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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter Plot with Marginal Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This time, we will make use of `GridSpec` to visualize a scatter plot with marginal histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Animal mass vs. longevity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('anage_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The given dataset is not complete. Filter the data so you end up with samples containing a body mass and a maximum longevity. Select all samples of the class Aves and with a body mass smaller than 20,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "longevity = 'Maximum longevity (yrs)'\n",
    "mass      = 'Body mass (g)'\n",
    "\n",
    "# Remove records with missing values.\n",
    "data = data[   np.isfinite(data[longevity]) \n",
    "             & np.isfinite(data[mass])\n",
    "           ]\n",
    "\n",
    "# Sort according to class.\n",
    "aves = data[data['Class'] == 'Aves']\n",
    "aves = data[data[mass] < 20000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a figure with a constrained layout. Create a `GridSpec` object of size 4x4. Create a scatter plot of size 3x3 and marginal histograms of size 1x3 and 3x1. Add labels and a figure title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the figure.\n",
    "fig = plt.figure(figsize=(8, 8), dpi=150, \n",
    "                 constrained_layout=True)\n",
    "\n",
    "# Create a GridSpec object.\n",
    "gs = fig.add_gridspec(4, 4)\n",
    "\n",
    "# Specify subplots.\n",
    "histx_ax   = fig.add_subplot(gs[0, :-1])\n",
    "histy_ax   = fig.add_subplot(gs[1:, -1])\n",
    "scatter_ax = fig.add_subplot(gs[1:, :-1])\n",
    "                             \n",
    "# Create the plots.\n",
    "scatter_ax.scatter(aves[mass], aves[longevity])\n",
    "histx_ax.hist(aves[mass], bins=20, density=True)\n",
    "histx_ax.set_xticks([])\n",
    "histy_ax.hist(aves[longevity], bins=20, \n",
    "              density=True, orientation='horizontal')\n",
    "histy_ax.set_yticks([])\n",
    "                             \n",
    "# Add labels.\n",
    "plt.xlabel('Body mass in grams')\n",
    "plt.ylabel('Maximum longevity in years')\n",
    "   \n",
    "# Add the super title.\n",
    "plt.suptitle('Scatter plot with marginal histograms')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adapted from ***Data Visualization with Python***, by Mario Döbler and Tim Großmann, Packt 2019, ISBN 978-1-78995-646-7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional material (c) 2024 by Ronald Mak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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