docs sweep

doc/python/box-plots.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.6.0
+      jupytext_version: 1.4.2
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.6
+    version: 3.7.7
   plotly:
     description: How to make Box Plots in Python with Plotly.
     display_as: statistical
@@ -36,13 +36,18 @@ jupyter:
     thumbnail: thumbnail/box.jpg
 ---
 
-A [box plot](https://en.wikipedia.org/wiki/Box_plot) is a statistical representation of numerical data through their quartiles. The ends of the box represent the lower and upper quartiles, while the median (second quartile) is marked by a line inside the box. For other statistical representations of numerical data, see [other statistical charts](https://plotly.com/python/statistical-charts/).
+<!-- #region -->
+A [box plot](https://en.wikipedia.org/wiki/Box_plot) is a statistical representation of the distribution of a variable through its quartiles. The ends of the box represent the lower and upper quartiles, while the median (second quartile) is marked by a line inside the box. For other statistical representations of numerical data, see [other statistical charts](https://plotly.com/python/statistical-charts/).
+
+
+Alternatives to box plots for visualizing distributions include [histograms](https://plotly.com/python/histograms/), [violin plots](https://plotly.com/python/violin/), [ECDF plots](https://plotly.com/python/ecdf-plots/) and [strip charts](https://plotly.com/python/strip-charts/).
 
 ## Box Plot with `plotly.express`
 
 [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 In a box plot created by `px.box`, the distribution of the column given as `y` argument is represented.
+<!-- #endregion -->
 
 ```python
 import plotly.express as px

doc/python/ecdf-plots.md

@@ -35,7 +35,9 @@ jupyter:
 
 ### Overview
 
-[Empirical cumulative distribution function plots](https://en.wikipedia.org/wiki/Empirical_distribution_function) are a way to visualize the distribution of a variabl, and Plotly Express has a built-in function, `px.ecdf()` to generate such plots. [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
+[Empirical cumulative distribution function plots](https://en.wikipedia.org/wiki/Empirical_distribution_function) are a way to visualize the distribution of a variable, and Plotly Express has a built-in function, `px.ecdf()` to generate such plots. [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
+
+Alternatives to ECDF plots for visualizing distributions include [histograms](https://plotly.com/python/histograms/), [violin plots](https://plotly.com/python/violin/), [box plots](https://plotly.com/python/box-plots/) and [strip charts](https://plotly.com/python/strip-charts/).
 
 ### Simple ECDF Plots
 
@@ -90,24 +92,30 @@ fig.show()
 
 By default, the Y value represents the fraction of the data that is *at or below* the value on on the X axis. Setting `ecdfmode` to `"reversed"` reverses this, with the Y axis representing the fraction of the data *at or above* the X value. Setting `ecdfmode` to `"complementary"` plots `1-ECDF`, meaning that the Y values represent the fraction of the data *above* the X value.
 
+In `standard` mode (the default), the right-most point is at 1 (or the total count/sum, depending on `ecdfnorm`) and the right-most point is above 0.
+
 ```python
 import plotly.express as px
 fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="standard",
-              title="ecdfmode='standard' (at or below X value, the default)")
+              title="ecdfmode='standard' (Y=fraction at or below X value, this the default)")
 fig.show()
 ```
 
+In `reversed` mode, the right-most point is at 1 (or the total count/sum, depending on `ecdfnorm`) and the left-most point is above 0.
+
 ```python
 import plotly.express as px
 fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="reversed",
-              title="ecdfmode='reversed' (at or above X value)")
+              title="ecdfmode='reversed' (Y=fraction at or above X value)")
 fig.show()
 ```
 
+In `complementary` mode, the right-most point is at 0 and no points are at 1 (or the total count/sum) per the definition of the CCDF as 1-ECDF, which has no point at 0.
+
 ```python
 import plotly.express as px
 fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="complementary",
-              title="ecdfmode='complementary' (above X value)")
+              title="ecdfmode='complementary' (Y=fraction above X value)")
 fig.show()
 ```
 
@@ -140,6 +148,35 @@ fig = px.ecdf(df, x="total_bill", color="sex", markers=True, lines=False)
 fig.show()
 ```
 
+### Marginal Plots
+
+ECDF plots also support [marginal plots](https://plotly.com/python/marginal-plots/)
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.ecdf(df, x="total_bill", color="sex", markers=True, lines=False, marginal="histogram")
+fig.show()
+```
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.ecdf(df, x="total_bill", color="sex", marginal="rug")
+fig.show()
+```
+
+### Facets
+
+ECDF Plots also support [faceting](https://plotly.com/python/facet-plots/)
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.ecdf(df, x="total_bill", color="sex", facet_row="time", facet_col="day")
+fig.show()
+```
+
 ```python
 
 ```

doc/python/graph-objects.md

@@ -56,13 +56,19 @@ Graph objects have several benefits compared to plain Python dictionaries:
 5. Graph object constructors and update methods accept "magic underscores" (e.g. `go.Figure(layout_title_text="The Title")` rather than `dict(layout=dict(title=dict(text="The Title")))`) for more compact code.
 6. Graph objects support attached rendering (`.show()`) and exporting functions (`.write_image()`) that automatically invoke the appropriate functions from [the `plotly.io` module](https://plotly.com/python-api-reference/plotly.io.html).
 
-### When to use Graph Objects Directly
+### When to use Graph Objects vs Plotly Express
 
-The recommended way to create figures is using the [functions in the plotly.express module](https://plotly.com/python-api-reference/), [collectively known as Plotly Express](/python/plotly-express/), which all return instances of `plotly.graph_objects.Figure`, so every figure produced with the plotly library, actually uses graph objects under the hood, unless manually constructed out of dictionaries.
+The recommended way to create figures is using the [functions in the plotly.express module](https://plotly.com/python-api-reference/), [collectively known as Plotly Express](/python/plotly-express/), which all return instances of `plotly.graph_objects.Figure`, so every figure produced with the `plotly` library actually uses graph objects under the hood, unless manually constructed out of dictionaries.
 
 That said, certain kinds of figures are not yet possible to create with Plotly Express, such as figures that use certain 3D trace-types like [`mesh`](/python/3d-mesh/) or [`isosurface`](/python/3d-isosurface-plots/). In addition, certain figures are cumbersome to create by starting from a figure created with Plotly Express, for example figures with [subplots of different types](/python/mixed-subplots/), [dual-axis plots](/python/multiple-axes/), or [faceted plots](/python/facet-plots/) with multiple different types of traces. To construct such figures, it can be easier to start from an empty `plotly.graph_objects.Figure` object (or one configured with subplots via the [make_subplots() function](/python/subplots/)) and progressively add traces and update attributes as above. Every `plotly` documentation page lists the Plotly Express option at the top if a Plotly Express function exists to make the kind of chart in question, and then the graph objects version below.
 
-Note that the figures produced by Plotly Express **in a single function-call** are [easy to customize at creation-time](/python/styling-plotly-express/), and to [manipulate after creation](/python/creating-and-updating-figures/) using the `update_*` and `add_*` methods. The figures produced by Plotly Express can always be built from the ground up using graph objects, but this approach typically takes **5-100 lines of code rather than 1**. Here is a simple example of how to produce the same figure object from the same data, once with Plotly Express and once without. The data in this example is in "long form" but [Plotly Express also accepts data in "wide form"](/python/wide-form/) and the line-count savings from Plotly Express over graph objects are comparable. More complex figures such as [sunbursts](/python/sunburst-charts/), [parallel coordinates](/python/parallel-coordinates-plot/), [facet plots](/python/facet-plots/) or [animations](/python/animations/) require many more lines of figure-specific graph objects code, whereas switching from one representation to another with Plotly Express usually involves changing just a few characters.
+Note that the figures produced by Plotly Express **in a single function-call** are [easy to customize at creation-time](/python/styling-plotly-express/), and to [manipulate after creation](/python/creating-and-updating-figures/) using the `update_*` and `add_*` methods. 
+
+### Comparing Graph Objects and Plotly Express
+
+The figures produced by Plotly Express can always be built from the ground up using graph objects, but this approach typically takes **5-100 lines of code rather than 1**. 
+
+Here is a simple example of how to produce the same figure object from the same data, once with Plotly Express and once without. The data in this example is in "long form" but [Plotly Express also accepts data in "wide form"](/python/wide-form/) and the line-count savings from Plotly Express over graph objects are comparable. More complex figures such as [sunbursts](/python/sunburst-charts/), [parallel coordinates](/python/parallel-coordinates-plot/), [facet plots](/python/facet-plots/) or [animations](/python/animations/) require many more lines of figure-specific graph objects code, whereas switching from one representation to another with Plotly Express usually involves changing just a few characters.
 
 ```python
 import pandas as pd
@@ -73,11 +79,17 @@ df = pd.DataFrame({
   "Number Eaten": [2, 1, 3, 1, 3, 2],
 })
 
+
+# Plotly Express
+
 import plotly.express as px
 
 fig = px.bar(df, x="Fruit", y="Number Eaten", color="Contestant", barmode="group")
 fig.show()
 
+
+# Graph Objects
+
 import plotly.graph_objects as go
 
 fig = go.Figure()
@@ -88,4 +100,8 @@ fig.update_layout(legend_title_text = "Contestant")
 fig.update_xaxes(title_text="Fruit")
 fig.update_yaxes(title_text="Number Eaten")
 fig.show()
-```
+```
+
+```python
+
+```

doc/python/histograms.md

@@ -36,14 +36,19 @@ jupyter:
     thumbnail: thumbnail/histogram.jpg
 ---
 
-In statistics, a [histogram](https://en.wikipedia.org/wiki/Histogram) is representation of the distribution of numerical data, where the data are binned and the count for each bin is represented. More generally, in plotly a histogram is an aggregated bar chart, with several possible aggregation functions (e.g. sum, average, count...).
+<!-- #region -->
+In statistics, a [histogram](https://en.wikipedia.org/wiki/Histogram) is representation of the distribution of numerical data, where the data are binned and the count for each bin is represented. More generally, in Plotly a histogram is an aggregated bar chart, with several possible aggregation functions (e.g. sum, average, count...) which can be used to visualize data on categorical and date axes as well as linear axes.
 
-If you're looking instead for bar charts, i.e. representing *raw, unaggregated* data with rectangular
+
+Alternatives to violin plots for visualizing distributions include [violin plots](https://plotly.com/python/violin/), [box plots](https://plotly.com/python/box-plots/), [ECDF plots](https://plotly.com/python/ecdf-plots/) and [strip charts](https://plotly.com/python/strip-charts/).
+
+> If you're looking instead for bar charts, i.e. representing *raw, unaggregated* data with rectangular
 bar, go to the [Bar Chart tutorial](/python/bar-charts/).
 
 ## Histograms with Plotly Express
 
 [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
+<!-- #endregion -->
 
 ```python
 import plotly.express as px
@@ -160,7 +165,7 @@ fig = px.histogram(df, x="total_bill", color="sex")
 fig.show()
 ```
 
-#### Using histfunc
+#### Aggregating with other functions than `count`
 
 For each bin of `x`, one can compute a function of data using `histfunc`. The argument of `histfunc` is the dataframe column given as the `y` argument. Below the plot shows that the average tip increases with the total bill.
 
@@ -193,7 +198,7 @@ fig.show()
 
 #### Visualizing the distribution
 
-With the `marginal` keyword, a subplot is drawn alongside the histogram, visualizing the distribution. See [the distplot page](https://plotly.com/python/distplot/)for more examples of combined statistical representations.
+With the `marginal` keyword, a [marginal](https://plotly.com/python/marginal-plots/) is drawn alongside the histogram, visualizing the distribution. See [the distplot page](https://plotly.com/python/distplot/) for more examples of combined statistical representations.
 
 ```python
 import plotly.express as px

doc/python/line-and-scatter.md

@@ -67,6 +67,15 @@ fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
 fig.show()
 ```
 
+Color can be [continuous](https://plotly.com/python/colorscales/) as follows, or [discrete/categorical](https://plotly.com/python/discrete-color/) as above.
+
+```python
+import plotly.express as px
+df = px.data.iris()
+fig = px.scatter(df, x="sepal_width", y="sepal_length", color='petal_length')
+fig.show()
+```
+
 The `symbol` argument can be mapped to a column as well. A [wide variety of symbols](https://plotly.com/python/marker-style/) are available.
 
 ```python
@@ -104,6 +113,53 @@ fig.update_traces(marker_size=10)
 fig.show()
 ```
 
+### Error Bars
+
+Scatter plots support [error bars](https://plotly.com/python/error-bars/).
+
+```python
+import plotly.express as px
+df = px.data.iris()
+df["e"] = df["sepal_width"]/100
+fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
+                 error_x="e", error_y="e")
+fig.show()
+```
+
+### Marginal Distribution Plots
+
+Scatter plots support [marginal distribution plots](https://plotly.com/python/marginal-plots/)
+
+```python
+import plotly.express as px
+df = px.data.iris()
+fig = px.scatter(df, x="sepal_length", y="sepal_width", marginal_x="histogram", marginal_y="rug")
+fig.show()
+```
+
+### Facetting
+
+Scatter plots support [faceting](https://plotly.com/python/facet-plots/).
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.scatter(df, x="total_bill", y="tip", color="smoker", facet_col="sex", facet_row="time")
+fig.show()
+```
+
+### Linear Regression and Other Trendlines
+
+Scatter plots support [linear and non-linear trendlines](https://plotly.com/python/linear-fits/).
+
+```python
+import plotly.express as px
+
+df = px.data.tips()
+fig = px.scatter(df, x="total_bill", y="tip", trendline="ols")
+fig.show()
+```
+
 ## Line plots with Plotly Express
 
 ```python