https://github.com/atelierarith/pyplotly.jl
Pythonista friendly Julia interface for plotly
https://github.com/atelierarith/pyplotly.jl
julia plotly python
Last synced: 3 months ago
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Pythonista friendly Julia interface for plotly
- Host: GitHub
- URL: https://github.com/atelierarith/pyplotly.jl
- Owner: AtelierArith
- License: mit
- Created: 2022-02-02T11:28:45.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-10-08T13:40:18.000Z (over 3 years ago)
- Last Synced: 2023-03-05T23:18:34.625Z (over 3 years ago)
- Topics: julia, plotly, python
- Language: Julia
- Homepage: https://discourse.julialang.org/t/pyplotly-jl/76035
- Size: 154 KB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PyPlotly
[](https://github.com/AtelierArith/PyPlotly.jl/actions/workflows/CI.yml?query=branch%3Amain)
[](https://AtelierArith.github.io/PyPlotly.jl/stable)
[](https://AtelierArith.github.io/PyPlotly.jl/dev)
# Introduction
I know there are Plotly.jl and PlotlyJS.jl. But, it would be nice to provide a Pythonista-friendly Julia interface for plotly since there are lots of examples on the internet regarding plotly written in Python.
# Just give it a try
```console
$ pip3 install numpy pandas plotly
$ git clone https://github.com/AtelierArith/PyPlotly.jl.git
$ cd PyPlotly
$ julia --project=@. -e 'using Pkg; Pkg.isinstance()'
```
# How to use
Let's assume you've written a Python script something like:
```python
import plotly.graph_objects as go
# Create random data with numpy
import numpy as np
np.random.seed(1)
N = 100
random_x = np.linspace(0, 1, N)
random_y0 = np.random.randn(N) + 5
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N) - 5
fig = go.Figure()
# Add traces
fig.add_trace(go.Scatter(x=random_x, y=random_y0,
mode='markers',
name='markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y1,
mode='lines+markers',
name='lines+markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y2,
mode='lines',
name='lines'))
fig.show()
```
The example above is taken from [Line and Scatter Plots](https://plotly.com/python/line-and-scatter/#line-and-scatter-plots). You can translate the python code into Julia code as below:
```julia
using PyPlotly # this exports `go` and `px`
using Random
Random.seed!(1)
N = 100
random_x = range(0, 1, length=N)
random_y0 = randn(N) .+ 5
random_y1 = randn(N)
random_y2 = randn(N) .- 5
fig = go.Figure()
# Add traces
fig.add_trace(go.Scatter(x=random_x, y=random_y0,
mode="markers",
name="markers"))
fig.add_trace(go.Scatter(x=random_x, y=random_y1,
mode="lines+markers",
name="lines+markers"))
fig.add_trace(go.Scatter(x=random_x, y=random_y2,
mode="lines",
name="lines"))
fig
```
# Display object in notebook
## JupyterLab
Try running the following example in your notebook
```julia
using PyPlotly
df = px.data.iris()
fig = px.scatter(
df,
x="sepal_width",
y="sepal_length",
color="species",
size="petal_length",
hover_data=["petal_width"],
)
fig
```
## Pluto
Code is available from [here](https://github.com/AtelierArith/PyPlotly.jl/blob/main/playground/pluto/jlsample.jl).
# Appendix
- [JuliaLang Live Coding (Making PyPlotly.jl) with a silent cat](https://www.youtube.com/watch?v=Nb_w0MV4OLs&t=1994s)
- [Japanese blog post](https://zenn.dev/terasakisatoshi/articles/ebac6e005b9200)
- [Julia discourse](https://discourse.julialang.org/t/pyplotly-jl/76035)
- [PyPlotly.jl + Dash.jl](https://github.com/plotly/Dash.jl/issues/50#issuecomment-1033972687)