<jupyter_start><jupyter_text><jupyter_code>n_num = [1, 2, 3, 4, 5]
n=len(n_num)
s=sum(n_num)
mean=s/n
print("Mean is",mean)
n_num = [1, 2, 3, 4, 5]
n=len(n_num)
n_num.sort()
if n%2==0:
  m1=n_num[n//2]
  m2=n_num[n//2-1]
  median=(m1+m2)/2
else:
  median=n_num[n//2]
print("Median is",median)
from collections import Counter 

n_num = [1, 2, 3, 4, 5, 5] 
n = len(n_num) 
  
data = Counter(n_num) 
get_mode = dict(data) 
mode = [k for k, v in get_mode.items() if v == max(list(data.values()))] 
  
if len(mode) == n: 
    get_mode = "No mode found"
else: 
    get_mode = "Mode is / are: " + ', '.join(map(str, mode)) 
      
print(get_mode)
def variance(data):
     n = len(data)
     mean = sum(data) / n
     deviations = [(x - mean) ** 2 for x in data]
     variance = sum(deviations) / (n-1)
     return variance
variance([4, 8, 6, 5, 3, 2, 8, 9, 2, 5])
import math
def variance(data):
     n = len(data)
     mean = sum(data) / n
     return sum((x - mean) ** 2 for x in data) / (n -1)


def stdev(data):
     var = variance(data)
     std_dev = math.sqrt(var)
     return std_dev
stdev([4, 8, 6, 5, 3, 2, 8, 9, 2, 5])
L = [1, "a" , "string" , 1+2] 
print (L) 
L.append(6) 
print (L)
L.pop() 
print (L) 
print (L[1])
tup = (1, "a", "string", 1+2) 
print(tup) 
print(tup[1])
month={1:'JAN',2:'FEB',3:'MAR',4:'APR',5:'MAY',6:'JUNE',7:'JULY',8:'AUG',9:'SEP',10:'OCT',11:'NOV',12:'DEC'}
print(month[1],month[3])
print(month)<jupyter_output><NAME>
{1: 'JAN', 2: 'FEB', 3: 'MAR', 4: 'APR', 5: 'MAY', 6: 'JUNE', 7: 'JULY', 8: 'AUG', 9: 'SEP', 10: 'OCT', 11: 'NOV', 12: 'DEC'}
{'fraction_non_alphanumeric': 0.23346055979643765, 'fraction_numerical': 0.058524173027989825, 'mean_word_length': 3.507163323782235, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '26333', 'license': 'Apache-2.0', 'n_tokens_mistral': 827, 'n_tokens_neox': 756, 'n_words': 186, 'path': 'DMDWass1.ipynb', 'repo_name': 'ChiranjeebDash/ChiranjeebDash_18cse052', 'size': '1573'}