<jupyter_start><jupyter_text>List<jupyter_code>l=[1,2,3,4,5]
a=l.append(3)

print(l)

l=[1,2,3,6,8]
b=l.pop()
print(l)
l=[1,2,3,4,5]
c=l.insert(2,8)
print(l)
l=[1,2,3,4,56]
d=l.reverse()
print(l)
l=[1,5,7,8,9,1,2]
e=l.sort()
print(l)<jupyter_output>[1, 1, 2, 5, 7, 8, 9]<jupyter_text>Dictonary<jupyter_code>d= {"1": "hi","2": "hello","3": 1964}
d.clear()
print(d)
d = {"name": "Ford","color": "green","year": 1964}
x = d.copy()
print(x)
car = {"brand": "Ford","model": "Mustang","year": 1964}
car.pop("model")
print(car)

car = {"brand": "Ford","model": "Mustang","year": 1964}
car.popitem()
print(car)
car = {"brand": "Ford","model": "Mustang","year": 1964}
car.update({"color": "White"})
print(car)<jupyter_output>{'brand': 'Ford', 'model': 'Mustang', 'year': 1964, 'color': 'White'}<jupyter_text>Set<jupyter_code>s = {"apple", "banana", "cherry"}
s.add("orange")
print(s)
s = {"apple", "banana", "cherry"}
s.clear()
print(s)
s = {"apple", "banana", "cherry"}
b=s.copy()
print(b)
s = {"apple", "banana", "cherry"}
s.pop()
print(s)
s = {"apple", "banana", "cherry"}
s.remove("apple")
print(s)<jupyter_output>{'cherry', 'banana'}<jupyter_text>Tuple<jupyter_code>t= (1, 3, 7, 8, 7, 5, 4, 6, 8, 5)
x =t.count(5)
print(x)
t= (1, 3, 7, 8, 7, 5, 4, 6, 8, 5)
x =t.index(5)
print(x)<jupyter_output>5<jupyter_text>Strings<jupyter_code>s = "hello, and welcome to my world."
x = s.capitalize()
print (x)
s = "hello, and welcome to my world."
x = s.upper()
print (x)
s = "hello, and welcome to my world."
x = s.lower()
print (x)
s = "hello, and welcome to my world."
x = s.find("hello")
print (x)
s = "hello, and welcome to my world."
x = s.replace("hello","hi")
print (x)<jupyter_output>hi, and welcome to my world.
{'fraction_non_alphanumeric': 0.2627565982404692, 'fraction_numerical': 0.05161290322580645, 'mean_word_length': 6.3852813852813854, 'pattern_counts': {'":': 16, '<': 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': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '283541', 'license': 'Apache-2.0', 'n_tokens_mistral': 863, 'n_tokens_neox': 804, 'n_words': 180, 'path': 'Untitled91.ipynb', 'repo_name': 'KotaBhandhavya/LetsUpgrade-Python-b7', 'size': '1705'}