long and wide

Author: nicolaskruchten

doc/python/2D-Histogram.md

@@ -42,7 +42,7 @@ A 2D histogram, also known as a density heatmap, is the 2-dimensional generaliza
 
 ## Density Heatmaps 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/) and produces [easy-to-style figures](/python/styling-plotly-express/). The Plotly Express function `density_heatmap()` can be used to produce density heatmaps.
+[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 Plotly Express function `density_heatmap()` can be used to produce density heatmaps.
 
 ```python
 import plotly.express as px

doc/python/2d-histogram-contour.md

@@ -40,7 +40,7 @@ A 2D histogram contour plot, also known as a density contour plot, is a 2-dimens
 
 ## Density Contours 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/) and produces [easy-to-style figures](/python/styling-plotly-express/). The Plotly Express function `density_contour()` can be used to produce density contours.
+[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 Plotly Express function `density_contour()` can be used to produce density contours.
 
 ```python
 import plotly.express as px

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/) and produces [easy-to-style figures](/python/styling-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/).
 
 Like the [2D scatter plot](https://plotly.com/python/line-and-scatter/) `px.scatter`, the 3D function `px.scatter_3d` plots individual data in three-dimensional space.
 

doc/python/bar-charts.md

@@ -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/) and produces [easy-to-style figures](/python/styling-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/).
 
 With `px.bar`, each row of the DataFrame is represented as a rectangular mark.
 

doc/python/box-plots.md

@@ -40,7 +40,7 @@ A [box plot](https://en.wikipedia.org/wiki/Box_plot) is a statistical representa
 
 ## 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/) and produces [easy-to-style figures](/python/styling-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.
 

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://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 "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.
+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.
 
 ```python
 import plotly.express as px

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/) 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.
+[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/). 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

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/) and produces [easy-to-style figures](/python/styling-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/).
 
 #### GeoJSON with `feature.id`
 
@@ -208,7 +208,6 @@ fig.show()
 ```python
 import plotly.graph_objects as go
 
-# Load data frame and tidy it.
 import pandas as pd
 df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')
 

doc/python/distplot.md

@@ -37,7 +37,7 @@ jupyter:
 
 Several representations of statistical distributions are available in plotly, such as [histograms](https://plotly.com/python/histograms/), [violin plots](https://plotly.com/python/violin/), [box plots](https://plotly.com/python/box-plots/) (see [the complete list here](https://plotly.com/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/) and produces [easy-to-style figures](/python/styling-plotly-express/).
+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 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

doc/python/dot-plots.md

@@ -37,9 +37,9 @@ jupyter:
 
 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`.
+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 "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-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/).
 
 ```python
 import plotly.express as px