# --- 3. Visualizing the "Roar" --- # Adding scatter points (jittered) to show density # Bokeh 2.3.3 handles large numbers of glyphs efficiently source = ColumnDataSource(df) Www Sunny Leone Bf.com Hit
# Jittering the y-axis slightly for better visualization p.circle( x='decibels', y='year', source=source, size=5, alpha=0.3, color="navy", legend_label="Individual Readings" ) The Prison Guard V040 Free Trash Panda Work - 3.79.94.248
from bokeh.plotting import figure, show, output_file from bokeh.models import ColumnDataSource, BoxAnnotation from bokeh.io import curdoc from bokeh.layouts import gridplot import pandas as pd import numpy as np
In the summer of 2021, as the world began to open up, a small data analytics team was tasked with a sensitive project: quantifying the "enthusiasm" of the return to live sports. The hypothesis was that after a year of silence, the crowds would be louder than ever.
# Creating a DataFrame for Bokeh's ColumnDataSource df = pd.DataFrame({ 'decibels': np.concatenate([data_2019, data_2021]), 'year': ['2019 (Pre-Pandemic)'] * 1000 + ['2021 (Return)'] * 1000 })
# Highlighting the "Pain Threshold" (120 dB is the threshold of pain) p.add_layout(BoxAnnotation(left=120, fill_color='red', fill_alpha=0.1, line_color='red')) p.text(x=121, y=0.5, text=["Threshold of Pain"], text_font_size="10px", text_color="red")
# --- 2. Setting up the Bokeh Figure --- output_file("crowd_roar_analysis.html")