improve scatter and line docs

Author: nicolaskruchten

Diff for: CHANGELOG.md

@@ -7,6 +7,7 @@ This project adheres to [Semantic Versioning](http://semver.org/).
 
 ### Added
   - Extra flags were added to the `gapminder` and `stocks` dataset to facilitate testing, documentation and demos [#3305](https://github.com/plotly/plotly.py/issues/3305)
+  - All line-like Plotly Express functions now accept `markers` argument to display markers, and all but `line_mapbox` accept `symbol` to map a field to the symbol attribute, similar to scatter-like functions [#3326](https://github.com/plotly/plotly.py/issues/3326)
 
 ### 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 for: doc/python/axes.md

@@ -51,7 +51,7 @@ Other kinds of subplots and axes are described in other tutorials:
 The different types of Cartesian axes are configured via the `xaxis.type` or `yaxis.type` attribute, which can take on the following values:
 
 - `'linear'` as described in this page
-- `'log'` (see the [log plot tutorial](/python/log-plots/))
+- `'log'` (see the [log plot tutorial](/python/log-plot/))
 - `'date'` (see the [tutorial on timeseries](/python/time-series/))
 - `'category'` (see the [categorical axes tutorial](/python/categorical-axes/))
 - `'multicategory'` (see the [categorical axes tutorial](/python/categorical-axes/))

Diff for: doc/python/bubble-charts.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: 3.6.7
+    version: 3.7.7
   plotly:
     description: How to make bubble charts in Python with Plotly.
     display_as: basic
@@ -36,7 +36,7 @@ jupyter:
 
 ## Bubble chart with plotly.express
 
-A [bubble chart](https://en.wikipedia.org/wiki/Bubble_chart) is a scatter plot in which a third dimension of the data is shown through the size of markers. For other types of scatter plot, see the [line and scatter page](https://plotly.com/python/line-and-scatter/).
+A [bubble chart](https://en.wikipedia.org/wiki/Bubble_chart) is a scatter plot in which a third dimension of the data is shown through the size of markers. For other types of scatter plot, see the [scatter plot documentation](https://plotly.com/python/line-and-scatter/).
 
 We first show a bubble chart example using 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/). The size of markers is set from the dataframe column given as the `size` parameter.
 
@@ -222,4 +222,4 @@ fig.show()
 
 ### Reference
 
-See https://plotly.com/python/reference/scatter/ for more information and chart attribute options!
+See https://plotly.com/python/reference/scatter/ for more information and chart attribute options!

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

@@ -41,7 +41,7 @@ This page shows examples of how to configure [2-dimensional Cartesian axes](/pyt
 The different types of Cartesian axes are configured via the `xaxis.type` or `yaxis.type` attribute, which can take on the following values:
 
 - `'linear'` (see the [linear axes tutorial](/python/axes/))
-- `'log'` (see the [log plot tutorial](/python/log-plots/))
+- `'log'` (see the [log plot tutorial](/python/log-plot/))
 - `'date'` (see the [tutorial on timeseries](/python/time-series/))
 - `'category'` see below
 - `'multicategory'` see below
@@ -64,6 +64,39 @@ fig.update_xaxes(type='category')
 fig.show()
 ```
 
+### Categorical Axes and Trace Types
+
+Every cartesian trace type is compatible with categorical axes, not just `bar`.
+
+Scatter plots where one axis is categorical are often known as [dot plots](https://plotly.com/python/dot-plots/).
+
+```python
+import plotly.express as px
+df = px.data.medals_long()
+
+fig = px.scatter(df, y="nation", x="count", color="medal", symbol="medal")
+fig.update_traces(marker_size=10)
+fig.show()
+```
+
+[Box plots]() and [violin plots]() are often shown with one categorical and one continuous axis.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+
+fig = px.box(df, x="sex", y="total_bill", color="smoker")
+fig.show()
+```
+
+```python
+import plotly.express as px
+df = px.data.tips()
+
+fig = px.violin(df, x="sex", y="total_bill", color="smoker")
+fig.show()
+```
+
 ### Controlling the Category Order 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/).

Diff for: doc/python/dot-plots.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: 3.6.7
+    version: 3.7.7
   plotly:
     description: How to make dot plots in Python with Plotly.
     display_as: basic
@@ -35,12 +35,21 @@ jupyter:
 
 #### Basic Dot Plot
 
-Dot plots (also known as [Cleveland dot plots](<https://en.wikipedia.org/wiki/Dot_plot_(statistics)>)) show changes between two (or more) points in time or between two (or more) conditions. Compared to a [bar chart](/python/bar-charts/), dot plots can be less cluttered and allow for an easier comparison between conditions.
+Dot plots (also known as [Cleveland dot plots](<https://en.wikipedia.org/wiki/Dot_plot_(statistics)>)) are [scatter plots](https://plotly.com/python/line-and-scatter/) with one categorical axis and one continuous axis. They can be used to show changes between two (or more) points in time or between two (or more) conditions. Compared to a [bar chart](/python/bar-charts/), dot plots can be less cluttered and allow for an easier comparison between conditions.
 
 For the same data, we show below how to create a dot plot using either `px.scatter` or `go.Scatter`.
 
 [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/).
 
+```python
+import plotly.express as px
+df = px.data.medals_long()
+
+fig = px.scatter(df, y="nation", x="count", color="medal", symbol="medal")
+fig.update_traces(marker_size=10)
+fig.show()
+```
+
 ```python
 import plotly.express as px
 import pandas as pd

Diff for: doc/python/line-and-scatter.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
@@ -34,7 +34,7 @@ jupyter:
     thumbnail: thumbnail/line-and-scatter.jpg
 ---
 
-## Scatter plot with Plotly Express
+## Scatter plots 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/).
 
@@ -55,9 +55,9 @@ fig = px.scatter(df, x="sepal_width", y="sepal_length")
 fig.show()
 ```
 
-#### Set size and color with column names
+#### Setting size and color with column names
 
-Note that `color` and `size` data are added to hover information. You can add other columns to hover data with the `hover_data` argument of `px.scatter`.
+Scatter plots with variable-sized circular markers are often known as [bubble charts](https://plotly.com/python/bubble-charts/). Note that `color` and `size` data are added to hover information. You can add other columns to hover data with the `hover_data` argument of `px.scatter`.
 
 ```python
 import plotly.express as px
@@ -67,7 +67,16 @@ fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
 fig.show()
 ```
 
-## Scatter plot in Dash
+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
+import plotly.express as px
+df = px.data.iris()
+fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", symbol="species")
+fig.show()
+```
+
+## Scatter plots in Dash
 
 [Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`.
 
@@ -80,7 +89,22 @@ snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
 IFrame(snippet_url + 'line-and-scatter', width='100%', height=630)
 ```
 
-## Line plot with Plotly Express
+### Scatter plots and Categorical Axes
+
+Scatters plots can be made on using any type of cartesian axis, including [linear](https://plotly.com/python/axes/), [logarithmic](https://plotly.com/python/log-plot/), [categorical](https://plotly.com/python/categorical-axes/) or [date](https://plotly.com/python/time-series/) axes.
+
+Scatter plots where one axis is categorical are often known as [dot plots](https://plotly.com/python/dot-plots/).
+
+```python
+import plotly.express as px
+df = px.data.medals_long()
+
+fig = px.scatter(df, y="nation", x="count", color="medal", symbol="medal")
+fig.update_traces(marker_size=10)
+fig.show()
+```
+
+## Line plots with Plotly Express
 
 ```python
 import plotly.express as px
@@ -99,7 +123,39 @@ fig = px.line(df, x='year', y='lifeExp', color='country')
 fig.show()
 ```
 
-## Scatter and line plot with go.Scatter
+The `markers` argument can be set to `True` to show markers on lines.
+
+```python
+import plotly.express as px
+df = px.data.gapminder().query("continent == 'Oceania'")
+fig = px.line(df, x='year', y='lifeExp', color='country', markers=True)
+fig.show()
+```
+
+The `symbol` argument can be used to map a data field to the marker symbol. A [wide variety of symbols](https://plotly.com/python/marker-style/) are available.
+
+```python
+import plotly.express as px
+df = px.data.gapminder().query("continent == 'Oceania'")
+fig = px.line(df, x='year', y='lifeExp', color='country', symbol="country")
+fig.show()
+```
+
+### Line plots on Date axes
+
+Line plots can be made on using any type of cartesian axis, including [linear](https://plotly.com/python/axes/), [logarithmic](https://plotly.com/python/log-plot/), [categorical](https://plotly.com/python/categorical-axes/) or date axes. Line plots on date axes are often called [time-series charts](https://plotly.com/python/time-series/).
+
+Plotly auto-sets the axis type to a date format when the corresponding data are either ISO-formatted date strings or if they're a [date pandas column](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html) or [datetime NumPy array](https://docs.scipy.org/doc/numpy/reference/arrays.datetime.html).
+
+```python
+import plotly.express as px
+
+df = px.data.stocks()
+fig = px.line(df, x='date', y="GOOG")
+fig.show()
+```
+
+## Scatter and line plots with go.Scatter
 
 If Plotly Express does not provide a good starting point, it is possible to use [the more generic `go.Scatter` class from `plotly.graph_objects`](/python/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/).
 

Diff for: doc/python/log-plot.md

@@ -29,6 +29,7 @@ jupyter:
     name: Log Plots
     order: 5
     permalink: python/log-plot/
+    redirect_from: python/log-plots/
     thumbnail: thumbnail/log.jpg
 ---
 
@@ -54,7 +55,7 @@ Setting the range of a logarithmic axis with Plotly Express works the same was a
 import plotly.express as px
 df = px.data.gapminder().query("year == 2007")
 
-fig = px.scatter(df, x="gdpPercap", y="lifeExp", hover_name="country", 
+fig = px.scatter(df, x="gdpPercap", y="lifeExp", hover_name="country",
                  log_x=True, range_x=[1,100000], range_y=[0,100])
 fig.show()
 ```