Axis labels are not shown for all subplots when using plotly express, facets and string labels
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Descripción
Problem summary
Whenever I have the following combination:
- plotly express
- facet plots
- labels used for axis are strings
The first subplot shows the axis labels properly, but all following subplots don't show labels at all:

If the axis labels are numbers, there is no problem.
From a comment of the initial issue I opened I now know that fig.update_traces(bingroup='x2', row=1, col=2) can work around the issue, but that's not a permanent solution.
Reproducible example
I originally created these plots in Python (plotly 5.18.0) and then used the .to_json(pretty=True) method to obtain the Javascript for the codepens.
- Manually building up the figure using
graph_objectsandmake_subplotsI get the following working visualization: https://codepen.io/sl1970/pen/BaMvNbp - If I use the convenient
plotly.expresslibrary and thefacetargument I get this broken visualization (no axis labels on the right plot): https://codepen.io/sl1970/pen/jOdXPoa
Original Python code
If necessary, this is the original Python code I used
import pandas as pd
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
df = pd.DataFrame(
{
"age": { "0": "Adult", "1": "Adult", "2": "Adult", "3": "Adult", "4": "Adult", "5": "Kid", "6": "Kid", "7": "Kid", "8": "Kid", "9": "Kid", },
"favourite_food": { "0": "Pizza", "1": "Noodles", "2": "Pizza", "3": "Pizza", "4": "Pizza", "5": "Burger", "6": "Pancake", "7": "Noodles", "8": "Pizza", "9": "Pancake", },
"favourite_drink": { "0": "Beer", "1": "Tea", "2": "Beer", "3": "Wine", "4": "Coffee", "5": "Coffee", "6": "Water", "7": "Beer", "8": "Tea", "9": "Wine", },
"max_running_speed": { "0": 4.7362803248, "1": 16.7084927714, "2": 8.1135697835, "3": 1.0704264989, "4": 4.6330187561, "5": 6.331593807, "6": 16.5221040135, "7": 3.2256763127, "8": 4.3084468631, "9": 6.3677742299, },
"number_of_bicycles": { "0": 4, "1": 2, "2": 1, "3": 3, "4": 4, "5": 3, "6": 3, "7": 3, "8": 4, "9": 2, },
}
)
df.set_index("age", inplace=True)
working = make_subplots(rows=1, cols=2, subplot_titles=["Food", "Drink"])
working.add_trace( go.Histogram( histfunc="count", histnorm="percent", x=df.loc["Adult"].favourite_food, name="Adult", legendgroup="Adult", ), row=1, col=1, )
working.add_trace( go.Histogram( histfunc="count", histnorm="percent", x=df.loc["Kid"].favourite_food, name="Kid", legendgroup="Kid" ), row=1, col=1, )
working.add_trace( go.Histogram( histfunc="count", histnorm="percent", x=df.loc["Adult"].favourite_drink, name="Adult", legendgroup="Adult", ), row=1, col=2, )
working.add_trace( go.Histogram( histfunc="count", histnorm="percent", x=df.loc["Kid"].favourite_drink, name="Kid", legendgroup="Kid" ), row=1, col=2, )
working.show()
broken = px.histogram(
df,
x=["favourite_food", "favourite_drink"],
facet_col="variable",
color=df.index,
barmode="group",
histnorm="percent",
text_auto=".2r",
).update_xaxes(matches=None, showticklabels=True).update_yaxes(matches=None, showticklabels=True)
broken.show()
Related
Guía de contribución
Primeros pasos
- Lee el issue completo y luego la guía de contribución del proyecto.
- Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
- Haz un fork del repositorio y trabaja en una rama.
- Abre un pull request que haga referencia al número del issue.
Línea de trabajo
Empieza con el manejo del histograma y de los facets de plotly.express descrito en el ejemplo reproducible de Python; después, compara su figura generada con el ejemplo funcional de make_subplots y los CodePens enlazados. La tarea estará terminada cuando las etiquetas de eje de tipo string categóricas se rendericen en cada facet sin requerir un workaround de bingroup, con una prueba de regresión que cubra este caso.
Escrito por el modelo de indexación a partir del texto del issue.
Evaluación
- Stack tecnológico
- pandas, python
- Área
- data-visualization
- Tipo de issue
- Error
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Estado de actividad
- Estancado
- Claridad
- Bastante claro
- Aptitud para principiantes
- 35/100