title: How can I plot the whiskers up to the Q1-1.5*IQR and Q3+1.5*IQR and not minimum and maximum values?
body: <p>I want to compare a data point with a boxplot. The plot shows me that the point is an outlier while when I calculated the lower (Q1-1.5<em>IQR) and upper (Q3+1.5</em>IQR) bounds, I found that the point shouldn't be an outlier. How can I plot the whiskers of my boxplot to the lower and upper bound? The <code>lower_allowed_limit</code> is 0.03658130324999995 but the value of my data point is 0.077414939.</p>
  <p><a href="https://i.stack.imgur.com/ek2Vb.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ek2Vb.png" alt="enter image description here" /></a></p>
  <pre><code>import numpy as np
  import pandas as pd
  import seaborn as sns
  
  data = {'Center': [0.26857881, 0.25526688, 0.27575725, 0.25816567, 0.27839591, 0.25704223, 0.27876854,
                     0.25689208, 0.24568341, 0.27170329, 0.24951651, 0.26341556, 0.23330706, 0.26628367,
                     0.24271549, 0.26151005, 0.2194737, 0.22327154, 0.20390595, 0.23187327, 0.22729037,
                     0.24013772, 0.21812675, 0.24829089, 0.22292593, 0.21533798, 0.2375203, 0.24358,
                     0.23935491, 0.21955327, 0.25495809, 0.22418302, 0.23168249, 0.22031974, 0.19427523,
                     0.21487167, 0.22327235, 0.2334787, 0.21033593, 0.21257535, 0.19528684, 0.20617609,
                     0.21584015, 0.20789747, 0.20814228, 0.21319687, 0.22782205, 0.20793727, 0.22442598,
                     0.22854906, 0.2328641, 0.23012169, 0.22649554, 0.21118934, 0.1980712, 0.20799725, 0.19317045,
                     0.20426635, 0.18019197, 0.18720767, 0.18573065, 0.19297233, 0.19230893, 0.20739394, 0.20091433,
                     0.20808377, 0.21997453, 0.21432004, 0.2145078, 0.22670847, 0.2195935, 0.21880863, 0.22720217,
                     0.2405563, 0.24436345, 0.22278882, 0.23400447, 0.2128166, 0.2344685, 0.21768168, 0.23376607,
                     0.21818298, 0.234481, 0.22627898, 0.29847948, 0.23503119, 0.2308161, 0.19334404, 0.09538444,
                     0.11038104, 0.11524165, 0.09856957, 0.12746572, 0.1558064, 0.15951489, 0.12194555, 0.11833563,
                     0.1014696, 0.11559569, 0.11146258, 0.1315745, 0.09123692, 0.09131569, 0.10194323, 0.11820753,
                     0.11682336, 0.12112035, 0.15115081, 0.08319368, 0.10231317, 0.09921997, 0.12303618, 0.10655393,
                     0.11144342, 0.09901149, 0.09016768, 0.09824667, 0.12649639, 0.14759194, 0.14387346]}
  
  X = pd.DataFrame(data)
  
  def is_tukey(x, y, k=1.5):
      first_quartile = np.quantile(x, .25)
      third_quartile = np.quantile(x, .75)
      iqr = third_quartile - first_quartile
      lower_allowed_limit = first_quartile - (k * iqr)
      upper_allowed_limit = third_quartile + (k * iqr)
      in_range = (y &gt; lower_allowed_limit) and (y &lt; upper_allowed_limit)
      return in_range, lower_allowed_limit
  
  y = 0.077414939
  
  is_tukey(X['Center'], y)
  
  sns.boxplot(X['Center'])
  sns.swarmplot([y], size=5, color='r')
  </code></pre>