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:

```
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)

```

Here is a sample of my dataframe:

```
{'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}}

```

This is a snap shot of what the heatmap looks like if you run my code:
[![Plot Image](https://i.stack.imgur.com/90BY2.png)](https://i.stack.imgur.com/90BY2.png)