I have a data which has very minute differences between each of them, i want to show the difference in scaling for that, i am using the below code for
fig = px.scatter_mapbox(df, lat="Lat", lon="Long", color="data",
color_continuous_scale=px.colors.sequential.Viridis, size_max=15, zoom=5,opacity=0.5)
the data column has the values such as this
14.27774 , 14.27654, 14.27456 somehow the scaling does not show any massive difference. If i check for
unique data in that
dataframe there are close to
120 of them. So is there a way to customise the scaling?
px uses the min and max of your data to map the colorscale to data values. It’s possible that you have some outliers values which flatten the contrast (you can check this by printing
df["data"].min() and max). You can use the
range_color argument of
px.scatter_mapbox to customize the bounds of the colorscale, in order to stretch the contrast, for example
range_color = [14.27, 14.28], or if you want to define the bounds programatically, you can compute the quantiles of the data with
scipy.stats.scoreatpercentile and take for example percentile 5 and 95 to eliminate outliers.
Thanks for the reply, do you have any example that uses
There is an example in the docstring of scipy.stats.scoreatpercentile, the output is a value which you can pass to