I am trying to make a plotly graph which could plot the evolution of a parameter's value along a period of time.

By now, what I've managed to get is plotting individual points. However, I'd like to keep the previos spots plotted while getting an increment on the slider.

My code:

import numpy as np
import plotly.express as px
import pandas as pd

df=pd.read_excel("file.xlsx")
fig=px.scatter(df, x="$R[\Omega]$", y="$X[\Omega]$", title='$U_1/I_1$',animation_frame="t[seg]")
fig["layout"].pop("updatemenus")
fig.show()

Data

df2=pd.DataFrame({"$R[\Omega]$":[-0.092034,-0.096416,-0.103026],
                         "$X[\Omega]$":[0.045707,0.047590,0.039953],
                         "t[seg]":[0.416244,0.417078,0.417912]})

CodePudding user response：

• have simulated data as I don't have access to your excel spreadsheet. Named columns consistently with what your code implies
• need to make sure layout has axes ranges set for all data
• after building figure, for each frame update the data in traces such that it contains all values from previous frames as well. (Note this assume that t[seg] is numeric)

full code

import numpy as np
import plotly.express as px
import pandas as pd

# df=pd.read_excel("file.xlsx")
# generate data in structure implied by question
df = pd.DataFrame(
    {
        "$R[\Omega]$": np.linspace(0, 100, 250),
        "$X[\Omega]$": np.sin(np.linspace(0, 50, 250)),
        "t[seg]": np.linspace(0, 100, 250) // 10,
    }
)
xcol = "$R[\Omega]$"
ycol = "$X[\Omega]$"
fcol = "t[seg]"
fig = px.scatter(
    df, x=xcol, y=ycol, title="$U_1/I_1$", animation_frame=fcol
)
fig["layout"].pop("updatemenus")
# prepare axes to show all values
fig.update_layout(
    {
        ax: {
            "range": [
                df[col].min(),
                df[col].max(),
            ]
        }
        for ax,col in zip(["xaxis", "yaxis"], [xcol,ycol])
    }
)

# add data into each frame from all preceeding traces
for fr in fig.frames[1:]:
    df_ = df.loc[df[fcol].le(float(fr.name))]
    for t in fr.data:
        t.update(x=df_[xcol].values, y=df_[ycol].values)
    
fig