<jupyter_start><jupyter_text>$ y=mx + c$ y = mx + c My name is<jupyter_code>L = [1,2,3,4,5,6,7,8]
L
L[1],L[3]
L[-1],L[-3]
len(L)
L.append(10)
L
sum(L)
max(L)
max(L), min(L)
L = [1,2.0,3,4,5,"Apple",True, False]
L
L[5]
type (L[1])
type (L[1]),type (L[6])
type (L[5])
type (L[7])
L = [[1,2,3],[3,4,5],[5,7,9]]
L
L[0]
L[1]
 L[1], L[2]
L[0][1]
L[1][2]
L1 = [1,2,3] ; L2 = [2,4,6]
L1+L2
L1 = [1,2,3] ; L2 = [2,4,6]
set(L1+L2)
L = [1,4,2,3,5,6,7]
L.append(10)
L
L.insert(2,1)
L
L.remove(1)
L
L.remove(10)
L
L = [1,4,2,3,5,6,7]
L.pop(-1)
L
L.pop(-2)
L
L.pop(1)
L
L = [1,10,2,30,5,60,7]
L.sort()
L
L.reverse()
L
L = [x for x in range(10)]
print(L)
L = [x for x in range(10)]
L
L = [x for x in range(30)]
print(L)

L = [x for x in range(30)]
L
L = [x for x in range(100) if x%2==0]
print(L)
L = [x for x in range(10) if x%4==0]
print(L)
import random as rn
rn.randint(0,100)
import random as rn
R = [rn.randint(0,10) for i in range(20)]
print(R)
import random as rn
R = [rn.randint(0,100) for i in range(200)]
print(R)
import collections
#High Performance Counting
C = collections.Counter(R)
print(C)<jupyter_output>Counter({64: 6, 22: 5, 42: 5, 32: 5, 61: 5, 47: 4, 73: 4, 14: 4, 9: 4, 68: 4, 33: 4, 65: 4, 49: 4, 5: 4, 93: 3, 36: 3, 10: 3, 97: 3, 0: 3, 91: 3, 95: 3, 28: 3, 45: 3, 78: 3, 59: 3, 27: 3, 89: 3, 43: 3, 66: 3, 79: 3, 69: 2, 24: 2, 17: 2, 96: 2, 51: 2, 16: 2, 52: 2, 67: 2, 21: 2, 44: 2, 71: 2, 70: 2, 75: 2, 39: 2, 6: 2, 35: 2, 60: 2, 80: 2, 77: 2, 99: 2, 1: 2, 40: 2, 76: 2, 13: 2, 98: 2, 84: 2, 30: 2, 57: 2, 11: 2, 87: 2, 90: 2, 48: 2, 29: 1, 26: 1, 72: 1, 88: 1, 2: 1, 85: 1, 83: 1, 31: 1, 50: 1, 7: 1, 63: 1, 54: 1, 62: 1, 81: 1, 94: 1, 15: 1, 56: 1, 41: 1, 86: 1, 8: 1, 12: 1, 3: 1, 18: 1, 37: 1, 74: 1, 4: 1})
{'fraction_non_alphanumeric': 0.2665505226480836, 'fraction_numerical': 0.2229965156794425, 'mean_word_length': 3.558201058201058, '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': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '349943', 'license': 'MIT', 'n_tokens_mistral': 1274, 'n_tokens_neox': 995, 'n_words': 333, 'path': 'Class 2.ipynb', 'repo_name': 'DNChhatkuli/Astrophysics', 'size': '1722'}