DataVisualization1.ipynb 302 KB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Visualization with Python\n",
    "\n",
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    "## Part 1: Python + Matplotlib\n",
    "\n",
    "### [Guy Allard](mailto://w.g.allard@lumc.nl)"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Matplotlib\n",
    "- Plotting library for Python\n",
    "- High quality figures suitable for publication\n",
    "- Integrates with IPython, Jupyter and NumPy (in PyLab mode)\n",
    "- Established and robust\n",
    "- Large community / user base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Interfaces\n",
    "1. Object Oriented  \n",
    "  - Best for larger development projects\n",
    "  - Have to keep track of figures and axes\n",
    "  - Steep learning curve\n",
    "<br><br>\n",
    "2. Pyplot State Machine  \n",
    "  - For interactive plotting\n",
    "  - Takes care of many housekeeping tasks\n",
    "  - Easier to learn than the OO interface\n",
    "<br><br>\n",
    "3. Pylab\n",
    "  - Modelled on matlab\n",
    "  - Imports common modules\n",
    "  - Handles most housekeeping tasks\n",
    "  - Easiest to learn\n",
    "  - The one we will be using!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Interfaces Example\n",
    "\n",
    "1. Object-oriented interface\n",
    "```python\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.arange(0, 10, 0.2)\n",
    "y = np.sin(x)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x, y)\n",
    "```\n",
    "\n",
    "2. State-machine environment (pyplot)\n",
    "```python\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.arange(0, 10, 0.2)\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y)\n",
    "```\n",
    "\n",
    "3. PyLab mode\n",
    "```python\n",
    "%pylab\n",
    "x = arange(0, 10, 0.2)\n",
    "y = sin(x)\n",
    "plot(x, y)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Getting help\n",
    "\n",
    "Consult the built-in documentation, for example:\n",
    "```\n",
    ">>> help(subplot)\n",
    "Help on function subplot in module matplotlib.pyplot:\n",
    "\n",
    "subplot(*args, **kwargs)\n",
    "    Return a subplot axes positioned by the given grid definition.\n",
    "...\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Useful Resources\n",
    "- Matplotlib Homepage\n",
    "  - https://matplotlib.org/\n",
    "<br><br>\n",
    "- Gallery\n",
    "  - https://matplotlib.org/gallery.html\n",
    "  - Many examples with source code\n",
    "<br><br>\n",
    "- Online documentation\n",
    "  - https://matplotlib.org/contents.html\n",
    "  - Full API documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# First Steps\n",
    "\n",
    "## Preparing the Jupyter Notebook\n",
    "1. Open a new Jupyter Notebook\n",
    "2. Run this code in the first empty cell:\n",
    "```\n",
    "%pylab inline\n",
    "```\n",
    "3. Now any pylab plotting commands will display in the notebook"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 1,
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   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Grab some data\n",
    "Use Pandas to load a dataset which contains population data for four countries"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 2,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
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    "populations = pd.read_csv(\n",
    "    'https://git.lumc.nl/courses/programming-course/raw/visualization-2018/visualization/data/populations.csv'\n",
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    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Take a quick look at the data"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Netherlands</th>\n",
       "      <th>Sweden</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950</td>\n",
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       "      <td>8.63930</td>\n",
       "      <td>4.28135</td>\n",
       "      <td>10.11365</td>\n",
       "      <td>7.01660</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1951</td>\n",
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       "      <td>8.67820</td>\n",
       "      <td>4.30370</td>\n",
       "      <td>10.26440</td>\n",
       "      <td>7.07040</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1952</td>\n",
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       "      <td>8.73040</td>\n",
       "      <td>4.33380</td>\n",
       "      <td>10.38210</td>\n",
       "      <td>7.12445</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1953</td>\n",
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       "      <td>8.77775</td>\n",
       "      <td>4.36930</td>\n",
       "      <td>10.49300</td>\n",
       "      <td>7.17145</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954</td>\n",
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       "      <td>8.81940</td>\n",
       "      <td>4.40570</td>\n",
       "      <td>10.61535</td>\n",
       "      <td>7.21360</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Belgium  Denmark  Netherlands   Sweden\n",
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       "0  1950  8.63930  4.28135     10.11365  7.01660\n",
       "1  1951  8.67820  4.30370     10.26440  7.07040\n",
       "2  1952  8.73040  4.33380     10.38210  7.12445\n",
       "3  1953  8.77775  4.36930     10.49300  7.17145\n",
       "4  1954  8.81940  4.40570     10.61535  7.21360"
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      ]
     },
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     "execution_count": 3,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Plot it!\n",
    "Let's make a plot the population of the Netherlands on the y-axis, and the year on the x-axis"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "plot(populations['Year'], populations['Netherlands']);"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Add titles and label the axes"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "plot(populations['Year'], populations['Netherlands'])\n",
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    "\n",
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    "title('Historical Population of The Netherlands')\n",
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    "\n",
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    "xlabel('Year')\n",
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    "\n",
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    "ylabel('Population (Millions)');"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Change some properties of the line\n",
    "\n",
    "How about a 5px thick orange line?"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "plot(populations['Year'], populations['Netherlands'], \n",
    "     linewidth=5, color='orange')\n",
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    "\n",
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    "title('Historical Population of The Netherlands')\n",
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    "\n",
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    "xlabel('Year')\n",
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    "\n",
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    "ylabel('Population (Millions)');"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Change some properties of the x-axis\n",
    "\n",
    "Label at five-year intervals  \n",
    "Display the label vertically"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "plot(populations['Year'], populations['Netherlands'], \n",
    "     linewidth=5, color='orange')\n",
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    "\n",
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    "title('Historical Population of The Netherlands')\n",
    "xlabel('Year')\n",
    "ylabel('Population (Millions)')\n",
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    "\n",
    "xticks(range(1950, 2016, 5), rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Change which years are displayed"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 8,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "plot(populations['Year'], populations['Netherlands'], \n",
    "     linewidth=5, color='orange')\n",
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    "\n",
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    "title('Historical Population of The Netherlands')\n",
    "xlabel('Year')\n",
    "ylabel('Population (Millions)')\n",
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    "xticks(range(1950, 2016, 5), rotation=90)\n",
    "\n",
    "xlim(1970, 1990);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Change the y-axis scale"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 9,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "plot(populations['Year'], populations['Netherlands'], \n",
    "     linewidth=5, color='orange')\n",
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    "\n",
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    "title('Historical Population of The Netherlands')\n",
    "xlabel('Year')\n",
    "ylabel('Population (Millions)')\n",
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    "xticks(range(1950, 2016, 5), rotation=90)\n",
    "xlim(1970, 1990)\n",
    "\n",
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    "ylim(13,15);"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Clean up the number formatting on the y-axis\n",
    "\n",
    "Integer tick labels"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 10,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "plot(populations['Year'], populations['Netherlands'], \n",
    "     linewidth=5, color='orange')\n",
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    "\n",
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    "title('Historical Population of The Netherlands')\n",
    "xlabel('Year')\n",
    "ylabel('Population (Millions)')\n",
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    "xticks(range(1950, 2016, 5), rotation=90)\n",
    "xlim(1970, 1990)\n",
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    "ylim(13,15)\n",
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    "\n",
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    "yticks(range(13,16));"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Plot multiple series\n",
    "\n",
    "Calling **plot** multiple times within the same cell will add multiple series to the chart\n",
    "\n",
    "Let's compare the Dutch with the Danes"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "plot(populations['Year'], populations['Netherlands'], color='orange')\n",
    "plot(populations['Year'], populations['Denmark'], color='red')\n",
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    "\n",
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    "title('Historical Populations of The Netherlands and Denmark')\n",
    "xlabel('Year')\n",
    "ylabel('Population (Millions)')\n",
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    "xticks(range(1950, 2016, 5), rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Add a legend\n",
    "\n",
    "1. Give each plotted line a label\n",
    "2. Add a legend to the figure"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 12,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "plot(populations['Year'], populations['Netherlands'],\n",
    "     color='orange', label='The Netherlands')\n",
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    "\n",
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    "plot(populations['Year'], populations['Denmark'],\n",
    "     color='red', label='Denmark')\n",
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    "\n",
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    "legend(loc='upper left')\n",
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    "\n",
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    "title('Historical Populations of The Netherlands and Denmark')\n",
    "xlabel('Year')\n",
    "ylabel('Population (Millions)')\n",
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    "xticks(range(1950, 2016, 5), rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Other plot types\n",
    "\n",
    "Let's load a different dataset and take a look at some different plot types"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 13,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
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     "execution_count": 13,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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    "flowers = pd.read_csv('https://git.lumc.nl/courses/programming-course/raw/visualization-2018/visualization/data/iris.csv')\n",
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    "\n",
    "flowers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Boxplots\n",
    "\n",
    "A simple boxplot of the sepal-length distribution"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 14,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "boxplot(flowers['sepal_length'], labels=['Sepal_length']);"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Boxplots\n",
    "\n",
    "Distributions of multiple features"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 15,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
      ]
     },
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     "execution_count": 15,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a list containing the numeric feature column names\n",
    "features = list(flowers.columns[:-1])\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 16,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 432x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "boxplot([flowers[f] for f in features], labels=features);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Controlling the size of the plot\n",
    "\n",
    "Let's change the shape of the boxplot"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 17,
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   "metadata": {
    "slideshow": {
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     "slide_type": "fragment"
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    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 720x360 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# make the figure 10 'units' wide and 5 'units' high\n",
    "figsize(10, 5)\n",
    "\n",
    "# plot the data\n",
    "boxplot([flowers[f] for f in features], labels=features);"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 18,
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   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "figsize(7,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Histogram"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 19,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 504x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "hist(flowers['petal_length'])\n",
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    "\n",
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    "title('Petal Length Distribution')\n",
    "xlabel('petal length')\n",
    "ylabel('count');"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Histogram\n",
    "\n",
    "change the number of 'bins'"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 20,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 504x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "hist(flowers['petal_length'], bins=20)\n",
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    "\n",
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    "title('Petal Length Distribution')\n",
    "xlabel('petal length')\n",
    "ylabel('count');"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Histogram\n",
    "\n",
    "Some formatting\n",
    "\n",
    "- Lines around the bars\n",
    "- color"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 21,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 504x288 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
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    "hist(flowers['petal_length'], bins=20, facecolor='teal', edgecolor='black', alpha=0.7)\n",
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    "\n",
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    "title('Petal Length Distribution')\n",
    "xlabel('petal length')\n",
    "ylabel('count');"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Subplots\n",
    "\n",
    "Separate plots with their own axes within a single figure\n",
    "\n",
    "The syntax can be confusing!"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 22,
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   "metadata": {
    "slideshow": {
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     "slide_type": "slide"
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    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 504x288 with 4 Axes>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1, 5):\n",
    "    subplot(2, 2, i)\n",
    "    xticks([]), yticks([])\n",
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    "    text(0.5, 0.5, 'subplot(2, 2, %d)' % i, ha='center', size=18, alpha=0.75);"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "subplot(2, 2, 1) indicates the first cell of a 2 row x 2 column matrix\n",
    "\n",
    "subplot(2, 2, 4) indicates the fourth cell of a 2 column x 2 row matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Subplots\n",
    "\n",
    "More complicated layouts"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 23,
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   "metadata": {
    "slideshow": {
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     "slide_type": "slide"
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    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 504x288 with 4 Axes>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
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    "subplot(1, 3, 1)            # 1 row, 3 columns, cell 1\n",
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    "xticks([]), yticks([])\n",
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    "text(0.5, 0.5, '(1, 3, 1)', ha='center', size=18, alpha=0.75)\n",
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    "\n",
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    "subplot(2, 3, 3)            # 2 rows, 3 columns, cell 3\n",
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    "xticks([]), yticks([])\n",
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    "text(0.5, 0.5, '(2, 3, 3)', ha='center', size=18, alpha=0.75)\n",
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    "\n",
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    "subplot(3, 2, 6)            # 3 rows, 2 columns, cell 6\n",
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    "xticks([]), yticks([])\n",
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    "text(0.5, 0.5, '(3, 2, 6)', ha='center', size=18, alpha=0.75)\n",
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    "\n",
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    "subplot(3, 3, 5)            # 3 rows, 3 columns, cell 5\n",
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    "xticks([]), yticks([])\n",
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    "text(0.5, 0.5, '(3, 3, 5)', ha='center', size=18, alpha=0.75);"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Subplots and Boxplots\n",
    "\n",
    "Compare how the features are distributed by species"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 24,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "['virginica', 'setosa', 'versicolor']\n"
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     ]
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    }
   ],
   "source": [
    "species = list(set(flowers.species))\n",
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    "print(species)"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 25,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# make a dataset for each species\n",
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    "setosa = flowers[flowers.species == 'setosa']\n",
    "versicolor = flowers[flowers.species == 'versicolor']\n",
    "virginica = flowers[flowers.species == 'virginica']"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 26,
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   "metadata": {
    "slideshow": {
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     "slide_type": "slide"
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    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 720x576 with 4 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(10, 8)\n",
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    "for cell, feature in enumerate(features):\n",
    "    subplot(2, 2, cell + 1)\n",
    "    boxplot(\n",
    "        [setosa[feature], versicolor[feature], virginica[feature]],\n",
    "        labels=species\n",
    "    )\n",
    "    ylabel(feature)"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 27,
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   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "figsize(7,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Sketch-style drawing\n",
    "\n",
    "using xkcd mode"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 28,
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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