Multiple consecutive bar plots with a time slider in Plotly, Python

I could not find a way to do it straight in the dataframe above, but it is possible to do it by "melting" the dataframe. The following code achieves what I was looking for, together with some beautification of the chart:

import pandas as pd
from io import StringIO
import plotly.express as px

string = """
DATE,ASSET1,ASSET2,ASSET3,ASSET4,ASSET5,ASSET6,ASSET7
2010-01-04,0.250000,0.0,0.250000,0.000000,0.25,0.000000,0.250000
2010-02-03,0.250000,0.0,0.250000,0.000000,0.25,0.000000,0.250000
2010-03-05,0.217195,0.0,0.250000,0.032805,0.25,0.000000,0.250000
2010-04-06,0.139636,0.0,0.250000,0.110364,0.25,0.000000,0.250000
2010-05-05,0.179569,0.0,0.218951,0.101480,0.25,0.000000,0.250000
2010-06-04,0.207270,0.0,0.211974,0.080756,0.25,0.000000,0.250000
2010-07-06,0.132468,0.0,0.250000,0.117532,0.25,0.000000,0.250000
2010-08-04,0.116353,0.0,0.250000,0.133647,0.25,0.000000,0.250000
2010-09-02,0.081677,0.0,0.250000,0.168323,0.25,0.000000,0.250000
2010-10-04,0.000000,0.0,0.250000,0.250000,0.25,0.009955,0.240045
"""

df = pd.read_csv(StringIO(string))

df = df.melt(id_vars=['DATE']).sort_values(by = 'DATE')

fig = px.bar(df, x="variable", y="value", animation_frame="DATE")
fig.update_layout(legend_title_text = None)
fig.update_xaxes(title = "Asset")
fig.update_yaxes(title = "Proportion")
fig.update_layout(autosize = True, height = 600)
fig.update_layout(hovermode="x")
fig.update_layout(plot_bgcolor="#F8F8F8")
fig.update_traces(
hovertemplate=
    '<i></i> %{y:.2%}'
)
fig.show()

This produces the following: