4-Visualization-with-Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ISRC Python Workshop: Visualization with Matplotlib\n",
    "\n",
    "___Visualization with Matplotlib and Seaborn___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "@author: Zhiya Zuo\n",
    "\n",
    "@email: zhiya-zuo@uiowa.edu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:51:57.841456Z",
     "start_time": "2018-01-28T18:51:57.835813Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following imports the main module in package `matplotlib`. Note that `%matplotlib inline` means we want the plot to be inline in our notebook instead of getting a pop-up windows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:51:58.541533Z",
     "start_time": "2018-01-28T18:51:58.514311Z"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization is one of the most important things of data analysis. Besides just producing ___readable___ plots, we should make an effort to improve the overall attractiveness of the plots. `matplotlib` is a powerful package for ___Python___ users. Let's start with an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we generate sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:00.129863Z",
     "start_time": "2018-01-28T18:52:00.114814Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "[ 2.01486134  5.25099205  8.51321917 11.03441301 14.57193933 17.29545064\n",
      " 20.35492655 23.4540255  26.15927674 29.59403265]\n"
     ]
    }
   ],
   "source": [
    "X_arr = np.arange(10)\n",
    "Y_arr = 3*X_arr + 2 + np.random.random(size=X_arr.size) # linear with some noise\n",
    "print(X_arr)\n",
    "print(Y_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot a simple scatter plot, we can use `plt.scatter()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:01.719648Z",
     "start_time": "2018-01-28T18:52:01.480937Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b4dbe48>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b14e128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_arr, Y_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See how easy this is? However, this is still too simple - we do not even have X or Y axis labels. Further we may want to change the scatter markers. Let's get a better plot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:03.125922Z",
     "start_time": "2018-01-28T18:52:02.667513Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'My First Plot')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b37f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use `+` as marker; color set as `g` (green); size proportion to Y values\n",
    "plt.scatter(X_arr, Y_arr, marker='+', c='g', s=Y_arr*10) \n",
    "# How about adding a line to it? Let's use `plt.plot()`\n",
    "# set line style to dashed; color as `k` (black) \n",
    "plt.plot(X_arr, Y_arr, linestyle='dashed', color='k')\n",
    "# set x/y axis limits: first two are xlow and xhigh; last two are ylow and yhigh\n",
    "plt.axis([0, 10, 0, 35])\n",
    "# set x/y labels\n",
    "plt.xlabel('My X Axis')\n",
    "plt.ylabel('My Y Axis')\n",
    "# set title\n",
    "plt.title('My First Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Anatomy of a Figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we go deeper, let's take a look at the structure of a figure in `matplotlib`:\n",
    "\n",
    "<img width=700 src=\"https://matplotlib.org/_images/anatomy1.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we have done things for many of the elements. There's no need to memorize them, though. We can always Google and find information on specific parts that we want to update."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coding style (personal recommendation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I myself prefer the following way of getting a figure with `matplotlib`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:31.602659Z",
     "start_time": "2018-01-28T18:52:30.002316Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b3a3048>]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b3a3588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# `plt.subplots()` returns a figure object (which is the whole thing as shown above)\n",
    "# and `axes` that control specific plots in the figure.\n",
    "# Here our \"subplots\" layout is by default 1 row and 1 col and therefore 1 plot\n",
    "fig, ax = plt.subplots()\n",
    "# Setting figure size\n",
    "fig.set_figwidth(6)\n",
    "fig.set_figheight(5)\n",
    "# plot should be done on the `axis`: ax\n",
    "ax.plot(X_arr, Y_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying what we did earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:32.072684Z",
     "start_time": "2018-01-28T18:52:31.609378Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'My First Plot')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117d36240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_figwidth(6)\n",
    "fig.set_figheight(5)\n",
    "# What we just did, applying to `ax`\n",
    "ax.scatter(X_arr, Y_arr, marker='+', c='g', s=Y_arr*10) \n",
    "ax.plot(X_arr, Y_arr, linestyle='dashed', color='k')\n",
    "ax.axis([0, 10, 0, 35])\n",
    "ax.set_xlabel('My X Axis')\n",
    "ax.set_ylabel('My Y Axis')\n",
    "ax.set_title('My First Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is especially useful when handling multiple plots in one figure. Let's use an example of 4 plots: 2x2 layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:33.871634Z",
     "start_time": "2018-01-28T18:52:32.984934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116763630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now the returned `ax` would be array with a shape a 2x2\n",
    "fig, ax_arr = plt.subplots(nrows=2, ncols=2)\n",
    "ax_arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot in each of them by adding `plot` to each of the `axes` using a `for` loop. (In reality we should plot different things in different subplots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:34.990482Z",
     "start_time": "2018-01-28T18:52:34.001898Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yzyu7M0VGLMINaK1ZvNtyFYuMWJyXNHQb+mF/Ns9+bxmxfHHfAK6UEYtwA2VVtcz4Pp4fYo7JiMXJSUO3ARmxCHeVlFPMI9/KiMVVSEOvp9T8Uh79VkYswr38ecSy8IHBDOkuG2o5O2noF+n0bbNeWpWAt1dDGbEIt3GqrJqXf0yQEYsLOm9DV0otAMYC+Vrr3tbH2gKLgSAgA7hNa33Kfmk6D601vxwqYPb6ZBKOFcuIxYVJbf+38upaFmw/zL9/TqesupanRofyTxmxuJS6zAa+AK7502PTgc1a6xBgs/VztxeTVcid/9nFPQv+oKiihnduj2TxpCHSzF3XF0htU2My8/VvGQybvY23Nx5kcPd2rJ88jMdGhkgzdzHnfYeutf5FKRX0p4dvBK60fvwlsA2YZsO8nEpqfilzNqaw7kAu7Zo35qXrw5kwKFBuPuHiPL22zWbN6vgc5mxM4ciJcgYGteXfE/vRv2tbo1MTF+liZ+gdtdY51o9zAbfcVi23qJJ3fzrI0r1H8W7UgMmjQnjg8m60aCKnHtyY29e21ppfDxXwpnVsGNbJhwX3RjO8p9zq0NXVuzNprbVSSp/t60qpScAkgMDAwPqGc4ii8ho++jmVL3ZkYNaaiYO78uiIHvi2kBNDnuRcte2KdQ2WseGb65L5Lf0EAW2a8s7tkdwQ6S+jFTdxsQ09Tynlp7XOUUr5Aflne6LWej4wHyA6Ovqsjd8ZVFSb+HznYT7ZlkZJVS039/XnydGhciNbz1Kn2nalugbLCua3N/zf2PDF68O5U8aGbudiG/oq4B5glvX3lTbLyAC1JjNL9hxl3uaD5BVXMSKsA09f3ZNefi2NTk04nlvVdm5RJfM2H2TJHsvY8ImRITw4TMaG7qouly1+h+Ukka9S6ijwIpZiX6KUuh84AtxmzyTtRWvNugO5vL0hhfSCMvoFtua9O6LkjuQewp1ru6iiho+2ydjQ09TlKpcJZ/nSSBvn4lD5JZVMXx7PluR8Qjq0YP7E/owO7ygnhTyIu9b2tpR8pi6L43hpFTf19efJUaEEtpOxoSfwyOOudfE5PPt9PGXVJp4b04v7hgbLSSHh8sqra3l9bRLf/J5JSIcWfHpPNBEBrY1OSziQRzX04soaXlqVwIp92fT2b8k7t/UlpKOP0WkJUW/7M08xZUkshwvKuP+yYJ6+uifeXnLC09N4TEPfmVbA00vjyC2u5PERPXh0RAiNG8kmWsK11ZjMvL/5EB9uS6NTS2++fVD2Kfdkbt/QK2tMvLUhhc+2HybYtznLHh5CVGAbo9MSot5S80t4cnEs8dlF3NIvgBdvCKelt5fRaQkDuXVDP5BdxJOLYziUX8rEwV155rowmjV267+y8ABms+aLnRm8uT6ZZo0b8snd/bimt5/RaQkn4JbdrdZk5pOf03j3p0O0bd6YL/8+kCtC2xudlhD1ll1YwdNLY9mZdoKRYR1445Y+dPDxNjot4STcrqFnFJTx5JIY9mcWMjbCj1dv6k3rZo2NTkuIetFa80NMNi+sTMBk1swa14fbB3SRy2zFf3Gbhq61ZuGuTF5bk4RXQ8W8O/pyY19/o9MSot5OlVUz44d41sbnEt21DXNv6yvXlYu/5BYNPb+4kqnL49iWcpzLQ3yZPT5C9igXbmGrdZFQYXk1064JY9KwbrJmQpyVyzf0NXE5zPghnsoaEzNvvIS7B3WlgRS8cHFlVbW8tjaJb3dlEtbJhy/vG0h4Z9lbSJybyzb0ovIaXlh1gJUxx4gMaMXc2/vSvX0Lo9MSot52Z5zkqSWxZJ0q56Fh3ZhyVajsiijqxCUb+s8HjzN1WSwnSquZMjqUf17ZnUYNZZGQcG1VtSbmbjrI/F/SCWjTlMWThjAwWO4eJOrOpRp6WZVlr4qFuyx7VXx2zwB6+7cyOi0h6i3hWBFTFseSklfChIGBzBjTS7a4FRfMZSpmT8ZJnloaS+bJch68PJinrpK9KoTrO71mYt7mQ7Rp1pjP7x3A8LAORqclXJTTN/SqWhPvbDrE/F/S6Ny6KYseHCz7lQu3kH68lClLYonJsqyZeOXG3rRpLmsmxMVz6oaecKyIp5bEkpxbwoSBXZgxJlwOQ4XLM5s1X/9+hDfWJdGkUUPemxDFDZGdjU5LuAGn7I61JjP//iWdd386SOtmjVlwbzQjwtzu5uvCAx0rrGDqsji2pxZwRWh7Zo+PoGNLWbovbMPpGvrhgjKmWJfuj+ljWbovh6HC1Wmt+X5/Ni+usizdf+3m3tw5MFCW7gubcpqGbjZrvtl1hDfWJtO4UQPm3dGXGyI7S8ELl3eitIpnv49nQ0IeA4La8PatkXRt19zotIQbcoqGnlNkOQz99VABw0LbM/uWCDq1ksNQ4fo2JuTyzIp4SipreebaMB64XJbuC/sxvKHnF1dy9Tu/UGOSw1DhXr76LYMXViYQ7teShQ9GEtZJlu4L+zK8oXdo6c0/h/fgmks6EeQrh6HCfVzb24/C8hoevqK73O5QOIThDR3g4Su6G52CEDbX3qcJj48MMToN4UHkbYMQQrgJaehCCOEmlNbaccGUOg4cOcuXfYEChyVjfFyJbXtdtdYOv3msk9a1xHavuHWqbYc29HNRSu3RWkd7SlyJbUxsR/PU77MnxnaGupaRixBCuAlp6EII4SacqaHP97C4EtszeOr32RNjG17XTjNDF0IIUT/O9A5dCCFEPUhDF0IIN2F4Q1dKXaOUSlFKpSqlpjswbhel1FalVKJSKkEp9YSjYp+RQ0Ol1H6l1GoHx22tlFqmlEpWSiUppYY4KO6T1u/1AaXUd0opt95S01Nr29Pq2hrbKWrb0IaulGoIfAhcC4QDE5RS4Q4KXws8pbUOBwYDjzgw9mlPAEkOjgkwD1ivtQ4DIh2Rg1LKH3gciNZa9wYaAnfYO65RPLy2Paauwblq2+h36AOBVK11uta6GlgE3OiIwFrrHK31PuvHJVj+8f0dERtAKRUAjAE+dVRMa9xWwDDgMwCtdbXWutBB4RsBTZVSjYBmwDEHxTWCR9a2h9Y1OEltG93Q/YGsMz4/igOb6mlKqSAgCtjlwLDvAlMBswNjAgQDx4HPrYfFnyql7L5vsdY6G3gbyARygCKt9UZ7xzWQp9a2R9U1OFdtG93QDaeUagEsByZrrYsdFHMskK+13uuIeH/SCOgHfKy1jgLKALvPd5VSbbC8Qw0GOgPNlVJ32zuuJ3N0bXtiXYNz1bbRDT0b6HLG5wHWxxxCKeWFpeAXaq1XOCouMBS4QSmVgeVQfIRS6hsHxT4KHNVan37HtgzLD4K9jQIOa62Pa61rgBXApQ6IaxRPrG1PrGtwoto2uqHvBkKUUsFKqcZYTiSsckRgZbnP3WdAktZ6riNinqa1fkZrHaC1DsLyd96itXbI/+ha61wgSynV0/rQSCDRAaEzgcFKqWbW7/1IjDlx5igeV9seWtfgRLVt6B2LtNa1SqlHgQ1Yzgwv0FonOCj8UGAiEK+UirE+9qzWeq2D4hvpMWChtdGkA/fZO6DWepdSahmwD8tVGPtxgqXS9iK1bQiH1zU4V23L0n8hhHATRo9chBBC2Ig0dCGEcBPnbehKKW+l1B9KqVjr0taXrY8HK6V2WZc1L7bOrYRwGVLbwt2cd4ZuPWvbXGtdar0UajuWpb1TgBVa60VKqU+AWK31x+d6LV9fXx0UFGSbzIX4k7179xZcyD1FbVXbUtfC3upa2+e9ykVbOn6p9VMv6y8NjADutD7+JfAScM6GHhQUxJ49e84XUoiLopQ6242a/5KtalvqWthbXWu7TjN06+5pMUA+sAlIAwq11rXWpxiyrFmI+pLaFu6kTg1da23SWvfFstptIBBW1wBKqUlKqT1KqT3Hjx+/yDSFgOzCCnamFdj0NS+2tqWuhS1tSsyjqKKm3q9zQVe5WHcv2woMAVpbdxaDcyxr1lrP11pHa62j27ev83hTiP+y/kAO1837laeXxlFjsv2+Txda21LXwhYqqk08syKeB7/aw39+Sa/369XlKpf2SqnW1o+bAqOxLGvdCoy3Pu0eYGW9sxHiTyprTMz4Pp6Hv9lH13bN+PbBQXg1tM3VtlLbwkjJucXc8MF2vvsjk4ev6M4To0Lq/Zp1WfrvB3xp3bC/AbBEa71aKZUILFJKvYplqetn9c5GiDOk5Jbw2Hf7OJhXykPDuvHUVT1p3MimSyektoXDaa35Zlcmr65OxMfbi6/vH8jlIbY5yqvLVS5xWPZT/vPj6VhmjkLYlNaahbsyeWV1Ij7ejfjq7wMZFmr7sYbUtnC0wvJqpi6LY2NiHleEtmfObZH4tmhis9c3dHMuIf6ssLya6cvjWZ+Qy7DQ9sy5NZL2PrYreCGMsiv9BJMXx1BQWsVzY3rx96HBNGigbBpDGrpwGn8cPsnkRfs5XlrFjOt6cf9lti94IRyt1mTm/S2pvL/lEIFtm7HiH0PpE9DKLrGkoQvDmcyaD7akMm/zQbq0bcbyf1xKREBro9MSot6yCyt4clEMf2ScZFw/f2be2JsWTezXdqWhC0MdK6xg8uIY/jh8kpuj/HnlJvsWvBCOsv5ADtOWx1NrMvPO7ZHcHBVg95jykyMMs/5ALtOWW64rn3tbJOP62b/ghbC3yhoTr6xOZOGuTCICWvHeHVEE+TrkftXS0IXjVdaYeHVNIt/8nklv/5a8P6EfwQ4qeCHs6cxLbScN68a/bH+p7TlJQxcOdTCvhMe+3U9KXgkPXh7M01eHObTghbCHP19q++XfB3KFHS61PR9p6MIhSqtq+WhrKp9uP4xPk0Z8ft8AhvfsYHRaQtRb4rFiXlmdyG/pJ7g8xJe5t/U17FJbaejCrsxmzbJ9R3lrQwrHS6oYF+XP9OvC6ODjbXRqQtRLQWkVczYeZPHuTFo19eKVm3pz18BAQy+1lYYu7GZ3xklm/phIfHYRUYGtmT+xP1GBbYxOS4h6qa418+XODN7bfIiKGhP3XBrE5JGhtGrmZXRq0tCF7R09Vc4b65JZE5eDXytv5t3RlxsiO2O5QZAQrklrzU9J+by2JpGME+UM79meGWPC6dGhhdGp/S9p6MJmyqpq+eTnNOb/ko5S8MTIEB66ohvNGkuZCdeWklvCK6sT2Z5aQPf2zfnivgFc6YTngOQnTdSb2az5ISabN9cnk1dcxY19OzPtmjA6t25qdGpC1MvJsmrmbkrh212Z+Hh78dL14dw1uKvNtnC2NWnool72HjnFzNWJxGYVEhnQio/u6k//rjInF66txmTmq9+OMO+ng5RVm5g4uCuTR4XSpnljo1M7J2no4qIcK6zgzfXJrIw5RgefJsy5NZKbo/xlMy3h8rYm5/PKmkTSj5dxeYgvz48NJ7Sjj9Fp1Yk0dHFBKmtMfPJzGp/8nIbW8NiIHjx8RXeay/4rwsWl5pfyyupEfj54nG6+zVlwbzTDe3ZwqZP58lMo6uyPwyeZtjyOwwVljInw45lrwwho08zotISolxqTmX//nMZ7m1Np4tWA58b04m9DglxyBbM0dHFeZVW1vLUhhS9/yyCgTVMWPjCIoT18jU5LiHpLOFbE00vjSMwpZmyEHy/dcIlN7yDkaNLQxTntSC1g2vI4sgsruGdIEE9f3VPGK8LlVdWa+HBLKh9tS6N1s8Z8cnd/rundyei06k1+MsVfKq6s4Y21SXz3RxbdfJuz5KEhDAhqa3RaQtRbTFYhU5fFcjCvlFv6BfD82F60bubcV6/UlTR08f9sTc7n2e/jySuu5KEruvHkqFC8vRoanZYQ9VJZY+KdTQf5z6/pdGzpzef3DmB4mPMtDqoPaejifxWWVzNzdSIr9mUT2rEFn9w9lMgucis44fr2ZJxk6rI40gvKmDAwkGeuC6Olt/F7r9iaNHQBWO4e9NwPBygsr+bxkSE8Mrw7TRrJu3Lh2sqra5m93nJC37+1+5/Ql4bu4QpKq3hxVQJr4nK4pHNLvvz7AC7pbJ87kgvhSDtTC5i2Io6jpzznhP55/3ZKqS7AV0BHQAPztdbzlFJtgcVAEJAB3Ka1PmW/VIUtaa35MS6Hl1YlUFpZy9NX92TSsG5Ou0eFPUhtu6eSyhpeX5vMd39kEuxhJ/Tr8t9VLfCGR31mAAAQZUlEQVSU1nqfUsoH2KuU2gTcC2zWWs9SSk0HpgPT7JeqsJW84kqe++EAmxLz6NulNW+NjyDERZY225jUtpvZmpLPsyusJ/SHdePJ0Z51Qv+8DV1rnQPkWD8uUUolAf7AjcCV1qd9CWxDit6paa1Zuucor6xJpLrWzHNjenHf0GAaeuj+K1Lb7uNUWTWvrPm/E/of3z2Uvh54Qv+CBkpKqSAgCtgFdLT+QADkYjlsFU4q80Q5z3wfx47UEwwKbsusWyII9m1udFpOQ2rbNWmtWRufy4urDlBYXsPjI3rwyIgeHntCv84NXSnVAlgOTNZaF5+5YY3WWiul9Fn+3CRgEkBgYGD9shUXzGTWfL7jMHM2HqRhA8VrN/dmwgBj73vobC6mtqWujXfm6DAioBVf3z+IXn4tjU7LUHVq6EopLywFv1BrvcL6cJ5Syk9rnaOU8gPy/+rPaq3nA/MBoqOj/7LpC/tIyS1h2vI4YrIKGRHWgddu7o1fK7npxJkutralro2jtWbx7ixeW5tEda2ZZ68L4+9Dg2nkQSf0z6YuV7ko4DMgSWs994wvrQLuAWZZf19plwzFBauuNfPRtlQ+3JqKj7eX3NPzLKS2Xc+RE2U8syKenWknGNytLbPGRRAko8P/VZd36EOBiUC8UirG+tizWIp9iVLqfuAIcJt9UhQXIiarkGnL4kjJK+HGvp15YWw47Vx49zg7k9p2EadHh29vTMGrQQNev7kPdwzoIqPDP6nLVS7bgbN910baNh1xsSqqTczZmMKCHYfp4OPNZ/dEM7KXnMs7F6lt15CSW8LU5XHEZhUyMqwDr8ro8Kzce9mUh9iZWsD0FfFkniznrkGBTL82DB833KdCeJaqWhMfbU3jo22W0eF7E6K4PsJPRofnIA3dhRVVWLa4XbQ7i6B2zVg0aTCDu7UzOi0h6m1/5immLY/jYF4pN/XtzAvXX0JbJ79BszOQhu6iNiZYNtMqKK2SLW6F2yivrmXOxoMs2HGYTi29WXBvNCPCZHRYV9LQXUxqfimvrklkW8pxwjr58Ok90UQEeN6KOOFetNasjDnGrHXJ5BZXcvfgQKZdI6PDCyUN3UUUVdQw76dDfPVbBk29GjLjul7cOzTIozbTEu4pNquQl39MYF9mIb39W/LBnVFEe8hmWrYmDd3JmcyaRbszmbPxIKfKq7ljQBeeuqqnS9/IVgiA/OJKZm9IYdneo/i2aMLsWyIY3z9ALkWsB2noTuy3tBPMXJ1IUk4xA4Pb8sLYcHr7y17lwrVV1phYsOMwH25Jpdpk5qEruvHo8B4yXrEBaehOKOtkOa+vTWLdgVz8Wzflwzv7cV2fTnK5lnBpWms2Jubx2pokMk+WMzq8IzOu6yUrPW1IGroTKauq5eNtacz/NZ2GSvHU6FAeHNZNrl4RLi85t5iZPyayM+0EIR1a8PX9A7k8pL3RabkdaehOwGzWrIzNZta6ZPKKq7ipb2emXRsmq+GEyztVVs3cTQdZuOsIPt5evHzDJdw1KFA20rITaegG2595ipd/TCQmq5CIgFZ8dFc/+neVM/zCtdWYzHzz+xHe/ekQpVW1TBzclcmjQmkji4PsShq6QY6XVPHGuiRW7MumvU8T3r41knFR/nKGX7i8HakFvLgqgdT8Ui7r4cvzY8Pp2ckjb3HocNLQDbAztYDHF8VQXFHDP67sziPDe9DCze9GLtxfjcnMnI0H+eTnNLq2a8Z//hbNqF4d5GS+A0kXcSCTWfP+lkPM23yIbr7NWfjAIHnnItzCscIKHvtuP3uPnOLOQYG8MDZcTuYbQBq6g+SXVDJ5UQw7004wLsqfV27qTXN5Vy7cwNbkfJ5cEkNNrZn3JkRxQ2Rno1PyWNJRHOD0iKW0qobZ4yO4tX+AHIYKl1djMvP2xhT+/XM6vfxa8uGdUXRr38LotDyaNHQ7khGLcFdnjljuGhTI8zJicQrS0O1ERizCXW1JzmPKklgZsTgh6TB2sCO1gCdkxCLcjIxYnJ80dBuSEYtwVzJicQ3S0G3kv0Ys/fx55UYZsQj3ICMW1yEdxwbOHLG8NT6CW6O7GJ2SEPX25xHLR3f1I1h2RnRq0tDrwWTWvLf5EO9tkRGLcC8yYnFN0tAvUkxWIa+tSWR3xikZsQi3UWMys2h3FnM2psiIxQWdtwMppRYAY4F8rXVv62NtgcVAEJAB3Ka1PmW/NJ1H2vFS3t6QwroDubRr3pi3b41kfP8Ao9MSF0Fq+/+YzZo18TnM2ZhCxolyBga15c3xETJicTF12ZT4C+CaPz02HdistQ4BNls/d2u5RZU8syKOq975hV8OHmfyqBB+njpcmrlr+wIPr22tNb8cPM4NH27nse/24+3VkM/vHcDihwZLM3dB532HrrX+RSkV9KeHbwSutH78JbANmGbDvJxGUXkNH/2cyhc7MjBrzd+GdOXR4T1oJzdpdnmeXtuxWYW8uT6ZnWknCGjTlHduj+TGSNnC2ZVd7NC3o9Y6x/pxLtDxbE9USk0CJgEEBgZeZDjHq6g28cXODD7elkpJVS039/XnydGhdGnbzOjUhH3VqbZdta7BMjacszGFtfGWseGL14dz56BAmjSSk56urt5n8bTWWimlz/H1+cB8gOjo6LM+z1nUmsws3XuUd386SF5xFSPCOvD01T3p5dfS6NSEg52rtl2trsEyNpy3+SBL9hzFu1EDnhgZwoPDusle/G7kYv8l85RSflrrHKWUH5Bvy6SMoLVm3YFc3t6QQnpBGf0CW/P+hH4MDJbbwXkYt6vtovIaPv45jc93HMasNRMHd+XRET3wlbGh27nYhr4KuAeYZf19pc0yMsDO1ALeXJ9M7NEiQjq0kDuteDa3qe3KGsvY8KOtlrHhTX39mSJjQ7dWl8sWv8NykshXKXUUeBFLsS9RSt0PHAFus2eS9pJRUMbzKw/w66ECOrfy5q3xEYzrF0BDOSnkEdy1trXWrIo9xutrk8grrmJ4z/ZMvSZMxoYeoC5XuUw4y5dG2jgXh9Fas3BXJq+tScKroeK5Mb24e3BXWQnnYdyxtk+WVfPcD/Gsjc8lsktr3rsjikHd2hmdlnAQjzsbkldcydRlcfx88DiXh/gye3wEfq2aGp2WEPW2NTmfqcvjKCyvZto1YUwa1k2ONj2MRzX0NXE5zPghnsoaEzNvvIS7B3WVa26FyyurquW1tUl8uyuTsE4+fHnfQMI7y3jFE3lEQy+qqOHFlQf4IeYYkQGtmHt7X7rLxvzCDew9coopS2LIPFnOQ8O6MeWqULme3IO5fUPfkVrAv5bGkl9SxeRRITwyvAdeDeuy44EQzqu61sy8zQf5eFsanVs3ZdGDg2VWLty3oVfWmJi1LpkvdmbQrX1zVvzjUiK7tDY6LSHq7WBeCU8ujiHhWDG3RQfw/NhwfLy9jE5LOAG3bOhxRwt5cnEMacfLuPfSIKZdE0bTxnIYKlyb2axZsOMwszek4NOkEfMn9ueqSzoZnZZwIm7V0GtNZj7cmsb7Ww7R3qcJ39w/iMtCfI1OS4h6O3qqnH8tjeX39JOMDu/IG+P6yEpP8f+4TUNPP17Kk0tiic0q5Ma+nZl5Q29aNZPDUOHatNYs35fNy6sS0MDs8RHc2j9AVjGLv+TyDV1rzde/H+H1tUk0adSQD+6MYmyE3GFFuL4TpVU8+308GxLyGBjcljm3RsqyfXFOLt3Qc4oqmLosjl8PFXBFaHtmj4+gY0tvo9MSot5+Ssxj+op4iitqePa6MO6/TBYJifNzyYautWZlzDFeWHmAGpPmlZt6c/egQDkMFS6vpLKGmT8msnTvUXr5teSbBwYS1kkWCYm6cbmGfrKsmhnfx7PuQC79u7Zhzq2RBMmtsoQb+C3tBP9aGktOUQWPDu/B4yNDaNxI1kyIunOphn76MLSoQvaqEO6jssbE7PUpLNhxmGDf5iz7x6X0C2xjdFrCBblEQy+prOGV1Yks2XOUsE4+fH3/QNkKVLiF2KxCpiyxrJm4Z0hXpl0bRrPGLvFjKZyQ01fOmYehjwzvzhMjQ+UwVLi8GpOZD7ak8sHWVNq3aMLX9w/k8pD2RqclXJzTNvTKGhNvbUjhs+2HCWrXjKUPX0r/rnIYKlzfobwSpiyJJT67iHFR/rx4wyW0aiprJkT9OWVDjztayJQlsaTmlzJxcFeeuU4OQ4XrO3Ppfosmjfjk7n5c09vP6LSEG3GqLvnnw9Cv/j6QYaFyGCpcX9ZJy9L9XYdPMqqXZel+ex9Zui9sy2kaemp+CU8uthyG3hzlz0vXXyJL94XL01qzZE8WM39MRCnFW+MjGC9L94WdGN7QzzwMbd64IR/f1Y9r+8hhqHB9+SWVTF8ez5bkfAZ3a8vbt0YS0EaW7gv7MbyhF5RV8d7mQwwL8eX1cX3o4CNL94V72JiQx47UAp4fG859lwbJ7Q6F3Rne0Dv4eLP6scvp0rapHIYKt3LnwECuCG0vG2oJhzG8oQMEtpOCF+6nQQMlzVw4lKzQEUIINyENXQgh3ITSWjsumFLHgSNn+bIvUOCwZIyPK7Ftr6vW2uELF5y0riW2e8WtU207tKGfi1Jqj9Y62lPiSmxjYjuap36fPTG2M9S1jFyEEMJNSEMXQgg34UwNfb6HxZXYnsFTv8+eGNvwunaaGboQQoj6caZ36EIIIerB8IaulLpGKZWilEpVSk13YNwuSqmtSqlEpVSCUuoJR8U+I4eGSqn9SqnVDo7bWim1TCmVrJRKUkoNcVDcJ63f6wNKqe+UUm69cY+n1ran1bU1tlPUtqENXSnVEPgQuBYIByYopcIdFL4WeEprHQ4MBh5xYOzTngCSHBwTYB6wXmsdBkQ6IgellD/wOBCtte4NNATusHdco3h4bXtMXYNz1bbR79AHAqla63StdTWwCLjREYG11jla633Wj0uw/OP7OyI2gFIqABgDfOqomNa4rYBhwGcAWutqrXWhg8I3ApoqpRoBzYBjDoprBI+sbQ+ta3CS2ja6ofsDWWd8fhQHNtXTlFJBQBSwy4Fh3wWmAmYHxgQIBo4Dn1sPiz9VSjW3d1CtdTbwNpAJ5ABFWuuN9o5rIE+tbY+qa3Cu2ja6oRtOKdUCWA5M1loXOyjmWCBfa73XEfH+pBHQD/hYax0FlAF2n+8qpdpgeYcaDHQGmiul7rZ3XE/m6Nr2xLoG56ptoxt6NtDljM8DrI85hFLKC0vBL9Rar3BUXGAocINSKgPLofgIpdQ3Dop9FDiqtT79jm0Zlh8EexsFHNZaH9da1wArgEsdENconljbnljX4ES1bXRD3w2EKKWClVKNsZxIWOWIwMpyN43PgCSt9VxHxDxNa/2M1jpAax2E5e+8RWvtkP/Rtda5QJZSqqf1oZFAogNCZwKDlVLNrN/7kRhz4sxRPK62PbSuwYlq29AbXGita5VSjwIbsJwZXqC1TnBQ+KHARCBeKRVjfexZrfVaB8U30mPAQmujSQfus3dArfUupdQyYB+WqzD24wQr6+xFatsQDq9rcK7alpWiQgjhJoweuQghhLARaehCCOEmpKELIYSbkIYuhBBuQhq6EEK4CWnoQgjhJqShCyGEm5CGLoQQbuJ/AHk3/aZ6xB9lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117805dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now the returned `ax` would be array with a shape a 2x2\n",
    "fig, ax_arr = plt.subplots(nrows=2, ncols=2)\n",
    "for ax_row in ax_arr:\n",
    "    for ax in ax_row:\n",
    "        ax.plot(X_arr, Y_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Different plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of the simpletst scatter/line plots, there are of course other options."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a Gaussian distribution for illustration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:36.613867Z",
     "start_time": "2018-01-28T18:52:36.600420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15.018546230524336, 0.9973813325728333)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu, sigma = 15, 1\n",
    "gaussian_arr = np.random.normal(mu, sigma, size=10000)\n",
    "np.mean(gaussian_arr), np.std(gaussian_arr, ddof=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:37.321621Z",
     "start_time": "2018-01-28T18:52:36.964449Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176f8cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "# `hist()` will return something but we usually do not need.\n",
    "freq_arr, bin_arr, _ = ax.hist(gaussian_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can actually customize and make it prettier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:38.273234Z",
     "start_time": "2018-01-28T18:52:37.827321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11562ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "# Facecolor set to green; transparency (`alpha`) level: 30%\n",
    "freq_arr, bin_arr, _ = ax.hist(gaussian_arr, facecolor='g', alpha=0.3)\n",
    "# Add grid\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:17:05.788237Z",
     "start_time": "2018-01-28T18:17:05.776588Z"
    }
   },
   "source": [
    "With the guaussian data, we can use a different form: box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:41.292074Z",
     "start_time": "2018-01-28T18:52:40.902371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x112b1aba8>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1133b8ef0>,\n",
       "  <matplotlib.lines.Line2D at 0x1133b8d30>],\n",
       " 'fliers': [],\n",
       " 'means': [<matplotlib.lines.Line2D at 0x11303a400>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1133b8c88>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x112b1a2b0>,\n",
       "  <matplotlib.lines.Line2D at 0x112574438>]}"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147d7668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(gaussian_arr, \n",
    "           vert=False,  # verticle\n",
    "           showfliers=False, # do not show outliers\n",
    "           showmeans=True, # show the mean\n",
    "           labels=['Gaussian'] # group name (label)\n",
    "          )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bar chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random data for bar chart input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:42.321189Z",
     "start_time": "2018-01-28T18:52:42.312011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36, 16, 67, 73])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_arr = np.array(['Spring', 'Summer', 'Fall', 'Winder'])\n",
    "freq_arr = np.random.randint(0, 100, 4) # generate 4 random integers for bar chart\n",
    "freq_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also add error bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:43.268921Z",
     "start_time": "2018-01-28T18:52:43.257325Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 5, 9, 8])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yerr_arr = np.random.randint(5, 10, 4)\n",
    "yerr_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:44.282059Z",
     "start_time": "2018-01-28T18:52:43.946397Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 4 artists>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11242bbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(bar_arr, freq_arr, # X and Y\n",
    "       yerr = yerr_arr, # error bars\n",
    "       color='gold',\n",
    "      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Group these plots together!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:46.683295Z",
     "start_time": "2018-01-28T18:52:45.521346Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Bar chart')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a62c9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax_arr = plt.subplots(nrows=2, ncols=2, \n",
    "                           # let their scales be different\n",
    "                           sharex=False, sharey=False)\n",
    "fig.set_figwidth(12)\n",
    "fig.set_figheight(8)\n",
    "# set global title\n",
    "fig.suptitle(\"My first subplots\")\n",
    "\n",
    "## first\n",
    "ax_arr[0, 0].scatter(X_arr, Y_arr, marker='+', c='g', s=Y_arr*10) \n",
    "ax_arr[0, 0].plot(X_arr, Y_arr, linestyle='dashed', color='k')\n",
    "ax_arr[0, 0].axis([0, 10, 0, 35])\n",
    "ax_arr[0, 0].set_title('My First Plot')\n",
    "## second \n",
    "ax_arr[0, 1].hist(gaussian_arr, facecolor='g', alpha=0.3)\n",
    "ax_arr[0, 1].set_title('Histogram')\n",
    "## third\n",
    "ax_arr[1, 0].boxplot(gaussian_arr, vert=False, showfliers=False,\n",
    "                     showmeans=True, labels=['Gaussian'])\n",
    "ax_arr[1, 0].set_title('Box plot')\n",
    "## last one\n",
    "ax_arr[1,1].bar(bar_arr, freq_arr, \n",
    "                yerr = yerr_arr, color='gold')\n",
    "ax_arr[1, 1].set_title('Bar chart')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Seaborn: Made life easier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, I would like to introduce [`seaborn`](https://seaborn.pydata.org/). This package really helps me get some good-looking plots for journal/conference submissions. Specifically, I would like to show you two examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:51.087827Z",
     "start_time": "2018-01-28T18:52:51.082151Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example 1: Heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I would use the example of correlataion matrix. First I load a sample data `iris`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:52.507148Z",
     "start_time": "2018-01-28T18:52:52.468461Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</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"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('iris')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the correlation between the first 3 columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:53.755854Z",
     "start_time": "2018-01-28T18:52:53.737185Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.11757</td>\n",
       "      <td>0.871754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.428440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.42844</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length\n",
       "sepal_length      1.000000     -0.11757      0.871754\n",
       "sepal_width      -0.117570      1.00000     -0.428440\n",
       "petal_length      0.871754     -0.42844      1.000000"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_df = df[df.columns[:3]].corr()\n",
    "corr_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting a heatmap in `seaborn` is very straightfoward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:55.743813Z",
     "start_time": "2018-01-28T18:52:55.288495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113395e80>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b0892e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_figwidth(8)\n",
    "fig.set_figheight(7)\n",
    "sns.heatmap(corr_df, \n",
    "            annot=True, # show the values\n",
    "            fmt=\".2f\", # format of the value\n",
    "            cmap=sns.light_palette(\"#F4D03F\", as_cmap=True), # color map to be used \n",
    "            ax=ax # plot on the `ax` we just instaniated\n",
    "           )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example 2: Linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the same dataset, let's plot the bivariate regression between sepal length and width for different species:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T18:52:57.863481Z",
     "start_time": "2018-01-28T18:52:56.969108Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11becb160>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bea3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"sepal_length\", y=\"sepal_width\", \n",
    "           data=df, # the data to be used\n",
    "           col=\"species\", # seperate data into column based on `species`\n",
    "           hue=\"species\", # color for each subplot (facet)\n",
    "           col_wrap=3, # wrap into a new row for every 2 columns\n",
    "           ci=None, # do not show confidence interval\n",
    "           palette=\"muted\", # color palette to use\n",
    "           size=4, # size of each subplot (facet)\n",
    "          )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `lmplot()` is a wrapper of ['FacetGrid'](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html) so we cannot set the `axes` for this figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some helpful tutorials...\n",
    "\n",
    "Finally, I list some tutorials that I think will greatly benefit the understanding of the packages mentioend above:\n",
    "\n",
    "- https://matplotlib.org/users/pyplot_tutorial.html\n",
    "- https://matplotlib.org/faq/usage_faq.html#parts-of-a-figure\n",
    "- https://seaborn.pydata.org/examples/index.html"
   ]
  }
 ],
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   "nbconvert_exporter": "python",
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  "toc": {
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   "types_to_exclude": [
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