<jupyter_start><jupyter_text>Wisdom of Crowds<jupyter_code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set()
#sns.set(style="ticks", context="talk")
#plt.style.use("dark_background")
df = pd.read_csv('02-1-bean-data-full-x.csv', header=None)
df.head()
df.describe()
plt.figure(figsize=(15,4))
sns.stripplot(x=df.values);
fig, ax = plt.subplots(3, 1, figsize=(15,9))
jitter=[0, .1, .4]
for k in range(3):
    sns.stripplot(x=df.values, jitter=jitter[k], ax=ax[k]);
sns.boxplot(data=df.values);
plt.figure(figsize=(15,4))
sns.boxplot(x=df.values);
plt.savefig('box-whisker.png')
plt.figure(figsize=(15,4))
sns.histplot(data=df.values);
plt.savefig('histplot.png')
plt.figure(figsize=(15,4))
sns.histplot(data=df.values, binrange=[0, 10001]);
plt.savefig('histplot.png')
plt.figure(figsize=(15,4))
sns.histplot(data=df.values, binrange=[0, 4000]);
plt.savefig('histplot.png')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.20188284518828453, 'fraction_numerical': 0.0397489539748954, 'mean_word_length': 7.469026548672566, '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': '420386', 'license': 'MIT', 'n_tokens_mistral': 439, 'n_tokens_neox': 412, 'n_words': 61, 'path': '02-2-3-jelly-bean-counts/.ipynb_checkpoints/02-2-3-jelly-beans-checkpoint.ipynb', 'repo_name': 'yongduek/ArtofStatistics', 'size': '956'}