02_Matplotlib_Exercises.ipynb

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      "name": "02_Matplotlib-Exercises.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/mohd-faizy/_Matplotlib_CodeDump/blob/main/02_Matplotlib_Exercises.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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        "id": "sTYBV1NvoDlg"
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      "source": [
        "\n",
        "# Matplotlib Exercises:\n",
        "\n",
        "Welcome to the exercises for reviewing matplotlib! Take your time with these, Matplotlib can be tricky to understand at first. These are relatively simple plots, but they can be hard if this is your first time with matplotlib, feel free to reference the solutions as you go along.\n",
        "\n",
        "Also don't worry if you find the matplotlib syntax frustrating, we actually won't be using it that often throughout the course, we will switch to using seaborn and pandas built-in visualization capabilities. But, those are built-off of matplotlib, which is why it is still important to get exposure to it!\n",
        "\n",
        "** * 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",
      "metadata": {
        "collapsed": true,
        "id": "7fLxfi8woDlw"
      },
      "source": [
        "import numpy as np\n",
        "x = np.arange(0,100)\n",
        "y = x*2\n",
        "z = x**2"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPZRTaMIoDlz"
      },
      "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",
      "metadata": {
        "collapsed": true,
        "id": "Z_Vyz_u2oDl0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "# plt.show() for non-notebook users"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrMXhfKVoDl1"
      },
      "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",
      "metadata": {
        "id": "nrFoWnzDoDl2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "3b57affe-ea54-4f9e-9f04-62358833bf6c"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.plot(x,y)\n",
        "ax.set_xlabel('x')\n",
        "ax.set_ylabel('y')\n",
        "ax.set_title('title')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'title')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJNjo69xoDl5"
      },
      "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",
      "metadata": {
        "id": "Hwpk9VjMoDl6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "e4bd499b-e47e-43a4-dce9-c96580310cba"
      },
      "source": [
        "fig = plt.figure()\n",
        "\n",
        "ax1 = fig.add_axes([0,0,1,1])\n",
        "ax2 = fig.add_axes([0.2,0.5,.2,.2])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7K1JLEHoDl7"
      },
      "source": [
        "** Now plot (x,y) on both axes. And call your figure object to show it.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHuUUd6noDl8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "6c5402fc-3fc9-4823-c045-df21c819a8ff"
      },
      "source": [
        "ax1.plot(x,y)\n",
        "ax1.set_xlabel('x')\n",
        "ax1.set_ylabel('y')\n",
        "\n",
        "\n",
        "ax2.plot(x,y)\n",
        "ax2.set_xlabel('x')\n",
        "ax2.set_ylabel('y')\n",
        "\n",
        "fig # Show figure object"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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NyWoVruEqHVOyTMlqZTZmlYZzyQLnkiXXdpWCKVmmZKmHjVmFci5Z4HHJUjXXfBXmjkfWcMENvzIltzBTsrQjG7PqzpQscC5Z6o/vAtXVHY+s4cIbHvL7kluYKVnaORuz6uKZTVv43A+Xc/0Dfl9yK/O4ZGnXfEeo5tzjWqZkaeBszKoZ55IFziVLg+W7QzVhStamzd188cePcsXPTMnSYNiYNaI8e5fA45Kl4fCdohHj9yXLuWRp+GzMGjbnkgXOJUsjxXeNhsWULFOyNLJszBoSU7LA45KlWvAdpEFzj2u5x7VUOzbmFnHrrbfy0Y9+lO7ububPn8+CBQsG/RzucS0wJUu1VtN3U0RcAbwDWJuZx1TGDgS+C0wAVgKnZ+bGiAjgMmAW8Afg/Zn5QC3raxXd3d2cd9553HbbbYwfP54pU6Ywe/ZsjjrqqAE/h3PJMiVL9bFbjZ//KuDtvcYWALdn5iTg9sp1gJnApMq/DuAbNa6tZfziF79g4sSJHH744ey5556cccYZLF26dECPfWbTFj7xj/+Ps6+6j/1fuQff/y8n8olTj7Apt5hlKzcw62s/ZdG/PMF7p7bxo4+dZFOWaqSmiTkzfxIRE3oNzwFOrlxeDNwFfLoyviQzE7gnIg6IiLGZ2VXLGlvB6tWrOfTQQ7dfHz9+PPfee+8O9+vs7KSzsxOAdevWAT0p6Y5H1jqX3KJMyVL97bIxR8RHgP+TmRtH6DXHVDXb3wJjKpfHAU9V3W9VZexljTkiOuhJ1LS1tY1QSQLo6Oigo6MDgPb2dgAO2X8v/vmTJ7PfXnsUWZoK4FyyVIyBvMvGAPdFxAPAFcCPKql22DIzI2JQz5WZnUAnQHt7+4jU0ezGjRvHU0/98W+eVatWMW7cuAE/3qbcWkzJUrF22Zgz8zMR8d+BtwFnA5dHxHXAosx8fAivuWbbR9QRMRZYWxlfDRxadb/xlTEN05QpU1ixYgVPPPEE48aN49prr+Xqq6/e6WNWrly5PTVDz0fbBx10UK1LbUrVy27lypXFFrMLpmSpeAN6x1WS7W/p+ej5JeBVwPci4rbM/NQgX/NGYB6wsPL/0qrxD0fEtcBU4Bnnl0fGqFGjuPzyyzn11FPp7u7mnHPO4eijj97pY9avX/+y6+3t7SxbtqyWZTatRlh2pmSpPAYyx/xRYC6wHvg28MnM3BIRuwErgH4bc0RcQ8+OXqMjYhXwWXoa8nURcS7wJHB65e4303Oo1GP0HC519hB/JvVh1qxZzJo1q+gyVEKmZKlcBvLuOxB4Z2Y+WT2YmVsj4h07e2BmntnPTdP7uG8C5w2gHkkjwJQsldNA5pg/u5PbHh7ZclRW2/bW1uCVcdlVp2S/L1kqlxihHawL0d7enmWfu5PKpHdK/vy7jjUlSwWIiPszs72v2/wTWWoRziVLjaHWp+RUg7v11ls54ogjmDhxIgsXLiy6nFI655xzOPjggznmmGO2j23YsIEZM2YwadIkZsyYwcaNPefnyUzOP/98Jk6cyLHHHssDD9T+dPCbNndz8U3Lec83f86W7q1cPX8qF592jE1ZKikbs/q17csvbrnlFpYvX84111zD8uXLiy6rdN7//vdz6623vmxs4cKFTJ8+nRUrVjB9+vTtf9TccsstrFixghUrVtDZ2cmHPvShmtbmOa6lxmNjVr+G8+UXreSkk07iwAMPfNnY0qVLmTdvHgDz5s3jBz/4wfbxuXPnEhFMmzaNp59+mq6ukT9cv6+UfMlpbzAlSw3Ad6n6NdAvv9CO1qxZw9ixYwE45JBDWLNmDdD3Ml29evX2+44E55Klxua7VaqxiKDn68Zry+OSpeZgY1a/hvvlF61szJgxdHV1MXbsWLq6ujj44IOB2i1TU7LUPJxjVr+qv/xi8+bNXHvttcyePbvoshrC7NmzWbx4MQCLFy9mzpw528eXLFlCZnLPPfew//77D+tjbPe4lpqP7171ayhfftGKzjzzTO666y7Wr1/P+PHjueiii1iwYAGnn346ixYt4rDDDuO6664Des5ZfvPNNzNx4kT23ntvrrzyyiG/rilZak6e+UtqMJ69S2p8nvlLahKmZKn5+Y6WGoB7XEutw8YslZwpWWotvrulkjIlS63JxiyVkN+XLLUu3+lSiZiSJdmYpZLonZIXzJzMvqZkqeV45i81pPvuu49jjz2WF154gd///vccffTRPPTQQ0WXNST9fROUTVlqTb7z1ZCmTJnC7Nmz+cxnPsOmTZs466yzOOaYY4oua9CcS5bUm2f+UsPavHkzU6ZMYa+99uLuu+9m9913L7qkAfPsXVJr88xfakq/+93veP7559myZQsvvPAC++yzT9ElDYjHJUvaGROzGtbs2bM544wzeOKJJ+jq6uLyyy8vuqSdMiVL2qZUiTkijgC+WzV0OPA/gAOADwDrKuMXZubNdS5PDWLJkiXsscce/NVf/RXd3d2ceOKJ3HHHHbz1rW8turQ+9U7Jn555pDt3SepToYk5InYHVgNTgbOB5zPziwN9vIlZZWdKltSXUiXmXqYDj2fmkxFRcCnSyHKPa0lDUfRW4gzgmqrrH46IucAy4OOZubGYsqSh2+HsXR+YyomvMyVLGpjCPsqOiD2BfweOzsw1ETEGWA8kcDEwNjPP6eNxHUAHQFtb2wlPPvlkHauWds49riUNRFk/yp4JPJCZawC2/Q8QEd8CburrQZnZCXRCzxxzHeqUdsmULGmkFNmYz6TqY+yIGJuZXZWrfwE05vkV1XJMyZJGUiFbj4jYB5gB/HXV8Ocj4jh6Pspe2es2qXRMyZJqoZDGnJm/B17da+x9RdQiDYUpWVKtuCWRBsGULKnWbMzSAJmSJdWDWxVpF0zJkurJxizthClZUr25hZH6sENKnj/Vc1xLqgsbs9SLKVlSkdzaSBXOJUsqAxuzhClZUnm45VFLMyVLKhsbs1qWKVlSGbkVUstxj2tJZWZjVksxJUsqO7dIagnVKfk1+5uSJZWXjVlNrzolnzWtjQUzJ7OvKVlSSbl1UtMyJUtqRDZmNSVTsqRG5ZZKTcWULKnR2ZjVNEzJkpqBWy01PFOypGZiY1ZDMyVLajZuwdSQeqfk78yfyptNyZKagI1ZDceULKmZuTVTw3AuWVIrsDGrIZiSJbWKwrZsEbESeA7oBl7KzPaIOBD4LjABWAmcnpkbi6pRxTMlS2o1RUeOUzJzfdX1BcDtmbkwIhZUrn+6mNJUNFOypFZUtq3cHODkyuXFwF3YmFuOKVlSKyuyMSfw44hI4JuZ2QmMycyuyu2/BcYUVp0Kcd/KDXzKlCyphRW5xXtLZq6OiIOB2yLikeobMzMrTftlIqID6ABoa2urT6WquU2bu/nCjx7lyrtNyZJaW2GNOTNXV/5fGxHfB94ErImIsZnZFRFjgbV9PK4T6ARob2/foXGr8TiXLEl/tFsRLxoR+0TEftsuA28DHgJuBOZV7jYPWFpEfaqPTZu7+dwPl/Oeb/6czS9t5er5U7nktDfYlCW1tKK2gGOA70fEthquzsxbI+I+4LqIOBd4Eji9oPpUY84lS1LfCtkSZuZvgD/tY/x3wPT6V6R6cY9rSdo5I4rqxpQsSbvmVlE1V73H9bgDTMmStDM2ZtVUdUp+37TDWDDzSPYxJUtSv9xCqiZMyZI0NDZmjbjq45JNyZI0OG4tNWJMyZI0fDZmjQjnkiVpZLjl1LCYkiVpZNmYNWS9j0u+YOZkU7IkDZNbUQ2aKVmSasfGrEHxm6AkqbbcompAqs9xbUqWpNqxMWuXeqdk55IlqXbcuqpfpmRJqj8bs/rk2bskqRhuafUypmRJKpaNWdt5XLIkFc+trjwuWZJKxMbc4jzHtSSVi1vgFmVKlqRysjG3IFOyJJWXW+MWYkqWpPKzMbcIU7IkNQa3zE3OlCxJjaXujTkiDgWWAGOABDoz87KI+FvgA8C6yl0vzMyb611fM/G4ZElqPEVspV8CPp6ZD0TEfsD9EXFb5bavZOYXC6ipqXj2LklqXHVvzJnZBXRVLj8XEQ8D4+pdR7MyJUtSYyt0ix0RE4A3AvcCbwY+HBFzgWX0pOqNxVXXWJxLlqTmsFtRLxwR+wLXAx/LzGeBbwCvA46jJ1F/qZ/HdUTEsohYtm7dur7u0nLuW7mBWV/7KVf87AnOmnoYP/rYSTZlSWpQhSTmiNiDnqb8ncy8ASAz11Td/i3gpr4em5mdQCdAe3t71r7a8jIlS1LzKWKv7AAWAQ9n5perxsdW5p8B/gJ4qN61NZL7n9zAJ/7R45IlqdkUsSV/M/A+4FcR8a+VsQuBMyPiOHoOoVoJ/HUBtZXeps3dfOnHj7LIPa4lqSkVsVf2vwDRx00es7wLy1Zu4JNVZ+/69Mwj2deULElNxa16AzAlS1LrsDGXXHVK9rhkSWp+buFLyrN3SVJrsjGXUO+5ZPe4lqTW4da+REzJkiQbc0mYkiVJYGMunClZklTNxlwgj0uWJPVmFyiAKVmS1B8bc515XLIkaWfsCHViSpYkDYSNuQ7c41qSNFB2hxoyJUuSBsvGXCOmZEnSUNgpRpgpWZI0HDbmEWRKliQNl11jBJiSJUkjxcY8TB6XLEkaSXaQITIlS5JqwcY8BM4lS5JqxW4yCKZkSVKt2ZgHyJQsSaoHO8sumJIlSfVkY94Jvy9ZklRvpesyEfF24DJgd+Dbmbmw3jWYkiVJRSlVY46I3YGvAzOAVcB9EXFjZi6vVw0elyxJKlLZOs6bgMcy8zcAEXEtMAeoeWM2JUuSyqBsjXkc8FTV9VXA1Fq/6Natybv/9938+t+fdY9rSVKhGq77REQH0AHQ1tY2Is+5225Bx0mHc9C+rzAlS5IKVbbGvBo4tOr6+MrYdpnZCXQCtLe350i98Jzjxo3UU0mSNGS7FV1AL/cBkyLitRGxJ3AGcGPBNUmSVDelSsyZ+VJEfBj4ET2HS12Rmb8uuCxJkuqmVI0ZIDNvBm4uug5JkopQto+yJUlqaTZmSZJKxMYsSVKJ2JglSSoRG7MkSSViY5YkqURszJIklYiNWZKkEonMETvddN1FxDrgyRF8ytHA+hF8vlbishsel9/QueyGzmU3PMNZfodl5kF93dDQjXmkRcSyzGwvuo5G5LIbHpff0Lnshs5lNzy1Wn5+lC1JUonYmCVJKhEb88t1Fl1AA3PZDY/Lb+hcdkPnshuemiw/55glSSoRE7MkSSViYwYi4u0R8WhEPBYRC4qup+wi4tCIuDMilkfEryPio5XxAyPitohYUfn/VUXXWlYRsXtEPBgRN1WuvzYi7q2sg9+NiD2LrrGMIuKAiPheRDwSEQ9HxH9wvRu4iPivlffsQxFxTUTs5brXt4i4IiLWRsRDVWN9rmvR42uVZfjLiDh+OK/d8o05InYHvg7MBI4CzoyIo4qtqvReAj6emUcB04DzKstsAXB7Zk4Cbq9cV98+Cjxcdf1S4CuZORHYCJxbSFXldxlwa2YeCfwpPcvQ9W4AImIccD7QnpnHALsDZ+C615+rgLf3GutvXZsJTKr86wC+MZwXbvnGDLwJeCwzf5OZm4FrgTkF11RqmdmVmQ9ULj9Hz8ZxHD3LbXHlbouB04qpsNwiYjzwn4BvV64H8Fbge5W7uOz6EBH7AycBiwAyc3NmPo3r3WCMAl4ZEaOAvYEuXPf6lJk/ATb0Gu5vXZsDLMke9wAHRMTYob62jbmnoTxVdX1VZUwDEBETgDcC9wJjMrOrctNvgTEFlVV2XwU+BWytXH818HRmvlS57jrYt9cC64ArK9MA346IfXC9G5DMXA18Efg3ehryM8D9uO4NRn/r2oj2ERuzhiwi9gWuBz6Wmc9W35Y9u/u7y38vEfEOYG1m3l90LQ1oFHA88I3MfCPwe3p9bO1617/KfOgcev7AeQ2wDzt+VKsBquW6ZmOG1cChVdfHV8a0ExGxBz1N+TuZeUNleM22j28q/68tqr4SezMwOyJW0jNt8lZ65k0PqHy8CK6D/VkFrMrMeyvXv0dPo3a9G5g/B57IzHWZuQW4gZ710XVv4Ppb10a0j9iY4T5gUmXPxD3p2RnixoJrKrXKnOgi4OHM/HLVTTcC8yqX5wFL611b2WXmBZk5Pr3TEJoAAAHASURBVDMn0LOu3ZGZ7wXuBN5duZvLrg+Z+VvgqYg4ojI0HViO691A/RswLSL2rryHty0/172B629duxGYW9k7exrwTNVH3oPmCUaAiJhFz7zf7sAVmfk/Cy6p1CLiLcBPgV/xx3nSC+mZZ74OaKPnW79Oz8zeO0+oIiJOBj6Rme+IiMPpSdAHAg8CZ2Xmi0XWV0YRcRw9O83tCfwGOJuegOF6NwARcRHwl/QcWfEgMJ+euVDXvV4i4hrgZHq+QWoN8FngB/SxrlX+0LmcnqmBPwBnZ+ayIb+2jVmSpPLwo2xJkkrExixJUonYmCVJKhEbsyRJJWJjliSpRGzMkiSViI1ZkqQSsTFLepmImFL5Ttm9ImKfyvf3HlN0XVKr8AQjknYQEZcAewGvpOf81P+r4JKklmFjlrSDynnj7wNeAE7MzO6CS5Jahh9lS+rLq4F9gf3oSc6S6sTELGkHEXEjPV9s8FpgbGZ+uOCSpJYxatd3kdRKImIusCUzr46I3YG7I+KtmXlH0bVJrcDELElSiTjHLElSidiYJUkqERuzJEklYmOWJKlEbMySJJWIjVmSpBKxMUuSVCI2ZkmSSuT/A0I9ATk7v0M8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2sEfseSuoDl9"
      },
      "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",
      "metadata": {
        "id": "IsZVPAXEoDl9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "902d6ff8-a25b-4429-d899-e376d51291e1"
      },
      "source": [
        "fig = plt.figure()\n",
        "\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax2 = fig.add_axes([0.2,0.5,.4,.4])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7Yg7nlkoDl-"
      },
      "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",
      "metadata": {
        "id": "IlSWMBfXoDl_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "4710182f-0ad5-4df4-f8f9-d569a6ec3418"
      },
      "source": [
        "ax.plot(x,z)\n",
        "ax.set_xlabel('X')\n",
        "ax.set_ylabel('Z')\n",
        "\n",
        "\n",
        "ax2.plot(x,y)\n",
        "ax2.set_xlabel('X')\n",
        "ax2.set_ylabel('Y')\n",
        "ax2.set_title('zoom')\n",
        "ax2.set_xlim(20,22)\n",
        "ax2.set_ylim(30,50)\n",
        "\n",
        "fig"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q18NIzmJoDmC"
      },
      "source": [
        "## Exercise 4\n",
        "\n",
        "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtg8oH9toDmD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "81afb9ab-d2e0-4815-a676-e111d6b2b972"
      },
      "source": [
        "# Empty canvas of 1 by 2 subplots\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LlR23IxnoDmE"
      },
      "source": [
        "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWDak0l9oDmF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "11eafbd7-5ab0-42b6-cbf6-a389b33cfd99"
      },
      "source": [
        "axes[0].plot(x,y,color=\"blue\", lw=3, ls='--')\n",
        "axes[1].plot(x,z,color=\"red\", lw=3, ls='-')\n",
        "fig"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByRKh4DaoDmG"
      },
      "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",
      "metadata": {
        "id": "y-z96rQxoDmH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "ca2a54db-9b8f-4018-8c6c-a4583a8af292"
      },
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(12,2))\n",
        "\n",
        "axes[0].plot(x,y,color=\"blue\", lw=5)\n",
        "axes[0].set_xlabel('x')\n",
        "axes[0].set_ylabel('y')\n",
        "\n",
        "axes[1].plot(x,z,color=\"red\", lw=3, ls='--')\n",
        "axes[1].set_xlabel('x')\n",
        "axes[1].set_ylabel('z')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'z')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}