<jupyter_start><jupyter_text>Workflow example JVD<jupyter_code>%matplotlib inline
import matplotlib.pyplot as plt

# plot parameters
FIGSIZE = (12,7)
plt.rcParams['figure.figsize'] = FIGSIZE
plt.style.use('seaborn')
from jupyterworkflow.data import get_fremont_data
data = get_fremont_data()
data.head()
data.resample('W').sum().plot(figsize=(FIGSIZE));

ax = data.resample('D').sum().rolling(365).sum().plot(figsize=FIGSIZE);
ax.set_ylim(0, None);
data.groupby(data.index.time).mean().plot(figsize=FIGSIZE);
pivoted = data.pivot_table('Total', index=data.index.time, columns=data.index.date)
pivoted.iloc[:5,:5]
# just for speed only the first 500
pivoted.iloc[:,:500].plot(legend=False, alpha=.05, figsize=FIGSIZE);<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17001338688085676, 'fraction_numerical': 0.022757697456492636, 'mean_word_length': 8.121951219512194, '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': '517736', 'license': 'MIT', 'n_tokens_mistral': 300, 'n_tokens_neox': 288, 'n_words': 43, 'path': 'JupyterWorkflow.ipynb', 'repo_name': 'piinghel/JupyterWorkflow_TemplateJVD', 'size': '747'}