diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9ddd0bd04c..e1a97c714b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,6 +11,7 @@ This project adheres to [Semantic Versioning](http://semver.org/).
   - `px.scatter` and `px.density_contours` now support new `trendline` types `'rolling'`, `'expanding'` and `'ewm'` [#2997](https://github.com/plotly/plotly.py/pull/2997)
   - `px.scatter` and `px.density_contours` now support new `trendline_options` argument to parameterize trendlines, with support for constant control and log-scaling in `'ols'` and specification of the fraction used for `'lowess'`, as well as pass-through to Pandas for `'rolling'`, `'expanding'` and `'ewm'` [#2997](https://github.com/plotly/plotly.py/pull/2997)
   - `px.scatter` and `px.density_contours` now support new `trendline_scope` argument that accepts the value `'overall'` to request a single trendline for all traces, including across facets and animation frames [#2997](https://github.com/plotly/plotly.py/pull/2997)
+  - A new `px.ecdf()` function for Empirical Cumulative Distribution Functions [#3330](https://github.com/plotly/plotly.py/pull/3330)
 
 ### Fixed
   - Fixed regression introduced in version 5.0.0 where pandas/numpy arrays with `dtype` of Object were being converted to `list` values when added to a Figure ([#3292](https://github.com/plotly/plotly.py/issues/3292), [#3293](https://github.com/plotly/plotly.py/pull/3293))
diff --git a/doc/python/box-plots.md b/doc/python/box-plots.md
index e113cb9738..bae88a224b 100644
--- a/doc/python/box-plots.md
+++ b/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
diff --git a/doc/python/ecdf-plots.md b/doc/python/ecdf-plots.md
new file mode 100644
index 0000000000..acc9a532b4
--- /dev/null
+++ b/doc/python/ecdf-plots.md
@@ -0,0 +1,182 @@
+---
+jupyter:
+  jupytext:
+    notebook_metadata_filter: all
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.2'
+      jupytext_version: 1.4.2
+  kernelspec:
+    display_name: Python 3
+    language: python
+    name: python3
+  language_info:
+    codemirror_mode:
+      name: ipython
+      version: 3
+    file_extension: .py
+    mimetype: text/x-python
+    name: python
+    nbconvert_exporter: python
+    pygments_lexer: ipython3
+    version: 3.7.7
+  plotly:
+    description: How to add empirical cumulative distribution function (ECDF) plots.
+    display_as: statistical
+    language: python
+    layout: base
+    name: Empirical Cumulative Distribution Plots
+    order: 16
+    page_type: u-guide
+    permalink: python/ecdf-plots/
+    thumbnail: thumbnail/figure-labels.png
+---
+
+### Overview
+
+[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
+
+Providing a single column to the `x` variable yields a basic ECDF plot.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.ecdf(df, x="total_bill")
+fig.show()
+```
+
+Providing multiple columns leverage's Plotly Express' [wide-form data support](https://plotly.com/python/wide-form/) to show multiple variables on the same plot.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.ecdf(df, x=["total_bill", "tip"])
+fig.show()
+```
+
+It is also possible to map another variable to the color dimension of a plot.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.ecdf(df, x="total_bill", color="sex")
+fig.show()
+```
+
+### Configuring the Y axis
+
+By default, the Y axis shows probability, but it is also possible to show raw counts by setting the `ecdfnorm` argument to `None` or to show percentages by setting it to `percent`.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.ecdf(df, x="total_bill", color="sex", ecdfnorm=None)
+fig.show()
+```
+
+If a `y` value is provided, the Y axis is set to the sum of `y` rather than counts.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.ecdf(df, x="total_bill", y="tip", color="sex", ecdfnorm=None)
+fig.show()
+```
+
+### Reversed and Complementary CDF plots
+
+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' (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' (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' (Y=fraction above X value)")
+fig.show()
+```
+
+### Orientation
+
+By default, plots are oriented vertically (i.e. the variable is on the X axis and counted/summed upwards), but this can be overridden with the `orientation` argument.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.ecdf(df, x="total_bill", y="tip", color="sex", ecdfnorm=None, orientation="h")
+fig.show()
+```
+
+### Markers and/or Lines
+
+ECDF Plots can be configured to show lines and/or markers.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+fig = px.ecdf(df, x="total_bill", color="sex", markers=True)
+fig.show()
+```
+
+```python
+import plotly.express as px
+df = px.data.tips()
+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
+
+```
diff --git a/doc/python/graph-objects.md b/doc/python/graph-objects.md
index b2af9eba6b..936ea6d696 100644
--- a/doc/python/graph-objects.md
+++ b/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()
-```
\ No newline at end of file
+```
+
+```python
+
+```
diff --git a/doc/python/histograms.md b/doc/python/histograms.md
index 214e61d45b..6c49c64531 100644
--- a/doc/python/histograms.md
+++ b/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
diff --git a/doc/python/line-and-scatter.md b/doc/python/line-and-scatter.md
index b99afa56d5..cbdde08de6 100644
--- a/doc/python/line-and-scatter.md
+++ b/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
diff --git a/doc/python/plotly-express.md b/doc/python/plotly-express.md
index 2238c5ead0..269df0e093 100644
--- a/doc/python/plotly-express.md
+++ b/doc/python/plotly-express.md
@@ -43,10 +43,10 @@ Plotly Express provides [more than 30 functions for creating different types of
 Plotly Express currently includes the following functions:
 
 * **Basics**: [`scatter`](/python/line-and-scatter/), [`line`](/python/line-charts/), [`area`](/python/filled-area-plots/), [`bar`](/python/bar-charts/), [`funnel`](/python/funnel-charts/), [`timeline`](https://plotly.com/python/gantt/)
-* **Part-of-Whole**: [`pie`](/python/pie-charts/), [`sunburst`](/python/sunburst-charts/), [`treemap`](/python/treemaps/), [`funnel_area`](/python/funnel-charts/)
-* **1D Distributions**: [`histogram`](/python/histograms/), [`box`](/python/box-plots/), [`violin`](/python/violin/), [`strip`](/python/strip-charts/)
+* **Part-of-Whole**: [`pie`](/python/pie-charts/), [`sunburst`](/python/sunburst-charts/), [`treemap`](/python/treemaps/), [`icicle`](/python/icicle-charts/), [`funnel_area`](/python/funnel-charts/)
+* **1D Distributions**: [`histogram`](/python/histograms/), [`box`](/python/box-plots/), [`violin`](/python/violin/), [`strip`](/python/strip-charts/), [`ecdf`](/python/ecdf-charts/)
 * **2D Distributions**: [`density_heatmap`](/python/2D-Histogram/), [`density_contour`](/python/2d-histogram-contour/)
-* **Matrix Input**: [`imshow`](/python/imshow/)
+* **Matrix or Image Input**: [`imshow`](/python/imshow/)
 * **3-Dimensional**: [`scatter_3d`](/python/3d-scatter-plots/), [`line_3d`](/python/3d-line-plots/)
 * **Multidimensional**: [`scatter_matrix`](/python/splom/), [`parallel_coordinates`](/python/parallel-coordinates-plot/), [`parallel_categories`](/python/parallel-categories-diagram/)
 * **Tile Maps**: [`scatter_mapbox`](/python/scattermapbox/), [`line_mapbox`](/python/lines-on-mapbox/), [`choropleth_mapbox`](/python/mapbox-county-choropleth/), [`density_mapbox`](/python/mapbox-density-heatmaps/)
@@ -61,8 +61,8 @@ The Plotly Express API in general offers the following features:
 * **A single entry point into `plotly`**: just `import plotly.express as px` and get access to [all the plotting functions](https://plotly.com/python-api-reference/plotly.express.html), plus [built-in demo datasets under `px.data`](https://plotly.com/python-api-reference/generated/plotly.data.html#module-plotly.data) and [built-in color scales and sequences under `px.color`](https://plotly.com/python-api-reference/generated/plotly.colors.html#module-plotly.colors). Every PX function returns a `plotly.graph_objects.Figure` object, so you can edit it using all the same methods like [`update_layout` and `add_trace`](https://plotly.com/python/creating-and-updating-figures/#updating-figures).
 * **Sensible, Overridable Defaults**: PX functions will infer sensible defaults wherever possible, and will always let you override them.
 * **Flexible Input Formats**: PX functions [accept input in a variety of formats](/python/px-arguments/), from `list`s and `dict`s to [long-form or wide-form Pandas `DataFrame`s](/python/wide-form/) to [`numpy` arrays and `xarrays`](/python/imshow/) to [GeoPandas `GeoDataFrames`](/python/maps/).
-* **Automatic Trace and Layout configuration**: PX functions will create one [trace](/python/figure-structure) per animation frame for each unique combination of data values mapped to discrete color, symbol, line-dash, facet-row and/or facet-column. Traces' `legendgroup` and `showlegend` attributed are set such that only one legend item appears per unique combination of discrete color, symbol and/or line-dash. Traces are automatically linked to a correctly-configured [subplot of the appropriate type](/python/figure-structure).
-* **Automatic Figure Labelling**: PX functions label axes, legends and colorbars based in the input `DataFrame` or `xarray`, and provide [extra control with the `labels` argument](/python/styling-plotly-express/).
+* **Automatic Trace and Layout configuration**: PX functions will create one [trace](/python/figure-structure) per animation frame for each unique combination of data values mapped to discrete color, symbol, line-dash, facet-row and/or facet-column. Traces' [`legendgroup` and `showlegend` attributes](https://plotly.com/python/legend/) are set such that only one legend item appears per unique combination of discrete color, symbol and/or line-dash. Traces are automatically linked to a correctly-configured [subplot of the appropriate type](/python/figure-structure).
+* **Automatic Figure Labelling**: PX functions [label axes, legends and colorbars](https://plotly.com/python/figure-labels/) based in the input `DataFrame` or `xarray`, and provide [extra control with the `labels` argument](/python/styling-plotly-express/).
 * **Automatic Hover Labels**: PX functions populate the hover-label using the labels mentioned above, and provide [extra control with the `hover_name` and `hover_data` arguments](/python/hover-text-and-formatting/).
 * **Styling Control**: PX functions [read styling information from the default figure template](/python/styling-plotly-express/), and support commonly-needed [cosmetic controls like `category_orders` and `color_discrete_map`](/python/styling-plotly-express/) to precisely control categorical variables.
 * **Uniform Color Handling**: PX functions automatically switch between [continuous](/python/colorscales/) and [categorical color](/python/discrete-color/) based on the input type.
@@ -71,6 +71,7 @@ The Plotly Express API in general offers the following features:
 * **A Pandas backend**: the 2D-cartesian plotting functions are available as [a Pandas plotting backend](/python/pandas-backend/) so you can call them via `df.plot()`.
 * **Trendlines**: `px.scatter` supports [built-in trendlines with accessible model output](/python/linear-fits/).
 * **Animations**: many PX functions support [simple animation support via the `animation_frame` and `animation_group` arguments](/python/animations/).
+* **Automatic WebGL switching**: for sufficiently large scatter plots, PX will automatically [use WebGL for hardware-accelerated rendering](https://plotly.com/python/webgl-vs-svg/).
 
 
 ### Plotly Express in Dash
@@ -101,7 +102,7 @@ fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")
 fig.show()
 ```
 
-**Read more about [trendlines](/python/linear-fits/) and [templates](/python/templates/).**
+**Read more about [trendlines](/python/linear-fits/) and [templates](/python/templates/) and [marginal distribution plots](https://plotly.com/python/marginal-plots/).**
 
 ```python
 import plotly.express as px
@@ -218,6 +219,33 @@ fig = px.area(df, x="year", y="pop", color="continent", line_group="country")
 fig.show()
 ```
 
+**Read more about [timeline/Gantt charts](/python/gantt/).**
+
+```python
+import plotly.express as px
+import pandas as pd
+
+df = pd.DataFrame([
+    dict(Task="Job A", Start='2009-01-01', Finish='2009-02-28', Resource="Alex"),
+    dict(Task="Job B", Start='2009-03-05', Finish='2009-04-15', Resource="Alex"),
+    dict(Task="Job C", Start='2009-02-20', Finish='2009-05-30', Resource="Max")
+])
+
+fig = px.timeline(df, x_start="Start", x_end="Finish", y="Resource", color="Resource")
+fig.show()
+```
+
+**Read more about [funnel charts](/python/funnel-charts/).**
+
+```python
+import plotly.express as px
+data = dict(
+    number=[39, 27.4, 20.6, 11, 2],
+    stage=["Website visit", "Downloads", "Potential customers", "Requested price", "Invoice sent"])
+fig = px.funnel(data, x='number', y='stage')
+fig.show()
+```
+
 ### Part to Whole Charts
 
 **Read more about [pie charts](/python/pie-charts/).**
@@ -252,7 +280,7 @@ fig = px.treemap(df, path=[px.Constant('world'), 'continent', 'country'], values
 fig.show()
 ```
 
-**Read more about [treemaps](/python/icicle-charts/).**
+**Read more about [icicle charts](/python/icicle-charts/).**
 
 ```python
 import plotly.express as px
@@ -292,26 +320,30 @@ fig = px.violin(df, y="tip", x="smoker", color="sex", box=True, points="all", ho
 fig.show()
 ```
 
+**Read more about [Empirical Cumulative Distribution Function (ECDF) charts](https://plotly.com/python/ecdf-charts/).**
+
 ```python
 import plotly.express as px
 df = px.data.tips()
-fig = px.strip(df, x="total_bill", y="time", orientation="h", color="smoker")
+fig = px.ecdf(df, x="total_bill", color="sex")
 fig.show()
 ```
 
-**Read more about [density contours, also known as 2D histogram contours](/python/2d-histogram-contour/).**
+**Read more about [strip charts](https://plotly.com/python/strip-charts/).**
 
 ```python
 import plotly.express as px
-df = px.data.iris()
-fig = px.density_contour(df, x="sepal_width", y="sepal_length")
+df = px.data.tips()
+fig = px.strip(df, x="total_bill", y="time", orientation="h", color="smoker")
 fig.show()
 ```
 
+**Read more about [density contours, also known as 2D histogram contours](/python/2d-histogram-contour/).**
+
 ```python
 import plotly.express as px
 df = px.data.iris()
-fig = px.density_contour(df, x="sepal_width", y="sepal_length", color="species", marginal_x="rug", marginal_y="histogram")
+fig = px.density_contour(df, x="sepal_width", y="sepal_length")
 fig.show()
 ```
 
@@ -330,24 +362,25 @@ fig.show()
 
 ```python
 import plotly.express as px
-
-fig = px.imshow([[1, 20, 30],
-                 [20, 1, 60],
-                 [30, 60, 1]])
+data=[[1, 25, 30, 50, 1], [20, 1, 60, 80, 30], [30, 60, 1, 5, 20]]
+fig = px.imshow(data,
+                labels=dict(x="Day of Week", y="Time of Day", color="Productivity"),
+                x=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
+                y=['Morning', 'Afternoon', 'Evening']
+               )
+fig.update_xaxes(side="top")
 fig.show()
 ```
 
 ```python
 import plotly.express as px
-import numpy as np
-img_rgb = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
-                    [[0, 255, 0], [0, 0, 255], [255, 0, 0]]
-                   ], dtype=np.uint8)
-fig = px.imshow(img_rgb)
+from skimage import io
+img = io.imread('https://upload.wikimedia.org/wikipedia/commons/thumb/0/00/Crab_Nebula.jpg/240px-Crab_Nebula.jpg')
+fig = px.imshow(img)
 fig.show()
 ```
 
-#### Maps
+#### Tile Maps
 
 **Read more about [tile maps](/python/mapbox-layers/) and [point on tile maps](/python/scattermapbox/).**
 
@@ -375,6 +408,8 @@ fig = px.choropleth_mapbox(df, geojson=geojson, color="Bergeron",
 fig.show()
 ```
 
+### Outline Maps
+
 **Read more about [outline symbol maps](/python/scatter-plots-on-maps/).**
 
 ```python
diff --git a/doc/python/violin.md b/doc/python/violin.md
index bcdd44f1fe..8e9d2a4e4a 100644
--- a/doc/python/violin.md
+++ b/doc/python/violin.md
@@ -5,8 +5,8 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.1
+      format_version: '1.2'
+      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: 3.6.8
+    version: 3.7.7
   plotly:
     description: How to make violin plots in Python with Plotly.
     display_as: statistical
@@ -36,15 +36,18 @@ jupyter:
     thumbnail: thumbnail/violin.jpg
 ---
 
+<!-- #region -->
 ## Violin Plot with Plotly Express
 
 A [violin plot](https://en.wikipedia.org/wiki/Violin_plot) is a statistical representation of numerical data. It is similar to a [box plot](https://plotly.com/python/box-plots/), with the addition of a rotated [kernel density](https://en.wikipedia.org/wiki/Kernel_density_estimation) plot on each side.
 
-See also the [list of other statistical charts](https://plotly.com/python/statistical-charts/).
+Alternatives to violin plots for visualizing distributions include [histograms](https://plotly.com/python/histograms/), [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/).
+
 
 ### Basic Violin 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/).
+<!-- #endregion -->
 
 ```python
 import plotly.express as px
diff --git a/packages/python/plotly/plotly/express/__init__.py b/packages/python/plotly/plotly/express/__init__.py
index 8bc5da5391..c8fab13b5f 100644
--- a/packages/python/plotly/plotly/express/__init__.py
+++ b/packages/python/plotly/plotly/express/__init__.py
@@ -34,6 +34,7 @@
     box,
     strip,
     histogram,
+    ecdf,
     scatter_matrix,
     parallel_coordinates,
     parallel_categories,
@@ -89,6 +90,7 @@
     "box",
     "strip",
     "histogram",
+    "ecdf",
     "choropleth",
     "choropleth_mapbox",
     "pie",
diff --git a/packages/python/plotly/plotly/express/_chart_types.py b/packages/python/plotly/plotly/express/_chart_types.py
index f335e78de3..3e1f97fc05 100644
--- a/packages/python/plotly/plotly/express/_chart_types.py
+++ b/packages/python/plotly/plotly/express/_chart_types.py
@@ -489,6 +489,72 @@ def histogram(
 )
 
 
+def ecdf(
+    data_frame=None,
+    x=None,
+    y=None,
+    color=None,
+    text=None,
+    line_dash=None,
+    symbol=None,
+    facet_row=None,
+    facet_col=None,
+    facet_col_wrap=0,
+    facet_row_spacing=None,
+    facet_col_spacing=None,
+    hover_name=None,
+    hover_data=None,
+    animation_frame=None,
+    animation_group=None,
+    markers=False,
+    lines=True,
+    category_orders=None,
+    labels=None,
+    color_discrete_sequence=None,
+    color_discrete_map=None,
+    line_dash_sequence=None,
+    line_dash_map=None,
+    symbol_sequence=None,
+    symbol_map=None,
+    marginal=None,
+    opacity=None,
+    orientation=None,
+    ecdfnorm="probability",
+    ecdfmode="standard",
+    render_mode="auto",
+    log_x=False,
+    log_y=False,
+    range_x=None,
+    range_y=None,
+    title=None,
+    template=None,
+    width=None,
+    height=None,
+):
+    """
+    In a Empirical Cumulative Distribution Function (ECDF) plot, rows of `data_frame`
+    are sorted by the value `x` (or `y` if `orientation` is `'h'`) and their cumulative
+    count (or the cumulative sum of `y` if supplied and `orientation` is `h`) is drawn
+    as a line.
+    """
+    return make_figure(args=locals(), constructor=go.Scatter)
+
+
+ecdf.__doc__ = make_docstring(
+    ecdf,
+    append_dict=dict(
+        x=[
+            "If `orientation` is `'h'`, the cumulative sum of this argument is plotted rather than the cumulative count."
+        ]
+        + _wide_mode_xy_append,
+        y=[
+            "If `orientation` is `'v'`, the cumulative sum of this argument is plotted rather than the cumulative count."
+        ]
+        + _wide_mode_xy_append,
+    ),
+)
+
+
 def violin(
     data_frame=None,
     x=None,
diff --git a/packages/python/plotly/plotly/express/_core.py b/packages/python/plotly/plotly/express/_core.py
index cc0e98375b..01c789ecab 100644
--- a/packages/python/plotly/plotly/express/_core.py
+++ b/packages/python/plotly/plotly/express/_core.py
@@ -660,6 +660,12 @@ def configure_cartesian_axes(args, fig, orders):
     if "is_timeline" in args:
         fig.update_xaxes(type="date")
 
+    if "ecdfmode" in args:
+        if args["orientation"] == "v":
+            fig.update_yaxes(rangemode="tozero")
+        else:
+            fig.update_xaxes(rangemode="tozero")
+
 
 def configure_ternary_axes(args, fig, orders):
     fig.update_ternaries(
@@ -1312,6 +1318,7 @@ def build_dataframe(args, constructor):
     wide_cross_name = None  # will likely be "index" in wide_mode
     value_name = None  # will likely be "value" in wide_mode
     hist2d_types = [go.Histogram2d, go.Histogram2dContour]
+    hist1d_orientation = constructor == go.Histogram or "ecdfmode" in args
     if constructor in cartesians:
         if wide_x and wide_y:
             raise ValueError(
@@ -1346,7 +1353,7 @@ def build_dataframe(args, constructor):
                 df_provided and var_name in df_input
             ):
                 var_name = "variable"
-            if constructor == go.Histogram:
+            if hist1d_orientation:
                 wide_orientation = "v" if wide_x else "h"
             else:
                 wide_orientation = "v" if wide_y else "h"
@@ -1360,7 +1367,10 @@ def build_dataframe(args, constructor):
         var_name = _escape_col_name(df_input, var_name, [])
 
     missing_bar_dim = None
-    if constructor in [go.Scatter, go.Bar, go.Funnel] + hist2d_types:
+    if (
+        constructor in [go.Scatter, go.Bar, go.Funnel] + hist2d_types
+        and not hist1d_orientation
+    ):
         if not wide_mode and (no_x != no_y):
             for ax in ["x", "y"]:
                 if args.get(ax) is None:
@@ -1457,14 +1467,18 @@ def build_dataframe(args, constructor):
         df_output[var_name] = df_output[var_name].astype(str)
         orient_v = wide_orientation == "v"
 
-        if constructor in [go.Scatter, go.Funnel] + hist2d_types:
+        if hist1d_orientation:
+            args["x" if orient_v else "y"] = value_name
+            args["y" if orient_v else "x"] = wide_cross_name
+            args["color"] = args["color"] or var_name
+        elif constructor in [go.Scatter, go.Funnel] + hist2d_types:
             args["x" if orient_v else "y"] = wide_cross_name
             args["y" if orient_v else "x"] = value_name
             if constructor != go.Histogram2d:
                 args["color"] = args["color"] or var_name
             if "line_group" in args:
                 args["line_group"] = args["line_group"] or var_name
-        if constructor == go.Bar:
+        elif constructor == go.Bar:
             if _is_continuous(df_output, value_name):
                 args["x" if orient_v else "y"] = wide_cross_name
                 args["y" if orient_v else "x"] = value_name
@@ -1474,13 +1488,24 @@ def build_dataframe(args, constructor):
                 args["y" if orient_v else "x"] = count_name
                 df_output[count_name] = 1
                 args["color"] = args["color"] or var_name
-        if constructor in [go.Violin, go.Box]:
+        elif constructor in [go.Violin, go.Box]:
             args["x" if orient_v else "y"] = wide_cross_name or var_name
             args["y" if orient_v else "x"] = value_name
-        if constructor == go.Histogram:
-            args["x" if orient_v else "y"] = value_name
-            args["y" if orient_v else "x"] = wide_cross_name
-            args["color"] = args["color"] or var_name
+
+    if hist1d_orientation and constructor == go.Scatter:
+        if args["x"] is not None and args["y"] is not None:
+            args["histfunc"] = "sum"
+        elif args["x"] is None:
+            args["histfunc"] = None
+            args["orientation"] = "h"
+            args["x"] = count_name
+            df_output[count_name] = 1
+        else:
+            args["histfunc"] = None
+            args["orientation"] = "v"
+            args["y"] = count_name
+            df_output[count_name] = 1
+
     if no_color:
         args["color"] = None
     args["data_frame"] = df_output
@@ -1778,12 +1803,18 @@ def infer_config(args, constructor, trace_patch, layout_patch):
             trace_patch["opacity"] = args["opacity"]
         else:
             trace_patch["marker"] = dict(opacity=args["opacity"])
-    if "line_group" in args:  # px.line, px.line_*, px.area
-        modes = set(["lines"])
+    if (
+        "line_group" in args or "line_dash" in args
+    ):  # px.line, px.line_*, px.area, px.ecdf
+        modes = set()
+        if args.get("lines", True):
+            modes.add("lines")
         if args.get("text") or args.get("symbol") or args.get("markers"):
             modes.add("markers")
         if args.get("text"):
             modes.add("text")
+        if len(modes) == 0:
+            modes.add("lines")
         trace_patch["mode"] = "+".join(modes)
     elif constructor != go.Splom and (
         "symbol" in args or constructor == go.Scattermapbox
@@ -1792,6 +1823,10 @@ def infer_config(args, constructor, trace_patch, layout_patch):
 
     if "line_shape" in args:
         trace_patch["line"] = dict(shape=args["line_shape"])
+    elif "ecdfmode" in args:
+        trace_patch["line"] = dict(
+            shape="vh" if args["ecdfmode"] == "reversed" else "hv"
+        )
 
     if "geojson" in args:
         trace_patch["featureidkey"] = args["featureidkey"]
@@ -1833,6 +1868,14 @@ def infer_config(args, constructor, trace_patch, layout_patch):
     if "trendline_options" in args and args["trendline_options"] is None:
         args["trendline_options"] = dict()
 
+    if "ecdfnorm" in args:
+        if args.get("ecdfnorm", None) not in [None, "percent", "probability"]:
+            raise ValueError(
+                "`ecdfnorm` must be one of None, 'percent' or 'probability'. "
+                + "'%s' was provided." % args["ecdfnorm"]
+            )
+        args["histnorm"] = args["ecdfnorm"]
+
     # Compute applicable grouping attributes
     for k in group_attrables:
         if k in args:
@@ -1984,11 +2027,11 @@ def make_figure(args, constructor, trace_patch=None, layout_patch=None):
                         trace_spec != trace_specs[0]
                         and (
                             trace_spec.constructor in [go.Violin, go.Box]
-                            and m.variable in ["symbol", "pattern"]
+                            and m.variable in ["symbol", "pattern", "dash"]
                         )
                         or (
                             trace_spec.constructor in [go.Histogram]
-                            and m.variable in ["symbol"]
+                            and m.variable in ["symbol", "dash"]
                         )
                     ):
                         pass
@@ -2047,6 +2090,24 @@ def make_figure(args, constructor, trace_patch=None, layout_patch=None):
             ):
                 trace.update(marker=dict(color=trace.line.color))
 
+            if "ecdfmode" in args:
+                base = args["x"] if args["orientation"] == "v" else args["y"]
+                var = args["x"] if args["orientation"] == "h" else args["y"]
+                ascending = args.get("ecdfmode", "standard") != "reversed"
+                group = group.sort_values(by=base, ascending=ascending)
+                group_sum = group[var].sum()  # compute here before next line mutates
+                group[var] = group[var].cumsum()
+                if not ascending:
+                    group = group.sort_values(by=base, ascending=True)
+
+                if args.get("ecdfmode", "standard") == "complementary":
+                    group[var] = group_sum - group[var]
+
+                if args["ecdfnorm"] == "probability":
+                    group[var] = group[var] / group_sum
+                elif args["ecdfnorm"] == "percent":
+                    group[var] = 100.0 * group[var] / group_sum
+
             patch, fit_results = make_trace_kwargs(
                 args, trace_spec, group, mapping_labels.copy(), sizeref
             )
diff --git a/packages/python/plotly/plotly/express/_doc.py b/packages/python/plotly/plotly/express/_doc.py
index 65d9f0588f..7d5282ef07 100644
--- a/packages/python/plotly/plotly/express/_doc.py
+++ b/packages/python/plotly/plotly/express/_doc.py
@@ -325,7 +325,11 @@
         "Setting this value is recommended when using `plotly.express.colors.diverging` color scales as the inputs to `color_continuous_scale`.",
     ],
     size_max=["int (default `20`)", "Set the maximum mark size when using `size`."],
-    markers=["boolean (default `False`)", "If `True`, markers are shown on lines.",],
+    markers=["boolean (default `False`)", "If `True`, markers are shown on lines."],
+    lines=[
+        "boolean (default `True`)",
+        "If `False`, lines are not drawn (forced to `True` if `markers` is `False`).",
+    ],
     log_x=[
         "boolean (default `False`)",
         "If `True`, the x-axis is log-scaled in cartesian coordinates.",
@@ -573,10 +577,26 @@
         "Sets the number of rendered sectors from any given `level`. Set `maxdepth` to -1 to render all the"
         "levels in the hierarchy.",
     ],
+    ecdfnorm=[
+        "string or `None` (default `'probability'`)",
+        "One of `'probability'` or `'percent'`",
+        "If `None`, values will be raw counts or sums.",
+        "If `'probability', values will be probabilities normalized from 0 to 1.",
+        "If `'percent', values will be percentages normalized from 0 to 100.",
+    ],
+    ecdfmode=[
+        "string (default `'standard'`)",
+        "One of `'standard'`, `'complementary'` or `'reversed'`",
+        "If `'standard'`, the ECDF is plotted such that values represent data at or below the point.",
+        "If `'complementary'`, the CCDF is plotted such that values represent data above the point.",
+        "If `'reversed'`, a variant of the CCDF is plotted such that values represent data at or above the point.",
+    ],
 )
 
 
-def make_docstring(fn, override_dict={}, append_dict={}):
+def make_docstring(fn, override_dict=None, append_dict=None):
+    override_dict = {} if override_dict is None else override_dict
+    append_dict = {} if append_dict is None else append_dict
     tw = TextWrapper(width=75, initial_indent="    ", subsequent_indent="    ")
     result = (fn.__doc__ or "") + "\nParameters\n----------\n"
     for param in getfullargspec(fn)[0]:
diff --git a/packages/python/plotly/plotly/tests/test_optional/test_px/test_facets.py b/packages/python/plotly/plotly/tests/test_optional/test_px/test_facets.py
index 6598599fb9..c1db2afe77 100644
--- a/packages/python/plotly/plotly/tests/test_optional/test_px/test_facets.py
+++ b/packages/python/plotly/plotly/tests/test_optional/test_px/test_facets.py
@@ -1,4 +1,3 @@
-import plotly
 import pandas as pd
 import plotly.express as px
 from pytest import approx
@@ -112,25 +111,21 @@ def bad_facet_spacing_df():
 def test_bad_facet_spacing_eror(bad_facet_spacing_df):
     df = bad_facet_spacing_df
     with pytest.raises(
-        ValueError, match="Use the facet_row_spacing argument to adjust this spacing\."
+        ValueError, match="Use the facet_row_spacing argument to adjust this spacing."
     ):
-        fig = px.scatter(
-            df, x="x", y="y", facet_row="category", facet_row_spacing=0.01001
-        )
+        px.scatter(df, x="x", y="y", facet_row="category", facet_row_spacing=0.01001)
     with pytest.raises(
-        ValueError, match="Use the facet_col_spacing argument to adjust this spacing\."
+        ValueError, match="Use the facet_col_spacing argument to adjust this spacing."
     ):
-        fig = px.scatter(
-            df, x="x", y="y", facet_col="category", facet_col_spacing=0.01001
-        )
+        px.scatter(df, x="x", y="y", facet_col="category", facet_col_spacing=0.01001)
     # Check error is not raised when the spacing is OK
     try:
-        fig = px.scatter(df, x="x", y="y", facet_row="category", facet_row_spacing=0.01)
+        px.scatter(df, x="x", y="y", facet_row="category", facet_row_spacing=0.01)
     except ValueError:
         # Error shouldn't be raised, so fail if it is
         assert False
     try:
-        fig = px.scatter(df, x="x", y="y", facet_col="category", facet_col_spacing=0.01)
+        px.scatter(df, x="x", y="y", facet_col="category", facet_col_spacing=0.01)
     except ValueError:
         # Error shouldn't be raised, so fail if it is
         assert False
diff --git a/packages/python/plotly/plotly/tests/test_optional/test_px/test_marginals.py b/packages/python/plotly/plotly/tests/test_optional/test_px/test_marginals.py
index ecb7927d62..0274068d27 100644
--- a/packages/python/plotly/plotly/tests/test_optional/test_px/test_marginals.py
+++ b/packages/python/plotly/plotly/tests/test_optional/test_px/test_marginals.py
@@ -14,7 +14,7 @@ def test_xy_marginals(px_fn, marginal_x, marginal_y):
     assert len(fig.data) == 1 + (marginal_x is not None) + (marginal_y is not None)
 
 
-@pytest.mark.parametrize("px_fn", [px.histogram])
+@pytest.mark.parametrize("px_fn", [px.histogram, px.ecdf])
 @pytest.mark.parametrize("marginal", [None, "rug", "histogram", "box", "violin"])
 @pytest.mark.parametrize("orientation", ["h", "v"])
 def test_single_marginals(px_fn, marginal, orientation):