title: yticks rotation argument removes frame from Seaborn heatmap
body: <p>When I pass the argument <code>plt.yticks(rotation = 0)</code>, my heatmap loses its frame edges. Setting <code>yticklabels = True</code> did not work.</p>
  
  <p>How can I prevent the loss of the left frame?</p>
  
  <p>Using the function and losing the left frame:
  <a href="https://i.stack.imgur.com/lTEHD.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/lTEHD.png" alt="enter image description here"></a></p>
  
  <p>Not using the function and setting the rotation to 90 produce a result that keeps the frame:
  <a href="https://i.stack.imgur.com/hmcGF.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/hmcGF.png" alt="enter image description here"></a></p>
  
  <p>Code: </p>
  
  <pre><code>import matplotlib.pyplot as plt
  import pandas as pd
  import seaborn as sns
  
  fig, ax = plt.subplots(figsize=(7.5,4.5)) 
  excel_file          = r"C:\some\path\to\a\spreadsheet.xlsx"
  xl                  = pd.ExcelFile(excel_file)
  df                  = xl.parse((xl.sheet_names)[0])
  df["Percentage"]    = df["Percentage"].apply(lambda x: round(x * 100, 2))
  df["Overlap Count"] = df["Overlap"]
  intersects          = df.pivot("Ward", "Overlap Count", "Percentage")
  ax = sns.heatmap(intersects, linewidths = 0.1, cmap = "gray_r", linecolor = "black", edgecolor = "b")
  plt.yticks(rotation = 0)
  ax.invert_yaxis()
  </code></pre>
  
  <p>Data:</p>
  
  <pre><code>Ward Overlap Percentage
  1   0   0.009222
  2   0   0.152653
  3   0   0.30053
  4   0   0.360239
  5   0   0.18537
  6   0   0.026462
  7   0   0.063586
  8   0   0.276085
  1   1   0.02033
  2   1   0.081188
  3   1   0.226472
  4   1   0.147386
  5   1   0.161132
  6   1   0.043077
  7   1   0.213306
  8   1   0.110658
  1   2   0.010786
  2   2   0.103747
  3   2   0.202578
  4   2   0.144904
  5   2   0.182761
  6   2   0.047456
  7   2   0.163646
  8   2   0.11166
  1   3   0.021125
  2   3   0.064839
  3   3   0.120019
  4   3   0.0945
  5   3   0.136404
  6   3   0.038833
  7   3   0.188478
  8   3   0.127329
  1   4   0.028342
  2   4   0.079106
  3   4   0.083844
  4   4   0.05823
  5   4   0.091276
  6   4   0.089948
  7   4   0.131098
  8   4   0.116538
  1   5   0.061603
  2   5   0.070535
  3   5   0.04057
  4   5   0.057444
  5   5   0.078471
  6   5   0.093779
  7   5   0.105894
  8   5   0.125726
  1   6   0.077076
  2   6   0.090837
  3   6   0.024996
  4   6   0.050841
  5   6   0.063669
  6   6   0.133617
  7   6   0.066882
  8   6   0.073919
  1   7   0.105353
  2   7   0.12705
  3   7   0.000991
  4   7   0.042617
  5   7   0.057453
  6   7   0.148017
  7   7   0.042073
  8   7   0.044024
  1   8   0.163147
  2   8   0.077301
  3   8   0
  4   8   0.018129
  5   8   0.032641
  6   8   0.171143
  7   8   0.019979
  8   8   0.014061
  1   9   0.17952
  2   9   0.066654
  3   9   0
  4   9   0.008686
  5   9   0.008022
  6   9   0.127626
  7   9   0.005059
  8   9   0
  1   10  0.139758
  2   10  0.031035
  3   10  0
  4   10  0.009222
  5   10  0.002765
  6   10  0.039951
  7   10  0
  8   10  0
  1   11  0.115179
  2   11  0.038788
  3   11  0
  4   11  0.005554
  5   11  3.64E-05
  6   11  0.033801
  7   11  0
  8   11  0
  1   12  0.06856
  2   12  0.016265
  3   12  0
  4   12  0.002247
  5   12  0
  6   12  0.006292
  7   12  0
  8   12  0
  </code></pre>