<jupyter_start><jupyter_text>Anscombe's Quartet dataset<jupyter_code>import seaborn as sns
sns.set(style = "ticks")
df = sns.load_dataset("anscombe")
print(open('anscombe').read())
import random
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf
import seaborn as sns
sns.set(style = "ticks")
import seaborn as sns
sns.set(style = "ticks")
df = sns.load_dataset("anscombe")
with open("anscombe") as df:
    for line in df:
read()
print(df)
display(df)
print(df.to_html())
from IPython.display import display, HTML
print "Dataframe 1:"
display(df)
print("Dataframe 1:")
display(df)
print("dataframe 2:")
display(HTML(df.to_html()))<jupyter_output>dataframe 2:
{'fraction_non_alphanumeric': 0.1256544502617801, 'fraction_numerical': 0.005235602094240838, 'mean_word_length': 6.574257425742574, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '323001', 'license': 'Apache-2.0', 'n_tokens_mistral': 294, 'n_tokens_neox': 279, 'n_words': 79, 'path': 'First Draft.ipynb', 'repo_name': 'Gerrydh/Fundamentals-of-DA-Assignments', 'size': '764'}