Seaborn_Visz/Quick-Into.ipynb

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   "source": [
    "'''\r\n",
    "Seaborn is an amazing visualization library for statistical graphics plotting in Python. \r\n",
    "It provides beautiful default styles and color palettes to make statistical plots more attractive.\r\n",
    "It is built on the top of matplotlib library and also closely integrated to the data structures\r\n",
    "from pandas.\r\n",
    "\r\n",
    "Seaborn aims to make visualization the central part of exploring and understanding data.\r\n",
    "It provides dataset-oriented APIs, so that we can switch between different visual representations\r\n",
    "for same variables for better understanding of dataset.\r\n",
    "'''"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!pip install seaborn"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "#Dist plot :  Seaborn dist plot  is used to plot a histogram, with some other variations like kdeplot and rugplot."
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "# Importing libraries\r\n",
    "import numpy as np\r\n",
    "import seaborn as sns\r\n",
    "  \r\n",
    "  \r\n",
    "# Selecting style as white,\r\n",
    "# dark, whitegrid, darkgrid \r\n",
    "# or ticks\r\n",
    "sns.set(style=\"white\")\r\n",
    "  \r\n",
    "# Generate a random univariate \r\n",
    "# dataset\r\n",
    "rs = np.random.RandomState(10)\r\n",
    "d = rs.normal(size=100)\r\n",
    "  \r\n",
    "# Plot a simple histogram and kde \r\n",
    "# with binsize determined automatically\r\n",
    "sns.distplot(d, kde=True, color=\"m\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\Roja Achary\\Anaconda3\\envs\\lab\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
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     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
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     },
     "metadata": {},
     "execution_count": 2
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     "output_type": "display_data",
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/jGeeeQbf//73cdttt0Gr1Y7553JycpCTk3PJczt37hz+9+XLl2P58uVXl5joW7o6BmC1eN4wO2fNX5KIv7x5FBWlrW59/2iaGJzaQ8jJycHbb7+Nl19+GR0dHbj99tvx6KOPoqSkROx8RKMyNvdALpdBZ5gkdRRJpE2LgDZEg6IvaqWOQh7A6WMIDocD9fX1qKurg81mQ2hoKJ5++mls27ZNzHxEV3RxmF2ozh9KDxxm5wy5XIZ5N8Sjsa4LjfVdUschN+dUIWzfvh1LlizBa6+9hjVr1qCgoACPP/443n77bfzlL38ROyPRZfX1mDHQ77nD7Jw1IysWPr5KfMO9BLpOTh1D6OzsxM6dO5GWlnbJ8xqNBr/73e9ECUY0lovD7Ly9EHx8lZg9Pw5Fn9egq2MAwaEaqSORm3JqD8Fut48og0ceeQQAeHUxSebiMDtfP88dZuesrEXxkMlkOPTVGamjkBsbdQ/h5z//OYxGI4qLi9HZ2Tn8vM1mQ20td09JOkODVnR3DWLyVIPUUSaEQK0fps6MxLFDZ7Fk1WSWJF2TUQth48aNqKqqQkVFBVatWjX8vEKhwMyZM0UPR3Qlxpbzy0XhXr5c9G3zlyTiZHETiovqsfDGZKnjkBsatRCmTZuGadOmYeHChTAY+JsYTRzG5h5o/NWYFOgjdZQJIzwqCPHJoTj0dR2yFydCoeT9r+jqjFoIP/3pT7Fjxw786Ec/uuzr+fn5ooQiGo3FYkOHqR9xiSFeM8zOWdlLEvHn1w/j1IlmZM6OljoOuZlRCyE3NxcA8O///u/jEobIGfU1nV43zM5ZKWl6hOkn4ZsvajFtVhQLk67KqPuUGRkZAICsrCxEREQgKysL3d3dOHToENLT08clINF31Va2QaVWINjLhtk5QyaXIXtJAlqbelBX3SF1HHIzTi0yPvXUU9i5cydqamrw7LPPoqmpCf/2b/8mdjaiEex2B+qqO2CICPS6YXbOypwdDf8AHxR+XiN1FHIzThVCaWkpnn76aezduxe33HILnn/+eTQ1NYmdjWiE+ppOmIdsMER6/r2Tr5VSpUDWonjUlLcNX7xH5AynCkEQBMjlchw4cADZ2dkAgMHBQVGDEV1ORWkrlEo5dAYWwmjmLIiDSq3gXgJdFacKITY2Frm5uWhsbERWVhb++Z//GampqWJnI7qEIAioONWK2MQQnlI5Bj+NGrOyY1F6rBndnQNSxyE34dR31fPPP49169bhrbfegkqlwpw5c/Af//EfYmcjukRrUw96uoeQOFkndRS3kL04EQBwkOMsyElOFYJGo8GcOXPQ09ODU6dOITMzk6MraNyVl7ZCJgMSUkKljuIWgoL9kDEzEke/OYvBAYvUccgNODXtdNu2bXj77bcRGvqPb0SZTIZ9+/aJFozouypKWxGTEAI/jVrqKG5jwfeScLK4CUcK63HD8hSp49AE51QhfPLJJygoKOD4CpJMV0c/TC29WHnzFKmjuBVDZCCS0nQ49NUZzF+S6LU3EiLnOLVkFBERwTIgSVWUGgEAqRn8OrxaC5Ymob/PghNHGqWOQhOcU3sI8+fPx29+8xssW7YMvr6+w89PnTpVtGBE31Ze2gpDRACCQ/151sxVik8KRWRMEIo+r8XMebG8oI+uyKlC2L17NwBgz549w8/xGAKNl4E+CxrOdHr1Grjd7riuIpw+Nwaf7C7F0W/qkZymh4+vksdiaASnCmH//v1i5yC6osoyIwQBSM0IlzqKZGxW+3XNJpLJAI2/GoX7z1+olpymZyHQCE4dQ+jv78ezzz6Lu+++G93d3XjqqafQ398vdjYiAOeXiwK1vgiP4nTTayWTyZA4OQzdXYPobOeSG12eU4Xwy1/+EgEBAejo6ICPjw/6+vrw1FNPiZ2NCOYhK2oq2pCeGcFRztcpOj4YarUCtRVtUkehCcqpQjh9+jS2bt0KpVIJPz8//Pa3v8Xp06fFzkaEqjIT7DYH0jMjpI7i9hQKOeKTQ2Fq7UWHqU/qODQBOVUIcvmlb7Pb7SOeIxJDWUkLJgX6ICYuWOooHiEuKRQKhQxHD56VOgpNQE79VJ87dy62bduGoaEhfPXVV3j44Ycxb948sbORl7OYbag+bUL6tAjIeKqkS6h9lIhJCEFFqRE93ZxYTJdyqhB+9rOfQaPRICAgAP/5n/+JtLQ0/Mu//IvY2cjLVZebYLM5kD6dy0WulJASBkHg0DsaacxC2Lt3L+666y689tpraGxsREBAAGbNmgUfH5/xyEderOxEC/wDfBCbECJ1FI+i8VcjZYoexUVnMTRolToOTSCjXofwwQcf4OWXX8aWLVuQlpYGmUyGkydP4rnnnoPZbMbKlSvHKyd5GavFjqrTJkyfE80ra0UwKzsWlaeMKC6qx8Ibk6WOQxPEqIXw1ltv4Y9//CMiIyOHn0tKSsL06dPxxBNPsBBINNXlJlgtdp5dJBJ9eAASJ4fh4JdnMG9xApRKDr2jMZaMrFbrJWVwUUJCAsxms2ihiMpOtEDjr0ZcIpeLxLJgaRL6es04Wcz7o9N5oxaCQnHl3xoEQXB5GCLg/NlFlWVGpGeGQ67g6c1iSUgJQ3hUIAr/XgPBwe9ncvIso2uVn5+PNWvWYMWKFcjLyxvx+meffYb169fj5ptvxkMPPYRz586JGYfcROUpI6wWOzJmRkkdxaPJZDIsWJqEjrZ+VJYZpY5DE8CoxxAqKiowa9asEc8LggCLZfRb8hmNRmzfvh27d++GWq3Gpk2bMG/ePCQnnz+A1dfXh6effhp//etfYTAYsGPHDrz44ot48sknr+OvQ57g5LEmBAb58uyicTAlMwL7Q8px4O81Xj08kM4btRD27t17zRsuLCxEdnY2tFotAGDVqlXYs2cPHn74YQDnj088/fTTwzfeSU1NRX5+/jV/HnmGgT4LasrbMG9xAi9GGwdyhRzZixOx54NTOHumkyXs5UYthKioa99lN5lM0Ol0w4/1ej1KSkqGHwcHB2P58uUAgKGhIbz66qu46667rvnzyDOUlbTA4RAwbRaXi8bLjKwYfFFQiaK/17AQvJxoxxAud9D5ctMqe3t7kZubi7S0NNxyyy1ixSE3UXqsCWGGSTBEctT1eFH7KDF3YTwqThnRbuTQO28mWiEYDAa0t7cPPzaZTNDr9Ze8x2Qy4Y477kBaWhqee+45saKQmzjXNYiztZ3ImBnFUdfjbO6ieCiVchR9XiN1FJKQU3dMuxYLFizAiy++iM7OTvj5+aGgoAC/+MUvhl+32+148MEHsXr1ajz00ENixSA3Unrs/PnwGTNHXvtCrnW5W3KmT4/AieJGzMyOxaSAqxtNw1tyegbRCsFgMGDr1q3YvHkzrFYrNm7ciMzMTOTm5mLLli1obW1FWVkZ7HY7Pv30UwBARkYG9xS8lCAIOHm0CZGxWoSE+Usdx+Nd7pacoTp/OOwCvvi0EmnTru6Mo6RUHQvBA4hWCACQk5ODnJycS57buXMnAGDatGkoLy8X8+PJjbQ0noOppRdrbsuQOorX8p/kg4joINTXdCApTQeViuMsvA0vA6UJ4fihBiiVcl6MJrHEyWGw2Rw4W9spdRSSAAuBJGe12nHyaBPSMyPg66eSOo5X04ZoEKrzx5mqdjgcDqnj0DhjIZDkyktaYB6yYca8GKmjEIDEVB3MQzY0neUoGW/DQiDJHTvUAG2IBvGJoVJHIQA6wyQEBPmitrKNQyy9DAuBJNXV0Y+66g7MyIrhqIoJQiaTIXFyGPp6zDC19kodh8YRC4EkdfxQIyADZsyNljoKfUtkjBa+firUVrSP/WbyGKKedkoEAIMDFpiHbCOet9sdOHrwLOISQuBwCCMulLoSm9Xu6oj0HXL5+b2EshMt6OoYQHCoRupINA5YCCQ685ANNRVtI55vaTyH/l4z0qeFX/b1K4mO07owHV1JTEIwqspMqK1sw+z5cVLHoXHAJSOSzJmqdmj81dBHBEgdhS5DqVQgLikErU096OvlLXO9AQuBJHGuaxBdHQOISwrlILsJLD45FHK5DGcqeSzBG7AQSBJ11R1QKOSIiQ+WOgqNwsdXhej4YDTWd2FoyCp1HBIZC4HGnXnIhuaGbkTHa6FSc17ORJeYEgaHQxgxDI88DwuBxt3ZM51wOATEJfFCNHfgH+CD8KhA1Nd08AwvD8dCoHFltztQX9OBMMMkBAT6Sh2HnJQ4WQeb1YGzZ7qkjkIiYiHQuGqs64J5yIakybqx30wTRnCoBiFhF4fecZyFp2Ih0Lix2x2oLm9DcKgGoXreBMfdJKaGYWjQiuaGbqmjkEhYCDRumuq7MTRoRUq6nqeauiF9eAAmBfqgtrKdQ+88FAuBxoXDIaC63ARtiB/CDJOkjkPXQCaTIWmyDr3nhtBm7JM6DomAhUDjoqm+C4MD3Dtwd5GxQfD1U6L2KkaNkPtgIZDoLh47CAr2gy6cYyrcmVwuR0JKGDra+p0eRkjug4VAois92oSBfgsmT+HegSeISQiBUiW/qoGE5B5YCCSqoUErDn5Vh1C9P/cOPIRKpUB8Uiham3rQe25I6jjkQiwEEtUXBZUYGrQiPTOCewceJCElDAqlHNXlJqmjkAuxEEg0xuYeHPq6DlNnRCJI6yd1HHIhtY8S8UmhaG44h74e7iV4ChYCiUJwCPj4ryfh66vEgqVJUschESRMDoNCIUNVOY8leAreMc0DXOkWldfKx1cJP436urZxuLAODXVduPkH0+GnUbkoGU0kPj5KxCWForayHV0dA9CG8Dab7o6F4AGudIvKa5WUqruuQuhs78e+j8qRlKbD9LnRONc16LJsNLEkTtahrqYDhw/UISElTOo4dJ24ZEQuZbc78P6uY5DLZVi3MZMHkj2cj+/5YwkVpa0wtfZKHYeuEwuBXOrvn1Sgqb4b627PRFAwDyR7g6RUHVRqBf7+SbnUUeg6sRDIZU6XtKDw7zWYPT8WU2dESh2HxonaR4lZ2bGoKDWisZ73S3B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     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "#Line plot : The line plot is one of the most basic plot in seaborn library. \r\n",
    "# This plot is mainly used to visualize the data in form of some time series, i.e. in continuous manner."
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "import seaborn as sns\r\n",
    "  \r\n",
    "  \r\n",
    "sns.set(style=\"dark\")\r\n",
    "fmri = sns.load_dataset(\"fmri\")\r\n",
    "  \r\n",
    "# Plot the responses for different\\\r\n",
    "# events and regions\r\n",
    "sns.lineplot(x=\"timepoint\",\r\n",
    "             y=\"signal\",\r\n",
    "             hue=\"region\",\r\n",
    "             style=\"event\",\r\n",
    "             data=fmri)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timepoint', ylabel='signal'>"
      ]
     },
     "metadata": {},
     "execution_count": 4
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "'''\r\n",
    "Lmplot :  The lmplot is another most basic plot.\r\n",
    "It shows a line representing a linear regression model along\r\n",
    "with data points on the 2D-space and x and y can be set as the\r\n",
    "horizontal and vertical labels respectively.\r\n",
    "'''"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "import seaborn as sns\r\n",
    "  \r\n",
    "sns.set(style=\"ticks\")\r\n",
    "  \r\n",
    "# Loading the dataset\r\n",
    "df = sns.load_dataset(\"anscombe\")\r\n",
    "  \r\n",
    "# Show the results of a linear regression\r\n",
    "sns.lmplot(x=\"x\", y=\"y\", data=df)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23f57ca0f98>"
      ]
     },
     "metadata": {},
     "execution_count": 5
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ],
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"349.514219pt\" version=\"1.1\" viewBox=\"0 0 349.313438 349.514219\" width=\"349.313438pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-08-23T19:59:40.151312</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
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