content: import numpy as np
  import pandas as pd
  from bokeh.plotting import figure, show, output_file
  
  # Generate some synthetic time series for six different categories
  cats = list("abcdef")
  yy = np.random.randn(2000)
  g = np.random.choice(cats, 2000)
  for i, l in enumerate(cats):
      yy[g == l] += i // 2
  df = pd.DataFrame(dict(score=yy, group=g))
  
  # Find the quartiles and IQR foor each category
  groups = df.groupby('group')
  q1 = groups.quantile(q=0.25)
  q2 = groups.quantile(q=0.5)
  q3 = groups.quantile(q=0.75)
  iqr = q3 - q1
  upper = q3 + 1.5*iqr
  lower = q1 - 1.5*iqr
  
  # find the outliers for each category
  def outliers(group):
      cat = group.name
      return group[(group.score > upper.loc[cat][0]) | (group.score < lower.loc[cat][0])]['score']
  out = groups.apply(outliers).dropna()
  
  # Prepare outlier data for plotting, we need coordinate for every outlier.
  outx = []
  outy = []
  for cat in cats:
      # only add outliers if they exist
      if not out.loc[cat].empty:
          for value in out[cat]:
              outx.append(cat)
              outy.append(value)
  
  output_file("boxplot.html")
  
  p = figure(tools="save", background_fill="#EFE8E2", title="", x_range=cats)
  
  # If no outliers, shrink lengths of stems to be no longer than the minimums or maximums
  qmin = groups.quantile(q=0.00)
  qmax = groups.quantile(q=1.00)
  upper.score = [min([x,y]) for (x,y) in zip(list(qmax.iloc[:,0]),upper.score) ]
  lower.score = [max([x,y]) for (x,y) in zip(list(qmin.iloc[:,0]),lower.score) ]
  
  # stems
  p.segment(cats, upper.score, cats, q3.score, line_width=2, line_color="black")
  p.segment(cats, lower.score, cats, q1.score, line_width=2, line_color="black")
  
  # boxes
  p.rect(cats, (q3.score+q2.score)/2, 0.7, q3.score-q2.score,
      fill_color="#E08E79", line_width=2, line_color="black")
  p.rect(cats, (q2.score+q1.score)/2, 0.7, q2.score-q1.score,
      fill_color="#3B8686", line_width=2, line_color="black")
  
  # whiskers (almost-0 height rects simpler than segments)
  p.rect(cats, lower.score, 0.2, 0.01, line_color="black")
  p.rect(cats, upper.score, 0.2, 0.01, line_color="black")
  
  # outliers
  p.circle(outx, outy, size=6, color="#F38630", fill_alpha=0.6)
  
  p.xgrid.grid_line_color = None
  p.ygrid.grid_line_color = "white"
  p.grid.grid_line_width = 2
  p.xaxis.major_label_text_font_size="12pt"
  
  show(p)
  