{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Style and Color\n",
    "\n",
    "We've shown a few 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": [
      "/home/s/Sukanya.Patra/.local/lib/python3.8/site-packages/scipy/__init__.py:143: UserWarning: A NumPy version >=1.19.5 and <1.27.0 is required for this version of SciPy (detected version 1.19.3)\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "tips = sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Styles\n",
    "\n",
    "You can set particular styles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11990cc88>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KA0GSBBgIkqTCQJAkAQaCJKkwECRJgIEgSSoMBEkSYCBIkgoDQZIEGAiSpMJAkCQBBoIk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\nIEkqDARJEmAgSJIKA0GSBMD/A24NpGLNpQIjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1198f6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex',data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c2ba9b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kYixJ6tCSHoTHHnuMI488kkGDBhGNRgFobW2N35+dnU0wGEz2WJLU4WUke4ePPfYYaWlp\n/OUvf2HLli2MHz+eDz/8MH5/OBwmLy8v2WNJUoeX9CDMmzcvfnv06NHcfPPN3HHHHaxbt47TTz+d\n1atXc8YZZyR7LEnq8JIehLaMHz+eSZMmEYlEKCgooLS0NNUjSVKHk9IgPPzww/HbNTU1KZxEkuSJ\naZIkwCBIkmIMgiQJMAiSpBiDIEkCDIIkKcYgSJIAgyBJijEIkiTAIEiSYgyCJAkwCJKkGIMgSQIM\ngiQpxiBIkgCDIEmKMQiSJMAgSJJiDIIkCUjBbyo3Nzdz4403Ul9fTyQS4Ve/+hXHHXccEyZMoFOn\nThQWFlJdXZ3ssSSpw0t6EJYuXUqPHj244447+OSTT/jpT39KUVERlZWVlJSUUF1dTV1dHcOGDUv2\naJLUoSX9LaOzzz6ba665BoCWlhbS09PZvHkzJSUlAAwePJgXXngh2WNJUoeX9CBkZmaSlZVFKBTi\nmmuu4dprryUajcbvz87OJhgMJnssSerwUvKh8nvvvceYMWO44IILOPfcc+nU6T9jhMNh8vLyUjGW\nJHVoSQ/C7t27GTt2LNdffz0XXHABACeccALr1q0DYPXq1RQXFyd7LEnq8JL+ofKsWbP45JNPmDlz\nJjNmzCAtLY2qqiqmTp1KJBKhoKCA0tLSZI8lSR1e0oNQVVVFVVXVl9bX1NQkexRJ0ud4YpokCTAI\nkqQYgyBJAgyCJCnGIEiSAIMgSYoxCJIkwCBIkmIMgiQJMAiSpBiDIEkCDIIkKcYgSJIAgyBJijEI\nkiTAIEiSYgyCJAkwCJKkGIMgSQIMgiQpJiPVA/xbNBpl8uTJbNmyhS5dunDrrbfy7W9/O9VjSVKH\ncdAcIdTV1dHU1ERtbS3jxo1j2rRpqR5JkjqUgyYI69ev58wzzwTg5JNPZtOmTSmeSJI6loMmCKFQ\niNzc3PhyRkYGra2tKZxIkjqWg+YzhJycHMLhcHy5tbWVTp3a7lVLSwsAO3fuPOD9NTQ08PGut4g0\nBg/4MXT4+TT4TxoaGsjKykrZDA0NDXy0YzdNwT0pm0EHn08/DP7P/5v/fs3892voFx00QTjttNN4\n9tlnKS0t5ZVXXqF///5fue2uXbsAqKioSNZ46kAuu+zpVI8gtemyP132tTzOrl276Nev35fWp0Wj\n0ejXsof/0ee/ZQQwbdo0jjnmmDa3bWxsZNOmTfTs2ZP09PRkjilJh6yWlhZ27drFSSedRNeuXb90\n/0ETBElSah00HypLklLLIEiSAIMgSYoxCJIkwCAc9tauXUtRURHLly/fa/1PfvITbrjhhjb/ZvHi\nxdx1113JGE8dQH19PcXFxYwePZpAIMDo0aOZOXPm17qPQCDA9u3bv9bH7IgOmvMQlDjHHnssy5cv\n55xzzgHgjTfeoLGxcZ9/k5aWlozR1EEUFhby8MMPp3oM/RcGoQMoKirirbfeIhQKkZOTw9KlSzn/\n/PN59913mT9/PitWrKCxsZEePXpwzz337PW38+bN44knniAtLY1zzz2XSy65JEXPQoeytr7d/tvf\n/pb169fT0tLCpZdeyo9//GMCgQBFRUW8+eabZGVlUVJSwnPPPUcwGOSBBx4gLS2NiRMnEgwGef/9\n96moqKCsrCz+mKFQiBtvvJGPP/4YgKqqqn2e5Kq9+ZZRB/GjH/2Ip5/+1xm4Gzdu5NRTT6W1tZWP\nPvqIuXPnsnDhQiKRCK+++mr8b7Zu3cry5ctZsGAB8+fP5+mnn+att95K0TPQoezvf//7Xm8ZLVu2\njHfeeYf58+fz8MMP87vf/Y5g8F+XkTnllFN46KGHaGpqIjMzkwceeICCggLWrl3Ljh07OO+887j/\n/vu5//77efDBB/faz7333ssPfvAD5s6dyy233MLkyZNT8GwPXR4hdABpaWmcd955VFdX07dvX04/\n/XSi0SidOnWic+fOVFZWkpmZyfvvv09zc3P879544w3effddxowZQzQaJRgM8o9//IP8/PzUPRkd\nkr74ltGcOXN47bXXGD16NNFolJaWFurr6wE44YQTAMjLy+O4446L396zZw9HHnkkc+fOZcWKFWRn\nZ+/1/wr/+p9ds2YNy5cvJxqN8sknnyTpGR4eDEIH0bdvXz777DNqamoYN24cO3bsIBQK8cwzz7Bw\n4UIaGxu58MIL9zq0P+aYYygsLOS+++4D4KGHHuL4449P1VPQIeyLbxkde+yxDBw4kFtuuYVoNMrM\nmTPjP4i1r8+vHnzwQU499VTKyspYs2YNq1at2uv+goICTjrpJM4991w++OADHn300a//yRzGDEIH\ncs4557B06VL69evHjh07yMjIIDMzk1GjRgFw9NFH8/7778e3Lyoq4owzzmDUqFE0NTVx8skn06tX\nr1SNr0PYF1/khw4dytq1a6moqOCzzz5j2LBhZGdn77VdW7eHDh3KlClTePLJJ8nNzaVz5840NTXF\n77/88supqqqitraWcDjMb37zmyQ8u8OH1zKSJAF+qCxJijEIkiTAIEiSYgyCJAkwCJKkGIMgSQIM\ngiQpxiBIkgCDIO23hoYGAoEAF110ERdffDEbN27k1Vdfpby8nAsvvJCxY8dSX19POBxm6NChvPji\niwCMHTuWBQsWpHh66at5prK0n+655x6ysrL4xS9+wbp169iwYQPLli1j1qxZ9O7dm+eeey5+Jc4X\nX3yRyZMnEwgEWLVqFbNnz071+NJXMgjSfnr55Ze5+uqrGThwIEOGDKGoqIiLL76Y/Px8otEoaWlp\nhMNhVqxYAcDkyZN58skneeqppzjyyCNTPL301by4nbSfTjvtNJ588kmeffZZ/vjHP/LII4/wne98\nh8WLFwP/urLnrl274ttv376drl27sm3bNoOgg5qfIUj76c477+Txxx/nZz/7GZMmTeL111/n448/\n5qWXXgLgkUce4brrrgNg/vz5ZGdnM3PmTCZOnPhff7pUSiXfMpL2086dOxk3bhzhcJj09HR++ctf\n0rt3b6ZOnUpTUxM5OTncfvvtAIwaNYpHH32UXr16MXXqVFpbW7nppptS/AykthkESRLgW0aSpBiD\nIEkCDIIkKcYgSJIAgyBJijEIkiTAIEiSYgyCJAmA/wfsG8me1rHmWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be1bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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 0x119986978>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c405550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "sns.countplot(x='sex',data=tips,palette='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spine Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FAMCIUAAAjAgFAMCIUAAAjAIWin/+858aNmyYzpw5o+rqaqWnp2vcuHGaPXt2oEYCANxA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c56d6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex',data=tips)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oAABGYQvFv/71L40ePVqHDh1SXV2dcnNzdc899+iJJ54I10gAgLMISyhaWlpUVFSkSy65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\nADAiFAAAo/8D+9EPXEct+eYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c779128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex',data=tips)\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Size and Aspect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use matplotlib's **plt.figure(figsize=(width,height) ** to change the size of most seaborn plots.\n",
    "\n",
    "You can control the size and aspect ratio of most seaborn grid plots by passing in parameters: size, and aspect. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11cabbf28>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PZvYD/duceyowWDKvJEmStBPTMnNV60AdM9gXA08HLo2IvwOGgPcDn4qI8cAKYHENuSRJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cabb940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Non Grid Plot\n",
    "plt.figure(figsize=(12,3))\n",
    "sns.countplot(x='sex',data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11cd69048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3+L5PfyrJ+nWD7NgwyKsvX19+ve+48fK6565NSH3lllV89aEZL8zdd7yKt92w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\noSsRkSMIgiAIQlciIkcQBEEQhK7k/wP8pTGG3q7v3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cd69748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grid Type Plot\n",
    "sns.lmplot(x='total_bill',y='tip',size=2,aspect=4,data=tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale and Context\n",
    "\n",
    "The set_context() allows you to override default parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e2a2128>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m3jHLtavTfYxLW9Mh80k11ZckSZI0IEOm+kTE+4C3MRMwO5fFXgp8eo4Sq1aM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\nY4HvMRM0B+tVg/Hg/dPA5cAzMvP4hR5fkiRJUvcMmUtEb9/JFQyPYE7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YfjHzVeAI4AeD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\npiRJkiSpNf8fgqvzmH+I+ZQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c82160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('poster',font_scale=4)\n",
    "sns.countplot(x='sex',data=tips,palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-222f1a0b2892>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfont_size_axes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlabel_font_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m ax = sns.distplot(df['Z'],width=0.3, \n\u001b[0m\u001b[1;32m      6\u001b[0m              fliersize=50, linewidth=12)\n\u001b[1;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linewidth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 6750x5250 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "fig = plt.figure(figsize=(45,35), dpi=150)\n",
    "font_size_axes=128\n",
    "label_font_size=128\n",
    "ax = sns.distplot(df['Z'],width=0.3, \n",
    "             fliersize=50, linewidth=12)\n",
    "ax.spines['left'].set_linewidth(5)\n",
    "ax.spines['left'].set_color('orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out the documentation page for more info on these topics:\n",
    "https://stanford.edu/~mwaskom/software/seaborn/tutorial/aesthetics.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marci/anaconda/lib/python3.5/site-packages/bs4/__init__.py:166: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
      "\n",
      "To get rid of this warning, change this:\n",
      "\n",
      " BeautifulSoup([your markup])\n",
      "\n",
      "to this:\n",
      "\n",
      " BeautifulSoup([your markup], \"lxml\")\n",
      "\n",
      "  markup_type=markup_type))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img alt=\"Titan the Pit Bull Terrier Pictures 1058495\" class=\"\" id=\"feature_image\" src=\"http://cdn-www.dailypuppy.com/dog-images/titan-the-pit-bull-terrier_60497_2016-08-07_w450.jpg\" style=\"width:450px;\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.puppyplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}