add examples from wishlish

Author: Joseph Damiba

doc/python/3d-surface-plots.md

@@ -163,6 +163,19 @@ fig = go.Figure(data=[
 fig.show()
 ```
 
+### Setting the Surface Color
+
+You can use the `surfacecolor` attribute to define the color of the surface of your figure. In this example, the surface color is drawn relation to the x-axis data. 
+
+```python
+import plotly.graph_objects as go
+import numpy as np
+x, y = 4 * np.pi * np.mgrid[0:1:20j, 0:1:20j]
+z = np.sin(x)
+fig = go.Figure(go.Surface(x=x, y=y, z=z, surfacecolor=x))
+fig.show()
+```
+
 #### Reference
 
 

doc/python/bar-charts.md

@@ -384,6 +384,26 @@ fig.show()
 
 See examples of horizontal bar charts [here](https://plotly.com/python/horizontal-bar-charts/).
 
+### Bar Charts With Multicategory Axis Type
+
+If your traces have arrays for `x` or `y`, then the axis type is automatically inferred to be `multicategory`. 
+
+```python
+import plotly.graph_objects as go
+x = [
+    ["BB+", "BB+", "BB+", "BB", "BB", "BB"],
+    [16, 17, 18, 16, 17, 18,]
+]
+fig = go.Figure()
+fig.add_bar(x=x,y=[1,2,3,4,5,6])
+fig.add_bar(x=x,y=[6,5,4,3,2,1])
+fig.update_layout(barmode="relative")
+fig.show()
+```
+
+
+
+
 ### Reference
 
 See https://plotly.com/python/reference/#bar for more information and chart attribute options!

doc/python/box-plots.md

@@ -489,6 +489,15 @@ fig.update_layout(
 fig.show()
 ```
 
+### Box Plot With Only Points
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.strip(df, x='day', y='tip')
+fig.show()
+```
+
 #### Reference
 
 See https://plotly.com/python/reference/#box for more information and chart attribute options!

doc/python/line-and-scatter.md

@@ -274,6 +274,25 @@ fig = go.Figure(data=go.Scattergl(
 fig.show()
 ```
 
+### Sparklines With go.Scatter
+
+Sparklines are scatter plots inside subplots, with gridlines, axis lines, and ticks removed. 
+
+```python
+import plotly.express as px
+from plotly.subplots import make_subplots
+df = px.data.gapminder()
+fig = make_subplots(2, 1)
+df1 = df.query("country == 'Canada'")
+fig.add_trace(go.Scatter(x=df1['year'], y=df1['gdpPercap'], mode='lines', name='Canada'), 1, 1)
+df2 = df.query("country == 'France'")
+fig.add_trace(go.Scatter(x=df2['year'], y=df2['gdpPercap'], mode='lines', name='France'), 2, 1)
+fig.update_layout(template=None, height=400)
+fig.update_xaxes(showgrid=False)
+fig.update_yaxes(showgrid=False)
+fig.show()
+```
+
 ### Reference
 
 See https://plotly.com/python/reference/#scatter or https://plotly.com/python/reference/#scattergl for more information and chart attribute options!

doc/python/line-charts.md

@@ -70,6 +70,20 @@ fig = px.line(df, x="year", y="lifeExp", color="continent",
 fig.show()
 ```
 
+### Sparklines with Plotly Express
+
+Sparklines are scatter plots inside subplots, with gridlines, axis lines, and ticks removed. 
+
+```python
+import plotly.express as px
+df = px.data.gapminder().query("continent == 'Oceania'")
+fig = px.line(df, x='year', y='gdpPercap', facet_row='country')
+fig.update_layout(template=None, height=400)
+fig.update_xaxes(showgrid=False)
+fig.update_yaxes(showgrid=False)
+fig.show()
+```
+
 ### Line Plot with go.Scatter
 
 If Plotly Express does not provide a good starting point, it is possible to use the more generic `go.Scatter` function from `plotly.graph_objects`. Whereas `plotly.express` has two functions `scatter` and `line`, `go.Scatter` can be used both for plotting points (makers) or lines, depending on the value of `mode`. The different options of `go.Scatter` are documented in its [reference page](https://plotly.com/python/reference/#scatter).

doc/python/sliders.md

@@ -88,6 +88,54 @@ The method determines which [plotly.js function](https://plot.ly/javascript/plot
 - `"update"`: modify **data and layout** attributes
 - `"animate"`: start or pause an animation
 
+### Update Slider
+```python
+import plotly.graph_objects as go
+import numpy as np
+
+# Create figure
+fig = go.Figure()
+
+# Add traces, one for each slider step
+for step in np.arange(0, 5, 0.1):
+    fig.add_trace(
+        go.Scatter(
+            visible=False,
+            line=dict(color="#00CED1", width=6),
+            name="𝜈 = " + str(step),
+            x=np.arange(0, 10, 0.01),
+            y=np.sin(step * np.arange(0, 10, 0.01))))
+
+# Make 10th trace visible
+fig.data[10].visible = True
+
+# Create and add slider
+steps = []
+for i in range(len(fig.data)):
+    step = dict(
+        method="update",
+        args=[{"visible":[False] * len(fig.data)}, # update for traces
+               {"title":str(i)} # update for layout 
+             ],
+    )
+    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()
+```
+
+
 ### Sliders in Plotly Express
 Plotly Express provide sliders, but with implicit animation. The animation can be ommited by removing `updatemenus` in the `layout`:
 

doc/python/violin.md

@@ -258,6 +258,10 @@ fig.update_layout(xaxis_showgrid=False, xaxis_zeroline=False)
 fig.show()
 ```
 
+### Violin Plot With Only Points
+
+See https://plotly.com/python/box-plots/#box-plot-with-only-points.
+
 #### Reference
 
 See https://plotly.com/python/reference/#violin for more information and chart attribute options!