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绘制热图图未渲染所有yAxis标签

Python问题
2022-01-01

Plotly heatmap plot not rendering all yaxis labels(绘制热图图未渲染所有yAxis标签)

本文介绍了绘制热图图未渲染所有yAxis标签的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我构建了一个带有热图的仪表板。然而，我注意到t=y轴上的一些标签没有显示。我只是拿到了限制版，我不确定出了什么问题。这是我的仪表板： 

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)

以下是我的数据框示例：

{'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}}
这是运行我的代码时热图的快照：

推荐答案

您可以使用布局的yaxis_nticks属性指定要显示的刻度数。

例如，数据帧中有多少行就可以有多少个刻度。

corr_fig.update_layout(title="Correlation heatmap",
                  yaxis={"title": 'Traits'},
                  xaxis={"title": 'Traits',"tickangle": 45},
                  yaxis_nticks=len(supervisor))

它呈现为

这篇关于绘制热图图未渲染所有yAxis标签的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持编程学习网！

本文标题为：绘制热图图未渲染所有yAxis标签

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