01_Matplotlib_Concepts_Lecture.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sidkatiyar09/Udemy_python_data_Science_1/blob/main/01_Matplotlib_Concepts_Lecture.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvXmkj6677Lt"
      },
      "source": [
        "___\n",
        "\n",
        "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
        "___\n",
        "# Matplotlib Overview Lecture"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nadMgEOS77Lv"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2kXA16B77Lv"
      },
      "source": [
        "Matplotlib is the \"grandfather\" library of data visualization with Python. It was created by John Hunter. He created it to try to replicate MatLab's (another programming language) plotting capabilities in Python. So if you happen to be familiar with matlab, matplotlib will feel natural to you.\n",
        "\n",
        "It is an excellent 2D and 3D graphics library for generating scientific figures. \n",
        "\n",
        "Some of the major Pros of Matplotlib are:\n",
        "\n",
        "* Generally easy to get started for simple plots\n",
        "* Support for custom labels and texts\n",
        "* Great control of every element in a figure\n",
        "* High-quality output in many formats\n",
        "* Very customizable in general\n",
        "\n",
        "Matplotlib allows you to create reproducible figures programmatically. Let's learn how to use it! Before continuing this lecture, I encourage you just to explore the official Matplotlib web page: http://matplotlib.org/\n",
        "\n",
        "## Installation \n",
        "\n",
        "You'll need to install matplotlib first with either:\n",
        "\n",
        "    conda install matplotlib\n",
        "or\n",
        "    pip install matplotlib\n",
        "    \n",
        "## Importing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "blHWQT_I77Lv"
      },
      "source": [
        "Import the `matplotlib.pyplot` module under the name `plt` (the tidy way):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MdE6FDYk77Lw"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqHatRCF77Lw"
      },
      "source": [
        "You'll also need to use this line to see plots in the notebook:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "L2N6izXV77Lw"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONmnb0vy77Lx"
      },
      "source": [
        "That line is only for jupyter notebooks, if you are using another editor, you'll use: **plt.show()** at the end of all your plotting commands to have the figure pop up in another window."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "0z0RQ9Kn77Lx"
      },
      "source": [
        "# Basic Example\n",
        "\n",
        "Let's walk through a very simple example using two numpy arrays:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGtQ2vyb77Lx"
      },
      "source": [
        "### Example\n",
        "\n",
        "Let's walk through a very simple example using two numpy arrays. You can also use lists, but most likely you'll be passing numpy arrays or pandas columns (which essentially also behave like arrays).\n",
        "\n",
        "** The data we want to plot:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a4sQg6O477Ly"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "x = np.linspace(0, 5, 11)\n",
        "y = x ** 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WjGVoaqa77Ly",
        "outputId": "09e601cb-af11-4685-fefb-b04da0ba32d3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T_yxioFf77Lz",
        "outputId": "62b0e85d-9e96-4fe4-fa07-b8526e37f9c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.  ,  0.25,  1.  ,  2.25,  4.  ,  6.25,  9.  , 12.25, 16.  ,\n",
              "       20.25, 25.  ])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yg6Hli2f77Lz"
      },
      "source": [
        "## Basic Matplotlib Commands\n",
        "\n",
        "We can create a very simple line plot using the following ( I encourage you to pause and use Shift+Tab along the way to check out the document strings for the functions we are using)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ExTpx47e77Lz",
        "outputId": "7e2c5374-7bd0-40f9-e40e-e3fd9f1d19f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-99a445d3758e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 'r' is the color red\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'X Axis Title Here'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Y Axis Title Here'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'String Title Here'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ],
      "source": [
        "plt.plot(x, y, 'r') # 'r' is the color red\n",
        "plt.xlabel('X Axis Title Here')\n",
        "plt.ylabel('Y Axis Title Here')\n",
        "plt.title('String Title Here')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jmF83Vk77Lz"
      },
      "source": [
        "## Creating Multiplots on Same Canvas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qK1anHhu77L0"
      },
      "outputs": [],
      "source": [
        "# plt.subplot(nrows, ncols, plot_number)\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(x, y, 'r--') # More on color options later\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(y, x, 'g*-');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAMZxAFZ77L0"
      },
      "source": [
        "___\n",
        "# Matplotlib Object Oriented Method\n",
        "Now that we've seen the basics, let's break it all down with a more formal introduction of Matplotlib's Object Oriented API. This means we will instantiate figure objects and then call methods or attributes from that object."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cPQ4mDrr77L0"
      },
      "source": [
        "## Introduction to the Object Oriented Method"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCGu0x9577L0"
      },
      "source": [
        "The main idea in using the more formal Object Oriented method is to create figure objects and then just call methods or attributes off of that object. This approach is nicer when dealing with a canvas that has multiple plots on it. \n",
        "\n",
        "To begin we create a figure instance. Then we can add axes to that figure:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1mV8BSoa77L0",
        "outputId": "659779f2-0c80-45d8-fed6-2b80ba3953a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.text.Text at 0x111c85198>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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8CLgBGAP8Fbi72iLNrLFNnZqEw/XXOySaVa+D2ZLmAq8CU4CxEfFW+tCDknLfGNDMyikC\nxo+Hq6+Ge+6B7bcvuiLLS5Ztxj8QEc/VqZ7V39tdT2Yl9NZb8NWvwu9+BzffDJtuWnRF1l816Xoq\nKiTMrJxeeQX23z+ZBnv33Q6JVtDf3WPNrAU991wys2m33ZIxiQ03LLoiq4cs6yjWy3KfmTW3Bx+E\nvfeGr38dLrgA1lqr6IqsXrK0KO7PeJ+ZNakbb4SDDoLJk+Hkk4uuxuqt0nkUw4AtgA0k7UpyDgXA\nRoAbnGYtICLZ/fWCC+D225MuJ2s9labH7g8cCwwHJna5/6/AGTnWZGYl0HVjv/vu8+6vrSzL9Ngj\nI+KGOtWz+nt7eqxZAV5/PdnY7803Yfp079nUzGq1MnuOpCmSbksvuqOk42pSoZmVTufGfkOGeGM/\nS2QJiqnAHSQbAwI8C3wzy8XTgFks6bEu9w2WdKekZyTdIcl/Dc1KonNjvyOO8MZ+9o4sQbFJREwD\nOgAiYiXJpoBZTCUZ6+hqLHBXRGxHslfUdzNey8xydNdd8LGPwQ9+AOPGgfeItk5ZgmK5pI2BAJC0\nB/BalotHxGzgldXuPhS4Ir19BXBYtlLNLC/e2M8qyXLC3beAm4EPSpoDDAFGV/Gem0bEYoCIWCTJ\nGwCYFSQC/vM/4ZprvLGf9azXoIiIeZL2BbYjWUvxTESsqGENntZkVoC33oLjjoPf/x7uv997NlnP\nKi24GwX8KSIWRcRKSbsBRwILJE2o4sCixZKGRsTidFHfi5WePGHChFW329raaGtr6+fbmlmnV16B\nww+HjTdONvbznk2to729nfb29j69psd1FJLmAZ+MiJcl/RNwHfB1YASwQ0Rk6n6S9D7glojYOf3+\nXODliDhX0unA4IgY28NrvY7CrMaeew4+/elkS47zzoMB3hq0pWVZR1EpKH4bER9Ob18CLImICen3\nj0bEiAwFXAO0ARsDi4HxwE3A9cCWwALgsxHxag+vd1CY1dCDDyYtiXHjvGeTJbIERaUxirUkrZ1O\nh/0E8C8ZX7dKRBzdw0OfzPJ6M6udX/4Sjj8+meF00EFFV2ONpNIP/GuBeyQtBd4A7gWQtA0Zp8ea\nWfG8sZ9Vq+JeT+maic2AOyNieXrftsDAiJiXe3HuejKrSteN/W691Rv72ZqqGqMoAweFWf95Yz/L\nolabAppZg/HGflZLDgqzJjN3rjf2s9rKNHvJzMqvc9D6nHPgkktgzJiiK7Jm4aAwawJLlsCxx8JL\nLyVrJd7//qIrsmbiriezBjdzJuy6K+y8M9x7r0PCas8tCrMGtXIlnHkmXHYZ/OIXsN9+RVdkzcpB\nYdaAXnghOTdigw1g3jwYNqzoiqyZuevJrMHcdBOMGpVsw3H77Q4Jy59bFGYN4s034TvfgV/9KgmL\nPfcsuiJrFW5RmDWAp5+GPfaARYvgkUccElZfDgqzEotIBqr32QdOOgmmTYP3vrfoqqzVuOvJrKSW\nLYMTT0xaEDNnwk47FV2RtSq3KMxKaO5cGDkyOaL0oYccElYsB4VZiXR0wMSJcOCB8IMfwOTJPs/a\niueuJ7OS8DYcVlZuUZiVgLfhsDJzi8KsQN6GwxqBg8KsIN6GwxqFu57MCuBtOKyRuEVhVkfehsMa\nkVsUZnXibTisUTkozHLmbTis0bnrySxH3obDmoFbFGY5efjhZG2Et+GwRuegMKuxzm04Pv1pOPts\nb8Nhjc9dT2Y11LkNx9Kl3obDmodbFGY18r//m3Q17bQTzJ7tkLDm4RaFWZUWLkzWRtxzD0yZAvvv\nX3RFZrXlFoVZP61cCZMmJRv5DR8OTz3lkLDm5BaFWT/MmgUnn5xsvTF7Nmy/fdEVmeXHQWHWB127\nmSZOhNGjQSq6KrN8uevJLIPuupnGjHFIWGtwi8KsF+5mslbnoDDrQdduph//GI480i0Ia03uejJb\nTXfdTB6LsFZWWItC0vPAa0AHsCIiPlpULWad3M1ktqYiu546gLaIeKXAGswAdzOZVVJk15MKfn8z\ndzOZZVBkiyKAX0t6G5gcET8vsBZrQe5mMsumyKDYKyIWShpCEhhPRcTs1Z80YcKEVbfb2tpoa2ur\nX4XWlDq7mWbNShbNuZvJWkl7ezvt7e19eo0iIp9q+lKENB5YFhETV7s/ylCfNYeVK+Hii+Gss+Br\nX4Nx42DgwKKrMiuWJCKi4q9KhbQoJG0IDIiI1yW9B9gP+F4RtVhrcDeTWf8V1fU0FLhRUqQ1XB0R\ndxZUizUxdzOZVa+QoIiIPwIjinhvaw2rdzPNn+9uJrP+8hYe1nS6djPNmQPbbVd0RWaNzUFhTcPd\nTGb58II3a3hdF81tuWXSzeRFc2a14xaFNayODvif/4EzznA3k1meHBTWcFasgGuvhfPOg3XWge99\nDw4/3C0Is7w4KKxhLF8Ol10GF1wA22yT/He//RwQZnlzUFjpLV2aTHX9yU9gn31g+nT4qDelN6sb\nD2ZbaS1YAN/4BvzDP8Cf/wz33gs33OCQMKs3B4WVzhNPwJe+BLvuCuuum3x/2WUeqDYrioPCSmP2\nbDj4YPjkJ5O9mP7wB/jRj2CLLYquzKy1eYzCCtU5xfXcc5MFc6edBtOmwQYbFF2ZmXVyUFghVp/i\nOnZsspJ6bf+NNCsd/7O0uuqc4jpxInzwg8l/P/UpT3E1KzMHhdXF6lNcr7/es5fMGoUHsy1XnVNc\nt93WU1zNGpWDwnLROcV15EhYbz1PcTVrZA4Kq6muU1x32CGZ4nreebD55kVXZmb95TEKq1pHB9x6\nK/zwh++cCeEprmbNw0Fh/dZ1iuu668Lpp3uKq1kz8j9p67OFC5OAuPDCZBdXT3E1a24OCstk4cJk\nttK0afD448k4xPTpMGpU0ZWZWd4UEUXX0CNJUeb6ml134TBmTHIGxHrrFV2dmdWCJCKiYn+Ag8Le\nxeFg1locFJaJw8GsdTkorEcOBzMDB4WtxuFgZqtzUNiqcLj+enjsMYeDmb2bg6JFORzMLCsHRQtx\nOJhZfzgompzDwcyq5aBoQg4HM6slB0WDe+ONJAzmzn3na8ECh4OZ1Y6DooF0FwrPPpsc9PORj8Bu\nuyVfu+zicDCz2nFQlFTWUNh5Z1h//aKrNbNm5qAoAYeCmZWZg6LOegqF7bd/JxAcCmZWJg6KHDkU\nzKwZlDooJB0ATAIGAFMi4txunlOKoChTKLS3t9PW1pbvmzQYfyZr8meyJn8m3csSFIWccCdpAHAx\n8Ang/4CHJM2IiKfzes+334ZXX4VXXqn89fLLa9735pvwoQ8lYTBqFJxwQnEtBf9lX5M/kzX5M1mT\nP5P+K+oo1I8Cv4uIBQCSrgMOBSoGRTU/7F9/HQYNgsGDu//aeOPk/OfuHhs0CAYMqMOnYmZWQkUF\nxRbAn7p8/2eS8FjDrru+88N++XLYaCP/sDczq6dCxigkHQnsHxH/kn7/BeCjEXHqas8rfoDCzKzJ\nlXKMAvgLsFWX74en971Lb8WbmVn+iuqMeQjYRtLWktYFPg/cXFAtZmZWQSEtioh4W9IpwJ28Mz32\nqSJqMTOzykq94M7MzIpXynlAkg6Q9LSkZyWdXnQ9ZSBpiqTFkh4rupaykDRc0t2SnpT0uKRTe39V\nc5O0nqQHJT2Sfibji66pLCQNkDRPkru5AUnPS/pt+nflNxWfW7YWRboY71m6LMYDPp/nYrxGIGlv\n4HXgyojYpeh6ykDSMGBYRDwqaSAwFzjUf1e0YUT8TdJawBzg1Iio+IOgFUj6V2A3YKOIOKToeoom\n6Tlgt4h4pbfnlrFFsWoxXkSsADoX47W0iJgN9Po/tJVExKKIeDS9/TrwFMkanZYWEX9Lb65HMg5Z\nrt8GCyBpOPBp4LKiaykRkTEDyhgU3S3Ga/l//FaZpPcBI4AHi62keGkXyyPAIuDXEfFQ0TWVwI+B\n7+DQ7CqAX0t6SNLXKj2xjEFh1idpt9N04Btpy6KlRURHROxKsj5pd0k7Fl1TkSR9Blictj6Vfhns\nFREjSVpaJ6fd290qY1BkWoxnBiBpbZKQ+K+ImFF0PWUSEX8FZgIHFF1LwfYCDkn75K8FPibpyoJr\nKlxELEz/uwS4kR62UYJyBoUX4/XMvw2t6XJgfkRcWHQhZSBpE0mD0tsbAJ+il802m11EnBERW0XE\nB0h+ntwdEV8quq4iSdowbYkj6T3AfsATPT2/dEEREW8DnYvxngSu82I8kHQNcB+wraQXJP1z0TUV\nTdJewDHAx9MpfvPSc05a2WbATEmPkozX3BERtxZck5XPUGB2Opb1AHBLRNzZ05NLNz3WzMzKpXQt\nCjMzKxcHhZmZVeSgMDOzihwUZmZWkYPCzMwqclCYmVlFDgprepLGSXoi3VJ5nqRRvTz/y+nOtN09\nNkPSF7t8P1nSt7t53lRJR2Ssb2tJj2d5bn+ub1atos7MNqsLSXuQ7GUzIiJWSvp7YN1eXnYsySrV\nRd08dirJgrYZwE4k2x6cUINSvaDJSsstCmt2mwFLI2IlQES8HBGLACSNlNSe7p55m6Rhko4EPgJc\nlbY+1ut6sYhYAPwM+BFwCXByRHRkKUTSeyTdJenhtHXT9UyEdSRdJWm+pGmS1u+hxqHVfiBmfeWg\nsGZ3J7BVemLiJZL+CVZtJngRcGREjAKmAj+IiBuAh4GjI2JkRLzVzTUvINlo74mImNOHWt4ADouI\njwAfT6/TaTvg4ojYEVgGnNRDjWf34f3MasJdT9bUImK5pJHAPiQ/nK+TNJbkNLydSPbj7zzA5f+6\nvLTS5osfTp+/fR/LGQCck4ZVB7C5pE3Tx16IiAfS21cBXwfu6KVGs7pwUFjTi2RDs1nArHTQ+EvA\nPJIWwV59uVb6A/sSks0IT5B0UkT8JOPLjwE2AXaNiA5JfwTW7yxz9bJJwqrPNZrVmruerKlJ2lbS\nNl3uGgEsAJ4BhqSD3Uhau8sBP38FNurhkicAz0bELODbwL9J2rint1/t+0HAi2lIfAzYustjW0va\nPb19NHBvLzWa1Y2DwprdQOCKdHrso8AOwIT0PPbRwLnp/Y8Ae6avuQK4dPXBbElDSI7TPA1WHfwy\nCTivh/e+NN0S/k+S5pB0KY2S9FvgCyRnfHd6muSUsfnAe4FLe6nRs6SsbrzNuJmZVeQWhZmZVeSg\nMDOzihwUZmZWkYPCzMwqclCYmVlFDgozM6vIQWFmZhX9P/pEO8YhfIBCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x111b3afd0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create Figure (empty canvas)\n",
        "fig = plt.figure()\n",
        "\n",
        "# Add set of axes to figure\n",
        "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)\n",
        "\n",
        "# Plot on that set of axes\n",
        "axes.plot(x, y, 'b')\n",
        "axes.set_xlabel('Set X Label') # Notice the use of set_ to begin methods\n",
        "axes.set_ylabel('Set y Label')\n",
        "axes.set_title('Set Title')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DOW0NltR77L0"
      },
      "source": [
        "Code is a little more complicated, but the advantage is that we now have full control of where the plot axes are placed, and we can easily add more than one axis to the figure:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ep7BujJz77L1",
        "outputId": "5b5bc8f2-efab-4d35-f536-9970c939cb60"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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jKKclCmMCxhJF3uR3fjC/ekOJSHngJeAiVe0qIrWBq1TV98pBzvlVgc9UtZ73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\nGGN8+n8k31hzMLs8rwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x111c73470>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Creates blank canvas\n",
        "fig = plt.figure()\n",
        "\n",
        "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n",
        "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n",
        "\n",
        "# Larger Figure Axes 1\n",
        "axes1.plot(x, y, 'b')\n",
        "axes1.set_xlabel('X_label_axes2')\n",
        "axes1.set_ylabel('Y_label_axes2')\n",
        "axes1.set_title('Axes 2 Title')\n",
        "\n",
        "# Insert Figure Axes 2\n",
        "axes2.plot(y, x, 'r')\n",
        "axes2.set_xlabel('X_label_axes2')\n",
        "axes2.set_ylabel('Y_label_axes2')\n",
        "axes2.set_title('Axes 2 Title');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkgXxnTl77L1"
      },
      "source": [
        "## subplots()\n",
        "\n",
        "The plt.subplots() object will act as a more automatic axis manager.\n",
        "\n",
        "Basic use cases:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rq2FRA7677L1",
        "outputId": "fcd9a9bc-b2f5-4223-b595-71ca83b93a4a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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OO03+aglHAFKVvfIKfOMb8P3vw5VXwp57lh2ReogFQKqq3/wGDjmkWN3zyCPwjneUHZF6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\nTgDzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10c243400>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Use similar to plt.figure() except use tuple unpacking to grab fig and axes\n",
        "fig, axes = plt.subplots()\n",
        "\n",
        "# Now use the axes object to add stuff to plot\n",
        "axes.plot(x, y, 'r')\n",
        "axes.set_xlabel('x')\n",
        "axes.set_ylabel('y')\n",
        "axes.set_title('title');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mA5C8esb77L1"
      },
      "source": [
        "Then you can specify the number of rows and columns when creating the subplots() object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tcWkO0ah77L1",
        "outputId": "6c01abd9-a7c2-4fc7-ad27-4a103309aa37"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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o6c/8d4EXL/PcovLV+9xFZntRuV5ktlvMdS9huMTOrAAvTUL38OSx/cAfT615bPKH+QLw\ngXnMBf6WtSvh/w48B3x3nu95au3f9fhh6PJn/aes/RTCMeBT83rPwO8AT0zmHgPu72nul4HzwP8C\nP2TtyGrh+Rpq7iKzvahcLzLbreXaX4aSpIb5v1BKUsMseUlqmCUvSQ2z5CWpYZa8JDXMkpekhlny\nktQwS16SGvZ/GNp0aN6HtcEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1121a05f8>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Empty canvas of 1 by 2 subplots\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mj1R75Kf77L1",
        "outputId": "9eedcd46-1528-47d6-d0fa-2593a5a09c9d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x111f0f8d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x1121f5588>], dtype=object)"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Axes is an array of axes to plot on\n",
        "axes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZmildYR77L2"
      },
      "source": [
        "We can iterate through this array:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lKR4aQgw77L2",
        "outputId": "9ee78091-b492-48d7-c4f9-e14be760672b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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JHY00lzqAiE2ZAoceCttu64/Bi+TFkUfCX/4Cf/gDDB0aOhqJgjqACI0dC9XV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\nQE8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1121a05f8>"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for ax in axes:\n",
        "    ax.plot(x, y, 'b')\n",
        "    ax.set_xlabel('x')\n",
        "    ax.set_ylabel('y')\n",
        "    ax.set_title('title')\n",
        "\n",
        "# Display the figure object    \n",
        "fig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVTOdsbE77L2"
      },
      "source": [
        "A common issue with matplolib is overlapping subplots or figures. We ca use **fig.tight_layout()** or **plt.tight_layout()** method, which automatically adjusts the positions of the axes on the figure canvas so that there is no overlapping content:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qGrXkV2W77L2",
        "outputId": "1255b652-1095-45b8-bbf6-c3ad775fa3ab"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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3MbnlZN1RPBablJu5evsq2ixrg0ktJqHOs3V0xyEyre8Pf4/vj3yP0D6hyOqd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\nRGRY/w/09Pct8weipgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10c21ec18>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
        "\n",
        "for ax in axes:\n",
        "    ax.plot(x, y, 'g')\n",
        "    ax.set_xlabel('x')\n",
        "    ax.set_ylabel('y')\n",
        "    ax.set_title('title')\n",
        "\n",
        "fig    \n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qB9tDsQ277L2"
      },
      "source": [
        "### Figure size, aspect ratio and DPI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXYKtMrt77L2"
      },
      "source": [
        "Matplotlib allows the aspect ratio, DPI and figure size to be specified when the Figure object is created. You can use the `figsize` and `dpi` keyword arguments. \n",
        "* `figsize` is a tuple of the width and height of the figure in inches\n",
        "* `dpi` is the dots-per-inch (pixel per inch). \n",
        "\n",
        "For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qEutZWwA77L3",
        "outputId": "4880fd2c-04b9-464e-8ffc-d66261b24206"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x11228ea58>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(8,4), dpi=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qBuXcYxE77L3"
      },
      "source": [
        "The same arguments can also be passed to layout managers, such as the `subplots` function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YfcR7RND77L3",
        "outputId": "77456741-4ce0-4f91-cbd7-ffddf3f90766"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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kvuLppzvC8ksvwYknFivLRx0FgweXXZ3Up1Wz4mxwliSpUWXC3LlFUJ40qdjsd8opMGFC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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x112565748>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(figsize=(12,3))\n",
        "\n",
        "axes.plot(x, y, 'r')\n",
        "axes.set_xlabel('x')\n",
        "axes.set_ylabel('y')\n",
        "axes.set_title('title');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QNYtfVlC77L3"
      },
      "source": [
        "## Saving figures\n",
        "Matplotlib can generate high-quality output in a number formats, including PNG, JPG, EPS, SVG, PGF and PDF. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5eo-soE77L4"
      },
      "source": [
        "To save a figure to a file we can use the `savefig` method in the `Figure` class:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a3vDZ15z77L4"
      },
      "outputs": [],
      "source": [
        "fig.savefig(\"filename.png\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fosuDa9i77L4"
      },
      "source": [
        "Here we can also optionally specify the DPI and choose between different output formats:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6QzVHmlU77L4"
      },
      "outputs": [],
      "source": [
        "fig.savefig(\"filename.png\", dpi=200)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRBZ_RJ377L4"
      },
      "source": [
        "____\n",
        "## Legends, labels and titles"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3n4fNnA77L4"
      },
      "source": [
        "Now that we have covered the basics of how to create a figure canvas and add axes instances to the canvas, let's look at how decorate a figure with titles, axis labels, and legends."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6kBeZsCK77L4"
      },
      "source": [
        "**Figure titles**\n",
        "\n",
        "A title can be added to each axis instance in a figure. To set the title, use the `set_title` method in the axes instance:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zzGjXRFW77L4"
      },
      "outputs": [],
      "source": [
        "ax.set_title(\"title\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmwYfmQU77L4"
      },
      "source": [
        "**Axis labels**\n",
        "\n",
        "Similarly, with the methods `set_xlabel` and `set_ylabel`, we can set the labels of the X and Y axes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "slWQog3a77L5"
      },
      "outputs": [],
      "source": [
        "ax.set_xlabel(\"x\")\n",
        "ax.set_ylabel(\"y\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YKlMlL0O77L5"
      },
      "source": [
        "### Legends"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VJjlyk2g77L5"
      },
      "source": [
        "You can use the **label=\"label text\"** keyword argument when plots or other objects are added to the figure, and then using the **legend** method without arguments to add the legend to the figure: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7hXyume477L5",
        "outputId": "6852d141-0218-4f9d-eb44-f6f70c60d196"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x113a3d8d0>"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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IBJ0h/xtC5QqV+Ufvf3hdyin8ttCEiIhIafppy0/MXD+TNcPWeF1KiWgMVkREgsaxrGM8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\nEswY6zgTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x11392f320>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure()\n",
        "\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "\n",
        "ax.plot(x, x**2, label=\"x**2\")\n",
        "ax.plot(x, x**3, label=\"x**3\")\n",
        "ax.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DiTlCp7n77L5"
      },
      "source": [
        "Notice how are legend overlaps some of the actual plot!\n",
        "\n",
        "The **legend** function takes an optional keyword argument **loc** that can be used to specify where in the figure the legend is to be drawn. The allowed values of **loc** are numerical codes for the various places the legend can be drawn. See the [documentation page](http://matplotlib.org/users/legend_guide.html#legend-location) for details. Some of the most common **loc** values are:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xlz4pH5i77L5",
        "outputId": "ad4d179c-fd11-4d2a-91e0-eb001bf49573"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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khyd5ttOzBBituSMF52QLVkTEK2VkZfDiDy8yc+NM5g6aS+e6nZ0uSfyYAlZE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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x11392f320>"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Lots of options....\n",
        "\n",
        "ax.legend(loc=1) # upper right corner\n",
        "ax.legend(loc=2) # upper left corner\n",
        "ax.legend(loc=3) # lower left corner\n",
        "ax.legend(loc=4) # lower right corner\n",
        "\n",
        "# .. many more options are available\n",
        "\n",
        "# Most common to choose\n",
        "ax.legend(loc=0) # let matplotlib decide the optimal location\n",
        "fig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7r9iFE3S77L5"
      },
      "source": [
        "## Setting colors, linewidths, linetypes\n",
        "\n",
        "Matplotlib gives you *a lot* of options for customizing colors, linewidths, and linetypes. \n",
        "\n",
        "There is the basic MATLAB like syntax (which I would suggest you avoid using for more clairty sake:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Wyk1HWr77L5"
      },
      "source": [
        "### Colors with MatLab like syntax"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HiXs0naf77L6"
      },
      "source": [
        "With matplotlib, we can define the colors of lines and other graphical elements in a number of ways. First of all, we can use the MATLAB-like syntax where `'b'` means blue, `'g'` means green, etc. The MATLAB API for selecting line styles are also supported: where, for example, 'b.-' means a blue line with dots:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tkug4S0u77L6",
        "outputId": "ced49f23-fbfd-450d-ab68-a21befdc69d3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x111fae048>]"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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TFrRu1oLWzZpz4k396NqWfes3b146lnr6QT9/bi6c9SEs2/gOgwfD5Mnlg/XI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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1122f5e80>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# MATLAB style line color and style \n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(x, x**2, 'b.-') # blue line with dots\n",
        "ax.plot(x, x**3, 'g--') # green dashed line"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJEI7f0N77L6"
      },
      "source": [
        "### Colors with the color= parameter"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwTP_soc77L6"
      },
      "source": [
        "We can also define colors by their names or RGB hex codes and optionally provide an alpha value using the `color` and `alpha` keyword arguments. Alpha indicates opacity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ieNaQov677L6",
        "outputId": "bfa2c0c1-0af9-4a37-ca65-b3a5ef588fc8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x112179390>]"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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1wTfrT2pF+B+5XYS/aOejN3yUmbfOZNQ5o6p9WlXxzjvBG4SqCGuHBnUS/aEtqAi3/xou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\noBYRqXEa1CIiNe7/A+LEV4vCOkiIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x112c79d30>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.plot(x, x+1, color=\"blue\", alpha=0.5) # half-transparant\n",
        "ax.plot(x, x+2, color=\"#8B008B\")        # RGB hex code\n",
        "ax.plot(x, x+3, color=\"#FF8C00\")        # RGB hex code "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEEEzhPS77L7"
      },
      "source": [
        "### Line and marker styles"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uymU3H3N77L7"
      },
      "source": [
        "To change the line width, we can use the `linewidth` or `lw` keyword argument. The line style can be selected using the `linestyle` or `ls` keyword arguments:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T9TCgESP77L7",
        "outputId": "33c76f03-5671-44d8-8dc8-b9a3dc20f79f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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ls2Zhqp2EgyH5+Rjx13HYWf/JgKzUeouuvXqFrOzaUlZYhpf//jJKvi5BemU6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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1124aaa90>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(12,6))\n",
        "\n",
        "ax.plot(x, x+1, color=\"red\", linewidth=0.25)\n",
        "ax.plot(x, x+2, color=\"red\", linewidth=0.50)\n",
        "ax.plot(x, x+3, color=\"red\", linewidth=1.00)\n",
        "ax.plot(x, x+4, color=\"red\", linewidth=2.00)\n",
        "\n",
        "# possible linestype options ‘-‘, ‘–’, ‘-.’, ‘:’, ‘steps’\n",
        "ax.plot(x, x+5, color=\"green\", lw=3, linestyle='-')\n",
        "ax.plot(x, x+6, color=\"green\", lw=3, ls='-.')\n",
        "ax.plot(x, x+7, color=\"green\", lw=3, ls=':')\n",
        "\n",
        "# custom dash\n",
        "line, = ax.plot(x, x+8, color=\"black\", lw=1.50)\n",
        "line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...\n",
        "\n",
        "# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...\n",
        "ax.plot(x, x+ 9, color=\"blue\", lw=3, ls='-', marker='+')\n",
        "ax.plot(x, x+10, color=\"blue\", lw=3, ls='--', marker='o')\n",
        "ax.plot(x, x+11, color=\"blue\", lw=3, ls='-', marker='s')\n",
        "ax.plot(x, x+12, color=\"blue\", lw=3, ls='--', marker='1')\n",
        "\n",
        "# marker size and color\n",
        "ax.plot(x, x+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n",
        "ax.plot(x, x+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n",
        "ax.plot(x, x+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, markerfacecolor=\"red\")\n",
        "ax.plot(x, x+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n",
        "        markerfacecolor=\"yellow\", markeredgewidth=3, markeredgecolor=\"green\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uRtu1xES77L8"
      },
      "source": [
        "### Control over axis appearance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1B44Iy_77L8"
      },
      "source": [
        "In this section we will look at controlling axis sizing properties in a matplotlib figure."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hxmZIPS77L8"
      },
      "source": [
        "## Plot range"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L8uaGw6n77L8"
      },
      "source": [
        "We can configure the ranges of the axes using the `set_ylim` and `set_xlim` methods in the axis object, or `axis('tight')` for automatically getting \"tightly fitted\" axes ranges:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P0qGfM8V77L8",
        "outputId": "d5924af2-4cbd-46bc-d8da-ba93a7d2f58b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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iOG1q29zKxoQKVeWz5Z/xYM0HXUcxUSYxOZFPln5C57qdXUcx2XDaYllV5wH7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\nM50TAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x11253afd0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
        "\n",
        "axes[0].plot(x, x**2, x, x**3)\n",
        "axes[0].set_title(\"default axes ranges\")\n",
        "\n",
        "axes[1].plot(x, x**2, x, x**3)\n",
        "axes[1].axis('tight')\n",
        "axes[1].set_title(\"tight axes\")\n",
        "\n",
        "axes[2].plot(x, x**2, x, x**3)\n",
        "axes[2].set_ylim([0, 60])\n",
        "axes[2].set_xlim([2, 5])\n",
        "axes[2].set_title(\"custom axes range\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IWYDP4Le77L8"
      },
      "source": [
        "# Special Plot Types\n",
        "\n",
        "There are many specialized plots we can create, such as barplots, histograms, scatter plots, and much more. Most of these type of plots we will actually create using seaborn, a statistical plotting library for Python. But here are a few examples of these type of plots:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ii-oitgw77L8",
        "outputId": "2fbb13c8-6254-4ba5-ba1e-9b8cdf43b2bc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x1122be438>"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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K2ZHdlHRvREye5uvlHAgARkxEnLGdPbaVA9jeGRFH848fl/TEVooBAGzOlsJc0pdtXyLp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\nYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x112497828>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(x,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2-1lMZew77L9",
        "outputId": "180e8992-4467-4697-abcb-625b3cd9efd4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([ 14.,  11.,   9.,  12.,   6.,   7.,  13.,  13.,   6.,   9.]),\n",
              " array([  28. ,  123.5,  219. ,  314.5,  410. ,  505.5,  601. ,  696.5,\n",
              "         792. ,  887.5,  983. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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H3fqHgV9asXtLffRK4E1J7gP+EXh1kk8BBxewL34APFRVe7r7n2Mc/Iv4ungNcF9V/XdV\nHQb+GfgtFrMvnnCyxz5Rn8wq6L8JnJ9ke5LTgcuBW2bU1kbySeCeqrpuxbpbgCu65XcCN69Yf3l3\n1cG5wPmMv3T2tFdVH6iqF1bVeYyf+9ur6u3AF1i8vlgGHkpyQbfqEuBuFvB1wXjI5hVJnpkkjPvi\nHharL8L//Sv3pI69G955LMnLuj58x4p9jm+GnzBfxvjKkwPA1fP+xHvWN8ZnsYcZX2F0F3Bn1wfP\nA27r+uJW4Dkr9rmG8afp+4HXzfsYZtQvv8OTV90sZF8Av8H45Gcv8HnGV90sal9c2x3XPsYfPp62\nKH0B3Ag8Avyc8ZvelcBzT/bYgZcCoy5br1tP235hSpIa54exktQ4g16SGmfQS1LjDHpJapxBL0mN\nM+glqXEGvSQ1zqCXpMb9L+T+/wUw8NjiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1131285f8>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from random import sample\n",
        "data = sample(range(1, 1000), 100)\n",
        "plt.hist(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ao3wnbbG77L9",
        "outputId": "a33863fd-1393-4608-b49e-b4edd885819f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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5u1GNRAlvQJLtLUlXSLq330qwX7bPsf2gpC9JOhIR9/dd0yYR3hi96ZTJ7dq94OzJvuvB\n/kTE6em6Sgcl/YTtH+67pk0ivDFqtg9oN7j/LCL+uu96sLqI+G9Jd0u6uu9aNonwLsPTB/K5VdJD\nEXFL34Vg/2xfbPvC6c/naXdZjlGdaCa8O7L9EUn/LOly24/ZfmvfNWF/bL9G0i9Lev205ewB26Ma\nvSX2vZLutn1Mu+cpPhURn+i5po2iVRAAEmLkDQAJEd4AkBDhDQAJEd4AkBDhDQAJEd4AkBDhDQAJ\nEd4AkND/A3l3rO9C5RPQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x113dd10b8>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
        "\n",
        "# rectangular box plot\n",
        "plt.boxplot(data,vert=True,patch_artist=True);   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hJLv1Js77L9"
      },
      "source": [
        "## Further reading"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_IDlSke77L9"
      },
      "source": [
        "* http://www.matplotlib.org - The project web page for matplotlib.\n",
        "* https://github.com/matplotlib/matplotlib - The source code for matplotlib.\n",
        "* http://matplotlib.org/gallery.html - A large gallery showcaseing various types of plots matplotlib can create. Highly recommended! \n",
        "* http://www.loria.fr/~rougier/teaching/matplotlib - A good matplotlib tutorial.\n",
        "* http://scipy-lectures.github.io/matplotlib/matplotlib.html - Another good matplotlib reference.\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": "3.5.1"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}