<jupyter_start><jupyter_text><jupyter_code>import numpy as np
import scipy.stats as stats
import pylab

std_normal=np.random.normal(loc=0,scale=1,size=1000)
for i in range(0,101):
  print(i,np.percentile(std_normal,i))
measurements=np.random.normal(loc=20,scale=5,size=100)
stats.probplot(measurements,dist='norm',plot=pylab)
pylab.show()
measurements=np.random.uniform(low=-1,high=1,size=10000)
stats.probplot(measurements,dist='norm',plot=pylab)
pylab.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15714285714285714, 'fraction_numerical': 0.04693877551020408, 'mean_word_length': 11.921052631578947, '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': '207416', 'license': 'MIT', 'n_tokens_mistral': 208, 'n_tokens_neox': 198, 'n_words': 22, 'path': 'q_qplot.ipynb', 'repo_name': 'ajnasaju3/ai', 'size': '490'}