Creating a heatmap with an uneven number of rows and columns, year on the y axis and month on the x, like so:
fig = px.imshow(
rounded_heatmap,
text_auto=True,
color_continuous_scale=["rgb(227,237,224)", 'rgb(52,87,94)'],
template='mytemplate',
x=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug','Sep', 'Oct', 'Nov', 'Dec'],
y=[2019,2020,2021,2022], )
When this draws, it interpolates additional "between year" y-axis labels. I want to disable this behavior - can't locate how to do in the docs.
It can be improved by specifying the y-axis type as category. Modify some of the examples in the reference to fit your data and specify the y-axis type as categorical.
import plotly.express as px
data=[[1, 25, 30, 50, 1], [20, 1, 60, 80, 30], [30, 60, 1, 5, 20]]
fig = px.imshow(data,
labels=dict(x="Day of Week", y="Time of Day", color="Productivity"),
x=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
y=[2020,2021,2022]
)
fig.update_xaxes(side="top")
fig.update_yaxes(type='category')
fig.show()