diff --git a/src/components/AllChartsModal.js b/src/components/AllChartsModal.js
index 88e144118..9dc215415 100644
--- a/src/components/AllChartsModal.js
+++ b/src/components/AllChartsModal.js
@@ -16,6 +16,7 @@ export default function AllChartsModal({ show, handleClose }) {
         ![
           'matplotlib',
           'drawarrow',
+          'pyfonts',
           'seaborn',
           'plotnine',
           'pypalettes',
diff --git a/src/functions/plot.js b/src/functions/plot.js
index f43cb3aa6..b1b2c39d8 100644
--- a/src/functions/plot.js
+++ b/src/functions/plot.js
@@ -31,6 +31,64 @@ const docUrl =
 //
 //
 const param1 = {
+  name: 'x',
+  description: <p>Positions of the data points along the x-axis.</p>,
+  basicUsage: `
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+
+fig, ax = plt.subplots()
+ax.plot(x=x, y=y)
+plt.show()
+`.trim(),
+  type: 'array-like',
+  howToUse: (
+    <p>
+      The <code>x</code> and <code>y</code> arrays must have the same length and
+      contain numerical values.
+    </p>
+  ),
+  img: '120_Basic_lineplot2',
+  post: '120-line-chart-with-matplotlib',
+};
+//
+//
+//
+//
+//
+const param2 = {
+  name: 'y',
+  description: <p>Positions of the data points along the y-axis.</p>,
+  basicUsage: `
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.linspace(0, 10, 100)
+y = np.sin(x)
+
+fig, ax = plt.subplots()
+ax.plot(x=x, y=y)
+plt.show()
+`.trim(),
+  type: 'array-like',
+  howToUse: (
+    <p>
+      The <code>x</code> and <code>y</code> arrays must have the same length and
+      contain numerical values.
+    </p>
+  ),
+  img: '120_Basic_lineplot2',
+  post: '120-line-chart-with-matplotlib',
+};
+//
+//
+//
+//
+//
+const param3 = {
   name: 'color',
   description: <p>Sets the color of the line or markers in the plot.</p>,
   basicUsage: `
@@ -47,14 +105,14 @@ plt.show()
   type: 'string',
   howToUse: <div>{colorHowToUse}</div>,
   img: 'tuto-plot-1-square',
-  post: 'matplotlib-line-plot',
+  post: '121-line-chart-customization',
 };
 //
 //
 //
 //
 //
-const param2 = {
+const param4 = {
   name: 'linestyle',
   description: <div>{linestyleDescription}</div>,
   basicUsage: `
@@ -71,14 +129,14 @@ plt.show()
   type: 'string',
   howToUse: <div>{linestyleHowToUse}</div>,
   img: 'tuto-plot-2-square',
-  post: 'matplotlib-line-styles',
+  post: '121-line-chart-customization',
 };
 //
 //
 //
 //
 //
-const param3 = {
+const param5 = {
   name: 'marker',
   description: <p>Specifies the marker style for the data points.</p>,
   basicUsage: `
@@ -104,14 +162,14 @@ plt.show()
     </p>
   ),
   img: 'tuto-plot-3-square',
-  post: 'matplotlib-markers',
+  post: '121-line-chart-customization',
 };
 //
 //
 //
 //
 //
-const param4 = {
+const param6 = {
   name: 'label',
   description: <p>Sets the label for the plot, which is used in the legend.</p>,
   basicUsage: `
@@ -135,14 +193,14 @@ plt.show()
     </p>
   ),
   img: 'tuto-plot-4-square',
-  post: 'matplotlib-legend',
+  post: '585-legend-for-categorical-data-matplotlib',
 };
 //
 //
 //
 //
 //
-const param5 = {
+const param7 = {
   name: 'alpha',
   description: <p>Controls the transparency of the plot elements.</p>,
   basicUsage: `
@@ -159,14 +217,14 @@ plt.show()
   type: 'float',
   howToUse: <div>{alphaHowToUse}</div>,
   img: 'tuto-plot-5-square',
-  post: 'matplotlib-transparency',
+  post: '121-line-chart-customization',
 };
 //
 //
 //
 //
 //
-const param6 = {
+const param8 = {
   name: 'linewidth',
   description: <div>{linewidthDescription}</div>,
   basicUsage: `
@@ -183,14 +241,14 @@ plt.show()
   type: 'float',
   howToUse: <div>{linewidthHowToUse}</div>,
   img: 'tuto-plot-6-square',
-  post: 'matplotlib-line-width',
+  post: '121-line-chart-customization',
 };
 //
 //
 //
 //
 //
-const param7 = {
+const param9 = {
   name: 'zorder',
   description: <div>{zorderDescription}</div>,
   basicUsage: `
@@ -209,12 +267,22 @@ plt.show()
   type: 'int',
   howToUse: <div>{zorderHowToUse}</div>,
   img: 'tuto-plot-7-square',
-  post: 'matplotlib-zorder',
+  post: '121-line-chart-customization',
 };
 
 export const plot = {
   name,
   description,
   docUrl,
-  parameters: [param1, param2, param3, param4, param5, param6, param7],
+  parameters: [
+    param1,
+    param2,
+    param3,
+    param4,
+    param5,
+    param6,
+    param7,
+    param8,
+    param9,
+  ],
 };
diff --git a/src/notebooks/pyfonts.ipynb b/src/notebooks/pyfonts.ipynb
deleted file mode 100644
index 0d41bb5e5..000000000
--- a/src/notebooks/pyfonts.ipynb
+++ /dev/null
@@ -1,232 +0,0 @@
-{
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "id": "e758e508-0bd1-41d6-844a-8d59c9a10e71",
-      "metadata": {},
-      "source": [
-        "## Libraries\n",
-        "\n",
-        "First, you need to **install** `pyfonts` with the following command: `pip install pyfonts`.\n",
-        "\n",
-        "With `pyfonts` you can easily change the font in your [matplotlib](/matplotlib) charts. It was created by [Joseph Barbier](https://barbierjoseph.com/).\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "9de62754-740e-42ca-af5f-6f40e4f27236",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "from pyfonts import load_font"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "id": "eb96d3d0-596c-444e-bdba-e85d99780ef0",
-      "metadata": {},
-      "source": [
-        "## Quick start\n",
-        "\n",
-        "The easiest way to get started is to use a font available on [Google font repo](/~https://github.com/google/fonts/) and add `?raw=true` at the end of the url.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "7d742b94-e3dc-4568-8b5d-41e249d8fe4a",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 3000x1800 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# load font\n",
-        "font = load_font(\n",
-        "   font_url=\"/~https://github.com/google/fonts/blob/main/apache/ultra/Ultra-Regular.ttf?raw=true\"\n",
-        ")\n",
-        "\n",
-        "# check how the font looks on a minimalist example\n",
-        "fig, ax = plt.subplots(figsize=(10, 6), dpi=300)\n",
-        "ax.text(\n",
-        "   x=0.5,\n",
-        "   y=0.5,\n",
-        "   s=f\"What an easy way to load fonts, isn't it?\",\n",
-        "   font=font,\n",
-        "   fontsize=20,\n",
-        "   ha=\"center\",\n",
-        ")\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 1- How to find fonts\n",
-        "\n",
-        "There are many fonts available on the web. The **easiest way** to find one is to follow these steps:\n",
-        "\n",
-        "- Browse [Google Font website](https://fonts.google.com/) to find a font that you like. Let's say you want to use [Amaranth](https://fonts.google.com/specimen/Amaranth?query=amaranth).\n",
-        "- Go to [Google font github repository](/~https://github.com/google/fonts/tree/main/ofl) and type the name of your desired font in the search bar. We find that Urbanist font file in **Bold** is named `/~https://github.com/google/fonts/blob/main/ofl/amaranth/Amaranth-Bold.ttf`.\n",
-        "- copy the url and add `?raw=true` at the end, which gives us `/~https://github.com/google/fonts/blob/main/ofl/amaranth/Amaranth-Bold.ttf?raw=true`\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 2 - Load a font in matplotlib\n",
-        "\n",
-        "Use `load_font()` by passing the font's URL.\n",
-        "\n",
-        "Then, provide the `load_font()` output directly to any [matplotlib](/matplotlib) function that accepts a font, such as `ax.text()`.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 3000x1800 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# load font\n",
-        "font = load_font(\n",
-        "   font_url=\"/~https://github.com/google/fonts/blob/main/apache/ultra/Ultra-Regular.ttf?raw=true\"\n",
-        ")\n",
-        "\n",
-        "# check how the font looks on a minimalist example\n",
-        "fig, ax = plt.subplots(figsize=(10, 6), dpi=300)\n",
-        "ax.text(\n",
-        "   x=0.5,\n",
-        "   y=0.5,\n",
-        "   s=f\"What an easy way to load fonts, isn't it?\",\n",
-        "   font=font,\n",
-        "   fontsize=20,\n",
-        "   ha=\"center\",\n",
-        ")\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "id": "f1373fd8-b869-4313-828c-b4bdd3c46b32",
-      "metadata": {},
-      "source": [
-        "## Different weight and style\n",
-        "\n",
-        "When you load a font, **you don't load all its extensions**: bold, italic, thin etc, but only the one from the url. If you want to be able to use a font and its **bold** version, for example, you need to load both fonts:\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "72b189b6-c30e-4909-90cf-7a2393c36c0e",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<Figure size 3000x1800 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "font = load_font(\n",
-        "   \"/~https://github.com/google/fonts/blob/main/ofl/amaranth/Amaranth-Regular.ttf?raw=true\"\n",
-        ")\n",
-        "bold_font = load_font(\n",
-        "   \"/~https://github.com/google/fonts/blob/main/ofl/amaranth/Amaranth-Bold.ttf?raw=true\"\n",
-        ")\n",
-        "\n",
-        "fig, ax = plt.subplots(figsize=(10, 6), dpi=300)\n",
-        "ax.text(\n",
-        "   x=0.5,\n",
-        "   y=0.5,\n",
-        "   s=f\"Congrats, you now have a cool font!\",\n",
-        "   font=font,\n",
-        "   fontsize=20,\n",
-        "   ha=\"center\",\n",
-        ")\n",
-        "ax.text(x=0.5, y=0.3, s=f\"And now it's bold\", font=bold_font, fontsize=20, ha=\"center\")\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "id": "e281fb0f-5aa2-463f-8dea-06253c008cd0",
-      "metadata": {},
-      "source": [
-        "## Going further\n",
-        "\n",
-        "You might be interested in:\n",
-        "\n",
-        "- the [official github repo of pyfonts](/~https://github.com/JosephBARBIERDARNAL/pyfonts) (give it a star!)\n",
-        "- how to [draw arrows in matplotlib](/drawarrow)\n",
-        "- how to create [beautiful annotation in matplotlib](/advanced-custom-annotations-matplotlib)\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "chartType": "table",
-    "description": "<a href='/~https://github.com/JosephBARBIERDARNAL/pyfonts'>PyFonts</a> is a Python library for <b>loading fonts</b> in matplotlib charts. It provides a simple way to access any fonts you can imagine.<br/><br/>This post will teach you how to use this library and all its <b>main features</b>, with concrete example and reproducible code.",
-    "family": "ranking",
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "keywords": "text, plot, matplotlib, annotation, color, customize, fonts, FontProperties",
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.13"
-    },
-    "seoDescription": "How to use the pyfonts library to customize fonts in matplotlib",
-    "slug": "pyfonts",
-    "title": "Introduction to PyFonts"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
diff --git a/src/notebooks/web-minimalist-area-chart.ipynb b/src/notebooks/web-area-chart-with-different-colors-for-positive-and-negative-values.ipynb
similarity index 99%
rename from src/notebooks/web-minimalist-area-chart.ipynb
rename to src/notebooks/web-area-chart-with-different-colors-for-positive-and-negative-values.ipynb
index 97251b2bc..c095ae710 100644
--- a/src/notebooks/web-minimalist-area-chart.ipynb
+++ b/src/notebooks/web-area-chart-with-different-colors-for-positive-and-negative-values.ipynb
@@ -180,11 +180,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Change colors\n",
+        "## 2 colors for positive and negative values\n",
         "\n",
         "Since there’s no built-in method to change the color based on a value, so we create **two area charts**:\n",
         "\n",
-        "- First chart: spans from the start date to `year_index` (the year when the value drops below 0).\n",
+        "- First chart: spans from the start date to `year_index` (the year is when the value drops below 0, and we find this info with the `flag` column).\n",
         "- Second chart: spans from `year_index` to the end date.\n",
         "\n",
         "Apply different colors to each chart, and done!\n"
@@ -385,7 +385,7 @@
         "ax_text(x=1978, y=-210000, s=s, font=font, fontsize=8, ha='left', highlight_textprops=[{'font':boldfont}])\n",
         "ax_arrow(tail_position=(1995, -300000), head_position=(2009, -50000), ax=ax, **arrow_props)\n",
         "\n",
-        "plt.savefig('../../static/graph/web-minimalist-area-chart.png', dpi=300, bbox_inches='tight')\n",
+        "plt.savefig('../../static/graph/web-area-chart-with-different-colors-for-positive-and-negative-values.png', dpi=300, bbox_inches='tight')\n",
         "plt.show()"
       ]
     },
@@ -405,14 +405,14 @@
   ],
   "metadata": {
     "chartType": "area",
-    "description": "This post guides you through reproducing an <a href='/area-plot'>area chart</a> with a <b>minimalist style</b>, two colors, and clean annotations. The process starts with a <b>basic example</b> and ends with a polished, <b>refined chart</b>.",
+    "description": "This post guides you through reproducing an <a href='/area-plot'>area chart</a> with two colors for positive and negative values, and clean annotations. The process starts with a <b>basic example</b> and ends with a polished, <b>refined chart</b>. Every step provides explanations and reproducible code.",
     "family": "evolution",
     "kernelspec": {
       "display_name": "Python 3 (ipykernel)",
       "language": "python",
       "name": "python3"
     },
-    "keywords": "area, chart, seaborn, matplotlib, python, arrows, annotations, highlight_text, drawarrow",
+    "keywords": "area, chart, seaborn, matplotlib, python, arrows, annotations, highlight_text, drawarrow, pyfonts",
     "language_info": {
       "codemirror_mode": {
         "name": "ipython",
@@ -425,8 +425,8 @@
       "pygments_lexer": "ipython3",
       "version": "3.10.13"
     },
-    "seoDescription": "How to create an area chart with 2 distincts colors in Python matplotlib with a nice minimalist style",
-    "slug": "web-minimalist-area-chart",
+    "seoDescription": "How to create an area chart with 2 distinct colors depending on whether the values are positive or negative.",
+    "slug": "web-area-chart-with-different-colors-for-positive-and-negative-values",
     "title": "Minimalist area chart"
   },
   "nbformat": 4,
diff --git a/src/notebooks/web-lollipop-with-colormap-and-arrow.ipynb b/src/notebooks/web-lollipop-with-colormap-and-arrow.ipynb
index f4520df51..059a58cd9 100644
--- a/src/notebooks/web-lollipop-with-colormap-and-arrow.ipynb
+++ b/src/notebooks/web-lollipop-with-colormap-and-arrow.ipynb
@@ -387,9 +387,11 @@
       "source": [
         "## Going further\n",
         "\n",
-        "This article explains how to reproduce a [map with scatter plot](/bubble-map) with annotations, colormap and nice features.\n",
+        "You might be interested in:\n",
         "\n",
-        "For more examples of **advanced customization** in map, check out [choropleth map of America](https://python-graph-gallery.com/choropleth-map-geopandas-python/). Also, you might be interested in [creating interactive map](https://python-graph-gallery.com/292-choropleth-map-with-folium/) with Folium.\n"
+        "- how to create a [dumbell chart](/web-lollipop-plot-with-python-mario-kart-64-world-records)\n",
+        "- how to use a [lollipop for a time series](/web-lollipop-plot-with-python-the-office)\n",
+        "- this tuto about [circular lollipop](/web-circular-lollipop-plot-with-matplotlib)\n"
       ]
     }
   ],
diff --git a/src/pages/area-plot.js b/src/pages/area-plot.js
index 58d6a6717..e37fb2e6f 100644
--- a/src/pages/area-plot.js
+++ b/src/pages/area-plot.js
@@ -197,9 +197,15 @@ export default function AreaPlot() {
             </Link>
           </Col>
           <Col xs={12} md={6}>
-            <Link to={'/web-minimalist-area-chart'}>
+            <Link
+              to={
+                '/web-area-chart-with-different-colors-for-positive-and-negative-values'
+              }
+            >
               <ChartImage
-                imgName={'web-minimalist-area-chart-square'}
+                imgName={
+                  'web-area-chart-with-different-colors-for-positive-and-negative-values-square'
+                }
                 caption={
                   'A minimalist area chart with clean annotations and style'
                 }
diff --git a/src/pages/color-palette-finder.js b/src/pages/color-palette-finder.js
index 35f63667a..15896c692 100644
--- a/src/pages/color-palette-finder.js
+++ b/src/pages/color-palette-finder.js
@@ -78,6 +78,7 @@ export default function Colors({ location }) {
       <TitleAndDescription
         title="Python Color Palette Finder"
         description={chartDescription}
+        chartType={'pypalettes'}
         isRaptiveEnabled={false}
       />
 
diff --git a/src/pages/drawarrow.js b/src/pages/drawarrow.js
index dd9d04bcc..05e4538c9 100644
--- a/src/pages/drawarrow.js
+++ b/src/pages/drawarrow.js
@@ -216,9 +216,11 @@ export default function Post() {
             </Link>
           </Col>
           <Col xs={12} md={4}>
-            <Link to="/web-minimalist-area-chart">
+            <Link to="/web-area-chart-with-different-colors-for-positive-and-negative-values">
               <ChartImage
-                imgName={'web-minimalist-area-chart-square'}
+                imgName={
+                  'web-area-chart-with-different-colors-for-positive-and-negative-values-square'
+                }
                 caption={'Minimalist style area chart'}
               />
             </Link>
@@ -239,7 +241,7 @@ export default function Post() {
                 {' '}
                 official github repo of drawarrow
               </a>{' '}
-              (give it a star!)
+              (⭐ give it a star!)
             </li>
             <li>
               how to draw an{' '}
diff --git a/src/pages/pyfonts.js b/src/pages/pyfonts.js
new file mode 100644
index 000000000..65689d98d
--- /dev/null
+++ b/src/pages/pyfonts.js
@@ -0,0 +1,337 @@
+import React from 'react';
+import TitleAndDescription from '../components/TitleAndDescription';
+import Layout from '../components/Layout';
+import Container from 'react-bootstrap/Container';
+import Contact from '../components/Contact';
+import Row from 'react-bootstrap/Row';
+import ChartFamilySection from '../components/ChartFamilySection';
+import { Link } from 'gatsby';
+import { Col } from 'react-bootstrap';
+import CodeChunk from '../components/CodeChunk';
+import Spacing from '../components/Spacing';
+import ChartImage from '../components/ChartImage';
+import { SEO } from '../components/SEO';
+
+const chartDescription = (
+  <>
+    <p>
+      <a href="/~https://github.com/JosephBARBIERDARNAL/pyfonts">PyFonts</a> is a
+      library that allows to load easily any font from the web and use it in
+      your matplotlib charts.
+    </p>
+    <p>
+      It was created by <a href="https://barbierjoseph.com/">Joseph Barbier</a>{' '}
+      in order to <b>simplify</b> the process to loading fonts in matplotlib and{' '}
+      <b>remove</b> the need to install them on your computer.
+    </p>
+  </>
+);
+
+export const Head = () => (
+  <SEO
+    title="PyFonts"
+    seoDescription="An overview of the PyFonts library in Python"
+  />
+);
+
+export default function Post() {
+  return (
+    <Layout title="PyFonts">
+      <TitleAndDescription
+        title="PyFonts: a simple way to load fonts for matplotlib"
+        description={chartDescription}
+        chartType={'pyfonts'}
+      />
+
+      <Container>
+        <h2 id="Quick">&#9201; Quick start</h2>
+        <p>
+          Before using pyfonts you need to <b>install</b> it. This can easily be
+          done with <code>pip</code>:
+        </p>
+        <br />
+        <CodeChunk>{codeInstall}</CodeChunk>
+        <br />
+        <Row className="align-items-center">
+          <Col md={6}>
+            <p>
+              pyfonts provides <b>1 simple function</b>:<code>load_font()</code>
+              : load a font from the web (Github) and return a matplotlib font
+              object.
+            </p>
+          </Col>
+          <Col md={6}>
+            <ChartImage
+              imgName="quickstart-pyfonts"
+              caption="Basic use case of pyfonts"
+            />
+          </Col>
+        </Row>
+        <CodeChunk>{quickCode}</CodeChunk>
+      </Container>
+
+      <Spacing />
+
+      <Container>
+        <h2>1- How to find fonts</h2>
+
+        <p>
+          There are many fonts available on the web. The{' '}
+          <strong>easiest way</strong> to find one is to follow these steps:
+        </p>
+
+        <ul>
+          <li>
+            Browse <a href="https://fonts.google.com/">Google Font website</a>{' '}
+            to find a font that you like. Let's say you want to use{' '}
+            <a href="https://fonts.google.com/specimen/Amaranth?query=amaranth">
+              Amaranth
+            </a>
+            .
+          </li>
+          <li>
+            Go to the{' '}
+            <a href="/~https://github.com/google/fonts/tree/main/ofl">
+              Google font GitHub repository
+            </a>{' '}
+            and type the name of your desired font in the search bar. We find
+            that <i>Amaranth font file</i> in <strong>Bold</strong> is named{' '}
+            <code>
+              /~https://github.com/google/fonts/blob/main/ofl/amaranth/Amaranth-Bold.ttf
+            </code>
+            .
+          </li>
+          <li>
+            Copy the URL and add <code>?raw=true</code> at the end, which gives
+            us{' '}
+            <code>
+              /~https://github.com/google/fonts/blob/main/ofl/amaranth/Amaranth-Bold.ttf?raw=true
+            </code>
+            .
+          </li>
+        </ul>
+      </Container>
+
+      <Spacing />
+
+      <Container>
+        <h2>2 - Load a font in matplotlib</h2>
+        <Row className="align-items-center">
+          <Col md={6}>
+            <p>
+              Use <code>load_font()</code> by passing the font's URL.
+            </p>
+            <p>
+              Then, provide the <b>output</b> of <code>load_font()</code>{' '}
+              directly to any <a href="/matplotlib">matplotlib</a> function that
+              accepts a font, such as <code>ax.text()</code>.
+            </p>
+          </Col>
+          <Col md={6}>
+            <ChartImage
+              imgName="usecase-pyfonts-1-square"
+              caption="how to load a font with pyfonts"
+            />
+          </Col>
+          <CodeChunk>{useFontCode}</CodeChunk>
+        </Row>
+      </Container>
+
+      <Spacing />
+
+      <Container>
+        <h2>Different weight and style</h2>
+        <Row className="align-items-center">
+          <Col md={7}>
+            <p>
+              When you load a font, <b>you don't load all its extensions</b>:
+              bold, italic, thin etc, but only the one from the url. If you want
+              to be able to use a font and its <b>bold</b> version, for example,
+              you need to load both fonts:
+            </p>
+          </Col>
+          <Col md={5}>
+            <ChartImage
+              imgName="usecase-pyfonts-2-square"
+              caption="Combine a normal font and a bold font with pyfonts"
+            />
+          </Col>
+        </Row>
+        <CodeChunk>{useFontCode2}</CodeChunk>
+      </Container>
+
+      <Spacing />
+
+      <Container>
+        <h2>Gallery of examples</h2>
+        <p>
+          Here are some examples of what you can do with <code>PyFonts</code>.
+          Click on the images to see the code.
+        </p>
+        <Row>
+          <Col xs={12} md={4}>
+            <Link to="/web-waffle-chart-for-time-series">
+              <ChartImage
+                imgName={'web-waffle-chart-for-time-series-square'}
+                caption={'Waffle chart with nice theme and fonts'}
+              />
+            </Link>
+          </Col>
+          <Col xs={12} md={4}>
+            <Link to="/web-lollipop-with-colormap-and-arrow">
+              <ChartImage
+                imgName={'web-lollipop-with-colormap-and-arrow-square'}
+                caption={'Lollipop chart with arrows'}
+              />
+            </Link>
+          </Col>
+          <Col xs={12} md={4}>
+            <Link to="/web-area-chart-with-different-colors-for-positive-and-negative-values">
+              <ChartImage
+                imgName={
+                  'web-area-chart-with-different-colors-for-positive-and-negative-values-square'
+                }
+                caption={'Minimalist style area chart'}
+              />
+            </Link>
+          </Col>
+        </Row>
+      </Container>
+
+      <Spacing />
+
+      <Container>
+        <h2>Going further</h2>
+        <p>
+          You might be interested in
+          <ul>
+            <li>
+              The{' '}
+              <a href="/~https://github.com/JosephBARBIERDARNAL/pyfonts">
+                {' '}
+                official github repo of pyfonts
+              </a>{' '}
+              (⭐ give it a star!)
+            </li>
+            <li>
+              Learn more on{' '}
+              <a href="custom-fonts-in-matplotlib">
+                how matplotlib handle fonts
+              </a>
+            </li>
+            <li>
+              How to <a href="/drawarrow">draw arrows in matplotlib</a>
+            </li>
+            <li>
+              How to create{' '}
+              <a href="/advanced-custom-annotations-matplotlib">
+                beautiful annotation in matplotlib
+              </a>
+            </li>
+          </ul>
+        </p>
+      </Container>
+
+      <Spacing />
+
+      <div className="greySection" id="related">
+        <Container>
+          <ChartFamilySection chartFamily="general" />
+        </Container>
+      </div>
+
+      <Spacing />
+
+      <Container>
+        <Contact />
+      </Container>
+
+      <Spacing />
+    </Layout>
+  );
+}
+
+const quickCode = `# Load pyfonts and matplotlib
+import matplotlib.pyplot as plt
+from pyfonts import load_font
+
+# load font from Github
+font = load_font(
+   font_url="/~https://github.com/google/fonts/blob/main/apache/ultra/Ultra-Regular.ttf?raw=true"
+)
+
+# Create a plot with an annotation that uses the loaded font
+fig, ax = plt.subplots(figsize=(10, 6), dpi=300)
+ax.text(
+   x=0.5,
+   y=0.5,
+   s=f"What an easy way to load fonts, isn't it?",
+   font=font,
+   fontsize=20,
+   ha="center",
+)
+plt.show()
+`;
+
+const axesCode = `import matplotlib.pyplot as plt
+from pyfonts import ax_arrow
+
+fig, ax = plt.subplots(figsize=(10,10))
+
+ax.scatter(x=[1, 5, 10], y=[5, 12, 3], s=300)
+ax_arrow(
+   tail_position=(2, 8),
+   head_position=(9.5, 3),
+   ax=ax,
+   color="green",
+   tail_width=10,
+   head_width=30,
+   head_length=30,
+   radius=0.4
+)
+plt.show()
+`;
+
+const useFontCode = `import matplotlib.pyplot as plt
+from pyfonts import load_font
+
+font = load_font(
+  "/~https://github.com/google/fonts/blob/main/ofl/amaranth/Amaranth-Bold.ttf?raw=true"
+)
+
+fig, ax = plt.subplots(figsize=(10, 10), dpi=300)
+ax.text(
+    x=0.5,
+    y=0.5,
+    s=f"Amaranth font\\nwith PyFonts!!!",
+    font=font,
+    fontsize=50,
+    ha="center",
+)
+plt.show()
+`;
+
+const useFontCode2 = `import matplotlib.pyplot as plt
+from pyfonts import load_font
+
+font = load_font(
+   "/~https://github.com/google/fonts/blob/main/ofl/amaranth/Amaranth-Regular.ttf?raw=true"
+)
+bold_font = load_font(
+   "/~https://github.com/google/fonts/blob/main/ofl/amaranth/Amaranth-Bold.ttf?raw=true"
+)
+
+fig, ax = plt.subplots(figsize=(6, 6), dpi=300)
+ax.text(
+   x=0.5,
+   y=0.5,
+   s=f"Congrats, you now have a cool font!",
+   font=font,
+   fontsize=20,
+   ha="center",
+)
+ax.text(x=0.5, y=0.3, s=f"And now it's bold", font=bold_font, fontsize=25, ha="center")
+plt.show()
+`;
+
+const codeInstall = `pip install pyfonts`;
diff --git a/src/util/list-of-best-charts.js b/src/util/list-of-best-charts.js
index bebdd1037..975149028 100644
--- a/src/util/list-of-best-charts.js
+++ b/src/util/list-of-best-charts.js
@@ -27,10 +27,10 @@ export const listOfBestCharts = [
     author: 'Benjamin Nowak',
   },
   {
-    link: 'web-minimalist-area-chart',
+    link: 'web-area-chart-with-different-colors-for-positive-and-negative-values',
     title: "Japan's population evolution",
     description: "Evolution of Japan's population between 1952 and 2024.",
-    img: 'web-minimalist-area-chart-square.png',
+    img: 'web-area-chart-with-different-colors-for-positive-and-negative-values-square.png',
     alt: 'Area chart',
     author: 'Joseph Barbier',
   },
diff --git a/src/util/sectionDescriptions.js b/src/util/sectionDescriptions.js
index 9e8d2608f..4f763e768 100644
--- a/src/util/sectionDescriptions.js
+++ b/src/util/sectionDescriptions.js
@@ -399,6 +399,22 @@ export const chartTypesInfo = [
     pythonURL: 'https://python-graph-gallery.com/drawarrow/',
     label: 'DrawArrow',
   },
+  {
+    id: 'pyfonts',
+    family: 'package',
+    logo: 'pyfonts',
+    dataToVizURL: '',
+    pythonURL: 'https://python-graph-gallery.com/pyfonts/',
+    label: 'PyFonts',
+  },
+  {
+    id: 'pypalettes',
+    family: 'package',
+    logo: 'pypalettes',
+    dataToVizURL: '',
+    pythonURL: 'https://python-graph-gallery.com/color-palette-finder/',
+    label: 'PyPalettes',
+  },
   {
     id: 'animation',
     family: 'general',
diff --git a/static/graph/quickstart-pyfonts-square.png b/static/graph/quickstart-pyfonts-square.png
new file mode 100644
index 000000000..400d2c204
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diff --git a/static/graph/quickstart-pyfonts.png b/static/graph/quickstart-pyfonts.png
new file mode 100644
index 000000000..d31a3b334
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diff --git a/static/graph/usecase-pyfonts-1-square.png b/static/graph/usecase-pyfonts-1-square.png
new file mode 100644
index 000000000..e78119f1d
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diff --git a/static/graph/usecase-pyfonts-1.png b/static/graph/usecase-pyfonts-1.png
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index 000000000..2c9f43216
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diff --git a/static/graph/usecase-pyfonts-2-square.png b/static/graph/usecase-pyfonts-2-square.png
new file mode 100644
index 000000000..243d9c7e5
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diff --git a/static/graph/usecase-pyfonts-2.png b/static/graph/usecase-pyfonts-2.png
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index 000000000..7b9420413
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diff --git a/static/graph/web-minimalist-area-chart-square.png b/static/graph/web-area-chart-with-different-colors-for-positive-and-negative-values-square.png
similarity index 100%
rename from static/graph/web-minimalist-area-chart-square.png
rename to static/graph/web-area-chart-with-different-colors-for-positive-and-negative-values-square.png
diff --git a/static/graph/web-minimalist-area-chart.png b/static/graph/web-area-chart-with-different-colors-for-positive-and-negative-values.png
similarity index 100%
rename from static/graph/web-minimalist-area-chart.png
rename to static/graph/web-area-chart-with-different-colors-for-positive-and-negative-values.png
diff --git a/static/section/pyfonts.png b/static/section/pyfonts.png
new file mode 100644
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