<jupyter_start><jupyter_text><jupyter_code>import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
df = sns.load_dataset('fmri')
df.head(3)
sns.relplot(data= df, x= 'timepoint', y= 'signal')
sns.relplot(data= df, x= 'timepoint', y= 'signal', kind='line')
df.describe()
df.timepoint.value_counts()
df.groupby('timepoint').agg('mean').plot()
sns.relplot(data= df, x= 'timepoint', y= 'signal', hue='region')
sns.relplot(data= df, x= 'timepoint', y= 'signal', hue='region', kind='line', style='event', markers=True, col='event')
sns.relplot(data= df, x= 'timepoint', y= 'signal', hue='region', kind='line', style='event', markers=True, col='subject', col_wrap=4)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2013793103448276, 'fraction_numerical': 0.002758620689655172, 'mean_word_length': 8.075, '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': '602818', 'license': 'MIT', 'n_tokens_mistral': 284, 'n_tokens_neox': 276, 'n_words': 65, 'path': 'Wizualizacje2.ipynb', 'repo_name': 'KonradBier/kBI', 'size': '725'}