13.7 Adding Annotations and Drawings on a Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/cover4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.7 Adding Annotations and Drawings on a Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import the required libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create two arrays, x and y, using numpy functions **np.arange()** and **np.array()**, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "x = np.arange(6)\n",
    "y = np.array([23,34,65,78,51,55])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we create a subplot using the function **plt.subplots()**. And we create a line plot for x and y using the function **plot()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x229f9ad4670>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(x, y, 'ko--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add annotation by using the function **annotate()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(3, 50, 'Maximum')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(x, y, 'ko--')\n",
    "ax.annotate('Maximum', xy = (x[3], y[3]), xytext =(3, 50),\n",
    "            arrowprops = dict(facecolor ='green', shrink = 0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we added another annotation pointing to the **minimum**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 50, 'Minimum')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(x, y, 'ko--')\n",
    "ax.annotate('Maximum', xy = (x[3], y[3]), xytext =(3, 50),\n",
    "            arrowprops = dict(facecolor ='green', shrink = 0.1))\n",
    "ax.annotate('Minimum', xy = (x[0], y[0]), xytext =(0, 50),\n",
    "            arrowprops = dict(facecolor ='green', shrink = 0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can aslo create shapes on figures, for example we can add a circle using the functions **plt.Circle()** and **add_patch()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x229f9c5a7c0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "circle = plt.Circle((0.5, 0.5), 0.2, fc='y')\n",
    "ax.add_patch(circle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add a rectangle using the functions **plt.Rectangle()** and **add_patch()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Rectangle at 0x229f9caec10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "rect = plt.Rectangle((0.3, 0.5), 0.5, 0.2, color='y', alpha= 0.5)\n",
    "ax.add_patch(rect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add a triangle using the functions **plt.Polygon()** and **add_patch()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Polygon at 0x229f9b37220>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "tri = plt.Polygon([[0.25, 0.25], [0.55, 0.5], [0.4, 0.7]], color='g', alpha=0.5)\n",
    "ax.add_patch(tri)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}