text: user1638: I have a heatmap and I would like to find some rectangles.
  
  I have already used clustermap. But here, I can not calculate these rectangles.
  The order of the data should not be changed.
  This Code is not working: 
  import pandas as pd
  import matplotlib.pyplot as plt
  import seaborn as sns
  tips2 = pd.read_csv('heatmapExport.csv')
  sns.set(color_codes=True)
  g = sns.clustermap(tips2, cbar="false")
  plt.show()
  
  Does somebody have an idea?
  Thanks a lot
  Here is an example of what I want:
  
  
  user196: Based on your description I think you should have a look at a technique called 'biclustering'. 
  The example on this page defines the goal of this technique as 'Finding subgroups of rows and columns which are as similar as possible to each other and as different as possible to the rest.'
  Since your examples are python-based, you could check out scikit-learn's implementations of biclustering.
  
original_text: 1638: <p>I have a heatmap and I would like to find some rectangles.
  <a href="https://i.stack.imgur.com/eUJrR.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/eUJrR.jpg" alt="enter image description here"></a></p>
  
  <p>I have already used <a href="http://seaborn.pydata.org/generated/seaborn.clustermap.html" rel="nofollow noreferrer">clustermap</a>. But here, I can not calculate these rectangles.
  The order of the data should not be changed.</p>
  
  <p>This Code is not working: </p>
  
  <pre><code>import pandas as pd
  import matplotlib.pyplot as plt
  import seaborn as sns
  tips2 = pd.read_csv('heatmapExport.csv')
  sns.set(color_codes=True)
  g = sns.clustermap(tips2, cbar="false")
  plt.show()
  </code></pre>
  
  <p>Does somebody have an idea?</p>
  
  <p>Thanks a lot</p>
  
  <p>Here is an example of what I want:</p>
  
  <p><a href="https://i.stack.imgur.com/3tgrc.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/3tgrc.jpg" alt="enter image description here"></a></p>
   196: <p>Based on your description I think you should have a look at a technique called 'biclustering'. </p>
  
  <p>The example on <a href="https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/Biclust" rel="nofollow noreferrer">this page</a> defines the goal of this technique as 'Finding subgroups of rows and columns which are as similar as possible to each other and as different as possible to the rest.'</p>
  
  <p>Since your examples are python-based, you could check out <a href="http://scikit-learn.org/stable/modules/biclustering.html" rel="nofollow noreferrer">scikit-learn's implementations of biclustering</a>.</p>
  