<jupyter_start><jupyter_text><jupyter_code>#Import dependencies
import numpy as np 
import matplotlib.pyplot as plt
%matplotlib inline 

# Parameters 
mean = 0 # mean
sd = 1 # standard deviation
sample_size = 1000

# Generate Sample
sample = np.random.normal(loc = mean, scale = sd, size = sample_size)

# Plot
plt.hist(sample, bins='auto') 
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10732984293193717, 'fraction_numerical': 0.015706806282722512, 'mean_word_length': 5.491525423728813, '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': '211470', 'license': 'MIT', 'n_tokens_mistral': 144, 'n_tokens_neox': 133, 'n_words': 35, 'path': 'normal_distribution.ipynb', 'repo_name': 'yniu87/stat_teach', 'size': '382'}