<jupyter_start><jupyter_text>play with numpy<jupyter_code>dir(arr) # dir to see all builtin names 

arr = np.arange(0,10)
arr

arr = np.zeros(7) 
print(arr)
dir(np)
np.random.randint(1,100,(2,2))
# 1 to 100 as range 
# (2,2) as Dimensions
who = np.random.choice(['styam','ronaldo','obit'],1) # choose any one 
print(who)
n = np.random.randint(1 , 100,50)
print(n)
n + 4
n*5
n /2
n//4
n%4
n -7
np.sin(n) # can be use for Mathematical operation
np.cos(n)
np.log(n)
np.std(n)
np.mean(n)
np.max(n)
n[:4] # till 4th index
n[4:] # from 4th index<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17926186291739896, 'fraction_numerical': 0.05799648506151142, 'mean_word_length': 4.643564356435643, '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': '400614', 'license': 'MIT', 'n_tokens_mistral': 284, 'n_tokens_neox': 266, 'n_words': 64, 'path': 'numpy intro.ipynb', 'repo_name': 'crin02/ml-library-intro', 'size': '569'}