src/notebooks/how-to-add-plot-inside-plot.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Libraries\n",
        "\n",
        "First, we need to install the following libraries:\n",
        "\n",
        "- [matplotlib](https://python-graph-gallery.com/matplotlib/): for plotting\n",
        "- `mpl_toolkits`: for adding sub plots\n",
        "- [pandas](https://python-graph-gallery.com/pandas/): for data manipulation\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
        "\n",
        "import pandas as pd"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Dataset\n",
        "\n",
        "Let's create a simple dataset for this post:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "x = ['Group A', 'Group B', 'Group C', 'Group D']\n",
        "y = [38, 47, 25, 30]\n",
        "\n",
        "df = pd.DataFrame(\n",
        "    {'x': x,\n",
        "     'y': y}\n",
        ")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simple chart\n",
        "\n",
        "Here is a simple [barplot](https://python-graph-gallery.com/barplot/) using `matplotlib`:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "ax.bar(df['x'], df['y'])\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Sub plot\n",
        "\n",
        "Having a smaller plot inside a bigger one is a good way to **show more information**, either for a legend or a zoom-in.\n",
        "\n",
        "In practice, we add an `Axes` object (which is **basically a plot**) inside another `Axes` object. This is done using the `inset_axes()` function from the `mpl_toolkits` library.\n",
        "\n",
        "For this, we have to define the **parent axes** (the bigger plot) and the **size** of the smaller plot.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "ax.bar(df['x'], df['y'])\n",
        "\n",
        "sub_ax = inset_axes(\n",
        "    parent_axes=ax,\n",
        "    width=\"40%\",\n",
        "    height=\"30%\",\n",
        "    borderpad=1  # padding between parent and inset axes\n",
        ")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Add content in the sub plot\n",
        "\n",
        "Once the sub plot is created, we can work on it as if it was a regular axes object:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "\n",
        "# main plot\n",
        "ax.bar(df['x'], df['y'])\n",
        "\n",
        "# create inset plot\n",
        "sub_ax = inset_axes(\n",
        "    parent_axes=ax,\n",
        "    width=\"40%\",\n",
        "    height=\"30%\",\n",
        "    borderpad=1  # padding between parent and inset axes\n",
        ")\n",
        "\n",
        "# add content inside inset plot\n",
        "random_variable = [1, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5]\n",
        "sub_ax.hist(\n",
        "    random_variable,\n",
        "    bins=5,\n",
        "    color='red',\n",
        "    alpha=0.5\n",
        ")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Customize the sub plot\n",
        "\n",
        "We can easily customize all features of the inset plot:\n",
        "\n",
        "- **dimensions** by modifying the `width` and `height` parameters\n",
        "\n",
        "- **position** by modifying the `loc` parameter\n",
        "\n",
        "- **background color** by modifying the `facecolor` parameter\n",
        "\n",
        "- and many more...\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "\n",
        "# main plot\n",
        "ax.bar(df['x'], df['y'], color='#d5e9f5')\n",
        "\n",
        "# create inset plot\n",
        "sub_ax = inset_axes(\n",
        "    parent_axes=ax,\n",
        "    width=3.5,\n",
        "    height=1.5,\n",
        "    loc='center',\n",
        "    axes_kwargs={\n",
        "        'facecolor': '#beface'\n",
        "    }\n",
        ")\n",
        "\n",
        "# add content inside inset plot\n",
        "random_variable = [1, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5]\n",
        "sub_ax.hist(\n",
        "    random_variable,\n",
        "    bins=5,\n",
        "    color='red',\n",
        "    alpha=0.5\n",
        ")\n",
        "sub_ax.set_title('Histogram of Random Variable')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Going further\n",
        "\n",
        "This article explains how to create a sub plot in a chart using [matplotlib](https://python-graph-gallery.com/matplotlib/).\n",
        "\n",
        "For more examples of **advanced customization**, check out this [beautiful chart](https://python-graph-gallery.com/web-ridgeline-by-text/) where the legend is actually a sub plot and how to [add an image in a chart](https://python-graph-gallery.com/513-add-logo-matplotlib/).\n"
      ]
    }
  ],
  "metadata": {
    "chartType": "colors",
    "description": "In <a href='https://python-graph-gallery.com/matplotlib/'>Matplotlib</a>, you can enhance the informativeness and visual appeal of your charts by incorporating a plot <b>within</b> the main plot.<br/><br/>This step-by-step guide elucidates the process and offers insights into customizing the inset plot to suit your needs.",
    "family": "general",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "keywords": "inset, plot, matplotlib, python, axis, subplot, subaxes, subplots, add_subplot, add_axes",
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.5"
    },
    "seoDescription": "How to add plot inside another plot in matplotlib?",
    "slug": "how-to-add-plot-inside-plot",
    "title": "How to add plot inside another plot"
  },
  "nbformat": 4,
  "nbformat_minor": 4
}