<jupyter_start><jupyter_text>Jupyter Data Science Workflow From exploratory analysis to reproducible science<NAME> / University of Washington eScience Institute<jupyter_code># Quick utility to embed the videos below
from IPython.display import YouTubeVideo
def embed_video(index, playlist='PLYCpMb24GpOC704uO9svUrihl-HY1tTJJ'):
    return YouTubeVideo('', index=index - 1, list=playlist, width=600, height=350)
embed_video(2)
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')
from jupyterworkflow.data import get_fremont_data
data = get_fremont_data()
data.head()
data.plot();
data.resample('W').sum().plot();
data.resample('D').sum().rolling(365).sum().plot()
data.groupby(data.index.time).mean().plot();
pivoted = data.pivot_table('Total', index = data.index.time, columns=data.index.date)
pivoted.iloc[:5, :5]
pivoted.plot(legend=False, alpha=0.01)
get_fremont_data??<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13714902807775378, 'fraction_numerical': 0.024838012958963283, 'mean_word_length': 7.828571428571428, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 6, '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': '92975', 'license': 'MIT', 'n_tokens_mistral': 347, 'n_tokens_neox': 325, 'n_words': 65, 'path': 'JupyterWorkflow.ipynb', 'repo_name': 'ycheng42/JupyterWorkflow', 'size': '935'}