udemy-ML/04-Matplotlib/03-Matplotlib-Styling-Plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# Matplotlib Styling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `matplotlib.pyplot` module under the name `plt` (the tidy way):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# COMMON MISTAKE!\n",
    "# DON'T FORGET THE .PYPLOT part\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE: For users running .py scripts in an IDE like PyCharm or Sublime Text Editor. You will not see the plots in a notebook, instead if you are using another editor, you'll use: *plt.show()* at the end of all your plotting commands to have the figure pop up in another window.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0,10)\n",
    "y = 2 * x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the **label=\"label text\"** keyword argument when plots or other objects are added to the figure, and then using the **legend** method without arguments to add the legend to the figure: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x151b84e0c08>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "ax.plot(x, x**2, label=\"x**2\")\n",
    "ax.plot(x, x**3, label=\"x**3\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how legend could potentially overlap some of the actual plot!\n",
    "\n",
    "The **legend** function takes an optional keyword argument **loc** that can be used to specify where in the figure the legend is to be drawn. The allowed values of **loc** are numerical codes for the various places the legend can be drawn. See the [documentation page](http://matplotlib.org/users/legend_guide.html#legend-location) for details. Some of the most common **loc** values are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lots of options....\n",
    "\n",
    "ax.legend(loc=1) # upper right corner\n",
    "ax.legend(loc=2) # upper left corner\n",
    "ax.legend(loc=3) # lower left corner\n",
    "ax.legend(loc=4) # lower right corner\n",
    "\n",
    "# .. many more options are available\n",
    "\n",
    "# Most common to choose\n",
    "ax.legend(loc=0) # let matplotlib decide the optimal location\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(loc=(1.1,0.5)) # manually set location\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting colors, linewidths, linetypes\n",
    "\n",
    "Matplotlib gives you *a lot* of options for customizing colors, linewidths, and linetypes. \n",
    "\n",
    "There is the basic MATLAB like syntax (which I would suggest you avoid using unless you already feel really comfortable with MATLAB). Instead let's focus on the keyword parameters.\n",
    "\n",
    "### Quick View:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colors with MatLab like syntax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With matplotlib, we can define the colors of lines and other graphical elements in a number of ways. First of all, we can use the MATLAB-like syntax where `'b'` means blue, `'g'` means green, etc. The MATLAB API for selecting line styles are also supported: where, for example, 'b.-' means a blue line with dots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151b8c263c8>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MATLAB style line color and style \n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, x**2, 'b.-') # blue line with dots\n",
    "ax.plot(x, x**3, 'g--') # green dashed line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Suggested Approach: Use keyword arguments\n",
    "\n",
    "### Colors with the color parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also define colors by their names or RGB hex codes and optionally provide an alpha value using the `color` and `alpha` keyword arguments. Alpha indicates opacity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151b8c7fa08>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, x+1, color=\"blue\", alpha=0.5) # half-transparant\n",
    "ax.plot(x, x+2, color=\"#8B008B\")        # RGB hex code\n",
    "ax.plot(x, x+3, color=\"#FF8C00\")        # RGB hex code "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line and marker styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linewidth\n",
    "\n",
    "To change the line width, we can use the `linewidth` or `lw` keyword argument. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151b6dda608>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "# Use linewidth or lw\n",
    "ax.plot(x, x-1, color=\"red\", linewidth=0.25)\n",
    "ax.plot(x, x-2, color=\"red\", lw=0.50)\n",
    "ax.plot(x, x-3, color=\"red\", lw=1)\n",
    "ax.plot(x, x-4, color=\"red\", lw=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linestyles\n",
    "\n",
    "There are many linestyles to choose from, here is the selection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151b6df5d48>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# possible linestype options ‘--‘, ‘–’, ‘-.’, ‘:’, ‘steps’\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "ax.plot(x, x-1, color=\"green\", lw=3, linestyle='-') # solid\n",
    "ax.plot(x, x-2, color=\"green\", lw=3, ls='-.') # dash and dot\n",
    "ax.plot(x, x-3, color=\"green\", lw=3, ls=':') # dots\n",
    "ax.plot(x, x-4, color=\"green\", lw=3, ls='--') # dashes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom linestyle dash\n",
    "\n",
    "The dash sequence is a sequence of floats of even length describing\n",
    "the length of dashes and spaces in points.\n",
    "\n",
    "For example, (5, 2, 1, 2) describes a sequence of 5 point and 1 point\n",
    "dashes separated by 2 point spaces.\n",
    "\n",
    "First, we see we can actually \"grab\" the line from the .plot() command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "lines = ax.plot(x,x)\n",
    "\n",
    "print(type(lines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "# custom dash\n",
    "lines = ax.plot(x, x+8, color=\"black\", lw=5)\n",
    "lines[0].set_dashes([10, 10]) # format: line length, space length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAFlCAYAAADiVIA6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de5iN9f7/8ddnTs5SOSRiKElK2aZEBxW6Ejr55lSSlJDTYDRj1vrVtWeZEWNQiS0JRVulYqfvrrR12G1hHIqSEsYh5w7OM2bm8/uDfPcYZjFm7nvN3M/HP1nv+6371bXXNq/rdq97GWutAAAAAC8IczsAAAAA4BTKLwAAADyD8gsAAADPoPwCAADAMyi/AAAA8AzKLwAAADwjwsmTVa1a1UZHRzt5SgAAAHjQihUr9lprq506d7T8RkdHKz093clTAgAAwIOMMRmnm3PbAwAAADyD8gsAAADPoPwCAADAMyi/AAAA8AzKLwAAADyD8gsAAADPoPwCAADAMyi/AAAA8AzKLwAAADyD8gsAAADPoPwCAADAMyi/AAAA8AzKLwAAADyD8gsAAIBztmHDBo0fP97tGOeM8gsAAICztm7dOvXo0UMNGzbU0KFDtXz5crcjnRPKLwAAAIL69ttv1blzZzVu3FhvvPGGcnNzJUmBQMDlZOeG8gsAAIAzSk9P1/3336/rrrtOb7/9tqy1eY4vWLBA33zzjUvpzh3lFwAAAPn85z//Ubt27XTDDTdo/vz5Be6mpaU5lOr8RbgdAAAAAKHBWqvPPvtMSUlJWrx4cdD9qlWratiwYerfv78D6YoG5RcAAMDjrLX6+OOPlZSUpK+++irofs2aNRUXF6c+ffqoQoUKDiQsOpRfAAAAj7LW6h//+IcCgcBZPbXhsssuU3x8vB5//HGVLVvWgYRFj/ILAADgMbm5uZo3b54CgYC+/fbboPv169dXQkKCHn30UUVFRTmQsPhQfgEAADwiOztbc+fO1ahRo7Ru3bqg+w0bNlRiYqK6deumiIjSURtLx38FAAAAzujYsWN6/fXXlZKSog0bNgTdv/baa+Xz+dSpUyeFh4c7kNA5lF8AAIBSKjMzU6+99ppGjx6tjIyMoPt/+ctf5Pf7de+99yosrHQ+EZfyCwAAUMocPnxYr7zyisaMGaNffvkl6H6LFi3k9/t19913yxjjQEL3UH4BAABKiQMHDmjy5MkaN26cdu/eHXS/VatW8vv9uvPOO0t96f0T5RcAAKCE+/333/XSSy9p/Pjx+vXXX4Pu33XXXfL5fLr11lsdSBdaKL8AAAAl1L59+zRhwgS98MIL2r9/f9D9jh07yufz6cYbb3QgXWii/AIAAJQwu3bt0rhx4/Tyyy/r0KFDQfc7deokn8+n66+/3oF0oY3yCwAAUEJs375dY8eO1dSpU3XkyJECd8PCwtS1a1eNHDlSjRs3dihh6KP8AgAAhLiMjAyNHj1a06dPV1ZWVoG7ERER6tGjhxISEtSgQQOHEpYclF8AAIAQtWHDBqWkpGjWrFnKzs4ucDcqKkq9evVSfHy8oqOjnQlYAlF+AQAAQsy6des0atQovfnmm8rNzS1wt2zZsurTp4/i4uJUu3ZthxKWXJRfAACAEPHNN98oEAho3rx5stYWuFuhQgX1799fQ4cO1SWXXOJQwpKP8gsAABACNm3apKZNmwYtvZUrV9bAgQM1ZMgQVa1a1aF0pUfp/NJmAACAEqZevXpq3779GY9feOGF+utf/6qMjAwFAgGKbyFRfgEAABxkrdXixYu1evXqfMd8Pl++WbVq1TR69GhlZGTI7/erSpUqTsQstSi/AAAADrDW6p///KduueUW3XnnnYqPj8+307x5c7Vt21aSVLNmTY0fP16bN2/WM888o0qVKjkduVQywe4rKUoxMTE2PT3dsfMBAACEgm3btumBBx7QqT1o2bJluuGGG/LMli5dqpUrV6pXr14qW7askzFLFWPMCmttzKlzrvwCAAAUs0suuUS//fZbvnkgEMg3a968ufr160fxLSaUXwAAgCKUnZ2tQ4cO5ZlFREQoISEh3+6CBQu0Zs0ap6JBZ1F+jTHTjTG7jTFrT5kPNMasN8Z8Z4wZU3wRAQAAQl9WVpamT5+uRo0a6bnnnst3vEePHqpTp87J102aNNFbb72lq6++2sGUOJsrvzMk3f3fA2PMHZLuk9TEWttYUmrRRwMAAAh9R48e1eTJk9WgQQP17t1bGzZs0OTJk7V37948e1FRUYqPj1ezZs30/vvva9WqVXrooYcUHh7uUnJvOqsPvBljoiV9YK295sTrtyRNtdYuOpeT8YE3AABQ2uzevVvR0dE6cuRInnliYmK+e3pzcnIUFhYmY4yTET2pqD/wdqWkW40xS40xnxtjbjjTojGmjzEm3RiTvmfPnkKeDgAAIDRVr15dTz31VL75iy++qN9//z3PLDw8nOLrssKW3whJF0q6SVKcpLfMGf6XtNZOtdbGWGtjqlWrVsjTAQAAhK7hw4crKioqz6xFixanfcID3FXY8rtN0rv2uGWSciXxHXsAAMCTatWqpd69e0uSOnbsqKVLl+qf//yn6tWr53IynCqikL/vfUl3SvrMGHOlpChJewv+LQAAAKVXYmKi+vTpo+uvv97tKChA0PJrjHlT0u2Sqhpjtkl6VtJ0SdNPPP4sS1JP6+RXxQEAAISYWrVqqVatWm7HQBBBy6+1ttsZDj1SxFkAAACAYsU3vAEAAMAzKL8AAADwDMovAAAAPIPyCwAAAM+g/AIAAMAzKL8AAADwDMovAAAAPIPyCwAAAM+g/AIAAMAzKL8AAADwDMovAAAAPIPyCwAAAM+g/AIAAMAzKL8AAADwDMovAAAIGVlZWW5HQClH+QUAAK7bu3evEhMTVadOHe3cudPtOCjFKL8AAMA1O3fuVFxcnKKjo5WcnKxdu3Zp3LhxbsdCKUb5BQAAjtu2bZsGDRqkevXqKTU1VYcOHTp5bPLkydq7d6+L6VCaUX4BAIBjNm3apKeeekr169fXiy++qKNHj+bbOXTokCZMmOBCOnhBhNsBAABA6ffTTz8pOTlZr7/+unJycgrcjYqKUnZ2tkPJ4DWUXwAAUGy+++47jRo1SnPnzlVubm6Bu+XKldNTTz2luLg4XXrppQ4lhNdQfgEAQJFbtWqVRo0apXnz5gXdrVixovr376+hQ4eqRo0aDqSDl1F+AQBAkVm2bJmSkpL0wQcfBN294IILNGjQIA0ePFgXX3yxA+kAyi8AACgCX375pQKBgD7++OOguxdddJFiY2M1YMAAValSxYF0wP+h/AIAgEKx1upf//qXkpKS9Pnnnwfdr169uoYPH65+/fqpYsWKDiQE8qP8AgCAc2Kt1f/+7/8qEAhoyZIlQfcvvfRSjRgxQk8++aTKly/vQELgzCi/AADgrOTm5mrBggUKBAJasWJF0P26desqPj5evXr1UpkyZRxICARH+QUAAAXKycnRO++8o1GjRmnNmjVB96+44gqNHDlSjzzyiCIjIx1ICJw9yi8AADit7OxszZkzR8nJyVq/fn3Q/UaNGikxMVFdunRRRAQVA6GJdyYAAMgjKytLs2bNUkpKijZu3Bh0v0mTJvL5fOrUqZPCwsIcSAgUHuUXAABIko4ePapXX31Vzz//vLZu3Rp0PyYmRn6/Xx07dpQxxoGEwPmj/AIA4HGHDh3S1KlTNXbsWO3YsSPo/s033yy/36+77rqL0osSh/ILAIBHHThwQJMmTVJaWpr27NkTdP/OO++Uz+fT7bffTulFiUX5BQDAY3777Te9+OKLmjBhgn777beg++3atZPP51PLli0dSAcUL8ovAAAesXfvXo0fP14vvfSS9u/fH3T//vvvV2JiomJiYhxIBziD8gsAQCm3c+dOpaamavLkyTp8+HCBu8YYPfTQQ0pMTFSTJk0cSgg4h/ILAEApdejQISUkJOiVV17R0aNHC9wNDw9X9+7dlZCQoEaNGjmUEHAe5RcAgFKqbNmy+uSTTwosvhEREerZs6cSEhJ0+eWXO5gOcAd
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "# custom dash\n",
    "lines = ax.plot(x, x+8, color=\"black\", lw=5)\n",
    "lines[0].set_dashes([1, 1,1,1,10,10]) # format: line length, space length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markers\n",
    "\n",
    "We've technically always been plotting points, and matplotlib has been automatically drawing a line between these points for us. Let's explore how to place markers at each of these points.\n",
    "\n",
    "### Markers Style\n",
    "\n",
    "Huge list of marker types can be found here: https://matplotlib.org/3.2.2/api/markers_api.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151b89eed08>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "# Use marker for string code\n",
    "# Use markersize or ms for size\n",
    "\n",
    "ax.plot(x, x-1,marker='+',markersize=20)\n",
    "ax.plot(x, x-2,marker='o',ms=20) #ms can be used for markersize\n",
    "ax.plot(x, x-3,marker='s',ms=20,lw=0) # make linewidth zero to see only markers\n",
    "ax.plot(x, x-4,marker='1',ms=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom marker edges, thickness,size,and style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArkAAAFlCAYAAADvSvB9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deXSU5eH28esmYVEWEQVBUBHCYpDViAhqBAFBUBDFDVxqW21VsFYUsnCqnUnYkQBhE6SsgiKLKEgRKSqugMgWDMkgsilBkEWQkOR+/4C3pz8VMpNk5pl58v2cM+cIGSdXPdPwPcMz9xhrrQAAAAA3KeP0AAAAAKCkEbkAAABwHSIXAAAArkPkAgAAwHWIXAAAALgOkQsAAADXiQ7Gg1566aW2bt26wXhoAAAABMn69aH9ftddV7x/f/369QettdV/72tBidy6detq3bp1wXhoAAAABIkxof1+xc1FY8yuc32NyxUAAADgOkQuAAAAXIfIBQAAgOsQuQAAAHAdIhcAAACuQ+QCAADAdYJyhBgAAADcxyqwM8aMbJCWFI5XcgEAAOA6RC4AAABch8gFAACA6xC5AAAAcB0iFwAAAK5D5AIAAMB1iFwAAAC4DpELAAAA1yFyAQAA4DpELgAAAFyHyAUAAIDrELkAAADQ1q1bC72PkQ3o5iQiFwAAoBT76quvdO+996pDhw5OTylRRC4AAEAp9MUXX+jOO+9U9+7d1a5dO/l8PqcnlahopwcAAAAgdD766CN5vV5t375dAwcO1JtvvqkKFSo4PavEEbkAAAAuZ63VBx98II/Ho927dyshIUGPPPKIypUr5/S0oCFyAQAAXMpaq+XLl8vj8ejw4cNKSkrSgw8+qOho9yeg+/8XAgAAlDIFBQVasmSJvF6vcnNzlZycrHvvvVdRUVFOTwsZIhcAAMAl8vPztWDBAnm9XpUrV06DBw/WXXfdpTJlSt9ZA0QuAABAhMvLy9PcuXOVmpqqatWqafjw4erSpYuMMQE9jnX2aNsSReQCAABEqNzcXM2cOVNDhgzRFVdcofT0dHXo0CHguHUjIhcAACDC/PLLL5o2bZqGDRuma665Rv/617908803Oz0rrBC5AAAAEeLnn3/W5MmTNXLkSMXFxWnBggVq3bq107PCEpELAAAQ5o4ePaoJEyZozJgxuummm7Rs2TK1aNHC6VlhjcgFAAClVqgvXQ30jV2HDx/W2LFjNX78eHXu3FmrVq1SkyZNgjPOZUrfeRIAAABh7uDBg0pMTFRMTIx27dqltWvXas6cOQRuAIhcAACAMPH9999rwIABatiwoQ4dOqT169frtddeU8OGDZ2eFnGIXAAAAIft3r1b/fr1U2xsrE6fPq1NmzZp0qRJqlu3rtPTIhaRCwAA4JCdO3fqySefVPPmzVWhQgVt27ZNaWlpqlOnjtPTIh6RCwAAEGKZmZl67LHHFBcXp+rVqyszM1MjRoxQzZo1nZ7mGpyuAAAAECJbtmxRamqq3n//ffXr10/Z2dmqWrWq07NciVdyAQAAQqRjx45q3ry5srOzNXjwYAI3iHglFwAA4BysAjtI1+j8B+FmZ2erYsWKxZkEP/FKLgAAQIgQuKFD5AIAAMB1/IpcY8xzxpitxpgtxpjXjTEVgj0MAAAAKKpCI9cYU1tSf0lx1tprJUVJeiDYwwAAAICi8vdyhWhJFxhjoiVdKGlf8CYBAAAAxVNo5Fpr90oaKek7SfslHbHW/vvX9zPGPGGMWWeMWZeTk1PySwEAAAA/+XO5wsWSeki6WtLlkioaY/r++n7W2inW2jhrbVz16tVLfikAAADgJ38uV+goaae1Nsdae1rSQkltgzsLAAAAKDp/Ivc7SW2MMRcaY4yk2yRlBHcWAAAAUHT+XJP7uaQFkjZI2nz235kS5F0AAABAkfn1sb7W2n9I+keQtwAAAAAlwq/IBQAAcJszp0Gd/83yRjY0Y1Di+FhfAABQquzfv1/PP/+8GjVq5PQUBBGRCwAASoXdu3frmWeeUZMmTZSfn69NmzY5PQlBROQCAABX8/l8euKJJ9SiRQtdeOGFysjI0JgxY1SnTh2npyGIiFwAAOBK33zzjR599FG1bt1al112mTIzMzV8+HBddtllTk9DCPDGMwAA4CpbtmxRSkqKVq1apf79+ysrK0tVq1Z1ehZCjFdyAQCAK2zYsEG9evVSx44d1bJlS2VnZys5OZnALaWIXAAAENE+++wzde/eXXfddZfi4+Pl8/n04osvqnLlyk5Pg4O4XAEAAESkNWvWyOv1KjMzU4MGDdKCBQtUoUKFgB7DcgyuaxG5AAAgYlhr9f7778vj8Wjfvn1KTExU3759Va5cOaenIcwQuQAAIOxZa/Xuu+/K6/XqyJEjSkpK0gMPPKDoaFIGv49nBgAACFsFBQVavHixvF6v8vPzlZycrF69eikqKsrpaQhzRC4AAAg7+fn5euONN5SSkqILLrhAL730krp3764yZXjPPPxD5AIAUIoYE9rvF+gbu06fPq05c+YoNTVV1atX18iRI3X77bfLhHo4Ih6RCwAAHHfq1CnNmDFDQ4cOVd26dTV58mTdeuutxC2KjMgFAACOOXnypKZOnarhw4fr2muv1axZs9SuXTunZ8EFiFwAABByx48f16RJkzRq1CjdcMMNWrhwoa6//nqnZ8FFiFwAABAyR48e1fjx45WWlqb4+Hi99957at68udOz4EJELgAACLpDhw4pLS1N6enp6tq1q1avXq3Y2FinZ8HFOIcDAAAEzYEDB5SQkKAGDRpoz549+uyzzzRr1iwCF0HHK7kAACBoGjdurAceeEAbNmzQVVdd5fQclCJELgAA+C+rwI7sMjr/QbibN29W7dq1izMJKBIuVwAAAEFD4MIpRC4AAABch8gFAACA6xC5AAAAcB0iFwAAAK5D5AIAAMB1iFwAAAC4DpELAAAA1yFyAQAA4DpELgAAAFyHyAUAAIDrRDs9AAAAhMapU6cklT/vfYxsaMYAQcYruQAAuNzJkyc1duxYxcTEOD0FCBkiFwAAlzp+/LhGjhypevXqafXq1Vq0aJHTk4CQ4XIFAABc5siRIxo/frzS0tLUvn17rVixQs2aNXN6FhBSRC4AAC5x6NAhpaWlacKECeratavWrFmja665xulZgCO4XAEAgAh34MABDRo0SA0aNNDevXv12WefaebMmQQuSjUiFwCACLVv3z4999xzaty4sY4dO6YNGzZo6tSpql+/vtPTAMcRuQAARJhdu3bp6aef1rXXXitjjLZs2aL09HRdddVVTk8DwgaRCwBAhMjOztaf/vQntWrVSpUrV9b27ds1evRoXX755X4/hrWhvQFOIXIBAAhz27dv1yOPPKIbbrhBtWvXVmZmpoYOHaoaNWo4PQ0IW0QuAABhavPmzbr//vt1yy23qFGjRsrOztbLL7+sSy65xOlpQNgjcgEACDPr169Xz5491blzZ11//fXy+XxKSkrSRRdd5PQ0IGIQuQAAhIlPPvlEd9xxh3r06KEOHTrI5/NpwIABqlSpktPTgIjDh0EAAFzPmNB+v0DecGWt1Zo1a+TxeOTz+TRo0CAtWrRI5cuXD95AoBQgcgEAcIC1Vv/+97/l9Xr1ww8/KDExUX369FHZsmWdnga4ApELAEAIWWv1zjvvyOPx6Oeff1ZSUpLuv/9+RUVFOT0NcBUiFwCAECgoKNDChQvl9XpljFFycrLuvvtulSnD22OAYCByAQAIory8PM2fP18pKSmqXLmyvF6vunXrJhPqC4WBUobIBQAgCE6fPq3Zs2crNTVVNWvW1JgxY9SpUyfiFggRIhcAgBI2adIkDR06VDExMZo6dari4+OdngSUOkQuAAAlbOnSpXr99dd14403Oj0FKLWIXABAqWcV2CUERuc/CPfdd98tzhwAJYC3dAIAAMB1iFwAAAC4jl+Ra4ypaoxZYIzZbozJMMZwkREAAADClr/X5KZJes9ae68xppykC4O4CQAAACiWQiPXGFNF0i2SHpMka22upNzgzgIAAACKzp/LFep
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "# marker size and color\n",
    "ax.plot(x, x, color=\"black\", lw=1, ls='-', marker='s', markersize=20, \n",
    "        markerfacecolor=\"red\", markeredgewidth=8, markeredgecolor=\"blue\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Thoughts\n",
    "\n",
    "After these 4 notebooks on Matplotlib, you should feel comfortable creating simple quick plots, more advanced Figure plots and subplots, as well as styling them to your liking. You may have noticed we didn't cover statistical plots yet, like histograms or scatterplots, we will use the seaborn library to create those plots instead. Matplotlib is capable of creating those plots, but seaborn is easier to use (and built on top of matplotlib!).\n",
    "\n",
    "We have an additional notebook called \"Additional-Matplotlib-Commands\" which you can explore for other concepts, mainly as a quick reference. We also highly encourage you to always do a quick Google Search and StackOverflow search for matplotlib questions, as there are thousands of already answered questions there and almost any quick matplotlib question already has an answer there.\n",
    "\n",
    "----\n",
    "----"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}