<jupyter_start><jupyter_text>Example NotebookHere's a simple test of the environment using Pandas, NumPy, Jupyter, etc., based on the [*10 Minutes to pandas*](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html) tutorial.<jupyter_code>import numpy as np
import pandas as pd<jupyter_output><empty_output><jupyter_text>First, a series with a `NaN` included:<jupyter_code>s = pd.Series([1, 3, 5, np.nan, 6, 8])
s<jupyter_output><empty_output><jupyter_text>Then some dates:<jupyter_code>dates = pd.date_range('20130101', periods=6)
dates<jupyter_output><empty_output><jupyter_text>Now a simple dataframe:<jupyter_code>df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
df<jupyter_output><empty_output><jupyter_text>import dataNow let's import data from SoundPrint.co :<jupyter_code>df = pd.read_csv("soundcheck.csv")
df.head()
df.shape<jupyter_output><empty_output><jupyter_text>Notes from <NAME> @ SoundPrint:There are ~11,700 SoundChecks of NYC Restaurants in this file organized as follows: For Columns: - *Venue ID* -- developer insisted on making the venue names anonymous, but we can retrieve a specific venue name should you wish; same venue ID means more than one soundcheck was taken for such venue - makes for more robust data) - *Restaurant Type* -- could be interesting segmented data analysis - *Zip code* -- could be good for seeing sound levels by Manhattan neighborhoods  - for the *Avg*, *Min*, *Max* sound levels: Avg is what most people care about, but those with _Hyperacusis_ - sensitivity to loud or sudden bursts of noise - care about Max - *Day of the week* -- could be some interested day of the week trends for sound levels (are places louder during weekend days)? - *Timestamp* -- sound levels by time of day could also be useful.<jupyter_code>%matplotlib inline

d2 = df["max_decibels"]
ax = d2.plot.hist(bins=50)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10303347280334728, 'fraction_numerical': 0.015167364016736401, 'mean_word_length': 5.956363636363636, 'pattern_counts': {'":': 0, '<': 26, '<?xml version=': 0, '>': 26, 'https://': 1, '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': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '319699', 'license': 'MIT', 'n_tokens_mistral': 638, 'n_tokens_neox': 603, 'n_words': 207, 'path': 'example.ipynb', 'repo_name': 'DerwenAI/decibels', 'size': '1916'}