09-data-visualization-with-matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Programming and Data Analysis\n",
    "\n",
    "> Data Visualization with Matplotlib\n",
    "\n",
    "Kuo, Yao-Jen <yaojenkuo@ntu.edu.tw> from [DATAINPOINT](https://www.datainpoint.com/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "from datetime import timedelta\n",
    "from urllib.error import HTTPError\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## About Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## What is `matplotlib`?\n",
    "\n",
    "> Matplotlib, short for matlab plotting library is a comprehensive library for creating static, animated, and interactive visualizations in Python.\n",
    "\n",
    "Source: <https://matplotlib.org/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Import Matplotlib with `import` command\n",
    "\n",
    "Matplotlib is officially aliased as `mpl`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## If Matplotlib is not installed, we will encounter a `ModuleNotFoundError`\n",
    "\n",
    "```\n",
    "Traceback (most recent call last):\n",
    "  File \"<stdin>\", line 1, in <module>\n",
    "ModuleNotFoundError: No module named 'matplotlib'\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Use `pip install` at Terminal to install matplotlib\n",
    "\n",
    "```bash\n",
    "pip install matplotlib\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Check version and its installation file path\n",
    "\n",
    "- `__version__` attribute\n",
    "- `__file__` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3.4\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/__init__.py\n"
     ]
    }
   ],
   "source": [
    "print(mpl.__version__)\n",
    "print(mpl.__file__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## We will use `matplotlib.pyplot` through this chapter, which is a plotting module in matplotlib, officially aliased as `plt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Why visualization?\n",
    "\n",
    "> Visualization plays a vital role among exploratory data analysis. Sometimes it is extremely difficult for human to understand raw data, functions, or formulas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The abstractness of raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.19541706, -1.05237904, -0.44243461, ..., -2.48707639,\n",
       "        0.92148783, -0.9189607 ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.normal(size=10000)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The abstractness of functions\n",
    "\n",
    "\\begin{equation}\n",
    "J(p) = -y_{true}log(p)-(1-y_{true})log(1-p)\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The abstractness of functions\n",
    "\n",
    "\\begin{equation}\n",
    "f(x) = \\frac{1}{1 + e^{-x}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The concreteness of visualization for raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.hist(arr, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The concreteness of visualization for functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps = 1e-06 # epsilon, a tiny number to avoid infinity\n",
    "p = np.linspace(0 + eps, 1 - eps, 10000)\n",
    "log_loss_0 = -np.log(1-p)\n",
    "log_loss_1 = -np.log(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(p, log_loss_0, label='$y_{true}=0, -log(1-p)$')\n",
    "ax.plot(p, log_loss_1, label='$y_{true}=1, -log(p)$')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The concreteness of visualization for functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 100)\n",
    "fx = 1 / (1 + np.exp(-x))\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, fx)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The classic visualizations of all time\n",
    "\n",
    "- [Charles Minard's map of Napoleon's disastrous Russian campaign of 1812](https://en.wikipedia.org/wiki/Charles_Joseph_Minard#/media/File:Minard.png).\n",
    "- [Hans Rosling's 200 Countries, 200 Years, 4 Minutes](https://youtu.be/jbkSRLYSojo)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Characteristics of a good visualization\n",
    "\n",
    "- Informative.\n",
    "- Simple.\n",
    "- Pretty."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Two ways to plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The most challenging part for matplotlib newbies is its various plotting syntaxes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## There are 2 plotting syntaxes to adapt\n",
    "\n",
    "- Matlab style.\n",
    "- Object-oriented style."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Plotting with matlab style: stateful interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = np.linspace(0, np.pi*2, 100)\n",
    "x2 = np.linspace(np.pi * 0.5, np.pi*2.5, 100)\n",
    "plt.figure()\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(x1, np.sin(x1))\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x2, np.cos(x2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Plotting with object-oriented style: object method interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1)\n",
    "axes[0].plot(x1, np.sin(x1))\n",
    "axes[1].plot(x2, np.cos(x2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## We will adapt the object-oriented style."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The general steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The general steps to create plots are:\n",
    "\n",
    "1. Creating arrays.\n",
    "2. Generating figure and axes objects(respectively or simultaneously).\n",
    "3. Calling various methods of axes object to generate main plot.\n",
    "4. Calling various methods of axes object to add elements.\n",
    "5. Show or save plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Creating arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 2*np.pi, 100)\n",
    "y = np.sin(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Generating figure and axes objects respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Generating figure and axes objects simultaneously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Calling various methods of axes object to generate main plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f921bcc5d90>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Calling various methods of axes object to add elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'x')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, y)\n",
    "ax.set_title('A simple plot')\n",
    "ax.set_xlabel('x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Show plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, y)\n",
    "ax.set_title('A simple plot')\n",
    "ax.set_xlabel('x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Save plots\n",
    "\n",
    "Various format is supported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eps': 'Encapsulated Postscript',\n",
       " 'jpg': 'Joint Photographic Experts Group',\n",
       " 'jpeg': 'Joint Photographic Experts Group',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'ps': 'Postscript',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics',\n",
       " 'tif': 'Tagged Image File Format',\n",
       " 'tiff': 'Tagged Image File Format'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.canvas.get_supported_filetypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, y)\n",
    "ax.set_title('A simple plot')\n",
    "ax.set_xlabel('x')\n",
    "fig.savefig('a_simple_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Different methods for different plot types\n",
    "\n",
    "- `scatter()` for scatter plots.\n",
    "- `hist()` for histograms.\n",
    "- `plot()` for lines.\n",
    "- `bar()` for bar plots.\n",
    "- `imshow()` for showing images in `ndarray` format.\n",
    "- ...etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Try importing 12-16-2021 data...\n",
      "Try importing 12-15-2021 data...\n",
      "Successfully imported 12-15-2021 data!\n"
     ]
    }
   ],
   "source": [
    "def get_latest_daily_report():\n",
    "    today = date.today()\n",
    "    day_delta = timedelta(days=1)\n",
    "    data_date = today\n",
    "    while True:\n",
    "        data_date_str = date.strftime(data_date, '%m-%d-%Y')\n",
    "        print(\"Try importing {} data...\".format(data_date_str))\n",
    "        daily_report_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/{}.csv\".format(data_date_str)\n",
    "        try:\n",
    "            daily_report = pd.read_csv(daily_report_url)\n",
    "            print(\"Successfully imported {} data!\".format(data_date_str))\n",
    "            break\n",
    "        except HTTPError:\n",
    "            data_date -= day_delta\n",
    "    return daily_report\n",
    "\n",
    "daily_report = get_latest_daily_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `scatter` for scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = daily_report[\"Incident_Rate\"].values\n",
    "y = daily_report[\"Case_Fatality_Ratio\"].values\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.scatter(x, y, s=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `hist` for histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+jl9cWXw58M/AcQaf/L/tAvT0uwx+RP0e8Fj7ev8s9wX8FvDd1tMTwF+08ZntaVF/7+EXH6jOdE8Mzo57vH0dfeX//az3NY0vbz8gSR2a5cMykqQlGO6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ/8P1QC2AXIMx/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = daily_report[\"Incident_Rate\"].values # cases per 100,000 persons\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.hist(x, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def get_confirmed_time_series():\n",
    "    request_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
    "    time_series = pd.read_csv(request_url)\n",
    "    id_cols = time_series.columns[:4]\n",
    "    time_series_long = pd.melt(time_series, id_vars=id_cols, var_name='Date', value_name='Confirmed')\n",
    "    date = pd.to_datetime(time_series_long['Date'])\n",
    "    time_series_long = time_series_long.drop('Date', axis=1)\n",
    "    time_series_long.insert(4, 'Date', date)\n",
    "    groupby_series = time_series_long.groupby(['Country/Region', 'Date'])['Confirmed'].sum()\n",
    "    out = pd.DataFrame(groupby_series).reset_index()\n",
    "    return out\n",
    "\n",
    "confirmed_time_series = get_confirmed_time_series()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `plot` for lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_ts = confirmed_time_series.groupby(\"Date\")[\"Confirmed\"].sum()\n",
    "x = global_ts.index\n",
    "y = global_ts.values\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `bar` for vertical bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEDCAYAAAAcI05xAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUP0lEQVR4nO3df5BlZX3n8feHASGKGoFZ1hWlWWMwlAHUWY0hmyimLBAjRsGIrq67uhNdjKZqlbAbyx1MrOBuXFIaNSFEMaVCxICJkEXIKiJGgRl+OfzwJ+MuiGFQ+TFAQIbv/nGeS1+a7uk7TN+eB+b9qurqc8899zzfc+65n/Pc557bnapCktSvnbZ3AZKkLTOoJalzBrUkdc6glqTOGdSS1DmDWpI6N7WgTvKxJDcnWT/BsicluaL9fCvJrdOqS5IeaTKt66iT/CqwCfirqnrWVjzud4BnV9V/nEphkvQIM7UedVVdCPx4fF6Spyc5N8m6JF9J8sx5HnoMcNq06pKkR5qdl7m9k4G3VNW3kzwf+Ahw6OjOJPsC+wFfXOa6JKlbyxbUSXYHfhk4I8lo9q5zFnsN8Nmq2rxcdUlS75azR70TcGtVHbyFZV4DHLs85UjSI8OyXZ5XVbcD1yc5GiCDg0b3J9kfeBLwteWqSZIeCaZ5ed5pDKG7f5IbkrwJeB3wpiRXAlcDR4495Bjg9PLP+UnSg0zt8jxJ0tLwm4mS1LmpfJi411571czMzDRWLUmPSuvWrbulqlbOd99UgnpmZoa1a9dOY9WS9KiU5PsL3efQhyR1zqCWpM4Z1JLUOYNakjpnUEtS5wxqSeqcQS1JnZvoOuokG4A7gM3AfVW1appFSZJmbc0XXl5UVbdMrRJJ0ryW+z+8SNJ2MXP8OVNvY8OJR0xlvZOOURdwXvtfh6vnWyDJ6iRrk6zduHHj0lUoSTu4SYP6kKp6DnA4cGz7D+MPUlUnV9Wqqlq1cuW8f1dEkvQwTBTUVfWD9vtm4CzgedMsSpI0a9GgTvK4JI8fTQMvAdZPuzBJ0mCSDxP3Bs5q/zl8Z+DTVXXuVKuSJD1g0aCuqu8BBy22nCRpOvxmoiR1zqCWpM4Z1JLUOYNakjpnUEtS5wxqSeqcQS1JnTOoJalzBrUkdc6glqTOGdSS1DmDWpI6Z1BLUucMaknqnEEtSZ0zqCWpcwa1JHXOoJakzhnUktQ5g1qSOmdQS1LnDGpJ6pxBLUmdM6glqXMGtSR1zqCWpM4Z1JLUOYNakjpnUEtS5wxqSeqcQS1JnTOoJalzEwd1khVJLk9y9jQLkiQ92Nb0qN8BXDutQiRJ85soqJPsAxwBnDLdciRJc03ao/4T4Djg/oUWSLI6ydokazdu3LgUtUmSmCCok7wMuLmq1m1puao6uapWVdWqlStXLlmBkrSjm6RHfQjw8iQbgNOBQ5N8cqpVSZIesGhQV9V/rap9qmoGeA3wxar6d1OvTJIEeB21JHVv561ZuKouAC6YSiWSpHnZo5akzhnUktQ5g1qSOmdQS1LnDGpJ6pxBLUmdM6glqXMGtSR1zqCWpM4Z1JLUOYNakjpnUEtS5wxqSeqcQS1JnTOoJalzBrUkdW6r/nGAJG2LmePPmXobG048YuptLDd71JLUOYNakjpnUEtS5wxqSeqcQS1JnTOoJalzBrUkdc6glqTOGdSS1DmDWpI6Z1BLUucMaknqnEEtSZ0zqCWpcwa1JHVu0aBOsluSS5JcmeTqJCcsR2GSpMEk/zjgHuDQqtqUZBfgoiT/u6q+PuXaJElMENRVVcCmdnOX9lPTLEqSNGuiMeokK5JcAdwMnF9VF8+zzOoka5Os3bhx4xKXKUk7romCuqo2V9XBwD7A85I8a55lTq6qVVW1auXKlUtcpiTtuLbqqo+quhW4ADhsGsVIkh5qkqs+Vib52Tb9M8CvA9dNuS5JUjPJVR9PBj6RZAVDsH+mqs6eblmSpJFJrvq4Cnj2MtQiSZqH30yUpM4Z1JLUOYNakjpnUEtS5wxqSeqcQS1JnTOoJalzBrUkdc6glqTOGdSS1DmDWpI6Z1BLUucMaknqnEEtSZ0zqCWpcwa1JHXOoJakzhnUktQ5g1qSOmdQS1LnDGpJ6pxBLUmdM6glqXMGtSR1zqCWpM4Z1JLUOYNakjq38/YuQNLymjn+nKm3seHEI6bexo7EHrUkdc6glqTOGdSS1DmDWpI6t2hQJ3lqki8luTbJ1UnesRyFSZIGk1z1cR/wX6rqsiSPB9YlOb+qrplybZIkJuhRV9VNVXVZm74DuBZ4yrQLkyQNtmqMOskM8Gzg4qlUI0l6iImDOsnuwN8Av1tVt89z/+oka5Os3bhx41LWKEk7tImCOskuDCH9qao6c75lqurkqlpVVatWrly5lDVK0g5tkqs+AvwlcG1V/a/plyRJGjdJj/oQ4PXAoUmuaD8vnXJdkqRm0cvzquoiIMtQiyRpHn4zUZI6Z1BLUucMaknqnEEtSZ0zqCWpcwa1JHXOoJakzhnUktQ5g1qSOmdQS1LnDGpJ6pxBLUmdM6glqXMGtSR1zqCWpM4Z1JLUOYNakjpnUEtS5wxqSeqcQS1JnTOoJalzBrUkdc6glqTOGdSS1DmDWpI6Z1BLUucMaknqnEEtSZ0zqCWpcwa1JHXOoJakzu28vQuQdkQzx58z9TY2nHjE1NvQ8rBHLUmdWzSok3wsyc1J1i9HQZKkB5ukR30qcNiU65AkLWDRoK6qC4EfL0MtkqR5LNmHiUlWA6sBnva0py3VaqWp8QM9PVIs2YeJVXVyVa2qqlUrV65cqtVK0g7Pqz4kqXNeR63tyuEHaXGTXJ53GvA1YP8kNyR50/TLkiSNLNqjrqpjlqMQSdL8HPqQww9S5/wwUZI6Z1BLUucMaknqnEEtSZ0zqCWpcwa1JHXOy/M64SVykhZij1qSOmePeoy9Wkk9skctSZ0zqCWpcwa1JHWuuzFqx4kl6cHsUUtS5wxqSeqcQS1JnTOoJalzBrUkdc6glqTOGdSS1DmDWpI6Z1BLUucMaknqnEEtSZ0zqCWpcwa1JHXOoJakzhnUktQ5g1qSOmdQS1LnDGpJ6txEQZ3ksCTfTPKdJMdPuyhJ0qxFgzrJCuDDwOHAAcAxSQ6YdmGSpMEkPernAd+pqu9V1b3A6cCR0y1LkjSSqtryAslRwGFV9eZ2+/XA86vqbXOWWw2sbjf3B7659OXOay/glmVqy7Z37La3d/u2/ehue9+qWjnfHTtP8ODMM+8h6V5VJwMnb2Vh2yzJ2qpatdzt2vaO1/b2bt+2d6y2x00y9HED8NSx2/sAP5hOOZKkuSYJ6kuBZyTZL8ljgNcAfzfdsiRJI4sOfVTVfUneBnwBWAF8rKqunnplk1v24Rbb3mHb3t7t2/aO1fYDFv0wUZK0ffnNREnqnEEtSZ3rLqiT/H6Sq5NcleSKJM9/GOt4+VJ81T3Jpm1dR1vP3kk+neR7SdYl+VqS31yKdW9DTZvb/h39zCxTu3uOtfnDJDeO3X7MIo+dSbJ+G9sfbff6JJ9P8rMLrTvJmiTvXGR9q5J8sE2fk+S1D6OmDUn22tL8JM9Ncn2SZ2/r8T22D65M8q0kX32465qz3lNG31peaJvafVv1ukrywiRnt+kleW3P08aCz3+SX0pycdtn1yZZs9TtL2aS66iXTZIXAC8DnlNV97Qneosv3vlU1d8xpStTkqyoqs1bsXyAzwGfqKrXtnn7Ai+fRntb4e6qOniBNsPw+cX9S91oVf0IOLi1swbYVFV/vNjj2p8yWAoPbHeSTwDHAu97uCurqrXA2nbzUuBfbWuBcyU5EPgs8FtVdTlwOdt2fI/vg3cBDwm+h3Pcjb4UN03TfG1vwSeAV1fVle043H+Z2++uR/1k4Jaqugegqm6pqh+0s/P7k1zSfn4OIMlvtDPd5Un+Icnebf4bk/xpmz41yQeT/GPr0R61tUW1M/qXknwa+Eab97nWO746w7cyR8tuSvK+1lv5OvBK4N6q+rPRMlX1/ar6UJIVSf5nkkvbO4jfnq+9dvvLST7TekAnJnld2xffSPL0RfbHmiQfS3JB2wdvb/P/IMk72vRMkluSfAW4DHhqko8mWdu28YSxbdyQ5IQkl7X2n9nm757k423eVUle1ea/JMO7iMuSnJFk9zn799Tx52XU45pvv48t86/bdv6bJE9Pcm57Pr6S5JlJHp+hB7pLW/4JSTbMeWq/BjylTZ8O7NaW3Wts2b3bft6U5Ob2vH67HU9XtjbWZXhH8t+A4zL0vD7Xft+e4V3DpUkOaevfM8l5rf4/Z/4vlY38AsOJ/vVVdUl7/KLHd5KdknykPXdnJ/n7sX28Isl1SS4CXgT8dOz42ZjkJ8AdSQ5s2/GDJLe2dW1I8kdJ/inJ3UnuTHJ9e/wFSSb+ckh7fi9I8tlWz6eSpN132FiNrxx7zPi2z3u8T8G/AG4CqKrNVXXNlNpZWFV18wPsDlwBfAv4CPBrbf4G4Pfb9BuAs9v0k5i9cuXNwAfa9BuBP23TpwJnMJyUDmD4uyWT1rOp/X4hcCew39h9e7TfPwOsB/Zstwv4jTb9P4CzgZMWWP9q4N1teleGntl+c9trt29lOJHtCtwInNDuewfwJ4vsjzXAP7bH7gX8CNgMXAPcBZzV2i3gJfNs4wrgAuDAsefjd9r0fwZOadPvH9UyVs9ewIXA49q83wPeM1bXO9tzdNRi+x2Yaft6f4Ze5cFt/v8BntGmnw98sU1/HHjF2L7+wNi6V7Tj4rB2++u0Y6PVvKHVdxHwurb9JzE83+8Dbhyr8dyx/TJ6LvYAPg38anvsS4Br230fHNsHR7T9vtc8x8cG4MfAS+fMfyOLHN/AUcDft/n/EvhJm7dba+8a4DrgXuAr7TFnttv7AYcyvBb3YPb4Wd+25y7g8PaYG4A/b9MXAKvGan/INs3z/N7G8CW6nRhOnL/Savx/wDMYTmKfYfY1P77t8x7vDzN7ZoD1c+atYTg+39P231nAbwO7LXc2djX0UVWbkjwX+LcMZ/q/zux41Gljv09q0/u0ZZ7MMERy/QKr/lwNb+Ov2Yaz7iVVNb7+t2d2nPmpDAfVjxgO9LPb/HUMB94Dkny4zbsX+D5w4FhP54ltPffO096lVXVTW8d3gfPa/G8w7CvY8v44p4Z3KvckuZnhYDsgyfnAe2kvwKo6b+wxr87wbmFnhpPEAcBV7b4zx7Zx1OP5dYYvRAFQVT9J8rL2uK+2ztJjGF6Qk5q7H1YCfwu8qqqubr3zXwbOaOuH4YQEcApwHEOP9D8A/wn43SRXMLww1wHnj8pdoP0NDD3l3YCPVtXdGcZLj0vyfoagum+ex70aOBr4TWAXhutxd0nyeIZ9/UqAqjqn9WAX8g/Am5N8oRYeipjv+P4V4Iw2/4dJvtTmPxO4v6pGY8nvAt7TerK/CKxr+/v6JHsC72LYdwU8lqH3vQI4N8lxDMf8tVuofzGXVNUNrZYrGJ6XTcD1VfXtNv+TzP4doXGTvv4nsdDzX1X13iSfYjjZvhY4huEks2x6G/oYvbW4oKr+O/A24FWju8YXa78/xHB2/UXamW6B1d4zNr2lt5lbcucDK0heyBBKL6iqgxh6d6O2f1rtdMzQa70DeM4DhVcdC7yYIXDC0DM9uP3sNxaUD7Q3zzbcP3b7fmY/a9jS/hh//PgL/hSGXspRwMaxbdyPoTfx4qo6EDhngfVtHms/PPSAD3D+2DYeUFVvmrPMfbRjsQXG+OcSc/fDbQy9rUPa7Z2AW8fWf3BV/QJAVX0VmEnya8CKqlrP7Pjsvq2dY9t6/hl4QpsebeceDEH+cob9/IEkhwLfZngr/A2GAHnGgzZ4dt/d3tbxSWBNVT2lqu5oi036BYbRHz/7yBaWme/4nvQ4/ybDfhgdj3eP3bcrQyD9JcO7kcvbvJ8y9LiPBj7Ptn3WNfe4HK1rkv0z6et/Ej9i6KGP24P2B5mq6rtV9VGG1+5B7SS2bLoK6iT7Jxk/6A9m6HUC/NbY71GP7IkMwwAA/37qBc56IvCTqrorw/jsL21h2ZuA3ZK8dWzeY9vvLwBvHRtH/fkkj9vGurZ2f5wFHAYcxNCTGXkCQ0je1npph0+wrvOYDRaSPIlhSOGQzH6u8NgkPz/ncRuA57bpIxl6oAu5F3gF8IYkr62q2xl6f0e39SfJQWPL/xXDu7CPj6+kqm4D3g68s+3/7wJ3J3kxw0lrJ4b98n3gewz79ULgQGDvYRX1SeCvGfY7DCecxzK7777A8EHd4a22g9tyFzIMp5DkcB4aEOPuZ+jB7Z/kvVtYbq6LgFe1seq9me0BXjc0O3yuwXCiCUNQXUkbs2+dkU0MQfVThiAfHedhOHG8mvnfTWyr64D9xmo8ZoHlluz1X1WbgJva80+SPRie/4uSHDEaO2c4KW9mGIpcNl0NfTCMUX8owyVT9wHfYTiQXgbsmuRihhfQ6Ilbw/CW90aGQNhvmeo8F3hLkqsYeiRfX2T5VwAntbeKGxlexL/HMLY4A1zWDoSNbdmHaw1buT+q6t72trgYhpxG869McjlwNUNQTXIJ1x8CH85wmdNmhnH0M5O8ETgtyWhI4t0Mn0OM/AXwt0kuYRhvntuLnlvznW1I5fwkdzKE3keTvJsh5E9nCB2AT7W6TptnPZcnuZJhuOaP2/afydCr3IPhLf8LGP5xxr4Mwz5vY+hVPbm9Vd+NoYcNQ9i9iOFvuN8IrGIIyN0YxrU/D7wFOKHtj8uALwP/d5HtvSfJkcCXk/zTYvun+ZtW53qGfX0xcFtV/XOSAq5Ocl9b1xVVtTnJqcBftOP6LoYe8x8CbwV+yOxxvjOwJ8NJfjT+veiVO5NqNa4GzklyC8NJ51nzLLqGpX39v4Hh+P1Au31CVX03yfsYXr93MeTS67YwDDUVj4ivkGf4BH5VVW3Pv0P8qJRkJ4arPI4ejQk+mrTx/yOr6vXbu5bllmT39rnPnsAlwCFV9cPtXZe2Xm89ai2jDF9OOBs461Ea0h9iGHZ46fauZTs5u707fQzwB4b0I9cjokctSTuyrj5MlCQ9lEEtSZ0zqCWpcwa1JHXOoJakzv1/x+JlVDNbhDwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "world_top_ten = daily_report.groupby('Country_Region')['Confirmed'].sum().sort_values()[-10:]\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "x = world_top_ten.index\n",
    "height = world_top_ten.values\n",
    "ax.bar(x, height)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `barh` for horizontal bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/M8lVTfsLm9mtJUkD0vVAe3CSNc2fLzXbDgE+VVVHVdWtwLuqahFwBL150o7oO/62qno88FHgnc22/wpsrKqFVXUEcHmS/YG/BE5s2o8Cfzq+mCRLmvAc3XLnxlm5YUkaVkM15JhkBLi1qq7ua/PSJEvo/V3MAw4Dbmj2fbH5uQp4YbN8IvDysYOr6pdJntMc963eSCa7AVeNL6aqlgHLAHaft6B28t4kSX26HmgT+f0M1UkOptfzOroJpk8Ce/S13dz83MK9f1cBxodRgK9W1amzUrEkaUpdH3Kcyj70Am5jkgPpTdA5la8Abx1bSbIvcDVwXJI/aLbtmeQxs1CvJGkSQx1oVXU9sBq4CfgE8K3tOOy/A/smuTHJ9cAzqurnwKuBzya5gV7AHTo7VUuSJtLpIceq2nvc+nrg8HHbXj3JsSN9y6PA4mZ5E3DaBO0vB47euYolSTtqqHtokqTuMNAkSZ3Q6SHH+7OF8+cy6txhkjRj7KFJkjrBQJMkdYKBJknqBJ+htWTtho2MLF3edhnbZb3P+iTtAuyhSZI6wUCTJHWCgSZJ6gQDTZLUCbtUoCU5MMn5SW5JsqqZIfoFbdclSWrfLhNo6c2ceTHwjap6VFU9gd5Emwdt5/FzZrE8SVLLdplAA44H7q6qvxvbUFW3VtWHk8xJ8tdJrk1yQ5I3AiRZnOSKJOcDa5v1f0ny+STfT/L+JK9IsjLJ2iSPbo57bpJrkqxO8rVmrjSSnJnkE0lWNL3E05vt703y9rG6krxvbJ8kaTB2pUB7LHDdJPteB2ysqqPpTeHyhmY2aoBjgHdV1WHN+uOAtwMLgVcCj6mqY4DzgLc1ba4Ejq2qo4ALgDP6rnUo8IfNed+d5EHAx2mmlEnyAHo9x8/s3O1KkqZjl/3F6iQfAZ4C3A3cChyR5MXN7rnAgmbfyqpa13fotVX10+YcP6I3AzXAWuAZzfJBwOeSzAN2A/qPX15Vm4HNSX4GHFhV65PcnuQo4EBgdVXdPkHNS4AlAHP2OWDn/gIkSVvZlXpoNwGPH1upqrcAJwAHAAHeVlVHNn8OrqqxoPrNuPNs7lu+p2/9Hu4N+A8D51TVQuCNwB6THL+l75jz6M1a/Rp6s1/fR1Utq6pFVbVozp5zp7hdSdJ07EqBdjmwR5I3923bs/n5ZeDNzfAfSR6TZK+duNZcYEOzfJ/ZqSfxJeAkekOeX96Ja0uSdsAuM+RYVZXk+cAHk5wB/Jxe7+vPgAuBEeC65m3InwPP34nLnQlcmGQDcDVw8LabQ1XdneQK4I6q2rIT15Yk7YBUVds1dELzMsh1wEuq6gdTtd993oKad9rZs17XTPDjxJLuL5KsqqpFE+3blYYc77eSHAb8EPj69oSZJGnm7TJDjvdnVXUz8Ki265CkYWYPTZLUCfbQWrJw/lxGfTYlSTPGHpokqRMMNElSJxhokqRO8BlaS9Zu2MjI0uVtlzEhf+9M0q7IHpokqRMMNElSJxhokqROMNAkSZ1goE0gyaa2a5AkTY+Btp2SzGm7BknS5Ay0bUiyOMkVSc4H1jbbLk6yKslNSZb0td2U5H1Jrk9ydZIDWytckoaQgTa1Y4B3VdVhzfprq+oJwCLg9CT7Ndv3Aq6uqscB3wDeMP5ESZYkGU0yuuXOjYOoXZKGhoE2tZVVta5v/fQk19ObyfoRwIJm+93Apc3yKnozaG+lqpZV1aKqWjRnz7mzWLIkDR+/FDK134wtJFkMnAg8qaruTLIC2KPZ/du6d/rvLfh3K0kDZQ9teuYCv2zC7FDg2LYLkiT1GGjTcxnwwCQ3AO+lN+woSbofcFhsAlW1d/NzBbCib/tm4FnbOqZZvgi4aFaLlCRtxR6aJKkTDDRJUic45NiShfPnMuq8Y5I0Y+yhSZI6wUCTJHWCgSZJ6gSfobVk7YaNjCxd3nYZAKz3WZ6kDrCHJknqBANNktQJBpokqRMMNElSJwxloCV5VzPj9A1J1iR54g6c45QkS2ejPknS9A3dW45JngQ8B3h8VW1Osj+w23TPU1WXAJfMdH2SpB0zjD20ecBtzZfzqarbquonSdYn+UCSlc2fPwBI8twk1yRZneRrSQ5str86yTnN8ieTfCjJt5PckuTFrd2dJA2pYQy0rwCPSPL9JOcmeXrfvl9V1THAOcDZzbYrgWOr6ijgAuCMSc47D3gKvd7f+ydqkGRJktEko1vu3DgDtyJJGjN0Q45VtSnJE4CnAs8APtf3LOyzfT8/2Cwf1LSZR29oct0kp764qu4Bbh7rxU1w7WXAMoDd5y2onb4ZSdLvDWMPjaraUlUrqurdwFuBF43t6m/W/PwwcE5VLQTeCOwxyWk39y1nJuuVJE1t6AItySFJFvRtOhK4tVl+Wd/Pq5rlucCGZvm0WS9QkrRDhm7IEdgb+HCShwK/A34ILKH37Gv3JNfQC/pTm/ZnAhcm2QBcDRw86IIlSVNLlY9yAJKsBxZV1W2DuN7u8xbUvNPOHsSlpuTHiSXtKpKsqqpFE+0buiFHSVI3DeOQ44SqaqTtGiRJO85Aa8nC+XMZdahPkmaMQ46SpE4w0CRJnWCgSZI6wUCTJHWCgSZJ6gQDTZLUCQaaJKkTDDRJUicYaJKkTvDjxC1J8mvge23X0aL9gYF8CPp+yHsfXsN8/zN174+sqgMm2uGnr9rzvcm+GD0MkowO6/1778N57zDc9z+Ie3fIUZLUCQaaJKkTDLT2LGu7gJYN8/1778NrmO9/1u/dl0IkSZ1gD02S1AkGmiSpEwy0FiQ5Kcn3kvwwydK26xmkJJ9I8rMkN7Zdy6AleUSSK5J8J8lNSd7edk2DkmSPJCuTXN/c+1lt1zRoSeYkWZ3k0rZrGbQk65OsTbImyeisXcdnaIOVZA7wfeC/AD8GrgVOraqbWy1sQJI8DdgEfKqqDm+7nkFKMg+YV1XXJXkIsAp4/jD8b58kwF5VtSnJg4ArgbdX1dUtlzYwSf4UWATsU1XPabueQUqyHlhUVbP6S+X20AbvGOCHVXVLVd0NXAA8r+WaBqaqvgH8ou062lBVP62q65rlXwPfAea3W9VgVM+mZvVBzZ+h+dd0koOAk4Hz2q6lywy0wZsP/Gvf+o8Zkv9T072SjABHAde0XMrANENua4CfAV+tqqG5d+Bs4AzgnpbraEsBX0myKsmS2bqIgTZ4mWDb0PxLVZBkb+ALwDuq6ldt1zMoVbWlqo4EDgKOSTIUQ85JngP8rKpWtV1Li46rqscDzwLe0jx6mHEG2uD9GHhE3/pBwE9aqkUD1jw/+gLwmar6Ytv1tKGq7gBWACe1W8nAHAec0jxHugA4Psmn2y1psKrqJ83PnwFfovfoZcYZaIN3LbAgycFJdgNeDlzSck0agObFiI8D36mq/9V2PYOU5IAkD22WHwycCHy31aIGpKr+vKoOqqoRev+9X15Vf9xyWQOTZK/mJSiS7AU8E5iVt5wNtAGrqt8BbwW+TO+lgM9X1U3tVjU4ST4LXAUckuTHSV7Xdk0DdBzwSnr/Ql/T/Hl220UNyDzgiiQ30PtH3VerauheXx9SBwJXJrkeWAksr6rLZuNCvrYvSeoEe2iSpE4w0CRJnWCgSZI6wUCTJHWCgSZJGojpfJw8yQf73gb+fpI7pjzGtxwlSYOwox8nT/I24Kiqeu222tlDkyQNxEQfJ0/y6CSXNd95/GaSQyc49FTgs1Od/4EzVKckSTtiGfCmqvpBkicC5wLHj+1M8kjgYODyqU5koEmSWtF8qPvJwIW9L8MBsPu4Zi8HLqqqLVOdz0CTJLXlAcAdzSwMk3k58JbtPZkkSQPXTJ+0LslLoPcB7ySPG9uf5BBgX3rff52SgSZJGohJPk7+CuB1zceLbwKe13fIqcAFtZ2v4/vaviSpE+yhSZI6wUCTJHWCgSZJ6gQDTZLUCQaaJKkTDDRJUicYaJKkTvj/EYsybvrbAn0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "y = world_top_ten.index\n",
    "width = world_top_ten.values\n",
    "ax.barh(y, width)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Techniques for adding elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## How to add title and axis labels\n",
    "\n",
    "- `set_title` to add title.\n",
    "- `set_xlabel` to add x-axis label.\n",
    "- `set_ylabel` to add y-axis label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "y = world_top_ten.index\n",
    "width = world_top_ten.values\n",
    "ax.barh(y, width)\n",
    "ax.set_title('The Ten Countries with Most Confirmed Cases.')\n",
    "ax.set_xlabel('Confirmed')\n",
    "ax.set_ylabel('Country Name')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## How to adjust axis ticks\n",
    "\n",
    "- `set_xticks` to adjust x-axis ticks.\n",
    "- `set_yticks` to adjust y-axis ticks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "y = world_top_ten.index\n",
    "width = world_top_ten.values\n",
    "ax.barh(y, width)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## How to adjust axis tick labels\n",
    "\n",
    "- `set_xticklabels` to adjust x-axis tick labels.\n",
    "- `set_yticklabels` to adjust y-axis tick labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "y = world_top_ten.index\n",
    "width = world_top_ten.values\n",
    "ax.barh(y, width)\n",
    "ax.set_yticks([7, 8, 9])\n",
    "ax.set_yticklabels([\"BRA\", \"IND\", \"USA\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## How to adjust axis limits\n",
    "\n",
    "- `set_xlim` to adjust the upper/lower limits of x-axis.\n",
    "- `set_ylim` to adjust the upper/lower limits of y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "y = world_top_ten.index\n",
    "width = world_top_ten.values\n",
    "ax.barh(y, width)\n",
    "ax.set_ylim(6.5, 9.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## How to add texts\n",
    "\n",
    "`text(x, y, 'Some Strings')` to add `'Some Strings'` at (x, y)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.barh(world_top_ten.index, world_top_ten.values)\n",
    "for i in range(world_top_ten.size):\n",
    "    v = world_top_ten.values[i]\n",
    "    ax.text(v + 1000000, i - 0.2, \"{:,}\".format(v))\n",
    "ax.set_xlim(0, world_top_ten.max() + 2e7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## How to add legends\n",
    "\n",
    "`legend()` and `label` parameter to add a legend to indicate categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def plot_lines_with_legends(country_names, ts_df):\n",
    "    fig = plt.figure()\n",
    "    ax = plt.axes()\n",
    "    for country in country_names:\n",
    "        country_data = ts_df[ts_df['Country/Region'].str.contains(country)]\n",
    "        x = country_data['Date']\n",
    "        y = country_data['Confirmed']\n",
    "        ax.plot(x, y, label=country)\n",
    "    ax.set_xlabel('Date')\n",
    "    ax.set_title('Cumulative COVID-19 confirmed cases')\n",
    "    ax.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lines_with_legends(['Taiwan', 'Singapore'], confirmed_time_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## How to solve the problem that matplotlib supporting only English and European fonts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 32047 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 35336 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 30906 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 35386 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 20154 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 25976 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 21488 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 28771 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 26399 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 32047 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 35336 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 30906 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 35386 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 20154 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 25976 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 21488 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/kuoyaojen/opt/miniconda3/envs/pyds/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 28771 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "tw = confirmed_time_series[confirmed_time_series['Country/Region'].str.contains('Taiwan')]\n",
    "ax.plot(tw['Date'].values, tw['Confirmed'].values, label='台灣')\n",
    "ax.set_title('COVID-19 累計確診人數')\n",
    "ax.set_xlabel('日期')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Calling `FontProperties` function from `matplotlib.font_manager`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib.font_manager import FontProperties\n",
    "\n",
    "my_font = FontProperties(fname=\"/System/Library/Fonts/STHeiti Light.ttc\") # Specify a font supporting Chinese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "tw = confirmed_time_series[confirmed_time_series['Country/Region'].str.contains('Taiwan')]\n",
    "ax.plot(tw['Date'].values, tw['Confirmed'].values, label='台灣')\n",
    "ax.set_title('COVID-19 累計確診人數', fontproperties=my_font)\n",
    "ax.set_xlabel('日期', fontproperties=my_font)\n",
    "ax.legend(prop=my_font)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## How to create a subplot grid?\n",
    "\n",
    "- `subplots(m, n)` to create a `ndarray` with a shape `(m, n)`.\n",
    "- There are (m, n) axes objects inside the `ndarray`.\n",
    "- Access axes objects through `[m, n]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(2, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 3)\n",
    "print(type(axes))\n",
    "print(axes.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_sin = np.linspace(0, 2*np.pi, 100)\n",
    "x_cos = np.linspace(0.5*np.pi, 2.5*np.pi, 100)\n",
    "y_sin = np.sin(x_sin)\n",
    "y_cos = np.cos(x_cos)\n",
    "fig, axes = plt.subplots(2, 3)\n",
    "axes[0, 1].plot(x_sin, y_sin)\n",
    "axes[1, 2].plot(x_cos, y_cos)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python Data Science",
   "language": "python",
   "name": "pyds"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}