plot_subplot-horizontal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Horizontal arrangement of subplots\n",
    "==================================\n",
    "\n",
    "An example showing horizontal arrangement of subplots with matplotlib.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.text(0.5, 0.5, 'subplot(2,1,1)', ha='center', va='center',\n",
    "        size=24, alpha=.5)\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.text(0.5, 0.5, 'subplot(2,1,2)', ha='center', va='center',\n",
    "        size=24, alpha=.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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