title: Plotly heatmap plot not rendering all yaxis labels
body: <p>I built an dash plotly dashboard with a heatmap. However I am noticing that t=some of the labels in my y-axis are not being shown. I am only getting a limited I am not sure what is going wrong. Here is my dashboard:</p>
  
  <pre><code>import dash
  import dash_table
  import plotly.graph_objs as go
  import dash_html_components as html
  import dash_core_components as dcc
  from dash.dependencies import Input,Output
  import pandas as pd
  import os
  import numpy as np
  #correlation dataframe
  correlation_df = supervisor[['Características (D)', 'Características (I)',
         'Características (S)', 'Características (C)', 'Motivación (D)',
         'Motivación (I)', 'Motivación (S)', 'Motivación (C)', 'Bajo Stress (D)',
         'Bajo Stress (I)', 'Bajo Stress (S)', 'Bajo Stress (C)','span','Mean Team Performance','employment span','Pay to team size ratio']]
  correlation_df  = correlation_df.corr()
  corr_fig = go.Figure()
  corr_fig.add_trace(go.Heatmap(
      z= correlation_df.values,
      x= ['Características (D)', 'Características (I)',
         'Características (S)', 'Características (C)', 'Motivación (D)',
         'Motivación (I)', 'Motivación (S)', 'Motivación (C)', 'Bajo Stress (D)',
         'Bajo Stress (I)', 'Bajo Stress (S)', 'Bajo Stress (C)','span','Mean Team Performance','employment span','Pay to team size ratio'],
      y= ['Características (D)', 'Características (I)',
         'Características (S)', 'Características (C)', 'Motivación (D)',
         'Motivación (I)', 'Motivación (S)', 'Motivación (C)', 'Bajo Stress (D)',
         'Bajo Stress (I)', 'Bajo Stress (S)', 'Bajo Stress (C)','span','Mean Team Performance','employment span','Pay to team size ratio'],
      hoverongaps=False
  ))
  corr_fig.update_layout(title="Correlation heatmap",
                    yaxis={"title": 'Traits'},
                    xaxis={"title": 'Traits',"tickangle": 45}, )
  app = dash.Dash()
  #html layout
  app.layout = html.Div(children=[
      html.H1(children='Dashboard', style={
          'textAlign': 'center',
          'height': '10'
      }),
      dcc.Graph(
          id='heatmap',
          figure=corr_fig.to_dict()
      )
      ])
  if __name__ == '__main__':
          app.run_server(debug=True)
  </code></pre>
  
  <p>Here is a sample of my dataframe:</p>
  
  <pre><code>{'Características (D)': {'Características (D)': 1.0,
    'Características (I)': -0.744432853713455,
    'Características (S)': 0.20085563028990697,
    'Características (C)': -0.039907357919985106,
    'Motivación (D)': 0.8232188768568326,
    'Motivación (I)': -0.6987940156295481,
    'Motivación (S)': 0.17336394623619988,
    'Motivación (C)': -0.03941838984936696,
    'Bajo Stress (D)': 0.8142337605566142,
    'Bajo Stress (I)': -0.48861318810993065,
    'Bajo Stress (S)': 0.3207614659369065,
    'Bajo Stress (C)': -0.0461134826855843,
    'span': 0.2874881163983965,
    'Mean Team Performance': 0.40633858242603244,
    'employment span': -0.09857697245687172,
    'Pay to team size ratio': 0.022958588188126107},
   'Características (I)': {'Características (D)': -0.744432853713455,
    'Características (I)': 1.0,
    'Características (S)': -0.3779100652350093,
    'Características (C)': -0.11879176229148546,
    'Motivación (D)': -0.8454566900924195,
    'Motivación (I)': 0.8314885901746485,
    'Motivación (S)': -0.5493813305976118,
    'Motivación (C)': 0.020902885445784,
    'Bajo Stress (D)': -0.4614762821424876,
    'Bajo Stress (I)': 0.8628000011272827,
    'Bajo Stress (S)': 0.07723803992022794,
    'Bajo Stress (C)': -0.26492408476089707,
    'span': -0.2923189384010105,
    'Mean Team Performance': -0.04150083345671622,
    'employment span': 0.4006484556146567,
    'Pay to team size ratio': 0.27081339758378836},
   'Características (S)': {'Características (D)': 0.20085563028990697,
    'Características (I)': -0.3779100652350093,
    'Características (S)': 1.0,
    'Características (C)': -0.7739057580439489,
    'Motivación (D)': 0.28928161764191546,
    'Motivación (I)': -0.14811042351159115,
    'Motivación (S)': 0.7823864767779756,
    'Motivación (C)': -0.6651182815949327,
    'Bajo Stress (D)': 0.10162624205618695,
    'Bajo Stress (I)': -0.5488737066087104,
    'Bajo Stress (S)': 0.46905181352171205,
    'Bajo Stress (C)': -0.4698328671560004,
    'span': -0.02087671997992093,
    'Mean Team Performance': -0.12496266913575294,
    'employment span': 0.27001694775950746,
    'Pay to team size ratio': 0.07931062556531454},
   'Características (C)': {'Características (D)': -0.039907357919985106,
    'Características (I)': -0.11879176229148546,
    'Características (S)': -0.7739057580439489,
    'Características (C)': 1.0,
    'Motivación (D)': -0.011616389427962759,
    'Motivación (I)': -0.292733356844308,
    'Motivación (S)': -0.4343733032773228,
    'Motivación (C)': 0.774357808826908,
    'Bajo Stress (D)': -0.04367706074639601,
    'Bajo Stress (I)': 0.0931714388059811,
    'Bajo Stress (S)': -0.6482541912883304,
    'Bajo Stress (C)': 0.7732581689662739,
    'span': 0.03775247426826095,
    'Mean Team Performance': -0.07825282894287325,
    'employment span': -0.5003613024138532,
    'Pay to team size ratio': -0.20937248430293648},
   'Motivación (D)': {'Características (D)': 0.8232188768568326,
    'Características (I)': -0.8454566900924195,
    'Características (S)': 0.28928161764191546,
    'Características (C)': -0.011616389427962759,
    'Motivación (D)': 1.0,
    'Motivación (I)': -0.6401977926528387,
    'Motivación (S)': 0.27806883694592277,
    'Motivación (C)': -0.2534345146499511,
    'Bajo Stress (D)': 0.35748019323906,
    'Bajo Stress (I)': -0.7219032007713697,
    'Bajo Stress (S)': 0.21293087519106632,
    'Bajo Stress (C)': 0.2698254124168881,
    'span': 0.5037240436882805,
    'Mean Team Performance': 0.48414442720369955,
    'employment span': -0.20711331594020507,
    'Pay to team size ratio': -0.3769998767635495},
   'Motivación (I)': {'Características (D)': -0.6987940156295481,
    'Características (I)': 0.8314885901746485,
    'Características (S)': -0.14811042351159115,
    'Características (C)': -0.292733356844308,
    'Motivación (D)': -0.6401977926528387,
    'Motivación (I)': 1.0,
    'Motivación (S)': -0.48288361435623983,
    'Motivación (C)': -0.4135335004412625,
    'Bajo Stress (D)': -0.5563645790627242,
    'Bajo Stress (I)': 0.45272622386580263,
    'Bajo Stress (S)': 0.31345796324782077,
    'Bajo Stress (C)': -0.1236088717264958,
    'span': -0.4334332491868192,
    'Mean Team Performance': -0.027223644357210867,
    'employment span': 0.08277408562811393,
    'Pay to team size ratio': 0.30770777808996924},
   'Motivación (S)': {'Características (D)': 0.17336394623619988,
    'Características (I)': -0.5493813305976118,
    'Características (S)': 0.7823864767779756,
    'Características (C)': -0.4343733032773228,
    'Motivación (D)': 0.27806883694592277,
    'Motivación (I)': -0.48288361435623983,
    'Motivación (S)': 1.0,
    'Motivación (C)': -0.23220036735524985,
    'Bajo Stress (D)': 0.12079023858043715,
    'Bajo Stress (I)': -0.5418626995091027,
    'Bajo Stress (S)': -0.12381340765657087,
    'Bajo Stress (C)': -0.3091698232697242,
    'span': 0.1503231802207429,
    'Mean Team Performance': -0.38838798587565976,
    'employment span': 0.09981399691805137,
    'Pay to team size ratio': -0.20858825983296703},
   'Motivación (C)': {'Características (D)': -0.03941838984936696,
    'Características (I)': 0.020902885445784,
    'Características (S)': -0.6651182815949327,
    'Características (C)': 0.774357808826908,
    'Motivación (D)': -0.2534345146499511,
    'Motivación (I)': -0.4135335004412625,
    'Motivación (S)': -0.23220036735524985,
    'Motivación (C)': 1.0,
    'Bajo Stress (D)': 0.18028688548066718,
    'Bajo Stress (I)': 0.386437402512207,
    'Bajo Stress (S)': -0.7351725371592022,
    'Bajo Stress (C)': 0.21452556505271267,
    'span': 0.15796613914842977,
    'Mean Team Performance': -0.11411844367303944,
    'employment span': -0.1335403092401566,
    'Pay to team size ratio': -0.16110863218572585},
   'Bajo Stress (D)': {'Características (D)': 0.8142337605566142,
    'Características (I)': -0.4614762821424876,
    'Características (S)': 0.10162624205618695,
    'Características (C)': -0.04367706074639601,
    'Motivación (D)': 0.35748019323906,
    'Motivación (I)': -0.5563645790627242,
    'Motivación (S)': 0.12079023858043715,
    'Motivación (C)': 0.18028688548066718,
    'Bajo Stress (D)': 1.0,
    'Bajo Stress (I)': -0.1849352428080063,
    'Bajo Stress (S)': 0.2529157606770202,
    'Bajo Stress (C)': -0.31055770095686547,
    'span': -0.11631187918782246,
    'Mean Team Performance': 0.05369401779765192,
    'employment span': -0.042901905999867325,
    'Pay to team size ratio': 0.4484652828139771},
   'Bajo Stress (I)': {'Características (D)': -0.48861318810993065,
    'Características (I)': 0.8628000011272827,
    'Características (S)': -0.5488737066087104,
    'Características (C)': 0.0931714388059811,
    'Motivación (D)': -0.7219032007713697,
    'Motivación (I)': 0.45272622386580263,
    'Motivación (S)': -0.5418626995091027,
    'Motivación (C)': 0.386437402512207,
    'Bajo Stress (D)': -0.1849352428080063,
    'Bajo Stress (I)': 1.0,
    'Bajo Stress (S)': -0.0981237735359993,
    'Bajo Stress (C)': -0.27961420029017486,
    'span': -0.06711566955045667,
    'Mean Team Performance': 0.06327392392569486,
    'employment span': 0.5471491483201977,
    'Pay to team size ratio': 0.17612214868518486},
   'Bajo Stress (S)': {'Características (D)': 0.3207614659369065,
    'Características (I)': 0.07723803992022794,
    'Características (S)': 0.46905181352171205,
    'Características (C)': -0.6482541912883304,
    'Motivación (D)': 0.21293087519106632,
    'Motivación (I)': 0.31345796324782077,
    'Motivación (S)': -0.12381340765657087,
    'Motivación (C)': -0.7351725371592022,
    'Bajo Stress (D)': 0.2529157606770202,
    'Bajo Stress (I)': -0.0981237735359993,
    'Bajo Stress (S)': 1.0,
    'Bajo Stress (C)': -0.3570697743190169,
    'span': -0.23885238917830093,
    'Mean Team Performance': 0.41404235485716345,
    'employment span': 0.33146618322475935,
    'Pay to team size ratio': 0.49978958145813196},
   'Bajo Stress (C)': {'Características (D)': -0.0461134826855843,
    'Características (I)': -0.26492408476089707,
    'Características (S)': -0.4698328671560004,
    'Características (C)': 0.7732581689662739,
    'Motivación (D)': 0.2698254124168881,
    'Motivación (I)': -0.1236088717264958,
    'Motivación (S)': -0.3091698232697242,
    'Motivación (C)': 0.21452556505271267,
    'Bajo Stress (D)': -0.31055770095686547,
    'Bajo Stress (I)': -0.27961420029017486,
    'Bajo Stress (S)': -0.3570697743190169,
    'Bajo Stress (C)': 1.0,
    'span': -0.01344626398272969,
    'Mean Team Performance': -0.08070306908833835,
    'employment span': -0.5968535698213163,
    'Pay to team size ratio': -0.2795657757692292},
   'span': {'Características (D)': 0.2874881163983965,
    'Características (I)': -0.2923189384010105,
    'Características (S)': -0.02087671997992093,
    'Características (C)': 0.03775247426826095,
    'Motivación (D)': 0.5037240436882805,
    'Motivación (I)': -0.4334332491868192,
    'Motivación (S)': 0.1503231802207429,
    'Motivación (C)': 0.15796613914842977,
    'Bajo Stress (D)': -0.11631187918782246,
    'Bajo Stress (I)': -0.06711566955045667,
    'Bajo Stress (S)': -0.23885238917830093,
    'Bajo Stress (C)': -0.01344626398272969,
    'span': 1.0,
    'Mean Team Performance': -0.19851531030268585,
    'employment span': 0.13994502995917002,
    'Pay to team size ratio': -0.802380461421258},
   'Mean Team Performance': {'Características (D)': 0.40633858242603244,
    'Características (I)': -0.04150083345671622,
    'Características (S)': -0.12496266913575294,
    'Características (C)': -0.07825282894287325,
    'Motivación (D)': 0.48414442720369955,
    'Motivación (I)': -0.027223644357210867,
    'Motivación (S)': -0.38838798587565976,
    'Motivación (C)': -0.11411844367303944,
    'Bajo Stress (D)': 0.05369401779765192,
    'Bajo Stress (I)': 0.06327392392569486,
    'Bajo Stress (S)': 0.41404235485716345,
    'Bajo Stress (C)': -0.08070306908833835,
    'span': -0.19851531030268585,
    'Mean Team Performance': 1.0,
    'employment span': 0.3992240651662481,
    'Pay to team size ratio': 0.38910257451919805},
   'employment span': {'Características (D)': -0.09857697245687172,
    'Características (I)': 0.4006484556146567,
    'Características (S)': 0.27001694775950746,
    'Características (C)': -0.5003613024138532,
    'Motivación (D)': -0.20711331594020507,
    'Motivación (I)': 0.08277408562811393,
    'Motivación (S)': 0.09981399691805137,
    'Motivación (C)': -0.1335403092401566,
    'Bajo Stress (D)': -0.042901905999867325,
    'Bajo Stress (I)': 0.5471491483201977,
    'Bajo Stress (S)': 0.33146618322475935,
    'Bajo Stress (C)': -0.5968535698213163,
    'span': 0.13994502995917002,
    'Mean Team Performance': 0.3992240651662481,
    'employment span': 1.0,
    'Pay to team size ratio': 0.04572394154746432},
   'Pay to team size ratio': {'Características (D)': 0.022958588188126107,
    'Características (I)': 0.27081339758378836,
    'Características (S)': 0.07931062556531454,
    'Características (C)': -0.20937248430293648,
    'Motivación (D)': -0.3769998767635495,
    'Motivación (I)': 0.30770777808996924,
    'Motivación (S)': -0.20858825983296703,
    'Motivación (C)': -0.16110863218572585,
    'Bajo Stress (D)': 0.4484652828139771,
    'Bajo Stress (I)': 0.17612214868518486,
    'Bajo Stress (S)': 0.49978958145813196,
    'Bajo Stress (C)': -0.2795657757692292,
    'span': -0.802380461421258,
    'Mean Team Performance': 0.38910257451919805,
    'employment span': 0.04572394154746432,
    'Pay to team size ratio': 1.0}}
  </code></pre>
  
  <p>This is a snap shot of what the heatmap looks like if you run my code:
  <a href="https://i.stack.imgur.com/90BY2.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/90BY2.png" alt="Plot Image"></a></p>