<jupyter_start><jupyter_text>list<jupyter_code>lst=[2,34,456,56.45,[1,2,3]]
lst
lst.append("vyshu")
lst
lst.copy()
lst.count(34)
lst.extend([1,2,3,'v'])
lst
lst.index(1)
lst.insert(89,667)
lst
lst.insert(0,38)
lst
lst.pop(2)
lst.remove(667)
lst.reverse()
lst
lst.clear()
lst<jupyter_output><empty_output><jupyter_text>dictionaries<jupyter_code>dit={"name":"vaishu","age":"20","school":"ghs","gender":"female"}
dit
dit.copy()
dit.get("name")
dit.items()
dit.keys()
dit.pop("age")
dit.fromkeys("school","ghs")
dit.update()
dit
dit.update({"color": "white"})
dit
dit.popitem()
dit.setdefault("name","srikanth")
dit.clear()
dit<jupyter_output><empty_output><jupyter_text>sets<jupyter_code>st={23,45,665,"nani","apple"}
st
st.add(567)
st
st.copy()
st.discard("apple")
st
seta={10,20,50,60}
setb={20,30,50}
seta.difference(setb)
seta={10,20,50,60}
setb={20,30,50}
setb.difference_update(seta)
setb
seta={10,20,50,60}
setb={20,30,50,23}
seta.intersection(setb)
seta={10,20,50,60}
setb={20,30,50}
seta.intersection_update(setb)
seta
seta.isdisjoint(setb)
seta.issubset(setb)
seta.clear()
seta<jupyter_output><empty_output><jupyter_text>tuple<jupyter_code>tup=("vyshu","nani","black",45,4793)
tup
tup.count("nani")
tup.index(45)<jupyter_output><empty_output><jupyter_text>strings<jupyter_code>st="nani"
st1="black"
st.capitalize()
st="NANI"
st.casefold()
st.center(20)
st1.count("l")
st1.encode()
st1.endswith("k")
st2="h\te\tl\tl\to"
st2.expandtabs(5)
st3="hello, world is good."
st3.find("world")
txt1="my name is{fname}, i like{color}".format(fname="nani", color="black")
txt1
st6= {"mamba":"name","x":"y"}
('{mamba} {x}'.format_map(st6))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2292418772563177, 'fraction_numerical': 0.07460890493381468, 'mean_word_length': 11.05072463768116, 'pattern_counts': {'":': 7, '<': 21, '<?xml version=': 0, '>': 21, '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': '537006', 'license': 'Apache-2.0', 'n_tokens_mistral': 844, 'n_tokens_neox': 781, 'n_words': 88, 'path': 'assignment1.ipynb', 'repo_name': 'Hakeemvaishnavi/letsupgradeassignment1', 'size': '1662'}