<jupyter_start><jupyter_text>Q1<jupyter_code>result = 1
for i in range (1,6): 
    result= i*result
print (result)<jupyter_output>120<jupyter_text>Q2<jupyter_code>result = 0
for i in range (1,6): 
    result= i+result
print (result)<jupyter_output>15<jupyter_text>Q3<jupyter_code>result = 1 
for i in range (3,10):
    result= i*result
print (result)<jupyter_output>181440<jupyter_text>Q4<jupyter_code>result = 1
for i in range (4,9):
    result= result*i
print (result)<jupyter_output>6720<jupyter_text>Q5<jupyter_code>result = 1
for i in range (6):
    if i==3:
        continue
    print (i)<jupyter_output>0
1
2
4
5<jupyter_text>Q6<jupyter_code>result=0
for word in 'this is my fourth string'.split():
#   print (word)
    result= result+1
print (result)<jupyter_output>5<jupyter_text>Q7<jupyter_code>num_list = [12,32,43,35]
while num_list:
    num_list.remove(num_list[0])
print(num_list)<jupyter_output>[]<jupyter_text>Q8<jupyter_code>my_str= 'this is my fourth string'.split()
result = 0
while (my_str):
# print (my_str)
    result = result + 1
    my_str.remove(my_str[0])
print (result)<jupyter_output>5<jupyter_text>Q9<jupyter_code>my_tweet = {"favorite_count":1138,
        "lang":"ch", 
        "coordinates": (-75.14310264, 40.05701649), 
        "entities":{
            "hashtags":[" Preds ", "Pens", " SingIntoSpring "]}}
result=0
for word in (my_tweet['entities']['hashtags']):
#   print (word)
    result= result+1
print (result)


result = 0
while (my_tweet['entities']['hashtags']):
# print (my_str)
    result = result + 1
    my_tweet['entities']['hashtags'].remove(my_tweet['entities']['hashtags'][0])
print (result)<jupyter_output>3
3<jupyter_text>Q10<jupyter_code>num_list = [12,32,43,34]
max_item = num_list[0]
for current_item in num_list:
    if max_item< current_item:
        max_item= current_item
print(max_item)
num_list = [12,32,43,34]
min_item = num_list[0]
for current_item in num_list:
    if min_item> current_item:
        min_item= current_item
print(min_item)<jupyter_output>12
{'fraction_non_alphanumeric': 0.1431401684001981, 'fraction_numerical': 0.05646359583952452, 'mean_word_length': 5.3924050632911396, 'pattern_counts': {'":': 5, '<': 32, '<?xml version=': 0, '>': 32, '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': '10', 'dir': 'jupyter-structured-clean-dedup', 'id': '229407', 'license': 'MIT', 'n_tokens_mistral': 903, 'n_tokens_neox': 844, 'n_words': 153, 'path': 'Lab 4.ipynb', 'repo_name': 'celiahines/IA-241', 'size': '2019'}