<jupyter_start><jupyter_text>Lab1---Ok for this lab we're going to reiterate a lot of the things that we went over in class.Our Goals are:- Defining variables- Using math- Manipulating variables- Changing data types- Playing with lists<jupyter_code># Define 4 variables, one of each type listed: [Integer, String, Boolean, Float]. Print all 4.
#test
#Integer
age = 19

#String
name = "Owen"

#Boolean
goes_to_marist = True

#Float
height = 74.5
# With whatever variables you'd like demonstrate the following: [Addition, Subtraction, Division, Multiplication, Modulo]. Print all 5.
Input1 = 7
Input2 = 10
print(Input1 + Input2)
print(Input1 - Input2)
print(Input1 / Input2)
print(Input1 * Input2)
print(Input1 % Input2)
# Using 2 variables and addition print the number 57
Integer1 = 50
Integer2 = 7
Solution = (Integer1 + Integer2)
if Solution < 58:
    print(Integer1 + Integer2)
else:
    print("Wrong")
# Change this variable to a string and print
number_into_string = 37
number_into_string = str(number_into_string)
print(type(number_into_string))
# Change this variable to a integer and print
string_into_number = '10'
string_into_number = int(string_into_number)
print(type(string_into_number))
# Change this variable to a float and print
string_into_float = '299.99'
string_into_float = float(string_into_float)
print(type(string_into_float))
# Change this variable to a boolean and print
number_into_boolean = 37
number_into_boolean = bool(number_into_boolean)
print(type(number_into_boolean))
# Make me a list of any type
Fast_Food = ["Mcdonalds", "Burger_King", "Wendys", "Chik-fil-a", "White_Castle"]
print(Fast_Food)<jupyter_output>['Mcdonalds', 'Burger_King', 'Wendys', 'Chik-fil-a', 'White_Castle']
{'fraction_non_alphanumeric': 0.09754672897196262, 'fraction_numerical': 0.028621495327102803, 'mean_word_length': 5.879518072289157, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 4, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '502866', 'license': 'MIT', 'n_tokens_mistral': 598, 'n_tokens_neox': 556, 'n_words': 184, 'path': 'JupyterNotebooks/Labs/Lab 1.ipynb', 'repo_name': 'owenbres01/CMPT-120L-910-20F', 'size': '1712'}