<jupyter_start><jupyter_text>Lab One---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.

#Integer
number = 100

#String
greeting = 'Hello World'

#Boolean
is_robot = True

#Float
number_decimal = 100.54

print(number)
print(greeting)
print(is_robot)
print(number_decimal)
# With whatever variables you'd like demonstrate the following: [Addition, Subtraction, Division, Multiplication, Modulo]. Print all 5.

count1 = 12
count2 = 3
count3 = 4

#Addition
print(count2 + count3)

#Subtraction
print(count1 - count3)

#Division
print(count3 / count2)

#Multiplication
print(count2 * count3)

#Modulo
print(count3 % count2)
# Using 2 variables and addition print the number 57

variable1 = 25
variable2 = 32

print(variable1 + variable2)
# 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

even_numbers = [2,4,6,8,10,12,14,16,18,20,22]
print(even_numbers)<jupyter_output>[2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
{'fraction_non_alphanumeric': 0.08944954128440367, 'fraction_numerical': 0.0481651376146789, 'mean_word_length': 5.737451737451737, 'pattern_counts': {'":': 0, '<': 4, '<?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': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '164862', 'license': 'MIT', 'n_tokens_mistral': 643, 'n_tokens_neox': 581, 'n_words': 189, 'path': 'JupyterNotebooks/Labs/Lab 1.ipynb', 'repo_name': 'Mad-Driscoll/CMPT-120L-902-21S', 'size': '1744'}