Lesson_7_Plotting/plotting_student.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "678b71e9",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/How-to-Learn-to-Code/python-class/blob/master/Lesson_7_Plotting/plotting_student.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "handmade-astrology",
   "metadata": {},
   "source": [
    "# Plotting in Python\n",
    "By the end of the lesson you will learn how to make simple plots using the matplotlib library"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "under-malpractice",
   "metadata": {},
   "source": [
    "## Learning objectives\n",
    "1. Basics of plotting\n",
    "2. How to scale and color\n",
    "3. Different types of plots\n",
    "    - Scatter plots\n",
    "    - Boxplots\n",
    "    - Histograms\n",
    "4. matplot lib axes\n",
    " \n",
    "We're going to use matplotlib, a Python library used for visualizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "interracial-publisher",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "plain-sunrise",
   "metadata": {},
   "source": [
    "First let's create a vector containing values from 0 to 100:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "rubber-insider",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.arange(0, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "approximate-orlando",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x) #use plot to plot our values\n",
    "plt.show() #to show the plot in the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "varying-windsor",
   "metadata": {},
   "source": [
    "It assumed our single data list to be the y-values and in the absence of an x-values list, [0, 1, 2, 3, ... 100] was used instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "stuffed-cabin",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.arange(0, 1, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "guided-honolulu",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forty-modem",
   "metadata": {},
   "source": [
    "Now we are passing in two lists to ```plt.plot```, which explicitly sets the x-values\n",
    "\n",
    "NOTE: when you pass in two lists to ```plt.plot```, their lengths must match. Here, our lists both contain 100 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "choice-vertical",
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 = np.arange(0, 1, 0.01)\n",
    "y2 = np.arange(2, 3, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "verbal-blocking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y1)\n",
    "plt.plot(x, y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flexible-stage",
   "metadata": {},
   "source": [
    "You can plot multiple curves on the same plot, but they have to be all the same length\n",
    "\n",
    "Right now, it's hard to tell by glancing which line belongs to which data. We can add labels and show a legend to make it easier to tell them apart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "available-success",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y1, label=\"plot 1\")\n",
    "plt.plot(x, y2, label=\"plot 2\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effective-extension",
   "metadata": {},
   "source": [
    "We can also add labels to the axes and a title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "moral-relation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y1, label=\"plot 1\")\n",
    "plt.plot(x, y2, label=\"plot 2\")\n",
    "plt.xlabel(\"x axis\")\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"This is a plot of two datasets\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "simplified-missouri",
   "metadata": {},
   "source": [
    "You can also change the line type (using ```linestyle```) and color (using ```color```)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "approved-pastor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y1, linestyle=\":\", label=\"line 1\") #include linestyle to change the appearance of the line\n",
    "plt.plot(x, y2, color=\"red\", label=\"line 2\") #include color to change the line's color\n",
    "plt.xlabel(\"x axis\")\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"This is a plot of two datasets\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "starting-collins",
   "metadata": {},
   "source": [
    "You can change the scaling of either axis by using either ```xlim``` or ```ylim```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "tutorial-reviewer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y1, linestyle=\":\", label=\"line 1\")\n",
    "plt.plot(x, y2, color=\"red\", label=\"line 2\")\n",
    "plt.xlim(0, 60) #sets the lower and upper bound of the x axis\n",
    "plt.ylim(0, 2.5) #sets the lower and upper bound of the y axis\n",
    "plt.xlabel(\"x axis\")\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"This is a plot of two datasets\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "handed-headline",
   "metadata": {},
   "source": [
    "You can see that this is the same plot as above, except it's more zoomed in because we changed the axes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hindu-frost",
   "metadata": {},
   "source": [
    "## Other types of plots in matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "vulnerable-welsh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot\n",
    "x = np.random.rand(50)\n",
    "y = np.random.rand(50)\n",
    "plt.scatter(x, y)\n",
    "plt.xlabel(\"x axis\")\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"Scatter plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fewer-consumption",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IbaiqHVU1W1WzMzMzKzKcdCRVNZEvadpahmAeOG1oeT1w94PYRpLUUMsQ3AhsSHJWkhOA84GrRra5Crhw8Omhc4FvVNVXGs4kSRqxutUTV9XBJNuAa4BVwM6q2p3k4sH67cDVwHnAHuA7wEWt5pEkjdcsBABVdTWLP+yHH9s+9H0Br245gyTp8DyzWJI6ZwgkqXOGQJI6ZwgkqXM51k5oSbIA7J32HNIS1gL7pj2ENMYZVTX2jNxjLgTSQ1mSuaqanfYc0tHwrSFJ6pwhkKTOGQJpZe2Y9gDS0fIYgSR1zj0CSeqcIZCkzhkCaQUk2ZnkniS3TnsW6WgZAmllXA5smvYQ0oNhCKQVUFWfBvZPew7pwTAEktQ5QyBJnTMEktQ5QyBJnTME0gpI8iHgeuCJSeaTvGraM0nL5SUmJKlz7hFIUucMgSR1zhBIUucMgSR1zhBIUucMgSR1zhBIUuf+Hx2UJGqOrvCOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot\n",
    "data = np.random.rand(50)\n",
    "plt.boxplot(data)\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"Boxplot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "split-lafayette",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Multiple boxplots\n",
    "data1 = np.random.rand(50)\n",
    "data2 = np.random.rand(50)\n",
    "\n",
    "# Make a list of the two datasets\n",
    "data = [data1, data2]\n",
    "plt.boxplot(data)\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"Boxplots for two datasets\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "simplified-privacy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram\n",
    "data = np.random.rand(100)\n",
    "plt.hist(data, bins=10, facecolor='green', edgecolor='black')\n",
    "plt.xlabel(\"x axis\")\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"Histogram\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distinct-circuit",
   "metadata": {},
   "source": [
    "# Plotting multiple graphs in one figure\n",
    "In matplotlib, there is a distinction between a \"figure\" and something called an \"axes\" object (see image below). The figure contains your plots and can be made up of more than one plot. Each plot is inside of the axes object. In all examples so far we've only had one plot so there was only one axes object inside of each figure. Think of the figure like a sheet of paper. We can draw multiple plots inside of boxes on the paper. The boxes represent the axes object. \n",
    "![Representation of the figure structure in matplotlib](matplotlib_figure.png)\n",
    "Figure from: https://towardsdatascience.com/what-are-the-plt-and-ax-in-matplotlib-exactly-d2cf4bf164a9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fresh-chance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A6cDRwHmSjk449N/N7Lj43ydzljMVdZnjUAeWLhrnra8ZZ4YEwAyJt76mu0c3zLylJim1M9o0qQ0KQS/T9KCOBx4wswfN7FngeuDsYsUaDB9YzI9VGya4af3E3mVh9phx0/qJngO+g3q0TVLqbtQlCuEMR1IbJKLw2CAJE1UmfYWgl2kM1DjwWNvvrfG2Tl4raZOk2yQdk3QhScskrZO0bvv27QOI25uiVx8YpQzBMrv3TXcs6hSFcIajvQ2CyDi10jiyrqJR9arwIehlmiQJJWzrnHv3feBwM9sp6QxgFXDktJPMrgKuAli8eHHS/L2hKWpgMYQBwzIHhcvs3oeSsVQge6MQAJJaUYj/rFQqpxBabdCSlWumJQ5lSZioOukrBL1MY6C2Aoe1/T4U2NZ+gJk93fb3rZL+VtJcM3s8HzGrp+rKUraBLHuiYNUZSwWTFIU4IeG410raRKRfHzGzzUkXk7QMWAawYMGCnEV18mJYJ68sJ7GX41u1XqYJ8X0POFLSEZL2B84Fbmk/QNKLpWg0XdLx8XV/nrewVVL1gOElt2wuNaMmhO59g8gShXgV8BmiKEQiZnaVmS02s8Xz5s3LT0pnaNqHAfZTUrGnd/LKGAOqOozYj74Gysx2AxcAq4EfADea2WZJ50s6Pz7sbcB9sfd3BXCumRUSwquKKgcMV22Y2LsoZidFGUhfTTxXUkUhzGxn/PetwJikueWJ6AxLZ2Pf+d0pyObkleEkdosMffiGjUGMs6eaqBsrzK0d265s+/uzwGfzFS0sqlwFuVcvqUgDmbZ7H+KEycDYG4UAJoiiEO9oP0DSi4Gfmpk1NQqRN8PWu7zrbVJjD9EUjefMMt9j6aJx1j3yBNfd/Rh7zPpO9RiEXg5uCBPmG7WSRJFUOWDYqxJVHXILIXkkdMxst6RWFGIGcE0rChHvv5IoCvFBSbuBSRoUhSjCgRm23hVRb7vp6XNmPLTyzMzX6zbVY/Hhh+SmW/0W4a16JZ7GGagivfmqBgy7VaKDZ49VbgSqTh6pC6MahSjKgRm23hVRb/NOLCpDt5IiQ51UOWG+UauZhz7gNyjdYtGfOCtxulmpVJ084oRNUfPpQsyQy3vMqAzd6py3lUSVE+YbZaBCWDuqCEJOWAhhtnkSozSpOmSKamSHrXdF1Nu89bQs3WqtAPPpc44LLnO3USG+0Lz5PMONVc9H6EaIn9D2cbFwKGo+3bD1rqh6m6eelq1bIUzM7aRRBiqkr1B+dNW9/MNdj05b5gSa1UiWkWmUFR8XC4ciDQEM3piG2Bh3UoWMoTnCjTJQoXjzqzZM7GOcWjSxkSwj0ygrofWkR5kiG9lhG9PQGuMk6iBjkTTKQIXiFV2+ess049RikEYy5HlGIfZWQupJV0Fo9WXUG1lncBploCAMZehlhLI2kqGPp4TYWwmlJ10FRdaX0Ayf03walcUXCt2MkMg+sTb0zMQQs/hCznosmqLqS9lTODwL04EG9qBCIMmDF/DOExdkbiRD7KG0E2pvJYSedBUUVV+6Gb5Lbtkc3CoRoeA9zuFxA1UAeY6FhT6ekvZZuymrK3G+FFVfuhm4HZNTexcyDmWViBBoipGtGjdQBZGXBx9qD6Wdfs/aTVnXPfIEN62fcCXOkaLqS78121rkYUhCjxqkoQlGNgR8DCpwmjCe0k1Zr7v7saDH1+pIUfUlaRmfbkzsmBxq7CjEcc2sNMHIhoD3oLoQUuipyM/Yl/GM3ZQy6Xs5vY530lFEfUkK5T7z7G6efCb5O2XtiRTt56ehDlGDfoQemq8LbqASGIX4cZnP2E1ZZ0iJRsqVOEw6DV9nHUpikLBWpzGcM3sMM7jwho1cvnpLLcYp62pkQxsr9hBfAqGndudBmc/YbZXn8044LLjFKZ30dIYTuzFIj7i1gOlfn3Mcv5x6jh2TU7X6QkGvUGsRKfR5XLPbVIKPrrq3sq9EeA8qgVGIH5f5jL0y/RYffkgwoVQnO+29qiUr1+Qe1qpzskFSqLWIyEVe1+w1VtwZ6SirDNxAJTAK8eOyn7HbuMiozlcKnUFCOkWEtZrmLBZhcAe9ZmcZd8vSrHKs2EN8CeT94bEQGYVndAZj0FUjisggDCmjL48wWhEGd5BrJpVxtzDtDCXvKaMMvAeVQCiLzuZNp8f01teM8837tzfqGZ3hGcbLz7tHHEqyQV5htCIiF4NcM6mMjWjFm/b+0qyxGbz1NeP7zFdsbS+jDNxAdaFpoackBbtp/UTpc6pCSt93kgkprBaKs5hXaC7J4I7tJ555djdHrPjGQM83iBHvVpZG1PMNZazYDdSIUOZaat0YhfT9JpDFIy/D4QjBWczLaHca3INmjbGrbT7ZIDoxiBHvVsbjc2axdsUpifeoogx8DGpE6LWWWlnpo6OQvt8E0o5Plr3CeZXkORbWSqF/aOWZHPCCmUztSc6QG/Saa1eckiqhpQ5j0G6gAqOozwykVaQiDUZeXqh/iqFY0iY7jJLDUVSDXlU4tS5LqAUZ4hvVcYoiQ2BJcepuFKUceQwQe5iwHNKEdEIaqyqaosbCqpzSEkLotB/BGahQGqAqjGSRkxKzrKVWlHLkkZHV7R1ddOMmwI1UmYzCfMF2imjQ0+jEqDrsEKCBGqSRzrsAqzKSRXukadZSKzIOnYcX2mvhWe9JlUsoKeB1pp9OhOKwV0VwBiprI11EAVa1vEoVqztAuSm8w3qhc2aPdV1Buy5L4DSFUFLA604vnajzUk95EJyBOmjW2N4vdLbTrZEuogCriq1X4ZHWIQ7dYtWGCXb+cnfPY5o4/hEydao/dWSUxvmSCMpArdowwa5npzdAY/upayNdRAFWFVt3j7Q3l6/ewtRzyeuCtdhPSpzwOMpx/FF+9roT8jhfGfUqlYGSdBrwN8AM4GozW9mxX/H+M4BngPea2fezCnP56i3T5gQAHPjCmV0fvIgCrDK27h5pd9I4Ha2FLdtDvcDIxvFHfQyj7oQ6zldWveo7D0rSDOBzwOnA0cB5ko7uOOx04Mj43zLg84MI03UyaZcxByhmfkJd5giMGr2cjqQFLVuh3lGar9PJKD97Ewi1LSqrXqXpQR0PPGBmDwJIuh44G/jPtmPOBr5kZgbcJWmOpJeY2Y+zCDNIb6iosJj3ZMKjmzd52VuO5cIbNiae06vXNQpx/FEfw2gCIbZFZdWrNCtJjAOPtf3eGm/LegySlklaJ2nd9u3bp91o0N5Q1mU+nHrSy5vstRRNCJ9skHSapC2SHpC0ImG/JF0R779H0qvzuG8Iz+40j7LqVRoDlfQxkM6BojTHYGZXmdliM1s8b968aSeE2p11wqGbM9LLual63bEyw+SdVP3sTjMpq16lCfFtBQ5r+30osG2AY1IRYnfWCZ80od4KM9lKC5N34pmhThGUVa9kXT7nu/cAaSbwQ+CNwATwPeAdZra57ZgzgQuIsvhOAK4ws+P7XHc78EjbprnA4wM8Q1m4fMNRV/kON7Pp3f0MSHobcJqZfSD+/W7gBDO7oO2YrwMrzew78e9/Bf7EzNYlXG8ZUS8L4Cigc2S6ru86JEKXsU7yDaxDfXtQZrZb0gXAaqI082vMbLOk8+P9VwK3EhmnB4jSzN+X4rr7CCxpnZktzv4I5eDyDceIy5dbmByiUDlwVdebjfa7zoXQZRwV+VLNgzKzW4mMUPu2K9v+NuBDwwrjOA2l1DC54zQF/x6U4xTP94AjJR0haX/gXOCWjmNuAd4TZ/OdCDw17PiT49SdkJY66hqyCASXbzhGVr6iwuQ9GNl3nSOhyzgS8vVNknAcx3GcKvAQn+M4jhMkbqAcx3GcICncQA2zxEu/c0uS752xXPdIulPSq9r2PSzpXkkbJU2br1KSfCdJeiqWYaOkj6c9tyT5lrfJdp+kPZIOifeV8f6ukfQzSfd12V9p/cuK61Ph8rk+9ZavXH0ys8L+EQ0I/wh4KbA/sAk4uuOYM4DbiOaBnAjcnfbckuR7HXBw/PfpLfni3w8Dcyt+fycBXx/k3DLk6zj+LGBNWe8vvscbgFcD93XZX1n9K6g+uD4NJ5/rU28ZS9WnontQe5d4MbNngdYSL+3sXeLFzO4C5kh6ScpzC5fPzO40syfjn3cRzU8pi2HeQRDvr4PzgOtylqEnZvZt4Ikeh1RZ/7Li+lSwfAWdW5R8jdenog3UMCuhp1ohvQT52nk/kXfQwoDbJa1XtPxM3qSV77WSNkm6TdIxGc8tQz4kzQZOA25q21z0+0tDlfUvK65P5cjn+jQ4uda/oudBDbPES+qlX4Yg9T0knUykUK9v27zEzLZJehFwh6T7Yw+jTPm+T7TW1U5JZwCriFbEDur9EYUj1ppZu/dV9PtLQ5X1LyuuT8XL5/o0HLnWv6J7UMMs8VLG0i+p7iHpt4GrgbPN7Oet7Wa2Lf7/Z8A/EXVjS5XPzJ42s53x37cCY5Lmpjm3DPnaOJeOcEQJ7y8NVda/rLg+FSyf69PQ5Fv/Ch5Qmwk8CBzB8wNjx3Qccyb7Dqp9N+25Jcm3gGh2/+s6th8A/Frb33cSrVhdtnwv5vkJ18cDj8bvMoj3Fx93EFHc+oAy31/bvRbSfVC3svpXUH1wfRpOPten/nKWpk9lKNUZRJ/r+BHwZ/G284Hz479F9DG3HwH3Aot7nVuBfFcDTwIb43/r4u0vjV/yJmBzhfJdEN9/E9Gg8+t6nVu2fPHv9wLXd5xX1vu7DvgxMEXkxb0/pPpXQH1wfRpOPten3vKVqk++1JHjOI4TJL6ShOM4jhMkbqAcx3GcIHED5TiO4wSJGyjHcRwnSNxAOY7jOEHiBspxHMcJEjdQjuM4TpC4gXIcx3GCxA2U4ziOEyRuoBzHcZwgcQPlOI7jBIkbKMdxHCdI3EAFiqRvSfrAgOdeIunLecvkOHXD9ajeuIEqEEmvl3SnpKckPSFpraTfqVqudiQ9LOl3e+w/WtI6SU/G//5F0tFlyuiMNk3Qo45jPyHJ0h4/yriBKghJvw58HfgMcAgwDlwK/KpKuQZgG/A2omeYC9wCXF+pRM7I0CA9AkDSbxLp04+rlqUOuIEqjpcBmNl1ZrbHzCbN7HYzuwemhw8kLYy9qplt1/hNSd+NPcd/lnRIx7HLJG2T9GNJF3UTRNKbJW2WtCMOebwi3n4t0RdOvyZpp6Q/7jzXzHaY2cMWfThMwB7gt4Z/PY6TikboURufBf4EeHbQFzJKuIEqjh8CeyT9vaTTJR08wDXeA/wBMB/YDVzRsf9k4EjgTcCKpJCBpJcRfQXzw8A84FYiRdrfzN5N9Enrs8zsQDP7q26CSNoB/JLIk/3LAZ7FcQahMXok6b8Dz5rZrQM8w0jiBqogzOxp4PWAAX8HbJd0i6TfyHCZa83sPjPbBXwMeLukGW37LzWzXWZ2L/AF4LyEa5wDfMPM7jCzKeBTwCzgdRmfZw5wENEnsTdkOddxBqUpeiTpQCLH7sMZ5B553EAViJn9wMzea2aHAq8k8uA+neESj7X9/QgwRjQO1G3//IRrzI/3tWR6Lj5vPIMcrXN3AVcCX5L0oqznO84gNESPLiUylA+lPN7BDVRpmNn9wBeJFAxgFzC77ZAXJ5x2WNvfC4Ap4PEe+7clXGMbcHjrhyTF5020ROsv/T7sRyR3ZgPnOMNSYz16I/BHkn4i6SfxuTdK+pM+5400bqAKQtLLJV0k6dD492FEoYO74kM2Am+QtEDSQcDFCZd5V5zmPRv4JPBVM9vTtv9jkmZLOgZ4H3BDwjVuBM6U9EZJY8BFRBlQd8b7fwq8tMdz/J6kRZJmxBlV/w94EvhBmvfgOMPQFD0iMlCvBI6L/20D/ifwuV7PP+q4gSqOXwAnAHdL2kWkUPcRVWzM7A4iRbgHWE+UStvJtUTe4k+AFwJ/1LH/34AHgH8FPmVmt3dewMy2AO8iSm54HDiLaDC3lUV0GfDRODPpIwkyzCEaHH4K+BFRBt9pZvbLvm/AcYanEXpkZj83s5+0/hFlwz5pZjvTvohRRFH2sFMnJC0EHgLGzGx3xeI4Ti1xPQof70E5juM4QeIGynEcxwmSvgZK0jWSfibpvi77JekKSQ9IukfSq/MX02knXtlBHpaoD65H4eF6FD5pelBfBE7rsf90olnYRwLLgM8PL5bjNI4v4nrkOJmY2e8AM/t2PJjYjbOBL8Vrtd0laY6kl5hZz8UQ586dawsX9rqs41TP+vXrHzezecNex/XIGVWG0aG+BioF4+w7E3trvK2nYi1cuJB169blcHsnRFZtmODy1VvYtmOS+XNmsfzUo1i6qH5zeyU90v+oXEitR5KWEfWyWLBgQSY9akq5OPVhGB3Kw0ApYVti7nqnYjnNZNWGCS6++V4mp6K5kBM7Jrn45nsBvDHsTmo9MrOrgKsAFi9enHqeSJpycQMWBl4OEXlk8W1l36VCDiV5qRDM7CozW2xmi+fNGzpq4gTK5au37G0EW0xO7eHy1VsqkqgWpNajQelXLi0DNrFjEuN5A7Zqw0TC1ZyiSFMOqzZMsGTlGo5Y8Q2WrFzT2DLKw0DdArwnzkI6EXiqX9zcaTbbdkxm2u4AJehRv3JxxyIM3JF4nr4hPknXAScBcyVtBT5BtBowZnYl0XdRziBaKuQZorWsGot3vfszf84sJhIaw/lzZlUgTRiEoEf9ysUdizAYxpEooi2qss1Lk8WX9G2U9v0GfCg3iQJm1MdW0lbU5acetc97Apg1NoPlpx5VprhBEYIe9SsXdyzCICRHIqnNu/CGjax75An+Yumxud+vE19JIgOjHALJElZYumicy95yLONzZiFgfM4sLnvLsZUb8VGJ23ejX7ksP/UoZo3N2OecUXcsqqBfOXRzGIpwJJLaPAP+4a5HS9GfPLL4Gk17r6FbutQohECyhhWWLhqv3CC1M+q93xa9yqW13UPY1dKvHMqMUHRr2yyWr+i64QaqB52NWjdGIQRS9/GJsuP2dSU0x2JUCcWR6BZuhHJ03w1UD5IatU7y8lxCT76o+/hE3Q2s47RTliOx/NSjuPCGjYnRozJ038egetCr8cpzbKUOaaN1H58oM27vOE1h6aJx3nnigmmzyMvSfe9B9aBbr2F8zizWrjglt/vUIfxURlihyF6kZxY6zmD8xdJjWXz4IZVEeNxA9aCsRi2P8FMZIcIiwwpFJzF4AkB68qpLoYetnfRUNTYZnIEKqVKX1agNO76T1Lh/+IaNXHjjRt55woJS5isMSxm9SE8A6E9ejoJnTTp5EJSBCrFSl9GoDdtT65bMYQZfvutRgOCNlCcxhEFejkJRDkdIDqxTPEElSYzqRNhhJ7b2a8Svu/uxnvtDwJMYwiAvR6EIh6MOyUROvgTVgxplL3qYnlqvuQoAeyz1Fxkqw5MYwiCv6QRFTEuoQzKRky9B9aDcix6MpBTwdmYo6VNDYRHq8kijRl7TCYqYlhCqAzvqS2gVSVA9KPeiB6PViP/pzffwzNRz0/afd8Jh07aFiCcxVE9eiUGDXqfXGFOIk8VDHDdvErKKwj+LFy+2pE9V+yDocHx01b1cd/dj7DFjhsR5JxwWfIJEyEhab2aLq5ajG930qI4kLS02a2zG3p50v/1VsGTlmlLmSuZNme3sMDoUVA8K3Iselr9YeqwbJKeW9BtjCnEuW6hhx17UqdcXnIFy+lNFL7OMe3rvuRkMWo5pGvvQHNgQw47QuwzqlGziBqpmVOH9lHHPOnl1TneylmN7Q7qflJhxWnVj34sQx837lUGden1BZfF14tkx06lirlgZ9xzVOXAhkYe+ZSnHznlNScap6sa+HyFmn/YrgzplSwfbg3KPOpkqvJ9u157YMcmqDRO5lEedvLom0m25rEtu2cwlbz5m6EnjSdu7rYAyQ+I5s9qEeUMLO/YrgxB7fd0I1kDVKU5aJmXGvFvhl155nnk5DaHG8keFbsZix+RUpjLOUo7dGtLnzHho5Zl971VHyhhn7VcGISabdCPYEJ971MkMMgFykNBNe/ilF3mF4er+vam600uvspRxlnKsKtSURR/yHGYoa6mmNGWwdNE4y089ivlzZrFtxySXr94S5BBKsAYqr8rbtHGsrDHvQZUizdeEW+ThNIQYyx8l+ulV2jLOUo5VOCVZ9CFvg1LWOGuaMqjLuobBhvjyiJM2dRwrS8x70FBpFqOTl8dbVizf09mnk6Rv7WQp47TlWEWoKYs+5D3MUGZUqF8Z1GUIJVgDNUzlbTVASeGpEAuhSAZVim5xbME+Y1J1C8M11WkZltazX/q1zTz5zNQ++/Is4yTnYNgVF7I4HFn0IW+DEtI4a12GUII1UDCYR520HEonoRVCkQyqFN16sG99zTjfvH97bXsfdfEcy6bVyO94ZoqDZ49hBk9NTuVaxkU4B1mv2UsfOg3dnNlj04x169hBCCl7LiRj2YugDdQgpBk7Ca0QimRQpahTpk8W6uI5lklnI//kM1PMGpvBX59zXK7lXYRzcMktmzNds5s+nPzyedMM3dh+YmyGmNpj+xw7qEEJSadCMpa9aJyB6tfQhFgIRTLMqtLt4Z5dv9pduKxlUBfPMQ/Shr7K6lXm7Rys2jDBjsnpPRyIDMySlWumPXs3fUh6B1PPGXNmjXHAC2bmZlBCmTMVkrHsReMMVK+P940HWghFk1UpVm2YYPlXN+3jOe6YnGL5VzbtvV6Z5JnUUBfPcViyhL7K6lXm7Rz0yn4T7L1X57Mn6cOFN2xMvM5Tk1Ns/MSbBpIvdEIxlr0INs18ULqlrn76nONYu+KU4AskBC5fvWUf49Ri6jkrfemhvNNhRyWdPUtKc1nzkfJOK+9lQDtrb7907jot/zNKNK4HVZeua8j0Uvyyx2qKCD/VwXMcliy9orJ6lXnrZq9oSRK96u6o9KzrRuMMFIxGA1QkvRS/bI/SkxoGI0s4rUynLk/d7GZUXjBzv8SxqaRMvX5jU96OVEsqAyXpNOBvgBnA1Wa2smP/ScA/Aw/Fm242s0/mJ6ZTJstPPWraGBTA2H4q3aNsSlJD2TqUtUdQR6eum1EBUmfq9Rubcqqlr4GSNAP4HPB7wFbge5JuMbP/7Dj0383s9wuQ0SmZpEmbc2aNZVrVOi+aEHqpQodGpUfQy6ikydTzOXDpqGr1lTQ9qOOBB8zsQQBJ1wNnA53K5bRR9+V0QvEmG9LQVqJDoZRhFWTJ1PNwcW+qXH0ljYEaBx5r+70VOCHhuNdK2gRsAz5iZps7D5C0DFgGsGDBguzS1gRfTidfGtDQ5qZDkF6P6u4k5U1TwsVlU2XPM02auRK2dWZxfh843MxeBXwGWJV0ITO7yswWm9niefPmZRJ0EKpaydy/Dut0kJsOQTo9qstq1WXin3QZjCoTldIYqK3AYW2/DyXy8PZiZk+b2c7471uBMUlzc5NyAKpUUM88czooXYfcSZrOqMyBy5sq54ilCfF9DzhS0hHABHAu8I72AyS9GPipmZmk44kM38/zFjYLVXZLs4QSPAwzEpSuQ+4kJdOAcHHpVJmo1NdAmdluSRcAq4lSZK8xs82Szo/3Xwm8DfigpN3AJHCumfX6UnjhVKmgaQvUx6pGgyp0yMdbymEUHMwqE5VSzYOKQw63dmy7su3vzwKfzVe04ahSQdMWaJ69vFFQlE7q9Mxl61AT0vNDZ5QczKp6no1cSQKqV9A0BZpXL2+UFKXFKD5zFhqSnh80Pq+qeBproOqgoHn18kZRUUbxmdPS2bPM+7tOTkQVwwh1ihrkQWMNFIQ/IJpXL28UB8RH8ZnT0KtnCeU6bE1vTMseRhjFqEHjPrdRJ/JKex3FTwWM4jOnoVvP8tKvbS512sUozMMqe17VKE4daHQPqg7k0csrcrwtVC+46jHGUOnWg2ytqdhOkSHRKkKwZdfVsocRRjFq4AaqAbQrysSOSWZI+3hWg2YETuyYRDy/5EFIIYU6jDFWQZ7fSBqGshvTqsJf/RzMPI3mKE4dcAMVIINU6tb+YZW0U9G7fZk0BEMQ+hhjFQzyjaQiKLsxDTFpJm+jmSZqEGrEY1B8DCowhond5xGjTrpGJ00OKdSdbuOal7z5mFLHS8oenwkt/LVqwwQX3bgp1zGjfmPWTRz38x5UYAzjCeahpGmObXJIoQlk+UZSUd512SHYkMJfLUOxp8tCIMMYzX5lG1ovcljcQAXGMEYmDyXtN4bhiQj1peyQaJn3Cylppl8UoiijGVovMg88xBcYw6RPDxtWWbVhgl2/2j1te+tbEb76sxMqIa1U3ssgFGk0mzj1wntQOTPsIOUwnuAwYZXOAd0WB88e4xNnlf+pd8fJSihJM92iEDOkQo1mSL3IvHADlSN5ZO0MG7sfVEm7hSVm7z8zCKV3nFDpdEpPfvk8blo/Mc1QFN2ja+LUCzdQOZLXIGUVnmAT49eOUzRJTulN6yd462vG+eb920s3FKH0IvPCDVRK0oTu8mjkq5rHkHcWVJXzMZo2F8QJl25O6Tfv387aFadUJFVzcAOVgrShu2Eb+SoXg8wzfl3lc4zigppF4sa+Nx55KBbP4ktB2gmww2bRVbkYZJ5ZUFU+xyguqFkUTZz4mTdNzJwLCe9BpaCbNzSxY5IlK9fs411e9pZjB/Y4q/bG8opfD/Mcw3rsVb/DJlHUxM8qemVF3bOJmXMh9ZqDNlChvKhuoTvB3u0t7/Kytxw7cOy5V4gwlHeRhkFDnXmE50JaUaDuFGHsqwjBFnnPpmXOhRYiDzbEF1J4ISl0177Kd4vOUNKqDRMsWbmGI1Z8gyUr1/SVvVuI8OSXzwvmXaRh0FBnHuG5steAazJFhK+qCMEWfc+li8ZZu+IUHlp5JstPPYrLV29JrfOhEVqIPFgDFdKLShqfSV5l63nvchAD220c6Jv3bw/mXaRh0PGsPDz2kFYUqDtFGPsqQrBl3TMkp3pQQguRBxviC+1FdY7PLFm5pmcoadD4fdI40IU3bEw8tspxlX4hx0HGs/IKzzVtLkhVFBG+ylrGeYS2ywr7NmGx1tBC5MH2oELPjunnXeZpYEN7F0V5ih6eC4/28NXaFafk8vXntGWcVz0rq16F5lQPQmg6GKyBCu1FddIvlJSnUQntXWQJv2YZhyszPJd1fNDJhyxlnFeYv6x6FZojOQihhciDDfHVITumVygpz/TT0N5FWk9xkIygMsJzoWUqjRppyzjPHkkZ9aopKechhciDNVAQ1ovKSt5GJaR3kTZOHWpMPlS5nH0JbTykH6E5kkVTxtSXoA1U3QnJqHQyTOVK6ymGGpMPVS5nX+rYIwlZ5/OkrCiEG6iSCWHC7bCVK62nGKoHHKpczr6MWo+kTlz6tc2lRCHcQJVIKGMfeYS40niKoXrAocrlTGdUeiR1YtWGCZ58ZipxX95RiGCz+JpIKJOPe60tmGc2W2gZQaHLVQc8+9Hp1V7lHYVI1YOSdBrwN8AM4GozW9mxX/H+M4BngPea2fdzlbQBhDL20S3EBeTeowvVAw5VrpAJJQLgVEuv9irvKETfHpSkGcDngNOBo4HzJB3dcdjpwJHxv2XA53OVsiGEMk8iaV5Vi5CXUKozkk6TtEXSA5JWJOyXpCvi/fdIenUVcvYilAiAUy3d2qs5s8Zyd1TShPiOBx4wswfN7FngeuDsjmPOBr5kEXcBcyS9JFdJG0AoE25bIa5ueDZbvjTFyQslAuBUS7d27JI3H5P7vdIYqHHgsbbfW+NtWY9B0jJJ6ySt2759e1ZZa09IYx9LF40zHkiPbgRohJMXSgTAqZYy27E0Y1BK2Na5mHeaYzCzq4CrABYvXtxtQfBGE9LYh2ezlUaSA3dCimPGgR93XkzSMqJeFgsWLMhV0F54fXFalNWOpTFQW4HD2n4fCmwb4BgnMHyeSWnk5uRBdY6e1xenbNIYqO8BR0o6ApgAzgXe0XHMLcAFkq4n8gyfMrNpnl8769evf1zSIz0OmQs8nkK+KmikbA8D/+3iXGXppI7v7fAcrl2Yk9dFj0p5zw8zcH0JuR5A+PJB+DK2yzewDvU1UGa2W9IFwGqiNPNrzGyzpPPj/VcCtxKlmD9AlGb+vhTXnddrv6R1Zra4/yOUj8s2GCMsWyFOHiTrUcjvGVy+PAhdxrzkSzUPysxuJTJC7duubPvbgA8NK4zjNJGinDzHaTq+1JHjlIA7eY6TnZCXOrqqagF64LINhstWDqE/i8s3PKHLmIt8ihw3x3EcxwmLkHtQjuM4zgjjBspxHMcJktIN1DCLZvY7twTZ3hnLdI+kOyW9qm3fw5LulbRR0roKZDtJ0lPx/TdK+njac0uQbXmbXPdJ2iPpkHhf0e/tGkk/k3Rfl/2V1beshKw7GWSsTIdSyleZHqWUrzJdiu9Rrj6ZWWn/iFJsfwS8FNgf2AQc3XHMGcBtRDPrTwTuTntuCbK9Djg4/vv0lmzx74eBuRW+t5OArw9ybtGydRx/FrCmjPcWX/8NwKuB+7rsr6S+FVQHKn2WkHUog3yV6NEg9yhbl+J7lKpPZfeghlk0M825hcpmZnea2ZPxz7uIZvuXwTDPXvl76+A84Loc798TM/s28ESPQ6qqb1kJWXdSy1ihDqWSr6Bzi5KvVF2C8vWpbAM1zMroqVZML1i2dt5P5Cm0MOB2SesVLeaZJ2lle62kTZJuk9Ra+z6Y9yZpNnAacFPb5iLfWxqqqm9ZCVl3ssjYTpk6lEW+KvQo0z0C1SXIuQ6WPVF3mEUzUy+mOSCpry/pZCLlen3b5iVmtk3Si4A7JN0fextlyfZ94HAz2ynpDGAV0beFgnlvRCGJtWbW7oEV+d7SUFV9y0rIutPv/tMPLF+H0spXlR6R8R4h6hLkXAfL7kENs2hm0Sump7q+pN8GrgbONrOft7ab2bb4/58B/0TUpS1NNjN72sx2xn/fCoxJmpvm3KJla+NcOkISBb+3NFRV37ISsu5kkbEqHUolX4V6lEq+NkLUJci7DhY5oJYwgDYTeBA4gucHyo7pOOZM9h1k+27ac0uQbQHRWmmv69h+APBrbX/fCZxWsmwv5vmJ18cDj8bvsPL3Fh93EFHs+oCy3lvbfRbSfVC3kvpWUB2o9FlC1qEM8lWiR1nKqUpdiq9fmj5VoWhnAD8kyuj4s3jb+cD58d8i+jz2j4B7gcW9zi1ZtquBJ4GN8b918faXxi98E7C5ItkuiO+9iWjw+XW9zi1Ttvj3e4HrO84r471dR/TRvykiL+79odS3AupA5c8Ssg6llK8yPUojX/y7El2K71OqPvlSR47jOE6Q+EoSjuM4TpC4gXIcx3GCxA2U4ziOEyRuoBzHcZwgcQPlOI7jBIkbKMdxHCdI3EA5juM4QfL/AWGoTEmY2LHlAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_rows = 2\n",
    "num_cols = 2\n",
    "count = 0\n",
    "\n",
    "fig, axes = plt.subplots(num_rows, num_cols)\n",
    "for row_num in range(num_rows):\n",
    "    for col_num in range(num_cols):\n",
    "        count += 1\n",
    "        ax = axes[row_num][col_num]\n",
    "        ax.scatter(np.random.rand(50), np.random.rand(50))\n",
    "        ax.set_title(f'Subplot {count}') \n",
    "        #set_title is used for an axes object, whereas a plt object uses just title\n",
    "fig.suptitle('Main title')\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#code adapted from above towardsdatascience link"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlikely-elements",
   "metadata": {},
   "source": [
    "The above example used iterative plotting to create and plot multiple plots in a ```for``` loop. However, if you have a set of plots already made that you want to include, you can use a different command called ```subplot```. The format is ```plt.subplot(number of rows, number of columns, plot number)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "spiritual-depth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 12)) #to specify how big the figure should be in order to make it readable\n",
    "plt.subplot(3, 1, 1)\n",
    "x = np.random.rand(50)\n",
    "y = np.random.rand(50)\n",
    "plt.scatter(x, y)\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"Scatter plot\")\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "data = np.random.rand(50)\n",
    "plt.boxplot(data)\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"Boxplot\")\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "data = np.random.rand(1000)\n",
    "plt.hist(data, bins=10, facecolor='green')\n",
    "plt.xlabel(\"x axis\")\n",
    "plt.ylabel(\"y axis\")\n",
    "plt.title(\"Histogram\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "positive-tomato",
   "metadata": {},
   "source": [
    "# In class exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "engaged-marble",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the IRIS.csv dataset (from: https://www.kaggle.com/datasets/arshid/iris-flower-dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cosmetic-school",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the petal_length and petal_width columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "accomplished-albuquerque",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a scatter plot of petal length vs width\n",
    "# Bonus: color the points based on species\n",
    "# Hint: the scatter plot function has a c option to specify color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aquatic-asthma",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label the x axis \"petal length\" and the y axis \"petal width\""
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Title the plot as \"Iris flower petal length vs width\"\n",
    "# If you colored by species, include a legend"
   ]
  }
 ],
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