 import numpy as np
from scipy.stats import chisquare

def calculate_chi_square(dataset1, dataset2):
  """Calculates the chi-square statistic between two datasets.

  Args:
    dataset1: A numpy array containing the first dataset.
    dataset2: A numpy array containing the second dataset.

  Returns:
    A float containing the chi-square statistic.
  """
  Dataset01 = [1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2]
  Dataset02 = [2, 1, 2, 2, 1, 2, 1]
  
  observed = np.array([np.sum(dataset1 == 1), np.sum(dataset1 == 2)])
  expected = np.array([np.sum(dataset2 == 1), np.sum(dataset2 == 2)])

  chi_square, p_value = chisquare(observed, expected)

  return chi_square, p_value

Dataset01 = [1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2]
Dataset02 = [2, 1, 2, 2, 1, 2, 1]
# Calculate the chi-square statistic and significance of correlation between the two datasets.
chi_square, p_value = calculate_chi_square(Dataset01, Dataset02)

print('Chi-square statistic:', chi_square)
print('Significance of correlation:', p_value)

import random

def extend_dataset(dataset, chi_square, p_value):
  """Extends a dataset while maintaining the same chi-square statistic and significance of correlation.

  Args:
    dataset: A numpy array containing the dataset to be extended.
    chi_square: The chi-square statistic of the original dataset.
    p_value: The significance of correlation of the original dataset.

  Returns:
    A numpy array containing the extended dataset.
  """

  extended_dataset = dataset.copy()

  while len(extended_dataset) < len(Dataset01):
    # Add a random number of 1s and 2s to the extended dataset.
    random_number = random.randint(1, 2)
    extended_dataset = np.append(extended_dataset, random_number)

    # Calculate the chi-square statistic and significance of correlation of the extended dataset.
    new_chi_square, new_p_value = calculate_chi_square(Dataset01, extended_dataset)

    # If the chi-square statistic and significance of correlation of the extended dataset are different from the original dataset, discard the extended dataset and try again.
    if new_chi_square != chi_square or new_p_value != p_value:
      extended_dataset = dataset.copy()

  return extended_dataset

# Extend Dataset 02 to the same number of data as Dataset 01, while maintaining the chi-square statistic and significance of correlation.
Dataset02 = extend_dataset(Dataset02, chi_square, p_value)

print('Extended Dataset 02:', Dataset02)

created 1 year ago

Python Online Compiler

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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

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