7_Seborn_Figure_Aesthetics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href='http://www.scienceacademy.ca'> <img style=\"float: left;height:70px\" src=\"Log_SA.jpeg\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Seaborn: statistical data visualization\n",
    "## Controlling figure aesthetics in seaborn\n",
    "\n",
    "Hi guys,<br>\n",
    "Welcome back to the seaborn: a statistical data visualization section. We are going to talk about one of the most important aspect in the data presentation and report writing which is figure aesthetics. This very important aspect if widely ignored by the early stage data scientist which could significantly reduce the impact of all your efforts at the end. Always pay attention to the figure aesthetics!<br>\n",
    "\n",
    "\n",
    "In the previous lectures, we have seen a couple of times how to control figure aesthetics in seaborn, but let's now go over it formally:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Girijesh\\Anaconda3\\envs\\DSMLBC_Py36\\lib\\site-packages\\IPython\\html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "tips = sns.load_dataset('tips')\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn figure styles\n",
    "### `set_style()`\n",
    "We can set particular styles using `set_style()`. `shift+tab` for doc string<br>\n",
    "There are five preset seaborn `themes: darkgrid, whitegrid, dark, white, and ticks`. They are each suited to different applications and personal preferences. The default theme is `darkgrid`. <br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bcbba23eb8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eBBwOvACsKPffAvx9Zl7V0uZM4OvALuWhRZnpPUttcs6gM9wALIiILuBA4C6K//e7AIdm5kEUvxj88ZY3RMTewPEU60bNBY6NiKi7cE0Ke0fEHS1/Pga8PTM/BBwCLImIN5Xn3puZfwJMAzZm5mHAz4CDgXdQ/FJyOEU4fHarz/k8cGtmHgJ8GrgKtc2eQWf4FsUPxqMUv20BbAaeB/41Ip4B3grs0PKedwNvA24t93uBdwJZR8GaVF42TBQRi4HZZU8AiutuVrm9ZW5hPUUIAAwC04FfA2dGxAKKJWxar1eA9wAfjojjy/2dJ+6vMPnZM+gAmfkoxVDQIuBfysM7Acdm5vHA6RTXQlfr24CfAoeUP8hXU0wESq/Vg8Dt5XX1YeDbwCPla6PdBXsWcHdmnghcx8uv1y3tXla2exwvXetqg2HQOf4N2D0zHyr3NwG/jYi1wH8BT1CMwwKQmfdR9ArujIjvU/QKflFvyZqkvgs8ExFrgB8Aw5k51Ob7FkbEauBMYFNETGt5/ULguLLH8T1g3cSWPbm5HIUkyZ6BJMkwkCRhGEiSMAwkSRgGkiQMA0kShoEkCZejkLZJRLyVYqG/N1As6bGIYgG1y4AZFAv+nVr+9yfAJzPz1oi4BfhOZl7ZSOHSGOwZSNvmk8DKzDwAWAzMo1hJ84TMfB/wFWB5eUftJ4ArI2IhsNkg0PbMO5ClbRARc4AbKZbq+A+KJZfvAR5uOW2nzNyjPP8qimXA98zMJ2ouV2qbPQNpG2TmWmBv4BaKJb6vAB7NzP0zc39gNsWy35RLhgewEXhXMxVL7TEMpG0QEV8C/iIzrwFOA/YHdo6IueUpn6BYMhzgMxQPaZkPrIgInzCn7ZbDRNI2iIjdKf6x76GYOL4Y+DlwOcWa+08Df0kxuXwXcGBm/jwivgpMyczPNFK4NAbDQJLkMJEkyTCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJ+H++1xLaZs9FUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bcb867ecf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# default countplot \n",
    "sns.countplot(x='sex',data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bcbbccd518>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j9+7dNDU1MW3aNAYOHMjMmTMJhUIMGjSIuXPn0q2bjZKkIAUag8rKSnr37s2dd97Je++9x3e+8x2GDBlCUVERI0eOZM6cOVRXV3PppZcGOZYkdXmB/hf8sssu4wc/+AEA8XiclJQUtm3bRn5+PgCjR49mw4YNQY4kSSLgGGRkZJCZmUk0GuWmm26iqKiIeDxOKBRK3N/Q0BDkSJIkOuED5D179jBlyhS+/e1vM378+MM+H4jFYmRnZwc9kiR1eYHGYN++fVxzzTXceuutXHnllQCcddZZ1NbWArB+/XpGjBgR5EiSJAKOQUlJCR988AH33XcfkUiESCRCUVERixcvZuLEiTQ3N1NQUBDkSJIkAj6aaNasWcyaNeuI5eXl5UGOIUn6Lx7QL0kyBpIkYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSQJYyBJwhhIkjAGkiSMgSSJgL/2si2tra3MmzePl19+mbS0NG6//Xa+9KUvdfZYktRlHBNbBlVVVTQ1NfH73/+eW265hZ/97GedPZIkdSnHxJbBpk2buOCCCwA4++yz2bp1a5vrtrS0ALB3797P9JyNB977TL+vE9OuXbs6ewT+9d6Bzh5Bx6DP+rf50XvmR++h/+2YiEE0GiUzMzNxOyUlhUOHDpGaeuR49fX1AEyePDmw+dR1XPz//m9njyAd1cUlF38uj1NfX3/U3fDHRAwyMzOJxWKJ262trUcNAcDQoUNZunQpffv2JSUlJagRJem41tLSQn19PUOHDj3q/cdEDM455xxqamoYN24cmzdvZvDgwW2u26NHD0aMGBHgdJJ0YvikA3NC8Xg8HuAsR/XR0USvvPIK8Xicn/70p5x++umdPZYkdRnHRAwkSZ3rmDi0VJLUuYyBJMkYSJKMwQmttraWM844g9WrVx+2fPz48cycOfOov/PYY4/xi1/8Iojx1AXs2rWLc845h0gkkvh37733fq7PEYlE2LFjx+f6mF3RMXFoqZInLy+P1atXc/nllwPw8ssvc/DgwU6eSl3JwIEDKSsr6+wx9CmMwQluyJAhvPHGGzQ0NJCVlUVlZSXjx49nz549lJeXs3btWg4ePMjJJ598xP/YysrKeOKJJwiFQowbN44pU6Z00qvQieauu+7ihRdeoLW1lalTpzJ27FgikQhnnHEGr776Kj179mTEiBE8/fTTfPDBBzz00EOkpKRQXFxMQ0MD77zzDuFwmHA4nHjMhoYGiouL2b9/PwCzZs3ijDPO6KyXeNxxN1EX8M1vfpO1a9cSj8epq6tj+PDhtLa28t5777FkyRJWrFhBS0sLf/3rXxO/89prr7FmzRqWLVvG0qVLqaqq4vXXX+/EV6Hj1WuvvXbYbqLKykp27drFI488wu9+9ztKSkr44IMPABg2bBgPP/wwTU1N9OjRg9/+9rcMHDiQjRs3snPnTi6//HIeeughHnzwQZYsWXLY85SUlDBq1CjKysqYP38+8+bNC/7FHsfcMugCxo8fz7x58xgwYEDi7O1u3bpx0kkncfPNN9OzZ0/27t3LoUOHEr/zyiuv8NZbbzF16lQA3n//fXbu3EleXl5nvAQdx/57N1FpaSnbtm0jEokAcOjQIXbv3g3AV77yFQCys7MZOHBg4ufGxka+8IUv8PDDD7N27VoyMzMP+3uFf//NPvfcczz55JPAv/9m1X7GoAsYMGAABw4coKysjJtvvpk333yTaDRKVVUVK1as4ODBg0yYMIGPn3+Yl5fHwIEDeeCBBwiFQixZssRNbn0u8vLyGDlyJPPnz6e1tZX77ruPAQMGfOrvPfTQQ5x99tmEw2Gee+451q1bd8Tjfutb32L8+PG8++67rFixIlkv4YRkDLqIcePGsWrVKk477TTefPNNUlJSSE9P56qrrgKgb9++vPPOO4n1hwwZwnnnncekSZNoampi2LBhnHLKKZ01vk4gY8aM4fnnnyccDnPgwAEuueSSw65a3JaLLrqI22+/nTVr1pCVlUVKSgpNTU2J+6+//nqKi4upqKggGo1yww03JPNlnHC8HIUkyQ+QJUnGQJKEMZAkYQwkSRgDSRLGQJKEMZAkYQykDtm7dy/f/e53mTBhAldeeSWbN2+mrq6OSZMmccUVV3DNNdckzvAeM2YMzz77LADXXnstS5cu7eTppbZ50pnUAffeey89evTguuuuo7a2lrq6Oh5//HFKSkr44he/yFNPPZW4iNqzzz7LvHnzmDJlCjU1NTzwwAOdPb7UJmMgdcCmTZu48cYbOe+887jwwgsZMmQIEydOJDc3N7FONBqluroagLlz5/LEE0/w5JNP0q9fv84aW/pUXptI6oBzzz2X1atX8+c//5k1a9awYsUKcnJyWLVqFQAtLS3s27cPgHg8zhtvvEF6ejp///vfjYGOaX5mIHXAz3/+c1atWsUVV1zBnDlz2L59O++//z4vvPACACtXruSHP/whAMuWLaNnz57cd999zJo1iwMHDnTm6NIncjeR1AF79uzhlltuIRaLkZKSwnXXXUf//v1ZsGABjY2NZGZmcscddxAKhZg0aRIrVqygf//+/OQnP6G1tdUvXNExyxhIktxNJEkyBpIkjIEkCWMgScIYSJIwBpIkjIEkCfj/7AaQ7k/xaWwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bcbbd56400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting white background\n",
    "sns.set_style('white')\n",
    "sns.countplot(x='sex',data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bcbbdaa438>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bcbbd70c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Passing ticks will add the ticks on the plot\n",
    "sns.set_style('ticks') # try darkgrid, whitegrid\n",
    "sns.countplot(x='sex',data=tips)#,palette='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing axes spines\n",
    "### `despine()`\n",
    "We can remove spine using **`despine()`** method. <br>\n",
    "By default:  \n",
    "`top=True, right=True, left=False, bottom=False`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bcbbea4160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex',data=tips)\n",
    "sns.despine() # top and right is True by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bcbcf040f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if we want to remove left and bottom, pass left = True, bottom = True\n",
    "sns.countplot(x='sex',data=tips)\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure size and Aspect\n",
    "We can simply use matplotlib's **`plt.figure(figsize=(width,height)` ** to change the size of most seaborn plots.\n",
    "\n",
    "We can control the size and aspect ratio of most seaborn grid plots by passing in parameters: size, and aspect. For example:<br>\n",
    "\n",
    "&#9758; In the 2nd line of the code below, when seaborn calls matplotlib, it actually set the figure size to the given size for seaborn.<br>\n",
    "So, we can actually use our matplotlib knowledge in combination with seaborn to set the figure size and aspect ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bcbcf96a90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bcbcf96b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3))\n",
    "sns.countplot(x='sex',data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1bcbcfa3828>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bcbbecc0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Another example with grid type plot\n",
    "sns.lmplot(x='total_bill',y='tip',data=tips,size=5,aspect=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling plot elements\n",
    "### `set_context()`\n",
    "The `set_context()` allows us to override default parameters:<br>\n",
    "Parameter context : `dict, None, or one of {paper, notebook, talk, poster}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bcbd0567f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bcbd0ad278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('paper',font_scale=3)\n",
    "sns.countplot(x='sex',data=tips,palette='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!\n",
    "For more on controlling figure aesthetics, check seaborn's official documentation [chick here](http://seaborn.pydata.org/tutorial/aesthetics.html#aesthetics-tutorial).<br> \n",
    "For more palette options, explore  [matplotlib color maps](https://matplotlib.org/examples/color/colormaps_reference.html)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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