<jupyter_start><jupyter_text>Exercise 1welcome every name out of `names`,1. Create a list of 3 names2. Create a fruitless function that prints "hello" and the name out of the name list3. The argument of your function is the name list<jupyter_code>names = ["Peet", "John", "Bram"]
def print_hello(names):
    for name in names:
        print(f'Hello {name}')
print_hello(names)<jupyter_output>Hello Peet
Hello John
Hello Bram<jupyter_text>Exercise 2- Write a Python program to calculate the sum of three given numbers- all the numbers need to be arguments in the function- if sum of the numbers is odd, then return a string saying the numbers are odd- Your functions does not have to print because it is fruitful- use a built-in function to calculate the sum, not an operator<jupyter_code>def odd_or_even(number1, number2, number3):
    total = sum([number1, number2, number3])
    if total % 2 != 0:
        return "This is odd"
    else:
        return "This is even"
odd_or_even(2, 1, 1)<jupyter_output><empty_output><jupyter_text>Exercise 3Write a Python program to concatenate all elements in a list into a string and return it<jupyter_code>def concatenate_list(mylist):
    return (" ").join(mylist)

my_list = ['These', 'words', 'will', 'be', 'together']
concatenate_list(my_list)<jupyter_output><empty_output><jupyter_text>Exercise 4Write a Python program to sum three given integers. However, if two values are equal, sum will be zero<jupyter_code>def new_sum(x, y, z):
    if x == y or y == z or x==z:
        total = 0
    else:
        total = x + y + z
    return total
new_sum(2,5,3)
def calculate_total(number1, number2, number3):
    list_of_numbers = [number1, number2, number3]
    total = sum(list_of_numbers)
    if number1 == number2 or number1 == number3 or number2 == number3:
        total = 0
    return total
calculate_total(1, 3, 2)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09216101694915255, 'fraction_numerical': 0.02065677966101695, 'mean_word_length': 4.291316526610644, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 16, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '140999', 'license': 'MIT', 'n_tokens_mistral': 630, 'n_tokens_neox': 607, 'n_words': 231, 'path': 'solutions/Week2/Week2-exercises-solutions.ipynb', 'repo_name': 'pyladiesams/Bootcamp-Data-Analysis-beginner-apr-may2020', 'size': '1888'}