Figures & SubPlots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figures and Subplots\n",
    "- figure - container thats holds all elements of plot(s)\n",
    "- subplot - appears within a rectangular grid within a figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_first_figure = plt.figure(\"My First Figure\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "subplot_1 = my_first_figure.add_subplot(2, 3, 1)\n",
    "subplot_6 = my_first_figure.add_subplot(2, 3, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.random.rand(50).cumsum(), 'k--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot_2 = my_first_figure.add_subplot(2,3,2)\n",
    "plt.plot(np.random.rand(50), 'go')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}