Plot 3d data with slider

plot_3d_time.py

import plotly.graph_objects as go
import numpy as np
import pandas as pd
df = pd.read_csv('/Users/andreylukyanenko/Downloads/df_numpy_3d_B4_pred.csv')
# Create figure
fig = go.Figure()

# Add traces, one for each slider step
for step in range(1, 66):
    df_small = df.loc[df['k'] == step]
    fig.add_trace(
        go.Scatter3d(
            visible=False,
            line=dict(color="#00CED1", width=6),
            name="𝜈 = " + str(step),
            x=df_small.i.values,
            y=df_small.j.values,
            z=df_small['k'],
            marker=dict(
        size=3,
        color=df_small.pred_P.values,                # set color to an array/list of desired values
        colorscale='Viridis',   # choose a colorscale
        opacity=0.8,
                colorbar=dict(thickness=20)
    )))

# Make 10th trace visible
fig.data[0].visible = True

# Create and add slider
steps = []
for i in range(len(fig.data)):
    step = dict(
        method="update",
        args=[{"visible": [False] * len(fig.data)},
              {"title": "Slider switched to step: " + str(i)}],  # layout attribute
    )
    step["args"][0]["visible"][i] = True  # Toggle i'th trace to "visible"
    steps.append(step)

sliders = [dict(
    active=10,
    currentvalue={"prefix": "Frequency: "},
    pad={"t": 50},
    steps=steps
)]

fig.update_layout(
    sliders=sliders
)

fig.show()