Plot Annotations using HTML

Question

I am improving my data visualization skills. One of the top recommendations is use the colors to improve the readability of plots. Well, I am picking the plots on the great book Storytelling with Data https://www.storytellingwithdata.com/ and coding them. The following one is on my desk now.

Using matplotlib to put this information mark with the green blocks may be a nightmare. I believe HTML will be easier for this task. On notebooks, this will be perfect, however, if I need to export this to a presentation, for example, I will need to use some print screen to finish my job. I try to use the magic %%capture to save the whole cell output into an image or a HTML. After some searches, I think this is impossible. Do you have some ideas on how I can handle this? Thanks a lot!

Answer 1

First, let me say that this site is not intended to be a code service. There is no presentation of the code you are working on. I am not affiliated with Plotly, and I created the graphs in Plotly because I think Plotly has elements that are a good example of a library where HTML can be used as an annotation. You are welcome to create your own to suit your own purposes.

df = df.replace('\%','', regex=True).astype(int)

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Bar(
    y=df.index,
    x=df['Bottom box'],
    name='Bottom box',
    orientation='h',
    marker=dict(
        color='rgb(196, 74, 70)',
        line=dict(color='rgb(196, 74, 70)', width=1)
    )
))
fig.add_trace(go.Bar(
    y=df.index,
    x=df['Middle'],
    name='Middle',
    orientation='h',
    marker=dict(
        color='rgb(178, 178, 178)',
        line=dict(color='rgb(178, 178, 178)', width=1)
    )
))
fig.add_trace(go.Bar(
    y=df.index,
    x=df['Top box'],
    name='Top box',
    orientation='h',
    marker=dict(
        color='rgb(81, 172, 201)',
        line=dict(color='rgb(81, 172, 201)', width=1)
    )
))
# bar labels
fig.add_trace(go.Scatter(
    y=df.index,
    x=[5,5,5,5],
    mode='text',
    text=['{}%'.format(x) for x in df['Bottom box']],
    textfont=dict(
        color='white',
        size=16),
))

fig.add_trace(go.Scatter(
    y=df.index,
    x=[95,95,95,95],
    mode='text',
    text=['{}%'.format(x) for x in df['Top box']],
    textfont=dict(
        color='white',
        size=16),
))

# title
fig.add_annotation(
    x=-0.15,
    y=1.25,
    xref='paper',
    yref='paper',
    text='Resultados da Pesquisa: Equipe X',
    font=dict(
        color='rgb(178, 178, 178)',
        size=24,
    ),
    showarrow=False,
)
# legend text
legend_txt = ('<span style="color:rgb(196, 74, 70);font-size:14px;">Discordo totalmente </span>|'
+'<span style="color:rgb(178, 178, 178);font-size:14px;"> Discordo | Neutro | Concordo |</span>'
+'<span style="color:rgb(81, 172, 201);font-size:14px;"> Concordo totalmente</span><br>'
+'<span style="color:rgb(178, 178, 178);font-size:14px;"> Porcentagem do Total</span>')
    
fig.add_annotation(
    x=-0.01,
    y=1.15,
    xref='paper',
    yref='paper',
    text=legend_txt,
    showarrow=False,
)

# right text
right_text = ('<span style="color:rgb(81, 172, 201);font-size:14px;"> Item A da presquisa </span><br>'
+'<span style="color:"black";font-size:14px;">com classification mais<br>alta para asequipe X<span>')

fig.add_annotation(
    x=1.25,
    y=0.98,
    xref='paper',
    yref='paper',
    text=right_text,
    showarrow=False,
)

# right text
right_text2 = ('<span style="rgb(178, 178, 178);font-size:14px;"> A insatisfacao foi <br>'
+'maior para o </span><span style="color:rgb(196, 74, 70);font-size:14px;">item <br>D da pesquisa</span>')

fig.add_annotation(
    x=1.20,
    y=0.05,
    xref='paper',
    yref='paper',
    text=right_text2,
    showarrow=False,
)

fig.update_layout(autosize=True,
                  height=600,
                  width=800,
                  barmode='stack',
                  template='plotly_white',
                  showlegend=False,
                  margin=dict(l=0,r=150,t=120,b=0)
                 )
fig.update_yaxes(showgrid=False,
                 autorange="reversed",
                 showline=True,
                 linecolor='white',
                 tickfont=dict(
                     size=16,
                     family='sans serif',
                 ))
fig.update_xaxes(
    side='top',
    linecolor='rgb(178, 178, 178)',
    ticks='inside',
    showgrid=False,
    showline=True,
    tickvals=np.arange(0,101,20),
    ticktext=['{}%'.format(x) for x in np.arange(0,101,20)],
    scaleanchor='x2',
)

fig.show()

Answer 2

After some more researches, I got that, by now, it is not possible to accomplish what I want. I could save the plots as images, create a html file and use a package or API to render the html into a image, but this comes with other problems that I want do avoid. This question (HTML to IMAGE using Python) is about a related problem and, on certain way, (not)solved my question.