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graph_objects and update_layout
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‎notebooks/3d-axes.md

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@@ -39,12 +39,14 @@ jupyter:
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### Range of axes
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The `go.Layout.scene` of a 3D figure can be configured through its `xaxis`, `yaxis` and `zaxis` parameters, in order to set the range, title, ticks, color etc. of the axes.
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3D figures have an attribute in `layout` called `scene`, which contains
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attributes such as `xaxis`, `yaxis` and `zaxis` parameters, in order to
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set the range, title, ticks, color etc. of the axes.
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For creating 3D charts, see [this page](https://plot.ly/python/3d-charts/).
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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N = 70
@@ -56,20 +58,20 @@ fig = go.Figure(data=[go.Mesh3d(x=(70*np.random.randn(N)),
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color='rgba(244,22,100,0.6)'
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)])
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fig.update(layout=go.Layout(scene = dict(
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xaxis = dict(nticks=4, range=[-100,100],),
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fig.update_layout(scene = dict(
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xaxis = dict(nticks=4, range=[-100,100],),
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yaxis = dict(nticks=4, range=[-50,100],),
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zaxis = dict(nticks=4, range=[-100,100],),),
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width=700,
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margin=dict(r=20, l=10, b=10, t=10)))
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margin=dict(r=20, l=10, b=10, t=10))
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fig.show()
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```
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### Fixed Ratio Axes
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import numpy as np
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@@ -90,23 +92,23 @@ for i in [1,2]:
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),
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row=i, col=j)
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fig.update(layout_width=700, layout_margin=dict(r=10, l=10, b=10, t=10))
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fig.update_layout(width=700, margin=dict(r=10, l=10, b=10, t=10))
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# fix the ratio in the top left subplot to be a cube
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fig.update(layout_scene_aspectmode='cube')
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fig.update_layout(scene_aspectmode='cube')
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# manually force the z-axis to appear twice as big as the other two
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fig.update(layout_scene2_aspectmode='manual',
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layout_scene2_aspectratio=dict(x=1, y=1, z=2))
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fig.update_layout(scene2_aspectmode='manual',
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scene2_aspectratio=dict(x=1, y=1, z=2))
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# draw axes in proportion to the proportion of their ranges
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fig.update(layout_scene3_aspectmode='data')
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fig.update_layout(scene3_aspectmode='data')
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# automatically produce something that is well proportioned using 'data' as the default
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fig.update(layout_scene4_aspectmode='auto')
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fig.update_layout(scene4_aspectmode='auto')
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fig.show()
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```
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### Set Axes Title
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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# Define random surface
@@ -125,20 +127,20 @@ fig.add_trace(go.Mesh3d(x=(70*np.random.randn(N)),
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color='pink'
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))
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fig.update(layout=go.Layout(scene = dict(
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xaxis_title='X AXIS TITLE',
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yaxis_title='Y AXIS TITLE',
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zaxis_title='Z AXIS TITLE'),
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width=700,
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margin=dict(r=20, b=10, l=10, t=10)))
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fig.update_layout(scene = dict(
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xaxis_title='X AXIS TITLE',
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yaxis_title='Y AXIS TITLE',
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zaxis_title='Z AXIS TITLE'),
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width=700,
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margin=dict(r=20, b=10, l=10, t=10))
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fig.show()
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```
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### Ticks Formatting
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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# Define random surface
@@ -151,8 +153,7 @@ fig = go.Figure(data=[go.Mesh3d(x=(60*np.random.randn(N)),
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)])
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# Different types of customized ticks
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fig.update(layout=go.Layout(
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scene = dict(
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fig.update_layout(scene = dict(
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xaxis = dict(
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ticktext= ['TICKS','MESH','PLOTLY','PYTHON'],
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tickvals= [0,50,75,-50]),
@@ -167,15 +168,15 @@ fig.update(layout=go.Layout(
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tick0=0, tickwidth=4),),
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width=700,
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margin=dict(r=10, l=10, b=10, t=10)
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))
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)
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fig.show()
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```
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### Background and Grid Color
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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N = 50
@@ -186,8 +187,7 @@ fig = go.Figure(data=[go.Mesh3d(x=(30*np.random.randn(N)),
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# xaxis.backgroundcolor is used to set background color
189-
fig.update(layout = go.Layout(
190-
scene = dict(
190+
fig.update_layout(scene = dict(
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xaxis = dict(
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backgroundcolor="rgb(200, 200, 230)",
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gridcolor="white",
@@ -207,10 +207,6 @@ fig.update(layout = go.Layout(
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margin=dict(
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r=10, l=10,
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b=10, t=10)
210-
))
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)
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fig.show()
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```
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```python
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```

‎notebooks/3d-mesh.md

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@@ -42,12 +42,11 @@ jupyter:
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`go.Mesh3d` draws a 3D set of triangles with vertices given by `x`, `y` and `z`. If only coordinates are given, an algorithm such as [Delaunay triangulation](https://en.wikipedia.org/wiki/Delaunay_triangulation) is used to draw the triangles. Otherwise the triangles can be given using the `i`, `j` and `k` parameters (see examples below).
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```python
45-
import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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# Download data set from plotly repo
49-
ds = np.DataSource()
50-
pts = np.loadtxt(ds.open('https://raw.githubusercontent.com/plotly/datasets/master/mesh_dataset.txt'))
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pts = np.loadtxt(np.DataSource().open('https://raw.githubusercontent.com/plotly/datasets/master/mesh_dataset.txt'))
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x, y, z = pts.T
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fig = go.Figure(data=[go.Mesh3d(x=x, y=y, z=z, color='lightpink', opacity=0.50)])
@@ -60,11 +59,10 @@ fig.show()
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The `alphahull` parameter sets the shape of the mesh. If the value is -1 (default value) then [Delaunay triangulation](https://en.wikipedia.org/wiki/Delaunay_triangulation) is used. If >0 then the [alpha-shape algorithm](https://en.wikipedia.org/wiki/Alpha_shape) is used. If 0, the [convex hull](https://en.wikipedia.org/wiki/Convex_hull) is represented (resulting in a convex body).
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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66-
ds = np.DataSource()
67-
pts = np.loadtxt(ds.open('https://raw.githubusercontent.com/plotly/datasets/master/mesh_dataset.txt'))
65+
pts = np.loadtxt(np.DataSource().open('https://raw.githubusercontent.com/plotly/datasets/master/mesh_dataset.txt'))
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x, y, z = pts.T
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fig = go.Figure(data=[go.Mesh3d(x=x, y=y, z=z,
@@ -79,7 +77,7 @@ fig.show()
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In this example we use the `ì`, `j` and `k` parameters to specify manually the geometry of the triangles of the mesh.
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```python
82-
import plotly.graph_objs as go
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import plotly.graph_objects as go
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fig = go.Figure(data=[
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go.Mesh3d(
@@ -108,7 +106,7 @@ fig.show()
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### Mesh Cube
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```python
111-
import plotly.graph_objs as go
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import plotly.graph_objects as go
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fig = go.Figure(data=[
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go.Mesh3d(
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# 8 vertices of a cube

‎notebooks/3d-scatter-plots.md

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v4upgrade: true
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---
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## 3D scatter plot with plotly express
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Like the [2D scatter plot](https://plot.ly/python/line-and-scatter/) `px.scatter`, the 3D function `px.scatter_3d` plots individual data in three-dimensional space. Note that plotly express functions take as argument a tidy [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html) such as the ones defined in ``px.data``.
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```python
45+
import plotly.express as px
46+
iris = px.data.iris()
47+
fig = px.scatter_3d(iris, x='sepal_length', y='sepal_width', z='petal_width',
48+
color='species')
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fig.show()
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```
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52+
A 4th dimension of the data can be represented thanks to the color of the markers. Also, values from the `species` column are used below to assign symbols to markers.
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54+
```python
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import plotly.express as px
56+
iris = px.data.iris()
57+
fig = px.scatter_3d(iris, x='sepal_length', y='sepal_width', z='petal_width',
58+
color='petal_length', symbol='species')
59+
fig.show()
60+
```
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62+
#### Style 3d scatter plot
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64+
It is possible to customize the style of the figure through the parameters of `px.scatter_3d` for some options, or by updating the traces or the layout of the figure through `fig.update`.
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```python
67+
import plotly.express as px
68+
iris = px.data.iris()
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fig = px.scatter_3d(iris, x='sepal_length', y='sepal_width', z='petal_width',
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color='petal_length', size='petal_length', size_max=18,
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symbol='species', opacity=0.7)
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# tight layout
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fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
75+
```
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### 3D Scatter Plot with go.Scatter3d
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#### Basic 3D Scatter Plot
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42-
Like the [2D scatter plot](https://plot.ly/python/line-and-scatter/) `go.Scatter`, the 3D function `go.Scatter3d` plots individual data in three-dimensional space.
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When data are not available as tidy dataframes, it is also possible to use the more generic `go.Scatter3D` from `plotly.graph_objs`.
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Like the [2D scatter plot](https://plot.ly/python/line-and-scatter/) `go.Scatter`, `go.Scatter3d` plots individual data in three-dimensional space.
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```python
45-
import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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48-
fig = go.Figure()
49-
50-
x0, y0, z0 = np.random.multivariate_normal(np.array([0, 0, 0]), np.eye(3), 100).transpose()
51-
fig.add_trace(go.Scatter3d(
52-
x=x0, y=y0, z=z0,
53-
mode='markers'
54-
))
88+
# Helix equation
89+
t = np.linspace(0, 10, 50)
90+
x, y, z = np.cos(t), np.sin(t), t
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56-
x1, y1, z1 = np.random.multivariate_normal(np.array([0,0,0]), np.eye(3), 100).transpose()
57-
fig.add_trace(go.Scatter3d(
58-
x=x1, y=y1, z=z1,
59-
mode='markers',
60-
marker_color='gray',
61-
))
62-
63-
# tight layout
64-
fig.update(layout_margin=dict(l=0, r=0, b=0, t=0))
65-
# marker common configuration for the two Scatter3d
66-
fig.update_traces(marker_line_color='lightgray', marker_opacity=0.8, marker_size=12,
67-
marker_line_width=1)
92+
fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z,
93+
mode='markers')])
6894
fig.show()
6995
```
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#### 3D Scatter Plot with Colorscaling
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#### 3D Scatter Plot with Colorscaling and Marker Styling
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```python
74-
import plotly.graph_objs as go
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100+
import plotly.graph_objects as go
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import numpy as np
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78-
x, y, z = np.random.multivariate_normal(np.array([0,0,0]), np.eye(3), 400).transpose()
103+
# Helix equation
104+
t = np.linspace(0, 20, 100)
105+
x, y, z = np.cos(t), np.sin(t), t
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80107
fig = go.Figure(data=[go.Scatter3d(
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x=x,
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91118
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# tight layout
94-
fig.update(layout_margin=dict(l=0, r=0, b=0, t=0))
121+
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
95122
fig.show()
96123
```
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‎notebooks/3d-surface-plots.md

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#### Topographical 3D Surface Plot
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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4545
import pandas as pd
4646

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fig = go.Figure(data=[go.Surface(z=z_data.values)])
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52-
fig.update(layout=go.Layout(title='Mt Bruno Elevation', autosize=False,
53-
width=500, height=500,
54-
margin=dict(l=65, r=50, b=65, t=90)
55-
))
52+
fig.update_layout(title='Mt Bruno Elevation', autosize=False,
53+
width=500, height=500,
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margin=dict(l=65, r=50, b=65, t=90))
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fig.show()
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```
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6362
Display and customize contour data for each axis using the `contours` attribute ([reference](plot.ly/python/reference/#surface-contours)).
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import pandas as pd
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@@ -80,19 +79,19 @@ fig = go.Figure(data=[
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project_z=True
8180
))
8281
])
83-
fig.update(layout=go.Layout(title='Mt Bruno Elevation', autosize=False,
84-
scene_camera_eye=dict(x=1.87, y=0.88, z=-0.64),
85-
width=500, height=500,
86-
margin=dict(l=65, r=50, b=65, t=90)
87-
))
82+
fig.update_layout(title='Mt Bruno Elevation', autosize=False,
83+
scene_camera_eye=dict(x=1.87, y=0.88, z=-0.64),
84+
width=500, height=500,
85+
margin=dict(l=65, r=50, b=65, t=90)
86+
)
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fig.show()
9089
```
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#### Multiple 3D Surface Plots
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```python
95-
import plotly.graph_objs as go
94+
import plotly.graph_objects as go
9695
import numpy as np
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z1 = np.array([

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