<jupyter_start><jupyter_text><jupyter_code># My First colab notebook code 
print("Hello, DPhi! Today I have executed my very first Python program. Thanks!")
x = input("What is your age?")
type(x)
x=int(x)
print(x)
type(x)
x = 36 / 4 * (3 + 2) * 4 + 2
print(x)
print(2*2**3)
a=5
b=16
c=a+b
print(c)
x=input("Enter a number-")
y=int(x)**3
print("The cube of ",x,' is ',str(y))
s = 'Python is easy to learn'
print(s[-5:])
print(s[16:])
L = [10, 20, 30, 25, 32, 45, 50]
L.append(20)
L.append(32)
print(L.count(32))
tup=(4,5,6,7,8)
print(tup[4])
 L1 = [10, 20, 30, 25, 32, 45, 50] 
 L2 = [80, 70, 78, 55, 62]
 S1=sum(L1)
 S2=sum(L2)
 print(S1-S2)
def hi():
  print("hello")

hi()
def bitcoin_to_USD(btc):
  amount = btc*537
  print("Total $ for ",btc,' bitcoins is :',round(amount,2))

bitcoin_to_USD(2.523)
import numpy
import numpy as np
def say(message, times=1):
  print(message*times)
say("Hello")
say("World",5)
def fun(x=1,y=2):
  x=x+y
  y+=1
  print(x,y)

fun(y=2,x=1)
#Create a NumPy array of the first five even numbers.
import numpy as np
count = 0
a = np.empty(0,dtype=int)
for i in range(1,20):
  if i%2 == 0:
    count +=1
    if count <= 5:
      a = np.append(a,i)
   
print(a)

  
a.dtype
list = [2, 5, 8, 6, 4, 12, 16, 15]
b=np.array(list)
b.ndim
c = np.linspace(17,31)
c
L = [10, 2, 7, 8, 3, 22, 54, 12, 13, 46, 23, 25, 41]

a = np.array(L)
a

a.sort()
a
b=np.delete(a,4)
b
c=np.array(L)
c
c=np.append(c,53)
c
c.reshape((2,7))
c.reshape((7,2))
c= np.linspace(0,25,num=25,dtype=int)
c
c=c.reshape(5,5)
c
c[0:2,3:5]
c[1:5:2,0:5:2]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.21424030514939607, 'fraction_numerical': 0.101716465352829, 'mean_word_length': 4.246666666666667, 'pattern_counts': {'":': 0, '<': 6, '<?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': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '597988', 'license': 'MIT', 'n_tokens_mistral': 885, 'n_tokens_neox': 791, 'n_words': 203, 'path': 'DPhi_Python_training.ipynb', 'repo_name': 'datakatha/practice', 'size': '1573'}