Merge pull request #2156 from plotly/px_styling

finally: how to style px

Author: nicolaskruchten

Diff for: doc/python/3d-scatter-plots.md

@@ -35,7 +35,7 @@ jupyter:
 
 ## 3D scatter plot with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 Like the [2D scatter plot](https://plot.ly/python/line-and-scatter/) `px.scatter`, the 3D function `px.scatter_3d` plots individual data in three-dimensional space.
 

Diff for: doc/python/bar-charts.md

@@ -5,7 +5,7 @@ jupyter:
     text_representation:
       extension: .md
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@@ -35,7 +35,7 @@ jupyter:
 
 ### Bar chart with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 With `px.bar`, each row of the DataFrame is represented as a rectangular mark.
 

Diff for: doc/python/box-plots.md

@@ -5,7 +5,7 @@ jupyter:
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       extension: .md
       format_name: markdown
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@@ -31,16 +31,16 @@ jupyter:
     page_type: example_index
     permalink: python/box-plots/
     redirect_from:
-    - /python/box/
-    - /python/basic_statistics/
+      - /python/box/
+      - /python/basic_statistics/
     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://plot.ly/python/statistical-charts/).
 
 ## Box Plot with `plotly.express`
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" 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.
 
@@ -73,13 +73,13 @@ fig.show()
 
 ### Choosing The Algorithm For Computing Quartiles
 
-By default, quartiles for box plots are computed using the `linear` method (for more about linear interpolation, see #10 listed on [http://www.amstat.org/publications/jse/v14n3/langford.html](http://www.amstat.org/publications/jse/v14n3/langford.html) and [https://en.wikipedia.org/wiki/Quartile](https://en.wikipedia.org/wiki/Quartile) for more details). 
+By default, quartiles for box plots are computed using the `linear` method (for more about linear interpolation, see #10 listed on [http://www.amstat.org/publications/jse/v14n3/langford.html](http://www.amstat.org/publications/jse/v14n3/langford.html) and [https://en.wikipedia.org/wiki/Quartile](https://en.wikipedia.org/wiki/Quartile) for more details).
 
-However, you can also choose to use an `exclusive` or an `inclusive` algorithm to compute quartiles. 
+However, you can also choose to use an `exclusive` or an `inclusive` algorithm to compute quartiles.
 
-The *exclusive* algorithm uses the median to divide the ordered dataset into two halves. If the sample is odd, it does not include the median in either half. Q1 is then the median of the lower half and Q3 is the median of the upper half.
+The _exclusive_ algorithm uses the median to divide the ordered dataset into two halves. If the sample is odd, it does not include the median in either half. Q1 is then the median of the lower half and Q3 is the median of the upper half.
 
-The *inclusive* algorithm also uses the median to divide the ordered dataset into two halves, but if the sample is odd, it includes the median in both halves. Q1 is then the median of the lower half and Q3 the median of the upper half.
+The _inclusive_ algorithm also uses the median to divide the ordered dataset into two halves, but if the sample is odd, it includes the median in both halves. Q1 is then the median of the lower half and Q3 the median of the upper half.
 
 ```python
 import plotly.express as px
@@ -92,7 +92,8 @@ fig.show()
 ```
 
 #### Difference Between Quartile Algorithms
-It can sometimes be difficult to see the difference between the linear, inclusive, and exclusive algorithms for computing quartiles. In the following example, the same dataset is visualized using each of the three different quartile computation algorithms. 
+
+It can sometimes be difficult to see the difference between the linear, inclusive, and exclusive algorithms for computing quartiles. In the following example, the same dataset is visualized using each of the three different quartile computation algorithms.
 
 ```python
 import plotly.express as px
@@ -103,7 +104,7 @@ df = pd.DataFrame(dict(
     linear=data,
     inclusive=data,
     exclusive=data
-)).melt(var_name="quartilemethod") 
+)).melt(var_name="quartilemethod")
 
 
 fig = px.box(df, y="value", facet_col="quartilemethod", color="quartilemethod",
@@ -204,7 +205,7 @@ fig.show()
 
 You can specify precomputed quartile attributes rather than using a built-in quartile computation algorithm.
 
-This could be useful if you have already pre-computed those values or if you need to use a different algorithm than the ones provided. 
+This could be useful if you have already pre-computed those values or if you need to use a different algorithm than the ones provided.
 
 ```python
 import plotly.graph_objects as go
@@ -217,9 +218,9 @@ fig.add_trace(go.Box(y=[
         [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
       ], name="Precompiled Quartiles"))
 
-fig.update_traces(q1=[ 1, 2, 3 ], median=[ 4, 5, 6 ], 
-                  q3=[ 7, 8, 9 ], lowerfence=[-1, 0, 1], 
-                  upperfence=[5, 6, 7], mean=[ 2.2, 2.8, 3.2 ], 
+fig.update_traces(q1=[ 1, 2, 3 ], median=[ 4, 5, 6 ],
+                  q3=[ 7, 8, 9 ], lowerfence=[-1, 0, 1],
+                  upperfence=[5, 6, 7], mean=[ 2.2, 2.8, 3.2 ],
                   sd=[ 0.2, 0.4, 0.6 ], notchspan=[ 0.2, 0.4, 0.6 ] )
 
 fig.show()

Diff for: doc/python/bubble-charts.md

@@ -38,7 +38,7 @@ jupyter:
 
 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://plot.ly/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 "tidy" data](/python/px-arguments/). The size of markers is set from the dataframe column given as the `size` parameter.
+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 "tidy" 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.
 
 ```python
 import plotly.express as px

Diff for: doc/python/bubble-maps.md

@@ -39,7 +39,7 @@ Plotly figures made with `px.scatter_geo`, `px.line_geo` or `px.choropleth` func
 
 ### Bubble map with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). With `px.scatter_geo`, each line of the dataframe is represented as a marker point. The column set as the `size` argument gives the size of markers.
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). With `px.scatter_geo`, each line of the dataframe is represented as a marker point. The column set as the `size` argument gives the size of markers.
 
 ```python
 import plotly.express as px

Diff for: doc/python/choropleth-maps.md

@@ -56,7 +56,7 @@ The GeoJSON data is passed to the `geojson` argument, and the data is passed int
 
 ### Choropleth Map with plotly.express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 #### GeoJSON with `feature.id`
 

Diff for: doc/python/distplot.md

@@ -37,7 +37,7 @@ jupyter:
 
 Several representations of statistical distributions are available in plotly, such as [histograms](https://plot.ly/python/histograms/), [violin plots](https://plot.ly/python/violin/), [box plots](https://plot.ly/python/box-plots/) (see [the complete list here](https://plot.ly/python/statistical-charts/)). It is also possible to combine several representations in the same plot.
 
-For example, the `plotly.express` function `px.histogram` can add a subplot with a different statistical representation than the histogram, given by the parameter `marginal`. [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+For example, the `plotly.express` function `px.histogram` can add a subplot with a different statistical representation than the histogram, given by the parameter `marginal`. [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 ```python
 import plotly.express as px

Diff for: doc/python/dot-plots.md

@@ -5,7 +5,7 @@ jupyter:
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@@ -35,11 +35,11 @@ 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)>)) 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` (for a tidy pandas DataFrame) or `go.Scatter`.
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 ```python
 import plotly.express as px
@@ -158,5 +158,4 @@ fig.show()
 
 ### Reference
 
-
 See https://plot.ly/python/reference/#scatter for more information and chart attribute options!

Diff for: doc/python/error-bars.md

@@ -35,7 +35,7 @@ jupyter:
 
 ### Error Bars with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). For functions representing 2D data points such as [`px.scatter`](https://plot.ly/python/line-and-scatter/), [`px.line`](https://plot.ly/python/line-charts/), [`px.bar`](https://plot.ly/python/bar-charts/) etc., error bars are given as a column name which is the value of the `error_x` (for the error on x position) and `error_y` (for the error on y position).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). For functions representing 2D data points such as [`px.scatter`](https://plot.ly/python/line-and-scatter/), [`px.line`](https://plot.ly/python/line-charts/), [`px.bar`](https://plot.ly/python/bar-charts/) etc., error bars are given as a column name which is the value of the `error_x` (for the error on x position) and `error_y` (for the error on y position).
 
 ```python
 import plotly.express as px

Diff for: doc/python/filled-area-plots.md

@@ -37,7 +37,7 @@ This example shows how to fill the area enclosed by traces.
 
 ## Filled area plot with plotly.express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 `px.area` creates a stacked area plot. Each filled area corresponds to one value of the column given by the `line_group` parameter.
 

Diff for: doc/python/funnel-charts.md

@@ -5,7 +5,7 @@ jupyter:
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@@ -24,23 +24,20 @@ jupyter:
     thumbnail: thumbnail/funnel.jpg
 ---
 
-
 ### Introduction
 
 Funnel charts are often used to represent data in different stages of a business process. It’s an important mechanism in Business Intelligence to identify potential problem areas of a process. For example, it’s used to observe the revenue or loss in a sales process for each stage, and displays values that are decreasing progressively. Each stage is illustrated as a percentage of the total of all values.
 
-
 ### Basic Funnel Plot with plotly.express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 With `px.funnel`, each row of the DataFrame is represented as a stage of the funnel.
 
-
 ```python
 import plotly.express as px
 data = dict(
-    number=[39, 27.4, 20.6, 11, 2], 
+    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()
@@ -76,6 +73,7 @@ fig.show()
 ```
 
 ### Setting Marker Size and Color
+
 This example uses [textposition](https://plot.ly/python/reference/#scatter-textposition) and [textinfo](https://plot.ly/python/reference/#funnel-textinfo) to determine information apears on the graph, and shows how to customize the bars.
 
 ```python
@@ -167,7 +165,6 @@ fig.show()
 
 #### Multiple Area Funnels
 
-
 ```python
 from plotly import graph_objects as go
 
@@ -204,4 +201,5 @@ fig.show()
 ```
 
 #### Reference
+
 See https://plot.ly/python/reference/#funnel and https://plot.ly/python/reference/#funnelarea for more information and chart attribute options!

Diff for: doc/python/histograms.md

@@ -41,7 +41,7 @@ bar, go to the [Bar Chart tutorial](/python/bar-charts/).
 
 ## Histogram with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 ```python
 import plotly.express as px

Diff for: doc/python/horizontal-bar-charts.md

@@ -37,7 +37,7 @@ See more examples of bar charts (including vertical bar charts) and styling opti
 
 ### Horizontal Bar Chart with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). For a horizontal bar char, use the `px.bar` function with `orientation='h'`.
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). For a horizontal bar char, use the `px.bar` function with `orientation='h'`.
 
 #### Basic Horizontal Bar Chart with Plotly Express
 

Diff for: doc/python/line-and-scatter.md

@@ -36,7 +36,7 @@ jupyter:
 
 ## Scatter plot with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 With `px.scatter`, each data point is represented as a marker point, which location is given by the `x` and `y` columns.
 

Diff for: doc/python/line-charts.md

@@ -37,7 +37,7 @@ jupyter:
 
 ### Line Plot with plotly.express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). With `px.line`, each data point is represented as a vertex (which location is given by the `x` and `y` columns) of a **polyline mark** in 2D space.
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). With `px.line`, each data point is represented as a vertex (which location is given by the `x` and `y` columns) of a **polyline mark** in 2D space.
 
 For more examples of line plots, see the [line and scatter notebook](https://plot.ly/python/line-and-scatter/).
 

Diff for: doc/python/linear-fits.md

@@ -37,7 +37,7 @@ jupyter:
 
 ### Linear fit trendlines with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 Plotly Express allows you to add [Ordinary Least](https://en.wikipedia.org/wiki/Ordinary_least_squares) Squares regression trendline to scatterplots with the `trendline` argument. In order to do so, you will need to install `statsmodels` and its dependencies. Hovering over the trendline will show the equation of the line and its R-squared value.
 

Diff for: doc/python/lines-on-maps.md

@@ -41,7 +41,7 @@ Plotly figures made with `px.scatter_geo`, `px.line_geo` or `px.choropleth` func
 
 ## Lines on Maps with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 ```python
 import plotly.express as px