0
        self.table = list(None for i in range(self.size))
        self.elementCount = 0
        self.comparisons = 0
   
   
    # method that checks if the hash table is full or not
    def isFull(self):
        if self.elementCount == self.size:
            return True
        else:
            return False
      
    
    # First hash function
    def h1(self, element):
        return element % self.size
       
    # Second hash function
    def h2(self, element):
        return 5-(element % 5)
           
   
    # method to resolve collision by double hashing method
    def doubleHashing(self, record):
        posFound = False
        # limit variable is used to restrict the function from going into infinite loop
        # limit is useful when the table is 80% full
        limit = self.size
        i = 1
        # start a loop to find the position
        while i <= limit:
            # calculate new position by quadratic probing
            newPosition = (self.h1(record.get_number()) + i*self.h2(record.get_number())) % self.size
            # if newPosition is empty then break out of loop and return new Position
            if self.table[newPosition] == None:
                posFound = True
                break
            else:
                # as the position is not empty increase i
                i += 1
        return posFound, newPosition
 
       
    # method that inserts element inside the hash table
    def insert(self, record):
        # checking if the table is full
        if self.isFull():
            print("Hash Table Full")
            return False
           
        posFound = False
       
        position = self.h1(record.get_number())
           
        # checking if the position is empty
        if self.table[position] == None:
            # empty position found , store the element and print the message
            self.table[position] = record
            print("Phone number of " + record.get_name() + " is at position " + str(position))
            isStored = True
            self.elementCount += 1
       
        # If collision occured 
        else:
            print("Collision has occured for " + record.get_name() + "'s phone number at position " + str(position) + " finding new Position.")
            while not posFound:
                posFound, position = self.doubleHashing(record)
                if posFound:
                    self.table[position] = record
                    #print(self.table[position])
                    self.elementCount += 1
                    #print(position)
                    #print(posFound)
                    print("Phone number of " + record.get_name() + " is at position " + str(position))
 
        return posFound
       
 
    # searches for an element in the table and returns position of element if found else returns False
    def search(self, record):
        found = False
        position = self.h1(record.get_number())
        self.comparisons += 1

        if(self.table[position] != None):
            if(self.table[position].get_name() == record.get_name()):
                print("Phone number found at position {}".format(position) + " and total comparisons are " + str(1))
                return position
           
            # if element is not found at position returned hash function
            # then we search element using double hashing
            else:
                limit = self.size
                i = 1
				
                newPosition = position
                # start a loop to find the position
                while i <= limit:
                    # calculate new position by double Hashing
                    position = (self.h1(record.get_number()) + i*self.h2(record.get_number())) % self.size
                    self.comparisons += 1
                    # if element at newPosition is equal to the required element
                   
                    if(self.table[position] != None):
                        if self.table[position].get_name() == record.get_name():
							
                            found = True
                            break
                       
                        elif self.table[position].get_name() == None:
                            found = False
                            break
                           
                        else:
                            # as the position is not empty increase i
                            i += 1
							
							
            if found:
                print("Phone number found at position {}".format(position) + " and total comparisons are " + str(i+1))
				#return position
            else:
                print("Record not Found")
                return found           
   
   
    # method to display the hash table
    def display(self):
        print("\n")
        for i in range(self.size):
            print("Hash Value: "+str(i) + "\t\t" + str(self.table[i]))
        print("The number of phonebook records in the Table are : " + str(self.elementCount)) 
by

Python Online Compiler

Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.

Taking inputs (stdin)

OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.

import sys
name = sys.stdin.readline()
print("Hello "+ name)

About Python

Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.

Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code 

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset = {"iPhone","Pixel","Samsung"}
print(myset)

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)

Supported Libraries

Following are the libraries supported by OneCompiler's Python compiler

NameDescription
NumPyNumPy python library helps users to work on arrays with ease
SciPySciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learnScikit-learn or Scikit-learn is the most useful library for machine learning in Python
PandasPandas is the most efficient Python library for data manipulation and analysis
DOcplexDOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling