<jupyter_start><jupyter_text>A Simple ExampleThis notebook shows the last time it was run, displays a dataframe, and then plots it.If you run `mkdocs serve` in the directory above this, and make changes here you'll see your docs update a moment after saving the notebook.![logo.png](attachment:logo.png)<jupyter_code>print(exported_value)
import pandas as pd
import numpy as np
import datetime
%matplotlib inline<jupyter_output><empty_output><jupyter_text>The last run time<jupyter_code>print(f"This notebook last ran at {datetime.datetime.now()}")<jupyter_output>This notebook last ran at 2018-09-18 09:02:51.502564<jupyter_text>Let's make a dataframe<jupyter_code>df = pd.DataFrame({"time":np.arange(0, 10, 0.1)})
df["amplitude"] =  np.sin(df.time)
df<jupyter_output><empty_output><jupyter_text>And plot it :)<jupyter_code>ax = df.plot()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11162255466052934, 'fraction_numerical': 0.028768699654775604, 'mean_word_length': 7.7, 'pattern_counts': {'":': 1, '<': 16, '<?xml version=': 0, '>': 16, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '99296', 'license': 'MIT', 'n_tokens_mistral': 325, 'n_tokens_neox': 301, 'n_words': 81, 'path': 'examples/binder_logo/docs/docs_example.ipynb', 'repo_name': 'MridulS/mknotebooks', 'size': '869'}