docs regen

Author: nicolaskruchten

docs/index.html

@@ -30,7 +30,7 @@ <h1 class="title"><code>plotly_express.colors.diverging</code></h1>
 </header>
 <section id="section-intro">
 <p>Diverging color scales are appropriate for continuous data that has a natural midpoint other otherwise informative special value, such as 0 altitude, or the boiling point
-of a liquid. The color scales in this module are mostly meant to be passed in as the <code>color_continuous_scale</code> argument to various functions, and to be used with the <code>color_continuous_midpoint</code> argument.</p>
+of a liquid.</p>
 </section>
 <section>
 </section>

docs/plotly_express/colors/diverging.html

@@ -56,7 +56,7 @@ <h2 class="section-title" id="header-submodules">Sub-modules</h2>
 </dd>
 <dt><code class="name"><a title="plotly_express.colors.qualitative" href="qualitative.html">plotly_express.colors.qualitative</a></code></dt>
 <dd>
-<section class="desc"><p>Qualitative color sequences are appropriate for data that has no natural ordering, such as categories, colors, names, countries etc. The color …</p></section>
+<section class="desc"><p>Qualitative color sequences are appropriate for data that has no natural ordering, such as categories, colors, names, countries etc.</p></section>
 </dd>
 <dt><code class="name"><a title="plotly_express.colors.sequential" href="sequential.html">plotly_express.colors.sequential</a></code></dt>
 <dd>

docs/plotly_express/colors/index.html

@@ -13,7 +13,7 @@
 gtag('config', 'UA-39373211-1');
 </script>
 <title>plotly_express.colors.qualitative API documentation</title>
-<meta name="description" content="Qualitative color sequences are appropriate for data that has no natural ordering, such as categories, colors, names, countries etc. The color …" />
+<meta name="description" content="Qualitative color sequences are appropriate for data that has no natural ordering, such as categories, colors, names, countries etc." />
 <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
 <link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
 <link href="https://fonts.googleapis.com/css?family=Dosis:500|Inconsolata|Open+Sans" rel="stylesheet">
@@ -29,7 +29,7 @@
 <h1 class="title"><code>plotly_express.colors.qualitative</code></h1>
 </header>
 <section id="section-intro">
-<p>Qualitative color sequences are appropriate for data that has no natural ordering, such as categories, colors, names, countries etc. The color sequences in this module are mostly meant to be passed in as the <code>color_discrete_sequence</code> argument to various functions.</p>
+<p>Qualitative color sequences are appropriate for data that has no natural ordering, such as categories, colors, names, countries etc.</p>
 </section>
 <section>
 </section>

docs/plotly_express/colors/qualitative.html

@@ -29,7 +29,7 @@
 <h1 class="title"><code>plotly_express.colors.sequential</code></h1>
 </header>
 <section id="section-intro">
-<p>Sequential color scales are appropriate for most continuous data, but in some cases it can be helpful to use a <a title="plotly_express.colors.diverging" href="diverging.html"><code>plotly_express.colors.diverging</code></a> or <a title="plotly_express.colors.cyclical" href="cyclical.html"><code>plotly_express.colors.cyclical</code></a> scale instead. The color scales in this module are mostly meant to be passed in as the <code>color_continuous_scale</code> argument to various functions.</p>
+<p>Sequential color scales are appropriate for most continuous data, but in some cases it can be helpful to use a <a title="plotly_express.colors.diverging" href="diverging.html"><code>plotly_express.colors.diverging</code></a> or <a title="plotly_express.colors.cyclical" href="cyclical.html"><code>plotly_express.colors.cyclical</code></a> scale instead.</p>
 </section>
 <section>
 </section>

docs/plotly_express/colors/sequential.html

@@ -45,7 +45,7 @@ <h2 class="section-title" id="header-functions">Functions</h2>
 <section class="desc"><p>Each row represents a country on a given year.</p>
 <p><a href="https://www.gapminder.org/data/">https://www.gapminder.org/data/</a></p>
 <h2 id="returns">Returns</h2>
-<p>A <code>pandas.DataFrame</code> with 1704 rows and the following columns: <code>['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap',
+<p>A <code>pandas.DataFrame</code> with 1704 rows and columns <code>['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap',
 'iso_alpha', 'iso_num']</code>.</p></section>
 </dd>
 <dt id="plotly_express.data.tips"><code class="name flex">
@@ -55,7 +55,7 @@ <h2 id="returns">Returns</h2>
 <section class="desc"><p>Each row represents a restaurant bill.</p>
 <p><a href="https://vincentarelbundock.github.io/Rdatasets/doc/reshape2/tips.html">https://vincentarelbundock.github.io/Rdatasets/doc/reshape2/tips.html</a></p>
 <h2 id="returns">Returns</h2>
-<p>A <code>pandas.DataFrame</code> with 244 rows and the following columns: <code>['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size']</code>.</p></section>
+<p>A <code>pandas.DataFrame</code> with 244 rows and columns <code>['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size']</code>.</p></section>
 </dd>
 <dt id="plotly_express.data.iris"><code class="name flex">
 <span>def <span class="ident">iris</span></span>(<span>)</span>
@@ -64,7 +64,7 @@ <h2 id="returns">Returns</h2>
 <section class="desc"><p>Each row represents a flower.</p>
 <p><a href="https://en.wikipedia.org/wiki/Iris_flower_data_set">https://en.wikipedia.org/wiki/Iris_flower_data_set</a></p>
 <h2 id="returns">Returns</h2>
-<p>A <code>pandas.DataFrame</code> with 150 rows and the following columns: <code>['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species',
+<p>A <code>pandas.DataFrame</code> with 150 rows and columns <code>['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species',
 'species_id']</code>.</p></section>
 </dd>
 <dt id="plotly_express.data.wind"><code class="name flex">
@@ -73,23 +73,23 @@ <h2 id="returns">Returns</h2>
 <dd>
 <section class="desc"><p>Each row represents a level of wind intensity in a cardinal direction.</p>
 <h2 id="returns">Returns</h2>
-<p>A <code>pandas.DataFrame</code> with 128 rows and the following columns: <code>['direction', 'strength', 'value']</code>.</p></section>
+<p>A <code>pandas.DataFrame</code> with 128 rows and columns <code>['direction', 'strength', 'value']</code>.</p></section>
 </dd>
 <dt id="plotly_express.data.election"><code class="name flex">
 <span>def <span class="ident">election</span></span>(<span>)</span>
 </code></dt>
 <dd>
 <section class="desc"><p>Each row represents voting results for an electoral district in the 2013 Montreal mayoral election.</p>
 <h2 id="returns">Returns</h2>
-<p>A <code>pandas.DataFrame</code> with 58 rows and the following columns: <code>['district', 'Coderre', 'Bergeron', 'Joly', 'total', 'winner', 'result']</code>.</p></section>
+<p>A <code>pandas.DataFrame</code> with 58 rows and columns <code>['district', 'Coderre', 'Bergeron', 'Joly', 'total', 'winner', 'result']</code>.</p></section>
 </dd>
 <dt id="plotly_express.data.carshare"><code class="name flex">
 <span>def <span class="ident">carshare</span></span>(<span>)</span>
 </code></dt>
 <dd>
 <section class="desc"><p>Each row represents the availability of car-sharing services near the centroid of a zone in Montreal.</p>
 <h2 id="returns">Returns</h2>
-<p>A <code>pandas.DataFrame</code> with 249 rows and the following columns: <code>['centroid_lat', 'centroid_lon', 'car_hours', 'peak_hour']</code>.</p></section>
+<p>A <code>pandas.DataFrame</code> with 249 rows and columns <code>['centroid_lat', 'centroid_lon', 'car_hours', 'peak_hour']</code>.</p></section>
 </dd>
 </dl>
 </section>