addressing PR comments

Author: nicolaskruchten

Diff for: doc/python/categorical-colors.md

@@ -31,7 +31,7 @@ jupyter:
     language: python
     layout: base
     name: Categorical Colors
-    order: 27
+    order: 28
     permalink: python/categorical-color/
     thumbnail: thumbnail/heatmap_colorscale.jpg
     v4upgrade: true
@@ -57,7 +57,8 @@ For example, in the `tips` dataset, the `smoker` column contains strings:
 ```python
 import plotly.express as px
 df = px.data.tips()
-fig = px.scatter(df, x="total_bill", y="tip", color="smoker", title="String 'smoker' values mean categorical colors")
+fig = px.scatter(df, x="total_bill", y="tip", color="smoker",
+                 title="String 'smoker' values mean categorical colors")
 
 fig.show()
 ```
@@ -67,7 +68,8 @@ The `size` column, however, contains numbers:
 ```python
 import plotly.express as px
 df = px.data.tips()
-fig = px.scatter(df, x="total_bill", y="tip", color="size", title="Numeric 'size' values mean continous color")
+fig = px.scatter(df, x="total_bill", y="tip", color="size",
+                 title="Numeric 'size' values mean continous color")
 
 fig.show()
 ```
@@ -78,7 +80,8 @@ Converting this column to strings is very straightforward, but note that the ord
 import plotly.express as px
 df = px.data.tips()
 df["size"] = df["size"].astype(str)
-fig = px.scatter(df, x="total_bill", y="tip", color="size", title="String 'size' values mean categorical colors")
+fig = px.scatter(df, x="total_bill", y="tip", color="size",
+                 title="String 'size' values mean categorical colors")
 
 fig.show()
 ```
@@ -91,7 +94,8 @@ df = px.data.tips()
 df["size"] = df["size"].astype(str) #convert to string
 df["size"] = df["size"].astype(float) #convert back to numeric
 
-fig = px.scatter(df, x="total_bill", y="tip", color="size", title="Numeric 'size' values mean continous color")
+fig = px.scatter(df, x="total_bill", y="tip", color="size",
+                 title="Numeric 'size' values mean continous color")
 
 fig.show()
 ```
@@ -128,7 +132,7 @@ fig = px.line(df, y="lifeExp", x="year", color="continent", line_group="country"
 fig.show()
 ```
 
-### Explicity Constructing a Color Sequence
+### Explicitly Constructing a Color Sequence
 
 The Plotly Express `color_discrete_sequence` argument accepts explicitly-constructed color sequences as well, as lists of CSS colors:
 
@@ -183,12 +187,7 @@ Plotly Express lets you specify an ordering over categorical variables with `cat
 import plotly.express as px
 df = px.data.gapminder().query("year == 2007")
 fig = px.bar(df, y="continent", x="pop", color="continent", orientation="h", hover_name="country",
-             color_discrete_map={
-                "Europe": "red",
-                "Asia": "green",
-                "Americas": "blue",
-                "Oceania": "goldenrod",
-                "Africa": "magenta"},
+             color_discrete_sequence=["red", "green", "blue", "goldenrod", "magenta"],
              category_orders={"continent": ["Oceania", "Europe", "Asia", "Africa", "Americas"]},
              title="Explicit color sequence with explicit ordering"
             )
@@ -200,7 +199,12 @@ fig.show()
 import plotly.express as px
 df = px.data.gapminder().query("year == 2007")
 fig = px.bar(df, y="continent", x="pop", color="continent", orientation="h", hover_name="country",
-             color_discrete_sequence=["red", "green", "blue", "goldenrod", "magenta"],
+             color_discrete_map={
+                "Europe": "red",
+                "Asia": "green",
+                "Americas": "blue",
+                "Oceania": "goldenrod",
+                "Africa": "magenta"},
              category_orders={"continent": ["Oceania", "Europe", "Asia", "Africa", "Americas"]},
              title="Explicit color mapping with explicit ordering"
             )

Diff for: doc/python/colorscales.md

@@ -46,10 +46,10 @@ In the same way as the X or Y position of a mark in cartesian coordinates can be
 
 This document explains the following four continuous-color-related concepts:
 
-- **color scales** represent a mapping between the range 0 to 1 and some color domain within which colors are to be interpolated (unlike [categorical color sequences](/python/categorical-color/) which are never interpolated). Color scale defaults depend on the `layout.colorscales` attributes of the active [template](/python/templates/), and can be explicitly specified using the `color_continuous_scale` argument for many [Plotly Express](/python/plotly-express/) functions or the `colorscale` argument in various `graph_objects` such as `layout.coloraxis` or `marker.colorscale` in `go.Scatter` traces. For example `[(0,"blue"), (1,"red")]` is a simple color scale that interpolated between blue and red via purple, which can also be implicitly represented as `["blue", "red"]` and happens to be one of the [built-in color scales](/python/builtin-colorscales) and therefore referred to as `"bluered"` or `plotly.colors.sequential.Bluered`.
-- **color ranges** represent the minimum to maximum range of data to be mapped onto the 0 to 1 input range of the color scale. Color ranges default to the range of the input data and can be explicitly specified using either the `range_color` or `color_continous_midpoint` arguments for many Plotly Express functions, or `cmin`/`cmid`/`cmax` or `zmin`/`zmid`/`zmax` for various `graph_objects` such as `layout.coloraxis.cmin` or `marker.cmin` in `go.Scatter` traces. For example, if a color range of `[100, 200]` is used with the color scale above, then any mark with a color value of 100 or less will be blue, and 200 or more will be red. Marks with values in between will be various shades of purple.
-- **color bars** are legend-like visible representations of the color range and color scale with optional tick labels and tick marks. Color bars can be configured with attributes inside `layout.coloraxis.colorbar` or in places like `marker.colorbar` in `go.Scatter` traces.
-- **color axes** connect color scales, color ranges and color bars to a trace's data. By default, any colorable attribute in a trace is attached to its own local color axis, but color axes may also be shared across attributes and traces by setting e.g. `marker.coloraxis` in `go.Scatter` traces. Local color axis attributes are configured within traces e.g. `marker.showscale` whereas shared color axis attributes are configured within the Layout e.g. `layout.coloraxis.showscale`.
+- **color scales** represent a mapping between the range 0 to 1 and some color domain within which colors are to be interpolated (unlike [categorical color sequences](/python/categorical-color/) which are never interpolated). Color scale defaults depend on the `layout.colorscales` attributes of the active [template](/python/templates/), and can be explicitly specified using the `color_continuous_scale` argument for many [Plotly Express](/python/plotly-express/) functions or the `colorscale` argument in various `graph_objects` such as `layout.coloraxis` or `marker.colorscale` in `go.Scatter` traces or `colorscale` in `go.Heatmap` traces. For example `[(0,"blue"), (1,"red")]` is a simple color scale that interpolated between blue and red via purple, which can also be implicitly represented as `["blue", "red"]` and happens to be one of the [built-in color scales](/python/builtin-colorscales) and therefore referred to as `"bluered"` or `plotly.colors.sequential.Bluered`.
+- **color ranges** represent the minimum to maximum range of data to be mapped onto the 0 to 1 input range of the color scale. Color ranges default to the range of the input data and can be explicitly specified using either the `range_color` or `color_continous_midpoint` arguments for many Plotly Express functions, or `cmin`/`cmid`/`cmax` or `zmin`/`zmid`/`zmax` for various `graph_objects` such as `layout.coloraxis.cmin` or `marker.cmin` in `go.Scatter` traces or `cmin` in `go.Heatmap` traces. For example, if a color range of `[100, 200]` is used with the color scale above, then any mark with a color value of 100 or less will be blue, and 200 or more will be red. Marks with values in between will be various shades of purple.
+- **color bars** are legend-like visible representations of the color range and color scale with optional tick labels and tick marks. Color bars can be configured with attributes inside `layout.coloraxis.colorbar` or in places like `marker.colorbar` in `go.Scatter` traces or `colorbar` in `go.Heatmap` traces.
+- **color axes** connect color scales, color ranges and color bars to a trace's data. By default, any colorable attribute in a trace is attached to its own local color axis, but color axes may also be shared across attributes and traces by setting e.g. `marker.coloraxis` in `go.Scatter` traces or `coloraxis` in `go.Heatmap` traces. Local color axis attributes are configured within traces e.g. `marker.showscale` whereas shared color axis attributes are configured within the Layout e.g. `layout.coloraxis.showscale`.
 
 
 ### Continuous Color with Plotly Express
@@ -61,7 +61,8 @@ For example, in the `tips` dataset, the `size` column contains numbers:
 ```python
 import plotly.express as px
 df = px.data.tips()
-fig = px.scatter(df, x="total_bill", y="tip", color="size", title="Numeric 'size' values mean continous color")
+fig = px.scatter(df, x="total_bill", y="tip", color="size", 
+                 title="Numeric 'size' values mean continous color")
 
 fig.show()
 ```
@@ -72,7 +73,21 @@ Converting this column to strings is very straightforward:
 import plotly.express as px
 df = px.data.tips()
 df["size"] = df["size"].astype(str)
-fig = px.scatter(df, x="total_bill", y="tip", color="size", title="String 'size' values mean discrete colors")
+fig = px.scatter(df, x="total_bill", y="tip", color="size", 
+                 title="String 'size' values mean discrete colors")
+
+fig.show()
+```
+
+If you have stringified numbers you can convert back just as easily:
+
+```python
+import plotly.express as px
+df = px.data.tips()
+df["size"] = df["size"].astype(str)
+df["size"] = df["size"].astype(float)
+fig = px.scatter(df, x="total_bill", y="tip", color="size", 
+                 title="Numeric 'size' values mean continous color")
 
 fig.show()
 ```