221211_Vyom Matplotlib Exercises.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * **\n",
    "\n",
    "# Exercises\n",
    "\n",
    "Follow the instructions to recreate the plots using this data:\n",
    "\n",
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.arange(0,100)\n",
    "y = x*2\n",
    "z = x**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1\n",
    "\n",
    "** Follow along with these steps: **\n",
    "* ** Create a figure object called fig using plt.figure() **\n",
    "* ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. **\n",
    "* ** Plot (x,y) on that axes and set the labels and titles to match the plot below:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "x = [0, 100]\n",
    "y = [0, 200]\n",
    "ax.plot(x,y)\n",
    "ax.set_xlim([0,100])\n",
    "ax.set_ylim([0,200])\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "ax.set_title('title')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2\n",
    "** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "first_ax = fig.add_axes([0,0,1,1])\n",
    "second_ax = fig.add_axes([0.2,0.5,.2,.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now plot (x,y) on both axes. And call your figure object to show it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "first_ax = fig.add_axes([0,0,1,1])\n",
    "second_ax = fig.add_axes([0.2,0.5,.2,.2])\n",
    "x = [0, 100]\n",
    "y = [0, 200]\n",
    "first_ax.plot(x,y)\n",
    "second_ax.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3\n",
    "\n",
    "** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_axes([0,0,1,1])\n",
    "ax2 = fig.add_axes([0.2,0.5,0.2,0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X9L49nClToFMnGDVKIS9hEvS5shI8J+ek8NasWePmzJnjnHNu69atrnbt2m7h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\nH51ua+eImT++AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c212cf8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X9L49nClToFMnGDVKIS9hEvS5shI8J+ek8NasWePmzJnjnHNu69atrnbt2m7h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\nH51ua+eImT++AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c212cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4\n",
    "\n",
    "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, axes= plt.subplots(nrows=1, ncols=2)\n",
    "axes[0].set_xlim(0,0.5)\n",
    "axes[0].set_ylim(0,0.5)\n",
    "axes[1].set_xlim(0,0.5)\n",
    "axes[1].set_ylim(0,0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, axes= plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "axes[0].set_xlabel('x')\n",
    "axes[0].set_ylabel('y')\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel('z')\n",
    "axes[0].set_xlim(0,0.5)\n",
    "axes[0].set_ylim(0,0.5)\n",
    "axes[1].set_xlim(0,0.5)\n",
    "axes[1].set_ylim(0,0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, axes= plt.subplots(nrows=1, ncols=2, figsize=(10,2))\n",
    "\n",
    "axes[0].set_xlabel('x')\n",
    "axes[0].set_ylabel('y')\n",
    "axes[1].set_xlabel('x')\n",
    "axes[1].set_ylabel('z')\n",
    "axes[0].set_xlim(0,0.5)\n",
    "axes[0].set_ylim(0,0.5)\n",
    "axes[1].set_xlim(0,0.5)\n",
    "axes[1].set_ylim(0,0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}