blob_id string | repo_name string | path string | length_bytes int64 | score float64 | int_score int64 | text string | is_english bool |
|---|---|---|---|---|---|---|---|
a0bb7d1e40305b400b3f64447cfc2af80c3a47a1 | achang6/wallstory | /pytuts/monkey/calc_ke.py | 503 | 4.1875 | 4 | # calculate kinetic energy
# welcome message
print('This program calculates the kinetic energy of a moving object.')
# receive mass
m_string = input('Enter the object\'s mass in kilograms: ')
# convert string input to float
m = float(m_string)
# receive velocity
v_string = input('Enter the object\'s velocity in m/s:... | true |
94c2f892a841e7e85e3b4b8668fa0c3c03aab561 | JagritiG/object_oriented_python | /15_polymorphism.py | 2,845 | 4.65625 | 5 | # Example of polymorphism
# Todo: Example of inbuilt polymorphic functions:
print(len("Python")) # len() returns length of a string
print(len([1, 2, 3, 4, 5])) # len() returns length of a list
# Todo: Example of user defined polymorphic function
def add(num1, num2, *args):
return num1 + num2 + sum(num for ... | true |
f72d71630eaebcdcf010cbc8c3abd3d5a8f2bfba | David-Lisboa/Python-Basico---Oceean | /02OperacoesMatematica.py | 424 | 4.21875 | 4 | # Operações Matematicas
soma = 1 + 2
subtracao = 3 - 1
multiplicacao = 3 * 2
divisao = 5 / 3
divisao_int = 5 // 3
print(soma)
print(type(soma))
print(subtracao)
print(type(subtracao))
print(multiplicacao)
print(type(multiplicacao))
print(divisao)
print(type(divisao))
print(divisao_int)
print(type(divisao_int))
p... | false |
4ce07b2cd30f6087d42555e915e9369574f6dde0 | trishulg/Lectures | /Lec4/SavingsProgram.py | 548 | 4.25 | 4 | # Get information from the user ? Input
balance = float(input('How much do u want to save : '))
if balance <= 0:
print('Looks like you already have enough')
balance = 0
payment = 1
else:
payment = float(input('How much will you save each period: '))
if payment <= 0:
payment = float(input('e... | true |
866f2ddd60097928168b3a1510f042e5cd4b9880 | Latas2001/python-program | /if elif else.py | 311 | 4.1875 | 4 | n=input("Enter the no.")
if n%2!=0:
print("This is odd no.")
print("Weird")
for n in range(2,5):z
elif n%2==0:
print("not weird")
break
for n in range(6,20):
elif n%2==0:
print("weird")
break
elif n%2==0 and n>20:
print("not weird")
else:
print("good bye"):
| false |
66e60bf7b0d5ef02153b9ba4bb0b99e405758a45 | Latas2001/python-program | /birthday reminder.py | 721 | 4.375 | 4 | dict={}
while True:
print("_______________Birthday App________________")
print("1.Show Birthday")
print("2.Add to Birthday List")
print("3.Exit")
choice = int(input("Enter the choice: "))
if choice==1:
if len(dict.keys())==0:
print("nothing to show....")
else:
... | true |
74cf296823ec6ed1d27702f4e8e778b488badf00 | shreyasingh18/HSBC-2021-WFS1-DEMOS | /python-examples/if_demo.py | 264 | 4.21875 | 4 | x = int(input("Enter value for x: "))
y = int(input("Enter value for y: "))
if x > y:
print("if: x > y")
print("if: x = ", x, "y = ", y)
else:
print("else: inside the else condition")
print("else: x = ", x, "y = ", y)
print("outside if condition") | false |
d115b3c8fd28f43e7259473a048e960b1f65423d | shreyasingh18/HSBC-2021-WFS1-DEMOS | /python-examples/nested_list_demo.py | 361 | 4.25 | 4 | items = [[1, 4, 3], [5, 8, 9, 10]]
#above list is nested list
print(len(items))
#finding the number of items of the particular index
print(len(items[0]))
#for loop to iterate the list
for x in items:
print(x)
print("--------------")
for x in items:
for y in x:
print(y)
print(items)
#deleting the items... | true |
fc3331e287fc69733621fe8b34ad67cddc70e270 | anishmarathe007/Assignments | /bookManagement.py | 1,199 | 4.1875 | 4 | data = {}
def insertIntoBook(name,author):
if name not in data.keys():
data[name] = author
print("Book Successfully Inserted!")
else:
print("Book with the same name already exists!")
def search(name):
if name in data.keys():
print(name, "Present. Author name is : ", data[name])
else:
print(... | true |
97a6b7d3bccc444a72db5cd4fa60b184aff9468a | shotokan/web_scraping_examples | /q1/d.py | 879 | 4.6875 | 5 | def _is_multiple_of_six(number):
"""
Function utility used to check if a number is multiple of six
:param number:
:return:
"""
return (number % 6) == 0
def _is_multiple_of_seven(number):
"""
Function utility used to check if a number is multiple of seven
:param number:
:return:... | true |
26be92cae05d7c34988a82611d73208c5092ebd5 | ktn-andrea/Scripts | /03/palindrom.py | 735 | 4.15625 | 4 | #!/usr/bin/env python3
def is_palindrome1(s):
return s == s[::-1]
def is_palindrome2(s):
i = 0
p = False
while i <= len(s)/2:
if s[i] == s[len(s)-i-1]:
p = True
else:
return False
i += 1
return p
def is_palindrome3(s):
if len(s) < 2:
... | false |
5dcb94625f39b6b106041a51f63baef27ce59177 | jeremytedwards/data-structures | /src/data_structures/sort_insertion.py | 1,801 | 4.125 | 4 | # coding=utf-8
import random
import timeit
def sort_insertion(origin_list):
"""Implement insertion sort."""
if len(origin_list) == 0:
return origin_list
else:
sorted_list = [origin_list.pop(0)]
while len(origin_list) > 0:
item = origin_list.pop(0)
for index,... | true |
d1fcc3ea09823f097bed3a97aaa43abd71a5ec9a | ayesha53/ayeshajawaid | /calculator.py | 435 | 4.25 | 4 | first_number=int(input("Enter number "))
second_number=int(input("Enter number "))
operator=input("Enter operator")
if operator=="+":
print(int(first_number)+int(second_number))
elif operator=="-":
print(int(first_number)-int(second_number))
elif operator=="*":
print(int(first_number)*int(second_number))
el... | false |
8d2680094b60b6fd59369f3031bb725c952acfde | ravindrasinghinbox/learn-python | /python-w3schools-v1/list.py | 1,427 | 4.1875 | 4 | # mylist = ["a", "c", "b"]
# print(mylist)
# print(mylist[0])
# print(mylist[-1])
# print(mylist[1:2])
# thislist = ["apple", "banana", "cherry", "orange", "kiwi", "melon", "mango"]
# print(thislist[-4:-1])
# # change list value
# mylist = ["a", "b", "c"]
# mylist[0] = "A"
# print(mylist)
# # loop list
... | false |
778636921c820b819ca1f8a698183fa15fc290a1 | ravindrasinghinbox/learn-python | /python-w3schools-v1/if-else.py | 786 | 4.40625 | 4 | # if
if(2>1):
print("2 is greater than 1")
# elif
if(5<1):
print("5 is less than 1")
elif (4 > 2):
print("4 is greater than 2")
# else
if(5<1):
print("5 is less than 1")
elif (4 < 2):
print("4 is less than 2")
else:
print("No input")
# short hand if
if(4<5):print("4 is greater th... | false |
2683e9f5a7b57b2281df5d8b05b8245cc319660a | sudiptoshahin/pythonmachinelearningbasic | /inputs.py | 1,324 | 4.1875 | 4 | #!/usr/bin/env python
# coding: utf-8
# In[ ]:
"""
input() and raw_input() both function take a string as
an argument and displays it as promot in shell. it waits for
the user to hit enter
for raw_input(), input line is treated as string and becomes
the value returend by the function
nput treats the typed line ... | true |
c75d6af83883238345dd0330087cddb25e655803 | shahnaaz20/debugging_part_4 | /cipher2.py | 927 | 4.34375 | 4 | def encrypt(message):
ascii_message = [ord(char)+3 for char in message]
encrypt_message = [ chr(char) for char in ascii_message]
return(''.join(encrypt_message))
def decrypt(message):
ascii_message = [ord(char)-3 for char in message]
decrypt_message = [ chr(char) for char in ascii_message]
r... | false |
75def06a03b2dd42b7af41318c36c5a53592247f | emojipeach/euler_problems_python | /0002.py | 664 | 4.28125 | 4 | print("""Each new term in the Fibonacci sequence is generated by adding the previous two terms. By starting with 1 and 2, the first 10 terms will be:""")
print("""1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...""")
print("""By considering the terms in the Fibonacci sequence whose values do not exceed four million, find the su... | true |
304bd6076896e403fd2973d669f1469b255220d4 | CrystalBRana/LabProjects2 | /q_n0_ 8.py | 299 | 4.375 | 4 | ''' Write a Python program which accepts the radius of a circle from the user and compute the area.
(area of circle =PI * r^2)'''
radius = float(input("Enter the radius of circle in centimeter:"))
area_of_circle = (3.14 * (radius**2))
print(f"The area of circle is {area_of_circle} square meter") | true |
2a0b4735c4db4ba0b831028b3a8a26c242a007f9 | CrystalBRana/LabProjects2 | /q.no.10.py | 402 | 4.1875 | 4 | # Write a python program to convert seconds to day, hour, minutes and seconds.
seconds = int(input('Insert second:'))
seconds_in_day = 60*60*24
seconds_in_hour = 60*60
seconds_in_minute = 60
days = seconds // seconds_in_day
hours = (seconds - (days * seconds_in_day))// seconds_in_hour
minutes = (seconds - (days * sec... | true |
af6475761f6aef23786189e00e99f2862639583a | SRaja001/MIT_Open | /HW/Problem_set_1.py | 2,844 | 4.25 | 4 | #Problem 0
#dob = raw_input('Please Enter your dat of birth MM/DD/YY: \n**')
#user = raw_input('Please enter your last name: \n**')
#print user, dob
###Problem 1
##
##balance = float(raw_input("Please enter the balance on your credit card: "))
##interest_rate = float(raw_input("Please enter the annual interest rate a... | true |
a11a6374068185e7cf39fca250b1e9f04b3f7f59 | jonathansilveira1987/EXERCICIOS_ESTRUTURA_DE_DECISAO | /exercicio20.py | 1,267 | 4.28125 | 4 | # 20. Faça um Programa para leitura de três notas parciais de um aluno. O programa deve calcular
# a média alcançada por aluno e apresentar:
# 1. A mensagem "Aprovado", se a média for maior ou igual a 7, com a respectiva média alcançada;
# 2. A mensagem "Reprovado", se a média for menor do que 7, com a respectiva médi... | false |
59f91514f7f4b3169801d00bf4a545fd1dddef91 | jonathansilveira1987/EXERCICIOS_ESTRUTURA_DE_DECISAO | /exercicio23.py | 592 | 4.25 | 4 | # 23. Faça um Programa que peça um número e informe se o número é inteiro ou decimal.
# Dica: utilize uma função de arredondamento.
# Desenvolvido por Jonathan Silveira - Instagram: @ jonathandev01
# Função de Arredondamento
# numero = float(input("Numero original: "))
# print("Arredondado :", round(numero))
numero ... | false |
a8e840fc577193db202ed5af696cb221e77db59a | kaidokariste/python | /01_LearnPythonHardWay/03_raw_terminal_input.py | 499 | 4.1875 | 4 | print("How old are you"),
age = input() # raw_input from python2 was renamed input in python3
print("How tall are you"),
height = input()
print("How much do you weight"),
weight = input()
print("So you're {} old, {} tall and {} heavy.".format(age,height,weight))
# you can define input text also as variable
age = inp... | true |
3a863353a9d02e208139ed825835b60db3adee0d | iEdwinTorres/backend-baby-names-assessment | /babynames.py | 2,733 | 4.21875 | 4 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# BabyNames python coding exercise.
# Copyright 2010 Google Inc.
# Licensed under the Apache License, Version 2.0
# http://www.apache.org/licenses/LICENSE-2.0
"""
Define the extract_names() function below and change main()
to call it.
For writing regex, it's nice to inc... | true |
bb3aceaea7a1f8ef838d20a859ab58b9982c580a | flovera1/CrackingTheCodeInterview | /Python/Chapter1ArraysAndStrings/stringCompression.py | 816 | 4.34375 | 4 | '''
Implement a method to perform basic string compression using the counts of repeated characters.
For example, the string aabcccccaaa would become a2b1c5a3. If the "compressed" string would not
become smaller than the original string, your method should return the original string.
You can assume the string has only ... | true |
079970bfa071570a912ba12d321c469e46a844ab | NedyalkoKr/Learning-Python | /Lesson 9 - Modularity/function_basics.py | 986 | 4.125 | 4 | # defining a new function
# x is the input to the function
# functions can accept one, or more or none parameters
# these parameters represent the initial data the function will use
# internaly
def square(x):
# returning the output of a function
return x * x
print(square(5))
# we can choose to bind paramete... | true |
43ce9efa69e5038b870c9bd863a26e2b4d9780fb | NedyalkoKr/Learning-Python | /Lesson 6 - Strings, Collections, and Iteration/lists.py | 922 | 4.59375 | 5 | # list is a ordered collection(sequence) of objects
# lists are mutable
# lists are iterable
# creating list object using list literal form
numbers = [1,2,3,4,5,6,7,8,9]
fruits = ["apple", "orange", "pear"]
# each list item position is map to a index that allows to reference and retrive that item
print(numbers[0])
... | true |
4c38a7789b6010f9fcd1cc16758354234c4901f1 | NedyalkoKr/Learning-Python | /Lesson 7 - Scalar Types, Operators, and Control Flow/nesting_conditionals.py | 405 | 4.28125 | 4 | h = 42
if h > 50:
print("Greater than 50")
else:
# nesting is not a bad pattern, but in python is better to flat
# than nested for readability
if h < 20:
print("Less than 20")
else:
print("Between 20 and 50")
# the same logic bu using flat structure
if h > 50:
print("Greater t... | true |
4387486343afdac38e51dd0971f9ce9637a2dd0e | luiseduardogfranca/Luis-Franca | /LP1-P1/Initial Knowledge Test/array_intersection.py | 1,370 | 4.15625 | 4 | def min_value(array):
min = array[0]
for value in array:
if value < min:
min = value
return min
# function to sort in ascending order
def sort_array(array):
new_array = []
for index in range(len(array)):
min = min_value(array)
new_array.append(min)
ar... | true |
4560924bf5c9fd07101e6cd2663c6add33352fff | luiseduardogfranca/Luis-Franca | /LP1-P1/Conditionals and Repetition Structure/playing_with_arrays.py | 1,195 | 4.15625 | 4 | def inverse_order(array):
return [array[index] for index in range(len(array) - 1, -1, -1)]
def left_shift(array):
new_array = [None] * len(array)
for index in range(len(array)):
new_array[index-1] = array[index]
return new_array
# i did it this way to learn a new way
def sort_by_decreas... | true |
00752314ecbd8f9be2862896576e36e76eaa6305 | ATHULKNAIR/PythonPrograms | /PrintSum.py | 241 | 4.15625 | 4 | # Write a program that takes three numbers and prints their sum. Every number
# is given on a separate line.
a = int(input('Enter First Number'))
b = int(input('Enter Second Number'))
c = int(input('Enter Third Number'))
print(a + b + c) | true |
6acd96fe92a6241537df088dd2df8dda02e7a6ac | rachelBurford/she_codes_python | /conditionals/dictionaries/Dictionaries_exercises/q1.py | 850 | 4.125 | 4 | prices = {
"Baby Spinach": 2.78,
"Hot Chocolate": 3.70,
"Crackers": 2.10,
"Bacon": 9.00,
"Carrots": 0.56,
"Oranges": 3.08
}
quantity = {
"Baby Spinach": 1,
"Hot Chocolate": 3,
"Crackers": 2,
"Bacon": 1,
"Carrots": 4,
"Oranges": 2
}
quantity2 = {
"Baby Spinach": 2,
... | true |
dcc907106126ba5a4491e86d9dca2aa648a365ee | vsingh1998/Code_in_place | /Lectures/Lecture4/add2numbers.py | 652 | 4.25 | 4 | """
File: add2numbers.py
--------------------
This program asks for the user inputs of two numbers and prints their sum.
"""
def main():
print("This is a program to calculate sum of two numbers.")
# ask the user to input first number
num1 = input("Enter first number: ")
# convert string into integer... | true |
34f78ebc1dd9e983bb699b85fad673b75bb318e6 | asnewton/Stack | /QueueByStack.py | 570 | 4.1875 | 4 | """ Implement Queue using Stacks """
from Stack import NewStack
class Queue:
myStack1 = NewStack()
myStack2 = NewStack()
def enQueue(self, item):
Queue.myStack1.push(item)
def deQueue(self):
while Queue.myStack1.isempty() is not True:
Queue.myStack2.push(Queue.myStack1.p... | true |
3bdaa7792f2fb8796ea50a7fa59d9204a79db3d0 | jakaprima/python-basic-minimalist | /tantangan/manipulasi_string_searching.py | 508 | 4.28125 | 4 | def LongestWord(sen):
# first we remove non alphanumeric characters from the string
# using the translate function which deletes the specified characters
sen = sen.translate(None, "~!@#$%^&*()-_+={}[]:;'<>?/,.|`")
# now we separate the string into a list of words
arr = sen.split(" ")
# the list max func... | true |
a9ca0cdcc9546f3dc38ee1f5afe01071ae2d038f | V-Marco/miscellaneous | /tkinter_bootcamp/grid.py | 420 | 4.53125 | 5 | from tkinter import *
# Create a root window
root = Tk()
# Create a label widget
myLabel1 = Label(root, text = "Hello, World!")
myLabel2 = Label(root, text = "Hi again!")
myLabel3 = Label(root, text = " ")
# Put them in the grid
# The positions are relative!
myLabel1.grid(row = 0, column = 0)
myLabel2.grid(r... | true |
bc63aacc6a2e74a5b190023f2fe991816e9cd332 | ShivaniAsokumar/problems-py | /Find_Second_Max.py | 2,532 | 4.15625 | 4 | """
! PROMPT: Find the second largest element in a given list.
* Input: Array of numbers
* Output: Second largest number
? What happens if an illegal argument is given. => Raise ValueError
* Secong largest number is smaller than max but larger than all other values.
// Brute Force Solution
* Find the maximum usi... | true |
38c04c9e3b6d113a3526adcda38fef3f12252623 | CamilliCerutti/Python-exercicios | /Curso em Video/ex005.py | 270 | 4.25 | 4 | # ANTECESSOR E SUCESSOR
# Faça um programa que leia um número Inteiro e mostre na tela o seu sucessor e seu antecessor.
x = int(input('Digite um número: '))
ant = x-1
suc = x+1
print(f' O antecessor do número que você escolheu é: \n{ant} e o sucessor é {suc}') | false |
d3f7181630d380ec71df0e9ba11851a48bd6eb1a | CamilliCerutti/Python-exercicios | /CURSO UDEMY/EXERCICIOS/EX003.PY | 442 | 4.125 | 4 | # Faça um programa que peça o primeiro nome do usuário. Se o nome tiver 4 letras ou menos escreva "Seu nome é curto"; se tiver entre 5 e 6 letras, escreva "Seu nome é normal"; maior que 6 escreva "Seu nome é muito grande"
nome = input('Digite seu nome: ')
letras = len(nome)
if letras <= 4:
print('Seu nome é curt... | false |
8d2b9e08b1470ce89c4eca976b3c664ef3889d87 | CamilliCerutti/Python-exercicios | /Curso em Video/ex075.py | 740 | 4.28125 | 4 | # ANALISE DE DADOS EM UMA TUPLA
# Desenvolva um programa que leia quatro valores pelo teclado e guarde-os em uma tupla. No final, mostre:
# A) Quantas vezes apareceu o valor 9.
# B) Em que posição foi digitado o primeiro valor 3.
# C) Quais foram os números pares.
tupla = (int(input('Digite um valor: ')), int(input('D... | false |
5193fe9313b4172185dbf05d83b213253686697d | CamilliCerutti/Python-exercicios | /Curso em Video/ex028.py | 613 | 4.1875 | 4 | # JOGO DE ADVINHAÇÃO V1.0
# Escreva um programa que faça o computador “pensar” em um número inteiro entre 0 e 5 e peça para o usuário tentar descobrir qual foi o número escolhido pelo computador. O programa deverá escrever na tela se o usuário venceu ou perdeu.
from random import randint
from time import sleep
num = ... | false |
009eb8f77f950e82f8a65bcb4f03ff6baf13395c | UlysseARNAUD-IPSSI/Module-python | /Chapitre 1 : Bases du langage Python/exercices/1.2 : Collections, boucles et dictionnaires/cityinfo.py | 1,421 | 4.3125 | 4 | #!/usr/bin/env python3
"""
Variables utilisées
"""
infoCity = {'Lyon': (513275, 47.87, 'Lyonnais'),
'Paris': (2206488, 105.40, 'Parisiens'),
'Brest': (139163, 49.51, 'Brestois'),
'Bordeaux': (249712, 49.36, 'Bordelais'),
}
cities = ['Lyon', 'Paris', 'Brest']
"""
Fonct... | false |
f91d47047a5b5bf2aace76fee32407c1d6c13818 | AnkitNigam1985/Data-Science-Projects | /Courses/DataFlair/pandas_pipe.py | 2,450 | 4.3125 | 4 | import numpy as np
import pandas as pd
#Pipe() used to apply any operation on all elements of series or dataframe
#Creating a function to be applied using pipe()
def adder(ele1, ele2):
return ele1+ele2
#Series
print("\nSeries:\n")
dataflair_s1 = pd.Series([11, 21, 31, 41, 51])
print("Original :\n",dataflair_s1)... | false |
7d9fec9bce16a86e0da440ca9c06c3b29d4a5d7d | RaimundoJSoares/Programs-in-Python | /Maior_e_menorNumero.py | 443 | 4.125 | 4 | a = int(input( 'Digite um numero'))
b = int(input('Digite o segundo numero'))
c = int(input('Digite o terceiro numero'))
#verificando quem é o menor
menor = a
if b < a and b < c:
menor = b
if c < a and c < b:
menor = c
print('O menor valor digitado foi {}'.format(menor))
#Verificando quem é o maior
maior = a
if... | false |
20b94d8b7f160bb48e2371e38165ed44558046ff | namphung1998/Comp123_code | /Final/Files/Q5.py | 1,052 | 4.28125 | 4 | import turtle
# Your job in this question is to write a function named
# drawSquares. The drawSquares draws a series of squares
# each one next to the other. The drawn squares start with
# 10 pixels to a side, and get bigger by 10 until they
# reach the input max size, after which they get smaller
# until they reach 1... | true |
90987e465b35469819d8c421424500fe06dbd2de | mitcheccles/tensortrade | /tensortrade/core/clock.py | 1,184 | 4.5625 | 5 | from datetime import datetime
class Clock(object):
"""A class to track the time for a process.
Attributes
----------
start : int
The time of start for the clock.
step : int
The time of the process the clock is at currently.
Methods
-------
now(format=None)
Get... | true |
d55539e09d65135646d3a27fc82ecb7e0cc33a18 | BenjaminAage/TileTraveller | /tile_traveller_def.py | 2,707 | 4.46875 | 4 |
# https://github.com/BenjaminAage/TileTraveller/blob/master/tile_traveller_def.py
# 1. Which implementation was easier and why?
# - It was quite hard to implement program #1 (without functions), as you had to figure out
# all the factors to have the program up and running. However, the function program (#2) w... | true |
f6875ce5510fbe4dce11a13281b5b0b1784a8899 | jeowsome/Python-Adventures | /Rock-Paper-Scissors/Find positions/main.py | 473 | 4.34375 | 4 | # put your python code here
numbers = input().split(' ') # read the input then create a new list for positions
to_find = input()
to_print = []
# when "iterating over the list of numbers", append all the found occurrences
for i in range(len(numbers)):
if numbers[i] == to_find:
to_print.append(str(i... | true |
7ee8bd96405b9f6a0d8b200d4627e4f371511db3 | jgazal/DSA_Python-FAD | /Python_FAD/Capitulo3_LoopsCondicionais/Range.py | 423 | 4.15625 | 4 | print("Range")
print("-----")
# Imprimindo números pares entre 50 e 101
for i in range(50, 101, 2):
print(i)
print("\n")
for i in range(3, 6):
print (i)
print("\n")
for i in range(0, -20, -2):
print(i)
print("\n")
lista = ['Morango', 'Banana', 'Maça', 'Uva']
lista_tamanho = len(lista)
for i in range(0, l... | false |
d449cdfec0e9cde9c047ed06aa6cf30360c9e108 | mauricioTechDev/daily-code-wars | /python/running-out-of-space.py | 746 | 4.15625 | 4 | # Kevin is noticing his space run out!
# Write a function that removes the spaces from the values and
# returns an array showing the space decreasing. For example,
# running this function on the array ['i', 'have','no','space']
# would produce ['i','ihave','ihaveno','ihavenospace'].
# SOLUTION W... | true |
3a5e51c219a91504012fa87e2f3db7abacd392f7 | raysmith619/Introduction-To-Programming | /exercises/prroduct.py | 664 | 4.34375 | 4 | # product.py
"""
Write a function product(factor1, factor2, factor3) that returns the
product of the values factor1, factor2, factor3.
Test it on the following:
.5, .4, .3;
1, 2, 3;
-1, -1, -1;
"""
def product(factor1, factor2, factor3):
""" Do product of 3 factors, returning the product
... | true |
9b25fa00573645aa4941e852ecbbc8499bee599a | raysmith619/Introduction-To-Programming | /exercises/functions/friends_family/simple_friends/list_friends.py | 498 | 4.15625 | 4 | #list_friends.py 23Sep2020 crs, Author
# Simple List Example
"""
Just list a list of friends names
"""
# Initial list of friends names
my_friends = [
"Ray Smith",
"Phil Fontain",
"Rich Parker",
]
# Simple loop to print out names from list
for name in my_friends:
print(name)
r''... | false |
f692537aac14503d8da3feecc163026fcc780f1c | raysmith619/Introduction-To-Programming | /exercises/turtle/turtle_onclick_rainbow.py | 837 | 4.4375 | 4 | # turtle_on_click_rainbow.py 27Nov2020 crs, from turtle_onclick
""" Adding color to turtle_onclick.py
Operation:
Repeat:
1. Position the mouse inside graphics screen
2. Click mouse (button one)
A line is draw to the mouse position
"""
from turtle import *
rainbow = ["red", "orange", "y... | true |
9cd3b65a6842ca1e873263202533ec990ff8f7ef | AxelSiliezar/ME021-Python | /Me021-Python/HW01/HW01_03.py | 305 | 4.21875 | 4 | import math
#given
M = input ("Enter value for M: ")
M = float(M)
m = input ("Enter value for m: ")
m = float (m)
r = input ("Enter value for r: ")
r = float (r)
G = 6.674*10**(-11) #Nm^2/kg^2
#G = universal gravitational constant
F = ((G)*(M*m/r**2))
print ("The value of F is: ",(F))
| false |
092092f0e0f92fa658a28b8a4ff6898a52868c1f | sashakrasnov/datacamp | /21-deep-learning-in-python/3-building-deep-learning-models-with-keras/03-fitting-the-model.py | 1,348 | 4.5 | 4 | '''
Fitting the model
You're at the most fun part. You'll now fit the model. Recall that the data to be used as predictive features is loaded in a NumPy matrix called predictors and the data to be predicted is stored in a NumPy matrix called target. Your model is pre-written and it has been compiled with the code from... | true |
f544c60fe5e01e1b88821505f3293bce45f8261b | sashakrasnov/datacamp | /21-deep-learning-in-python/4-fine-tuning-keras-models/06-building-your-own-digit-recognition-model.py | 2,475 | 4.5625 | 5 | '''
Building your own digit recognition model
You've reached the final exercise of the course - you now know everything you need to build an accurate model to recognize handwritten digits!
We've already done the basic manipulation of the MNIST dataset shown in the video, so you have X and y loaded and ready to model ... | true |
5a936e185653a9333474874e60db2dd78565cb30 | sashakrasnov/datacamp | /22-network-analysis-in-python-1/1-introduction-to-networks/03-specifying-a-weight-on-edges.py | 1,641 | 4.3125 | 4 | '''
Specifying a weight on edges
Weights can be added to edges in a graph, typically indicating the "strength" of an edge. In NetworkX, the weight is indicated by the 'weight' key in the metadata dictionary.
Before attempting the exercise, use the IPython Shell to access the dictionary metadata of T and explore it, f... | true |
e1b882e222d1cc893c2bcf8512372943370b71c2 | sashakrasnov/datacamp | /06-importing-data-in-python-2/3-diving-deep-into-the-twitter-api/03-load-and-explore-twitter-data.py | 1,212 | 4.53125 | 5 | '''
Load and explore your Twitter data
Now that you've got your Twitter data sitting locally in a text file, it's time to explore it! This is what you'll do in the next few interactive exercises. In this exercise, you'll read the Twitter data into a list: tweets_data.
Instructions
* Assign the filename 'tweets.txt... | true |
1575384d98d8fbab917cfe024e3686b910f42d54 | sashakrasnov/datacamp | /15-statistical-thinking-in-python-1/1-graphical-exploratory-data-analysis/05-computing-the-ecdf.py | 1,722 | 4.40625 | 4 | '''
Computing the ECDF
In this exercise, you will write a function that takes as input a 1D array of data and then returns the x and y values of the ECDF. You will use this function over and over again throughout this course and its sequel. ECDFs are among the most important plots in statistical analysis. You can writ... | true |
20eebc469dec4f8c152a61f7ea5c04b53092e976 | sashakrasnov/datacamp | /24-data-types-for-data-science/4-handling-dates-and-times/03-pieces-of-time.py | 1,804 | 4.25 | 4 | '''
Pieces of Time
When working with datetime objects, you'll often want to group them by some component of the datetime such as the month, year, day, etc. Each of these are available as attributes on an instance of a datetime object.
You're going to work with the summary of the CTA's daily ridership. It contains the... | true |
0f35c9a6a623c41d4590847e59962315672ce2b3 | sashakrasnov/datacamp | /29-statistical-simulation-in-python/2-probability-and-data-generation-process/07-driving-test.py | 2,037 | 4.65625 | 5 | '''
Driving test
Through the next exercises, we will learn how to build a data generating process (DGP) through progressively complex examples.
In this exercise, you will simulate a very simple DGP. Suppose that you are about to take a driving test tomorrow. Based on your own practice and based on data you have gathe... | true |
206f83801de4bbda63c100e3026939cf9085e7f6 | sashakrasnov/datacamp | /26-manipulating-time-series-data-in-python/1-working-with-time-series-in-pandas/06-calculating-stock-price-changes.py | 1,590 | 4.25 | 4 | '''
Calculating stock price changes
You have learned in the video how to calculate returns using current and shifted prices as input. Now you'll practice a similar calculation to calculate absolute changes from current and shifted prices, and compare the result to the function .diff().
'''
import pandas as pd
yahoo ... | true |
dbad60a48570c819043be5067ba14a0748a48fd3 | sashakrasnov/datacamp | /29-statistical-simulation-in-python/4-advanced-applications-of-simulation/01-modeling-corn-production.py | 1,494 | 4.3125 | 4 | '''
Modeling Corn Production
Suppose that you manage a small corn farm and are interested in optimizing your costs. In this exercise, we will model the production of corn.
For simplicity, let's assume that corn production depends on only two factors: rain, which you don't control, and cost, which you control. Rain is... | true |
19fb72baf7e1c430977fc551d85e516d2e87dadc | sashakrasnov/datacamp | /09-manipulating-dataframes-with-pandas/4-grouping-data/03-computing-multiple-aggregates-of-multiple.columns.py | 1,806 | 4.25 | 4 | '''
Computing multiple aggregates of multiple columns
The .agg() method can be used with a tuple or list of aggregations as input. When applying multiple aggregations on multiple columns, the aggregated DataFrame has a multi-level column index.
In this exercise, you're going to group passengers on the Titanic by 'pcl... | true |
db126fa6dca8af406e3eb64cec30bb4e302f7ef4 | sashakrasnov/datacamp | /24-data-types-for-data-science/3-meet-the-collections-module/04-safely-appending-to-a-keys-value-list.py | 1,648 | 4.75 | 5 | '''
Safely appending to a key's value list
Often when working with dictionaries, you know the data type you want to have each key be; however, some data types such as lists have to be initialized on each key before you can append to that list.
A defaultdict allows you to define what each uninitialized key will contai... | true |
0453443a2fd4382dd5faf56577be5ed76779b069 | sashakrasnov/datacamp | /08-pandas-foundations/1-data-ingestion-and-inspection/07-plotting-series-using-pandas.py | 1,935 | 4.84375 | 5 | '''
Plotting series using pandas
Data visualization is often a very effective first step in gaining a rough understanding of a data set to be analyzed. Pandas provides data visualization by both depending upon and interoperating with the matplotlib library. You will now explore some of the basic plotting mechanics wit... | true |
a8550d5ba734efaadd56fd5332aa2666f8b2fefa | sashakrasnov/datacamp | /06-importing-data-in-python-2/2-interacting-with-apis-to-import-data-from-the-web/01-loading-and-exploring-a-json.py | 924 | 4.65625 | 5 | '''
Loading and exploring a JSON
Now that you know what a JSON is, you'll load one into your Python environment and explore it yourself. Here, you'll load the JSON 'a_movie.json' into the variable json_data, which will be a dictionary. You'll then explore the JSON contents by printing the key-value pairs of json_data ... | true |
1f9b6dc325f192935d30604dae151f875e5ad35c | sashakrasnov/datacamp | /21-deep-learning-in-python/1-basics-of-deep-learning-and-neural-networks/02-the-rectified-linear-activation-function.py | 1,716 | 4.625 | 5 | '''
The Rectified Linear Activation Function
As Dan explained to you in the video, an "activation function" is a function applied at each node. It converts the node's input into some output.
The rectified linear activation function (called ReLU) has been shown to lead to very high-performance networks. This function ... | true |
8bb2983a52a9607911c0ea69f4a453fef39b45de | sashakrasnov/datacamp | /08-pandas-foundations/2-exploratory-data-analysis/09-separate-and-summarize.py | 1,569 | 4.125 | 4 | '''
Separate and summarize
Let's use population filtering to determine how the automobiles in the US differ from the global average and standard deviation. How the distribution of fuel efficiency (MPG) for the US differ from the global average and standard deviation?
In this exercise, you'll compute the means and sta... | true |
6b582bf7eb3e5e3fc4ae9912c68cd2be5dccac6d | sashakrasnov/datacamp | /14-interactive-data-visualization-with-bokeh/1-basic-plotting-with-bokeh/08-plotting-data-from-pandas-dataframes.py | 1,796 | 4.21875 | 4 | '''
Plotting data from Pandas DataFrames
You can create Bokeh plots from Pandas DataFrames by passing column selections to the glyph functions.
Bokeh can plot floating point numbers, integers, and datetime data types. In this example, you will read a CSV file containing information on 392 automobiles manufactured in ... | true |
cf5ed84b81c429740decf635f089ce6cf2b1b1a4 | sashakrasnov/datacamp | /24-data-types-for-data-science/2-dictionaries--the-root-of-python/06-working-with-dictionaries-more-pythonically.py | 1,852 | 4.25 | 4 | '''
Popping and deleting from dictionaries
Often, you will want to remove keys and value from a dictionary. You can do so using the del Python instruction. It's important to remember that del will throw a KeyError if the key you are trying to delete does not exist. You can not use it with the .get() method to safely d... | true |
4ea71b51359e8486ed5c5e65bea639653363814b | sashakrasnov/datacamp | /19-machine-learning-with-the-experts-school-budgets/2-creating-a-simple-first-model/06-combining-text-columns-for-tokenization.py | 2,114 | 4.125 | 4 | '''
Combining text columns for tokenization
In order to get a bag-of-words representation for all of the text data in our DataFrame, you must first convert the text data in each row of the DataFrame into a single string.
In the previous exercise, this wasn't necessary because you only looked at one column of data, so... | true |
1b100714dada7b59a0ebab1e83f8de2d942aead0 | sashakrasnov/datacamp | /28-machine-learning-for-time-series-data-in-python/3-predicting-time-series-data/01-introducing-the-dataset.py | 1,453 | 4.53125 | 5 | '''
Introducing the dataset
As mentioned in the video, you'll deal with stock market prices that fluctuate over time. In this exercise you've got historical prices from two tech companies (Ebay and Yahoo) in the DataFrame prices. You'll visualize the raw data for the two companies, then generate a scatter plot showing... | true |
ff58963682d6b0c385af84e7cfd8e569ebb0f43c | sashakrasnov/datacamp | /21-deep-learning-in-python/2-optimizing-a-neural-network-with-backward-propagation/01-coding-how-weight-changes-affect-accuracy.py | 2,751 | 4.4375 | 4 | '''
Coding how weight changes affect accuracy
Now you'll get to change weights in a real network and see how they affect model accuracy!
Have a look at the following neural network: https://s3.amazonaws.com/assets.datacamp.com/production/course_3524/datasets/ch2ex4.png
Its weights have been pre-loaded as weights_0. ... | true |
c909f3767520223e6be10318e530cbc924ef2b76 | sashakrasnov/datacamp | /24-data-types-for-data-science/2-dictionaries--the-root-of-python/01-creating-and-looping-through-dictionaries.py | 1,832 | 4.90625 | 5 | '''
Creating and looping through dictionaries
You'll often encounter the need to loop over some array type data, like in Chapter 1, and provide it some structure so you can find the data you desire quickly.
You start that by creating an empty dictionary and assigning part of your array data as the key and the rest as... | true |
a31ee59404efdb8fa7481075543179c1fec6412b | sashakrasnov/datacamp | /08-pandas-foundations/1-data-ingestion-and-inspection/05-reading-a-flat-file.py | 1,610 | 4.4375 | 4 | '''
Reading a flat file
In previous exercises, we have preloaded the data for you using the pandas function read_csv(). Now, it's your turn! Your job is to read the World Bank population data you saw earlier into a DataFrame using read_csv(). The file has been downloaded as world_population.csv.
The next step is to r... | true |
341a39673f57049a3f28d111b7b4e46428714cc8 | sashakrasnov/datacamp | /26-manipulating-time-series-data-in-python/1-working-with-time-series-in-pandas/04-set-and-change-time-series-frequency.py | 1,210 | 4.1875 | 4 | '''
Set and change time series frequency
In the video, you have seen how to assign a frequency to a DateTimeIndex, and then change this frequency.
Now, you'll use data on the daily carbon monoxide concentration in NYC, LA and Chicago from 2005-17.
You'll set the frequency to calendar daily and then resample to month... | true |
5a33d4bb2add5882a2a6637aecf90330ed47776d | sashakrasnov/datacamp | /14-interactive-data-visualization-with-bokeh/1-basic-plotting-with-bokeh/09-the-bokeh-columndatasource.py | 1,733 | 4.125 | 4 | '''
The Bokeh ColumnDataSource (continued)
You can create a ColumnDataSource object directly from a Pandas DataFrame by passing the DataFrame to the class initializer.
In this exercise, we have imported pandas as pd and read in a data set containing all Olympic medals awarded in the 100 meter sprint from 1896 to 2012... | true |
f8cdaee2db28f409fc8bf13ea75d0abb9a509a99 | sashakrasnov/datacamp | /22-network-analysis-in-python-1/4-bringing-it-all-together/08-finding-important-collaborators.py | 1,909 | 4.125 | 4 | '''
Finding important collaborators
Almost there! You'll now look at important nodes once more. Here, you'll make use of the degree_centrality() and betweenness_centrality() functions in NetworkX to compute each of the respective centrality scores, and then use that information to find the "important nodes". In other ... | true |
21fce1ba844f4c7273ed86a18e21b7725bc92f5f | sashakrasnov/datacamp | /24-data-types-for-data-science/1-fundamental-data-types/06-determining-set-differences.py | 1,822 | 4.65625 | 5 | '''
Determining set differences
Another way of comparing sets is to use the difference() method. It returns all the items found in one set but not another. It's important to remember the set you call the method on will be the one from which the items are returned. Unlike tuples, you can add() items to a set. A set wil... | true |
8b9d573be4b1eebe8a6d3ee66252f8c3442890a0 | sashakrasnov/datacamp | /29-statistical-simulation-in-python/2-probability-and-data-generation-process/03-game-of-thirteen.py | 1,668 | 4.40625 | 4 | '''
Game of thirteen
A famous French mathematician Pierre Raymond De Montmart, who was known for his work in combinatorics, proposed a simple game called as Game of Thirteen. You have a deck of 13 cards, each numbered from 1 through 13. Shuffle this deck and draw cards one by one. A coincidence is when the number on t... | true |
d2f59c0803cbe2036e08d903216d7fb254a8fa1b | sashakrasnov/datacamp | /29-statistical-simulation-in-python/1-basics-of-randomness-and-simulation/05-simulating-the-dice-game.py | 1,556 | 4.46875 | 4 | '''
Simulating the dice game
We now know how to implement the first three steps of a simulation. Now let's consider the next step - repeated random sampling.
Simulating an outcome once doesn't tell us much about how often we can expect to see that outcome. In the case of the dice game from the previous exercise, it's... | true |
f8bf2d9477abd2cfef2629ad87f03ce171d34e26 | sashakrasnov/datacamp | /22-network-analysis-in-python-1/3-structures/01-identifying-triangle-relationships.py | 2,208 | 4.125 | 4 | '''
Identifying triangle relationships
Now that you've learned about cliques, it's time to try leveraging what you know to find structures in a network. Triangles are what you'll go for first. We may be interested in triangles because they're the simplest complex clique. Let's write a few functions; these exercises wi... | true |
fd12b8f0353d46fac24d7d2efd50ba1185a290a5 | sashakrasnov/datacamp | /07-cleaning-data-in-python/4-cleaning-data-for-analysis/06-custom-functions-to-clean-data.py | 2,461 | 4.21875 | 4 | '''
Custom functions to clean data
You'll now practice writing functions to clean data.
The tips dataset has been pre-loaded into a DataFrame called tips. It has a 'sex' column that contains the values 'Male' or 'Female'. Your job is to write a function that will recode 'Male' to 1, 'Female' to 0, and return np.nan f... | true |
b754d2966c71ec0c22f1af9caa7bdf933f8c3616 | sashakrasnov/datacamp | /11-analyzing-police-activity-with-pandas/1-preparing-the-data-for-analysis/03-dropping-rows.py | 1,300 | 4.46875 | 4 | '''
Dropping rows
When you know that a specific column will be critical to your analysis, and only a small fraction of rows are missing a value in that column, it often makes sense to remove those rows from the dataset.
During this course, the driver_gender column will be critical to many of your analyses. Because on... | true |
c6c9a3c3dc132e3cfb2cfc349f23350c9d163dba | sashakrasnov/datacamp | /04-python-data-science-toolbox-2/3-bringing-it-all-together!/07-writing-a-generator-to-load-data-in-chunks-3.py | 1,737 | 4.46875 | 4 | '''
Writing a generator to load data in chunks (3)
Great! You've just created a generator function that you can use to help you process large files.
Now let's use your generator function to process the World Bank dataset like you did previously.
You will process the file line by line, to create a dictionary of the co... | true |
5ca7c9d133c9dcf4a63deed0f3741827294f3203 | sashakrasnov/datacamp | /32-introduction-to-pyspark/2-manipulating-data/03-selecting.py | 1,895 | 4.5 | 4 | '''
The Spark variant of SQL's SELECT is the .select() method. This method takes multiple arguments - one for each column you want to select. These arguments can either be the column name as a string (one for each column) or a column object (using the df.colName syntax). When you pass a column object, you can perform o... | true |
6ed2d41343506384911094ba689c134531af95a4 | sashakrasnov/datacamp | /26-manipulating-time-series-data-in-python/4-putting-it-all-together-building-a-value-weighted-index/03-import-index-component-price-information.py | 1,973 | 4.125 | 4 | '''
Import index component price information
Now you'll use the stock symbols for the companies you selected in the last exercise to calculate returns for each company.
'''
import pandas as pd
import matplotlib.pyplot as plt
listings = pd.read_excel('../datasets/stock_data/listings.xlsx', sheet_name='nyse', na_value... | true |
7056ea7898204358ce56b6d531df2f3fe587ffcb | sashakrasnov/datacamp | /10-merging-dataframes-with-pandas/2-concatenating-data/04-concatenating-pandas-dataframes-along-column-axis.py | 2,317 | 4.15625 | 4 | '''
Concatenating pandas DataFrames along column axis
The function pd.concat() can concatenate DataFrames horizontally as well as vertically (vertical is the default). To make the DataFrames stack horizontally, you have to specify the keyword argument axis=1 or axis='columns'.
In this exercise, you'll use weather dat... | true |
0a7757a9fa33be538833f52f832c9872ff36c672 | sashakrasnov/datacamp | /10-merging-dataframes-with-pandas/1-preparing-data/04-sorting-dataframe-with-the-index-and-columns.py | 2,593 | 4.96875 | 5 | '''
Sorting DataFrame with the Index & columns
It is often useful to rearrange the sequence of the rows of a DataFrame by sorting. You don't have to implement these yourself; the principal methods for doing this are .sort_index() and .sort_values().
In this exercise, you'll use these methods with a DataFrame of tempe... | true |
32de5c51962ed107e39b8c67a51b85be88214d39 | sashakrasnov/datacamp | /27-visualizing-time-series-data-in-python/4-work-with-multiple-time-series/01-load-multiple-time-series.py | 1,332 | 4.125 | 4 | '''
Load multiple time series
Whether it is during personal projects or your day-to-day work as a Data Scientist, it is likely that you will encounter situations that require the analysis and visualization of multiple time series at the same time.
Provided that the data for each time series is stored in distinct colu... | true |
0b8caa4f6f082d1f8c456b94dc1e87903a66f69e | sashakrasnov/datacamp | /24-data-types-for-data-science/2-dictionaries--the-root-of-python/04-adding-and-extending-dictionaries.py | 2,763 | 4.65625 | 5 | '''
Adding and extending dictionaries
If you have a dictionary and you want to add data to it, you can simply create a new key and assign the data you desire to it. It's important to remember that if it's a nested dictionary, then all the keys in the data path must exist, and each key in the path must be assigned indi... | true |
bbfd20e82c8d9bfe591e3345ed94c2e94702eb7e | sashakrasnov/datacamp | /12-introduction-to-databases-in-python/4-creating-and-manipulating-your-own-databases/06-updating-individual-records.py | 2,179 | 4.28125 | 4 | '''
Updating individual records
The update statement is very similar to an insert statement, except that it also typically uses a where clause to help us determine what data to update. You'll be using the FIPS state code using here, which is appropriated by the U.S. government to identify U.S. states and certain other... | true |
9d47888970d4d2bba4d9555352eb0a23c2e17a2d | sashakrasnov/datacamp | /18-linear-classifiers-in-python/3-logistic-regression/01-regularized-logistic-regression.py | 1,519 | 4.1875 | 4 | '''
Regularized logistic regression
In Chapter 1 you used logistic regression on the handwritten digits data set. Here, we'll explore the effect of L2 regularization. The handwritten digits dataset is already loaded, split, and stored in the variables X_train, y_train, X_valid, and y_valid. The variables train_errs an... | true |
4d1c8c000e06f5459daf9ce36ffc09a46bab346d | sashakrasnov/datacamp | /17-supervised-learning-with-scikit-learn/1-classification/02-k-nearest-neighbors-predict.py | 2,532 | 4.28125 | 4 | '''
k-Nearest Neighbors: Predict
Having fit a k-NN classifier, you can now use it to predict the label of a new data point. However, there is no unlabeled data available since all of it was used to fit the model! You can still use the .predict() method on the X that was used to fit the model, but it is not a good indi... | true |
b574a6df1c7c3fda951b2db54dee2bf19c76a8db | geohotweb/programing | /ecuaciones/leyendo_numeros.py | 279 | 4.34375 | 4 | #Este programa va leyendo numeros y mostrandolos por pantalla mientras los numeros que se introducen sean positivos.
num = int(input('Introduce un numero: '))
while num >= 0:
print(num)
num = int(input('Introduce otro numero: '))
print('Ha finalizado mi trabajo, adiós!.')
| false |
aa1b56ed11db7fd4ed50f0346226807a05b6db15 | geohotweb/programing | /ecuaciones/vocales_consonantes.py | 664 | 4.125 | 4 | #Este programa determina si el caracter introducido es vocal mayuscula o vocal minuscula y si es consonante mayuscula o minuscula tambien dice si hay un caraccter desconocido.
#! usr/bin/python
letra_o_vocal = input('Introduce la primera vocal o consonante: ')
mayusculas1 = 'BCDFGHJKLMNPQRSTVWXYZ'
minusculas1 = 'bcd... | false |
eda2584a11a3e4fb1a77f8082ce455df3b4c4713 | Xinyuan-wur/algorithms-in-bioinformatics | /clustering/assignment_kmeans_skeleton.py | 1,917 | 4.25 | 4 | #!/usr/bin/env python
"""
Author:
Student number:
Implementation of the k-means clustering algorithm
Hints:
- write a function to obtain Euclidean distance between two points.
- write a function to initialize centroids by randomly selecting points
from the initial set of points. You can use the random.sample() me... | true |
c162c550ba25f29822159f0c4fca5421e1dedd37 | 12reach/PlayWithPython | /primary/functions.py | 1,622 | 4.6875 | 5 | #!/usr/bin/python3
# functions and parameters are two important part of a program
# a function do the repetitive job so that we need not write same thing more and more
# functions do many things
# we will see it later in our detailed functions series
# let us define a function that pass two parameters and those param... | true |
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