Can anyone point me to an example of how to make a plot with 1000 (or more) lines in plotly? Looking to plot 1000 Monte Carlo simulations. I’ve tried to use cufflinks to plot a DataFrame but I get an error that it will take too long.

Thanks!

Can anyone point me to an example of how to make a plot with 1000 (or more) lines in plotly? Looking to plot 1000 Monte Carlo simulations. I’ve tried to use cufflinks to plot a DataFrame but I get an error that it will take too long.

Thanks!

I`m not sure if I understood you the right way. I made a plot to visualise marathon runners pace a while ago. Please, take a look.

Marathon analysis.

There are 2 similar charts near the end of notebook. It could take a while to render that notebook.

Hi @dpsugasa,

plotly.py/plotly.js won’t do so well if you represent each of 1000+ lines as individual traces. You’ll have much better luck if you group all of your lines (or at least all of your lines per color) into a single trace. This can be done by including `nan`

values to separate your individual lines. I would also recommend using the `scattergl`

trace types instead of `scatter`

since `scattergl`

is GPU optimized to handle much larger dataset sizes.

Here’s an example of plotting 1000 lines with 100 points each. Here representing 1000 trials of a 1D random walk for 100 iterations.

```
import numpy as np
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
N = 1000
# create list of the line segments you want to plot
all_xs = [np.arange(100, dtype='float64') for _ in range(N)]
all_ys = [(np.random.rand(100) - 0.5).cumsum() for _ in range(N)]
# append nan to each segment
all_xs_with_nan = [np.concatenate((xs, [np.nan])) for xs in all_xs]
all_ys_with_nan = [np.concatenate((ys, [np.nan])) for ys in all_ys]
# concatinate segments into single line
xs = np.concatenate(all_xs_with_nan)
ys = np.concatenate(all_ys_with_nan)
fig = go.Figure(data=[
go.Scattergl(x=xs, y=ys, mode='lines', opacity=0.05, line={'color': 'darkblue'})
])
iplot(fig)
```

Hope that helps!

-Jon

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fantastic, thank you.

thank you, very helpful. And interesting notebook!

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