{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fff56b76",
   "metadata": {},
   "source": [
    "---   \n",
    " <img align=\"left\" width=\"75\" height=\"75\"  src=\"https://upload.wikimedia.org/wikipedia/en/c/c8/University_of_the_Punjab_logo.png\"> \n",
    "\n",
    "<h1 align=\"center\">Department of Data Science</h1>\n",
    "<h1 align=\"center\">Course: Tools and Techniques for Data Science</h1>\n",
    "\n",
    "---\n",
    "<h3><div align=\"right\">Instructor: Muhammad Arif Butt, Ph.D.</div></h3>    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05dbb7e4",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">Lecture 3.25 (Data Visualization-V)</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9d52a95",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/arifpucit/data-science/blob/master/Section-3-Python-for-Data-Scientists/Lec-3.25(Data-Visualization-V).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117c331e",
   "metadata": {},
   "source": [
    "## _Data Visualization with Seaborn_\n",
    "\n",
    "**Read Documentation for details:** \n",
    "https://seaborn.pydata.org"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10a00218",
   "metadata": {},
   "source": [
    "<img align=\"left\" width=\"500\" height=\"500\"  src=\"images/intromatlab.png\"  >\n",
    "<img align=\"right\" width=\"400\" height=\"500\"  src=\"images/matplotlibadvantages.png\"  >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de1adc70",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f19dca3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5da4b29f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "344623d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44a334d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0677fbfc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e42b820a",
   "metadata": {},
   "source": [
    "## Learning agenda of this notebook\n",
    "1. Overview of Seaborn Library\n",
    "2. Download and Install Seaborn\n",
    "3. Built-in Datasets of Seaborn Library\n",
    "4. Plotting with Seaborn\n",
    "    - The `relplot()` method\n",
    "    - The `displot()` method\n",
    "    - The `catplot()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dab15048",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e5d568a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "312404c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d533666d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02ce5f5f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d8acceb0",
   "metadata": {},
   "source": [
    "## 2. Download and Install Seaborn Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ad09fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To install this library in Jupyter notebook\n",
    "import sys\n",
    "#!{sys.executable} -m pip install --upgrade pip\n",
    "!{sys.executable} -m pip install seaborn --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ecd0249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('0.11.2',\n",
       " ['/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn'])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.__version__ , sns.__path__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da1f4a94",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "715d4837",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86d46a83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39948056",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1e811d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "323dce5b",
   "metadata": {},
   "source": [
    "## 3. Built-in Sets of Seaborn Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2cbc7e5",
   "metadata": {},
   "outputs": [
    {
     "ename": "URLError",
     "evalue": "<urlopen error [Errno 8] nodename nor servname provided, or not known>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1353\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1354\u001b[0;31m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0m\u001b[1;32m   1355\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1251\u001b[0m         \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1252\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1297\u001b[0m             \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1298\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1246\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1247\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1006\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    948\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1414\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    917\u001b[0m         \u001b[0;34m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 918\u001b[0;31m         self.sock = self._create_connection(\n\u001b[0m\u001b[1;32m    919\u001b[0m             (self.host,self.port), self.timeout, self.source_address)\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m    786\u001b[0m     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    788\u001b[0m         \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[0;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m    917\u001b[0m     \u001b[0maddrlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 918\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    919\u001b[0m         \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mgaierror\u001b[0m: [Errno 8] nodename nor servname provided, or not known",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/1t/g3ylw8h50cjdqmk5d6jh1qmm0000gn/T/ipykernel_37492/4093529098.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mssl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_default_https_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mssl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_unverified_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dataset_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/utils.py\u001b[0m in \u001b[0;36mget_dataset_names\u001b[0;34m()\u001b[0m\n\u001b[1;32m    518\u001b[0m     \"\"\"\n\u001b[1;32m    519\u001b[0m     \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"https://github.com/mwaskom/seaborn-data\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 520\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    521\u001b[0m         \u001b[0mhtml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m         \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maudit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'urllib.Request'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;31m# post-process response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0m\u001b[1;32m    543\u001b[0m                                   '_open', req)\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    500\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    503\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m   1395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1396\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1397\u001b[0;31m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0m\u001b[1;32m   1398\u001b[0m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[1;32m   1399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1355\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[1;32m   1356\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1357\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1358\u001b[0m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1359\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno 8] nodename nor servname provided, or not known>"
     ]
    }
   ],
   "source": [
    "# To handle URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] ... unable to get local issuer certificate>\n",
    "import ssl\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "\n",
    "print(sns.get_dataset_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef3b014e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3fa2e8e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d6fb2d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39578358",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "21652a7d",
   "metadata": {},
   "source": [
    "### a. CAR_CRASHES Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "909ee9b0",
   "metadata": {},
   "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>total</th>\n",
       "      <th>speeding</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>not_distracted</th>\n",
       "      <th>no_previous</th>\n",
       "      <th>ins_premium</th>\n",
       "      <th>ins_losses</th>\n",
       "      <th>abbrev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.8</td>\n",
       "      <td>7.332</td>\n",
       "      <td>5.640</td>\n",
       "      <td>18.048</td>\n",
       "      <td>15.040</td>\n",
       "      <td>784.55</td>\n",
       "      <td>145.08</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.1</td>\n",
       "      <td>7.421</td>\n",
       "      <td>4.525</td>\n",
       "      <td>16.290</td>\n",
       "      <td>17.014</td>\n",
       "      <td>1053.48</td>\n",
       "      <td>133.93</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.6</td>\n",
       "      <td>6.510</td>\n",
       "      <td>5.208</td>\n",
       "      <td>15.624</td>\n",
       "      <td>17.856</td>\n",
       "      <td>899.47</td>\n",
       "      <td>110.35</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.4</td>\n",
       "      <td>4.032</td>\n",
       "      <td>5.824</td>\n",
       "      <td>21.056</td>\n",
       "      <td>21.280</td>\n",
       "      <td>827.34</td>\n",
       "      <td>142.39</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.0</td>\n",
       "      <td>4.200</td>\n",
       "      <td>3.360</td>\n",
       "      <td>10.920</td>\n",
       "      <td>10.680</td>\n",
       "      <td>878.41</td>\n",
       "      <td>165.63</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total  speeding  alcohol  not_distracted  no_previous  ins_premium  \\\n",
       "0   18.8     7.332    5.640          18.048       15.040       784.55   \n",
       "1   18.1     7.421    4.525          16.290       17.014      1053.48   \n",
       "2   18.6     6.510    5.208          15.624       17.856       899.47   \n",
       "3   22.4     4.032    5.824          21.056       21.280       827.34   \n",
       "4   12.0     4.200    3.360          10.920       10.680       878.41   \n",
       "\n",
       "   ins_losses abbrev  \n",
       "0      145.08     AL  \n",
       "1      133.93     AK  \n",
       "2      110.35     AZ  \n",
       "3      142.39     AR  \n",
       "4      165.63     CA  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df_cc = sns.load_dataset('car_crashes')\n",
    "df_cc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "512754e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51, 8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69de333b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa969e7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21b8438e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b1272ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9ab5cec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23850e90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea6075e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8f2d23fc",
   "metadata": {},
   "source": [
    "### b. FLIGHTS Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "494efe41",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949</td>\n",
       "      <td>Jan</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>Feb</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949</td>\n",
       "      <td>Mar</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949</td>\n",
       "      <td>Apr</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949</td>\n",
       "      <td>May</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960</td>\n",
       "      <td>Aug</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960</td>\n",
       "      <td>Sep</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960</td>\n",
       "      <td>Oct</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960</td>\n",
       "      <td>Nov</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960</td>\n",
       "      <td>Dec</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year month  passengers\n",
       "0    1949   Jan         112\n",
       "1    1949   Feb         118\n",
       "2    1949   Mar         132\n",
       "3    1949   Apr         129\n",
       "4    1949   May         121\n",
       "..    ...   ...         ...\n",
       "139  1960   Aug         606\n",
       "140  1960   Sep         508\n",
       "141  1960   Oct         461\n",
       "142  1960   Nov         390\n",
       "143  1960   Dec         432\n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flights = sns.load_dataset('flights')\n",
    "df_flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "537b4ff5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e22899c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d685e9c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7da15d6a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d72343af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb4ac87c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a42866f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6c2c929",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2af720e3",
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7c05a952",
   "metadata": {},
   "source": [
    "### c. TIPS Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c59dcca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\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\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tips = sns.load_dataset('tips')\n",
    "df_tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec07dc57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b993ef41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "079eac49",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09ec4694",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16470367",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea31a0bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca4d8ef0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "858599e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6a0c7e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce4b3abb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c520e5d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "53fd51f5",
   "metadata": {},
   "source": [
    "### d. IRIS Dataset\n",
    "<img align=\"center\" width=\"700\" height=\"500\"  src=\"images/iris.png\"  >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0af3c16b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6923bcfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\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\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris = sns.load_dataset('iris')\n",
    "df_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "905393f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "setosa        50\n",
       "versicolor    50\n",
       "virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e197fe64",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "067a9cae",
   "metadata": {},
   "outputs": [],
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   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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   "metadata": {},
   "source": [
    "### e. TITANIC Dataset\n",
    "<img align=\"center\" width=\"600\" height=\"300\"  src=\"images/titanic_sinking.jpeg\"  >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fbac2996",
   "metadata": {},
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic = sns.load_dataset('titanic')\n",
    "df_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "148b5d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88cf00b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a773de0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2bebc15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c54e08ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "691e1e67",
   "metadata": {},
   "source": [
    "## Programming with Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ae2ae5",
   "metadata": {},
   "source": [
    "#### Option 1: (Use Axes-Level Functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "748dfd6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "sns.boxplot(x='sex', y='age', data=df_titanic, ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d94870",
   "metadata": {},
   "source": [
    "#### Option 2: (Use Figure-Level Functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4b50f465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.catplot(x ='sex', y='age', kind='box', data = df_titanic);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3536fd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_context(context='paper')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8263b0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9d49baa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d191bdd6",
   "metadata": {},
   "source": [
    "## 4. Plotting Graphs with Seaborn\n",
    "<img align=\"center\" width=\"600\" height=\"300\"  src=\"images/seaborn_functions.png\"  >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5354c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('fivethirtyeight')  \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ec2e3993",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(style='white') # 'dark', 'darkgrid' white', 'whitegrid'\n",
    "sns.set_context(context='paper', font_scale=1.5) # talk', 'poster'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b2d8227",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87ceb3b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b37e6ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49cf6274",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6ea09ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61b89ffb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b6506fa5",
   "metadata": {},
   "source": [
    "### a. The `sns.relplot()` Method\n",
    "- Line Plot\n",
    "- Scatter Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "603f8877",
   "metadata": {},
   "source": [
    "**Example: Line Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d71a048b",
   "metadata": {},
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f34acf5d",
   "metadata": {},
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9a10355",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34515e44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a522d13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afad2824",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcbc4480",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c49cb56e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"sepal_width\", y=\"sepal_length\", data=df_iris,  kind='line');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "355e00bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "033cb642",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60779219",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37084687",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "af8c9d1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 464.77x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"sepal_width\", y=\"sepal_length\", data=df_iris,  kind='line', hue='species');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ff704cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7f0423c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c3e98ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35171ee5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b55b113a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "416f12ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 464.77x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"sepal_width\", y=\"sepal_length\", data=df_iris,  kind='line', hue='species', style='species');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f038f14",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6256aa08",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86927229",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0982c783",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "072302f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46f365aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecadbecf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b0e9a20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "536521bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d5a2dfc5",
   "metadata": {},
   "source": [
    "**Example: Scatter Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0642ae8d",
   "metadata": {},
   "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>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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "13071a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 7)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tips.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bb6efe8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fd157a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cfcb7f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f77081e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eff1ce03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='total_bill', y='tip', data=df_tips, kind='scatter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c3fbcb8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87951b88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e26b2e44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d8e4c1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c957e9d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 451.77x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='total_bill', y='tip', data=df_tips, kind='scatter', hue='sex');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6aacd186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 451.77x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='total_bill', y='tip', data=df_tips, kind='scatter', hue='sex', style='sex');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abcc237a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd873a0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e3381f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 811.77x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='total_bill', y='tip', data=df_tips, kind='scatter', hue='sex', style='sex', col='sex');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d1c48dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d40e0a2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "608e4c45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1806d1c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ecca0cd4",
   "metadata": {},
   "source": [
    "**Example: Sub-Plots using FacetGrid**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0f501431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 794.77x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='total_bill', y='tip', data=df_tips, kind='scatter', hue='day',col='day', col_wrap=2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f04d212",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5828e239",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f30c9c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14955af1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29088beb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d01c6bee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "763a0233",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaaa314a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ba802f3d",
   "metadata": {},
   "source": [
    "### b. The `sns.catplot()` Method\n",
    "- Categorical estimate plots:\n",
    "    - `pointplot` (with ``kind=\"point\"``)\n",
    "    - `barplot` (with ``kind=\"bar\"``)\n",
    "    - `countplot` (with ``kind=\"count\"``)\n",
    "    \n",
    "- Categorical distribution plots:\n",
    "    - `boxplot` (with ``kind=\"box\"``)\n",
    "    - `violinplot` (with ``kind=\"violin\"``)\n",
    "    - `boxenplot` (with ``kind=\"boxen\"``)\n",
    "\n",
    "- Categorical scatterplots:\n",
    "    - `stripplot` (with ``kind=\"strip\"``; the default)\n",
    "    - `swarmplot` (with ``kind=\"swarm\"``)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908246b9",
   "metadata": {},
   "source": [
    "**Example: Bar Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5c39ac41",
   "metadata": {},
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb7e5087",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "918b650f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d902760e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61e70c72",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d696c88f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ecef894",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09d1723f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3f177a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex', y ='survived',kind='bar', data = df_titanic);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e314d547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex', y ='tip',kind='bar', data = df_tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f8f50271",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='size', y ='tip',kind='bar', data = df_tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7d676274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='day', y ='tip',kind='bar', data = df_tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a110d02",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9dda219c",
   "metadata": {},
   "source": [
    "**Example: Count Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "db7a1344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex',kind='count', data = df_titanic);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "79271d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kb7zxhu644w6FhYWpZcuWKi4u9h5XXFysqKgoBQf7vf0AUCf8Vq+zZ89q7NixcjgcevbZZ2vtczgcWrp0qbp06SK3263c3FwNGzbMX6Pdtqorz8vjvhzoMYyzBDdVkC0s0GPgNuO3uK5fv15lZWVau3at3nzzTe/2vn37auHChcrOzpbD4ZDb7VZaWhpvw/IDj/uyTr2UFOgxjLtnyrZAj4DbkN/imp6ervT09J/cP3PmTM2cOdNf4wCAUXz8FQAMIK4AYABxBQADiCsAGEBcAcAA4goABhBXADCAuAKAAcQVAAwgrgBgAHEFAAOIKwAYQFwBwADiCgAGEFcAMIC4AoABxBUADCCuAGAAcQUAA4grABhAXAHAAOIKAAYQVwAwgLgCgAHEFQAMIK4AYABxBQADiCsAGEBcAcAA4goABhBXADCAuAKAAcQVAAwgrgBgAHEFAAOIKwAYQFwBwADiCgAGEFcAMIC4AoABxBUADCCuAGAAcQUAA4grABhAXAHAAOIKAAYQVwAwgLgCgAHEFQAMIK4AYABxBQADiCsAGEBcAcCAehPXQ4cOadSoUbrvvvuUkpKiwsLCQI8EADetXsTV5XLp0Ucf1cCBA/Xpp59q6tSpeuqpp3Ty5MlAjwYAN6VexHXXrl1yOp165JFHFBISoqSkJPXq1UsbNmwI9GgAcFPqRVyPHDmimJgYWSwW77bo6GgdPnw4gFMBwM2rF3GtqKhQaGhorW2hoaGqrKwM0EQAcGuCAz2AJNntdjmdzlrbnE6n7Ha7T4/v2LGjibEajD7/mBvoEeq3NX0DPUG91uflPoEeoX5LTLzm7kOHDv3o9noR15iYGL3yyiu1thUXFysuLu66j/2pJwYAgVQvTgskJCQoKChIy5cvl8vl0vbt27Vr1y4NHjw40KMBwE2xeDweT6CHkKSvvvpKs2fP1sGDBxUZGalnn31Wv/zlLwM9FgDclHoTVwBoTOrFaQEAaGyIKwAYQFyBBuT48eOBHgE+qhdvxaqvxo8fr927d0uSqqqq5PF4ZLVaJUndu3fXBx98oI8++kgRERGBHLNe6t+/v8rLyxUUFFRr+xNPPKH09PRa23Jzc3XgwAEtXrzYnyMGxJkzZ5STk6PCwkJVVFTozjvvVGpqqiZPnqymTZte87HZ2dm6fPmyZs6c6adp/edWXpf6irheww/feztnzhxVVFRowYIFkqQTJ04oKSkpUKM1CC+88IJSUlKue1xmZqYfpqkfpkyZotatW2vTpk0KCwvT119/raefflrfffed5syZc83Hnj171ucP1jQ0t/K61FecFrhFf/3rX5WcnKy4uDhNnz5dVVVVkqQxY8YoLy/Pe9y6des0ZMgQ79ejRo3SqFGjlJCQoC+++CIgswdKx44dNWfOHPXs2VM5OTlasmSJJk6cGOix/GLv3r1KSUlRWFiYJKl9+/bKyspSeHi4JGnNmjUaOnSounfvrl/84hfef8xXrFihDRs26K233tL48eMDNb4x13pddu3addUHivr376/Nmzd7v16+fLlSU1MVHx+vMWPG6PTp035/Dv+LlestOn78uDZs2KCysjKNGDFCffr08Ub0Wvbs2aMVK1YoPj5ezZo188Ok9cvFixe1c+dOuVwuvfbaa4Eex28GDRqk6dOny+FwqFevXoqLi1PPnj3Vs2dP7d69W4sXL9batWsVExOjffv26Xe/+51SU1M1YcIEHTlyRHa7vVGeFrjW67Jr167rPn7jxo1auXKlQkNDNX78eC1btizgK17ieosmTpyo0NBQtW3bVl27dtWxY8d8elx4eLjuv/9+w9MF1vTp0zVjxgzv9127dvWGNCUlRVar1XsO+3Yxd+5cvfPOO3rvvff097//XRUVFYqLi9OMGTPUpUsXrV+/XnfffbfOnDmjiooKNWvWTKWlpYEe27hrvS6+GDlypCIjIyVJAwYM0M6dO02O6xPieotatGjh/TokJETV1dU+Pe6uu+4yNVK9sWDBgp8853o7PP8f06RJEw0fPlzDhw9XdXW1Dh48qBUrVmjs2LH617/+pdzcXG3evFkRERHq0qWLPB6PampqAj22cdd6XbKzs6/7+DvvvNP7dXBwsOrDZ6M452pIkyZNaoX23Llztfb/8Nq1t6Pb8fm///776tWrl/dSmkFBQfr5z3+uuXPn6ty5c1q9erX279+vf/7zn9q0aZNycnLqRSRMu97r0rx5c7ndbu/xHo9H58+fD9S4PiOuhkRFRWnbtm1yOp06deqU1q1bF+iREGA9e/ZU8+bNNWPGDJ04cUKSVF5eriVLlig2NlbfffedQkJCFBISosrKSv35z3/WhQsXvGGxWq26ePFiIJ+CEdd7Xdq2bSu3262tW7eqpqZGr7/+ui5duhTgqa+PuBry/bnYxMREZWZmyuFwBHokBJjNZtOaNWtktVo1evRo3XfffXI4HDp37pxeffVVTZgwQc2aNVNiYqKSk5NVXl6uPn366MiRI5Kk1NRUFRYWatSoUQF+JnXreq9LZGSkpk2bpnnz5ql37946ceKE4uPjAz32dXHhFgAwgJUrABhAXAHAAOIKAAYQVwAwgLgCgAHEFQAMIK64Lfz73/9Wx44dAz0GbiPEFQAMIK4AYABxRaNUUlKiRx55RN26ddPQoUN1+PBh7759+/bp4YcfVlxcnLp27arf/OY3+u9//ytJmj17tkaPHl3rZ+Xn56t///63xUVUUHeIKxqdqqoqZWRkyG63Kz8/X5MnT/besufixYuaMGGCOnfurHfeeUdvvfVWrQtQOxwOffbZZ/rmm2+8P2/jxo0aMmTIbXklL9w84opG58MPP9SpU6c0b948xcbG6le/+pX31ihOp1Pjx4/XM888o3bt2qlz584aPXq0d2UbHx+vNm3aaNOmTZKuXJ3p448/5sI7uGFcLBuNzuHDh3XPPfd470slXbkLgnTlIt0jR47U2rVrdfDgQR09elQHDhyodUFqh8Oh9957T+np6dq0aZM6dOig2NhYfz8NNHCsXNHo/Nif7yEhIZKk0tJSDR06VNu2bVNMTIwmTZqk5557rtaxDodDn3/+uU6cOKGNGzeyasVNYeWKRqdDhw46efKkysvLvbeTOXDggKQr50+Dg4O1cuVKb4RXrFgh6coV7i0Wi6KiotS1a1fl5+dr//79WrRoUWCeCBo0Vq5odHr37q3o6GhNmzZNhw4dUmFhoZYvXy5JioyMVHl5uXbs2KETJ05o3bp1WrZsmSTJ5XJ5f0ZaWpry8vLUo0cP743vgBtBXNHoBAcHa8WKFQoODtZvf/tbzZs3T+np6ZKuXM1/5MiR3ts4/+1vf9Pzzz8vi8Wi/fv3e3/GoEGDVFVVpaFDhwbqaaCB404EwI84fPiwfv3rX2vnzp1q3rx5oMdBA8Q5V+AHzpw5o08//VSrV6/WkCFDCCtuGqcFgB+oqKhQVlaWnE6npkyZEuhx0IBxWgAADGDlCgAGEFcAMIC4AoABxBUADCCuAGAAcQUAA/4Ptmi7kSgh1XAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='day',kind='count', data = df_tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e83c5124",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b59b6745",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e1351ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 424.185x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex',kind='count', data = df_titanic, hue='survived');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64897aba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f92629e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "921460a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "542770df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3803dc30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78cb9cd1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c02d4704",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fc66859",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "496ed1dc",
   "metadata": {},
   "source": [
    "**Example: Box Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e2a40a9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex', y='age', kind='box', data = df_titanic);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4449b888",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05a025b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49ecbc11",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9c28fcf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1b8150fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 424.185x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex', y='age', kind='box', data = df_titanic, hue='survived');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "379881a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b8b8888",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2970e5d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a8ed1dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b6cca92",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fc82796",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4dbf6461",
   "metadata": {},
   "source": [
    "**Example: Violin Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "86e1c44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex', y='age', kind='violin', data = df_titanic);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23160c2a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e2ee5d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cab8d8e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "81fb3bc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KA8h6zt0BpTbkWywcx6G8oqqj3Pn5EnUBlJWckSCq8FJadg4BtQkAwKqNsLfYwqIydlNTE6rrGjDS1HNiGWnyo6i4BD5fz8eR4CBaYklLSwPLsvD7fxig8/v9MJvNiImJQUnJDxv6lJSUIDU1FQqFaA0q3jU0NECmMfR6nFemQX2IJxar1QqXx9t1YtEHUF5RBY7jJIgsfFRVVYLTtCeWtv8Nhy7GgoICGDVyxOt6LlEzwuRHIMDizBl6SAkFoiWWWbNmwWQy4bnnnoPX68WpU6fw8ccfY8mSJVi+fDleeuklNDQ0oLa2Fi+//DJWrlwpVmiCsFqtCPSwhqVdQKFFdYivvj979ix0KjkiVBffHIbp/XB7fbSnxiC0trbC3mJDQGNpe0GugFJvRnl5uaRx8aGgoADpJn93VY86qOXAcLMMx48fFycwMiiiJRa1Wo133nkHZ86cwZw5c3DHHXdg9erVWLRoEe655x6MHTsWy5cvx/Lly5GVlYXVq1eLFZogrNb6LrckvhCn0qMuxBcRlpSUINnIdXlzMKs4GDVynD1L4ywDVVpaCkYmB6c2drzm15hRWloqXVA8OX7kMDIMfVvnNFLvwPGj+cIGRHghal9TcnIyXnnllYteV6vVWLNmDdasWSNmOIKqrq1FoIepxu1YlQ6NNaHdFVZ8pghJmq5vDgwDJBs5FBcXY+bMmSJHFh5KSkqgMEYCsh+eA91KMwqLQrtbyOfzobDoDJaN73r9yoUyzD58cOwoOI4D01sTh0iKSroIpKa2tvOWxN3gVHo4WltCemp1UeFJpBi6vzkkaxwoKgyfWUxiO326CG6Vpe0v329tzeoicfp0kXRB8eD06dMIBNhuF0ZeKMPctlCSulWDHyUWgTTU14PtQ2JpPyZUVxV7PB6cq6pBSjflOID2PTUosQzU8RMn4dNGdtraOqCPRnNjfUhXbSgoKECqRdbtwsgLRWlYROkVNM4SAiixCMDpdMLlsINT9z4rDHI1ZEpVyO5bUlJSAnDAsC5mhLVLNQZQXdcQtnu1C8nj8eBc2VkE9DGdtrbm1CbIVRqcPHlS6hAH7PiRPIzU9+87kWF0o4ASS9CjxCKA9qY6q+pDYmEYyLUmVFeH5krqwsJCJFvkUPXw1JmgC0CtkKGoKLS7bqRw6tQpcIwMrC6q8w8YBqwxNmSf3jmOw/HjxzGyj91g7UYaPTiW/51AURG+UGIRQFVVFRQaPSBX9ul4n9IQsonl1MkCpGp7ntUjlwEpZganTlF3WH8dOXIEMlMsILs4c7s1MTj4XWiuRq+pqUGjrRUZ5v4llgyzD2fLK6lidpCjxCKAqqoqQGvu8/FehR5l50JzsduJ48cwwtj7xINUrR0njx8VIaLwknvwEJza2C5/5jcl4ExRYUiuwC8oKECUXoFITc8LIy+UbAhAKWdCugtwKKDEIoDy8nPwyHsfuG/HaswoLSsTMCJhOBwOnKuq6VN3xkizn4oI9pPL5cKpEycQMCV2+XNWFw1GpsCRI0dEjmzwCgoKMNLo7v3ACyhkQJoFlFiCHCUWARSfLYX/+/IbfcFqzKitrgLL9u/pTWqnTp2CSi5DUg8D9+3STX7UN9lgtYb2YlAx5efnAzI5AvquWyyQyRAwJ2L/gQOixsWHE0fzkN7NjpG9SdM5UEALJYMaJRYBnDtXDlbT964wVmOGz+sNuSnHBQUFSLO0jaH0Jvr7PTUKCgoEjytc7Pv6a/jNSZ0WRl7IbUjC3q/2hVQtNq/Xi+LScqT3c3ylXZrJ3zapIYQ+81BDiYVnNputra6TNqLP7+GUWshVmpAr0dGf6aIMA2SYfJRY+ohlWezduw9uU3KPx/ktyWhqbAip4ozFxcVgWQ7De1hU25N0kx9NLfaQexAbSiix8Ky0tBQyuaJTXadeMQwYfWRI1dNiWRYnTp7EyH48dY40unCcpor2ybFjx2C3t8Jv7jmxcEotZJYE7N69W5zAeFBYWIhks7zPCyMvFKFua/0WFhbyGxjhDSUWnhUXF0NuiAKY/v3TOpUmFJ0pFigq/pWVlcHudPcrsWSY/SgqKYXHM7C+9aFk5xdfgItIBhS9b3TnNKVi+46dIdM1dLrwFFJ0A58uzDBti24psQQvSiw8Kyo6A5eq7+Mr7VhtJE6dPi1ARMI4fvw4Ek0KGJV9v5mlGv1gwNF6ll74fD588cUuuMypfTreH5GKhnpryMy6O32qACmGwW3YlaJz4kxhaHzeoYgSC89OnCqEvx/jK+0CukhUV1aEzNP80SP5fS533k4pA9IsTMiuFhfLgQMH4PH54I8Y3qfjOaUGXMRwbNm6VeDIBs/v96O0vKrHoqV9kWIIoCjEqzuHM0osPPL7/agoL7u4/EYfsLpIgEPIjLMcO5KHUeb+J8EMgwNH82icpSeffPoZPJYRvW5rfT5XRDp27vwi6BdLnjt3Dv5AAMMMvU9R70mywY9GW2tIF+EMZ5RYeHT27FkEAgEEBpBYIFNAYYwMiX7j+vp61Fgb+12OAwAyzT4cLygIuTU7YrFarfju0LfwRGf2631+czICkAX9IH5JSQmi9QroFIMbD4rVslApZCHzIDbUUGLhUWFhIZTGyD7XCLuQSx2JU6eCP7EcO3YMEToFYrX9Tw4ZZj+cLg/dELqxefNmyIzR/W/1ymRwRKRj/YaNgsTFl9LSUiTpB9cNBgAyBkgyMiE3RX+ooMTCo5MnT8Gt7v/4Sju/LgrHCoJ/QPLokSPINHl63ae8K3olh2SLAseOHeM/sBDn9/ux4ZNP4YjMxED+cX0xo3Dm9OmgriJdWnIGiRp+uusSNW6UltIDSjCixMKjowUF8OmiB/z+gC4alRXlcLv7X0NJTEfzDiFjAHWe2mXo7TiSf5jHiMLD119/DbvDAV9k2oDez6mNQNRwrP/4Y54j40/Z2RIk6gY3vtIuQetDaXHwJtGhjBILTzweDyrKyxDQDzyxsLpIAAxOB/G0Y7vdjpLyCoyyDHy66CiLD0eP5IfMuguxfPDROrgjRg64KxUAnFGjsXPnF2hpaeExMn74/X5U1liR2Ifacn2RqA+gvPwcL+ci/KLEwpO2QXdmQDPCOsjkkJtigrpya0FBATRKGZIHMasn0+xHQ1ML7V1+npKSEpw4fhye2DGDOk/AlAio9diyZQtPkfGnqqoKAZblrcWSqGsr7dLa2srL+Qh/KLHw5OTJk5CbYrrckKk/nOpIHD0evPW0jhw5ggwzC9kAxlfaRWpYxBhonOV869avByKTwfWjKnaXGAb2yEx8uG49AgF+buB8KSsrg1mrgL4fi2p7EqtlIZcxOHeOWi3BhhILT44eL4BTHTno8wT0MSgI4gH8o3mHkGkc/O59mUYXjhzJH3xAYaC1tRWff74DzqhRvJzPF52BZpsNubm5vJyPL+Xl5bx1gwFte7PEGeUoC8G9jMIdJRaeHD9e0P2+Gf0QMMSiubEe9fX1PETFL4/Hg1Oniwc1vtIu0+TBkcOHeIgq9G3btg1Q6RAwD+PnhHIVPBFp+OCjdfycjydni88gUc3vAs5EjZemrgchSiw8sFqtsDU1IGCIGfS5OJUBCo0+KOs+FRYWggGLEcbBr0MYZfGhoroOTU1NPEQWujiOw0frN8AemTGgKcbd8caMwZG8PFRWVvJ2zsEqKT7NyxqW8w3TulFSFPxrv4YaSiw8KCgogEKjB6cyDP5kDAO/PiYo9y05cuQI0iMYqAY3jAQASNCxMGnkQ36cJT8/H/XWOvijMng9L6uLgMwSj083beL1vAPl8/lQXlGNFCO/4z7JhgCKi0OnKvhQQYmFBwUFBfDrY3h74vRoo3A4/ygv5+JT/uGDyOzjxl69YRhglNmL/Pw8Xs4Xqj7d9Bn8ESnglBrez+2IGInNW7bC7+e3lTAQpaWlCARYDOO5xZJibJsZ1tDQwOt5yeBQYuHB4SNH4dYOvhusXcAQh5LiIvh8gx/L4Ivf70dBwUmMjuDvxjDK5Eb+dwd5O1+oaW1txd69e+HmubXSzh8xAk6XOygG8U+dOoUkswLavtfV7JMYDQujRk5bMQQZSiyD5PV6UVpcDJaH8ZV2AV00WJYNqoWShYWF8Pv9yDDzl+zGRPhwtrwiKBfzieGrr74Co9QgYEwQ5gJyBXyWFGzZtl2Y8/dDwfFjGKEf/GzCCzEMkGbyB2XX8VBGiWWQTp8+DZbjBrXi/iJyBeTG4Bpnyc/PR1oEM+DtZLuSpA/AoJbjyJEj/J00hGzZth1Ocyqvg/YX8kSm4cD+/XA4HIJdoy+OHD6E0WavIOfONDiR/923gpybDAwllkE6fvw4ZKaYfu2d0RdOTTTy8oPnhnsodz/GGPkZX2knY4AxFi8Ofzf0ph03Nzej4Pgx+CJHCHqdgDEekCsl7Q6rra1FtbURo3mYpt6VMRF+FBaVwOnkv0VEBoYSyyB9l5cPl5rH1sr3AoY4HD12LCjqaXk8HhScPImxEfzfGMaaXfju2wO8nzfY7d+/H3KNYXAlgPqCkcFjTsaXe/YIe50e5ObmIsGkQMwAtlnoizSTHxolg8OHqbBpsKDEMggsy+LYsWPwG+N5P3fAGAdHa0tQlKs4duwYGI7DyAFs7NWbcRE+lFfVoq6ujvdzB7Nv9h+Ay5AoaDdYO59pGA4ePCRZiZdv9u3FRDO/rd3zyRhgQoQH+7/eJ9g1SP/0O7EUFxdjx44dcDqdOHfuXFA8UUvl7NmzcDudCBjjeD83p9RCYYjE0aPSTzs+ePAgxkQGoBTgMSROxyLOoMChQ0OnO4xlWRw6dAh+U5Io1/ObEuFxuySZOWW323Hou8OYFtP/baz7Y1q0C1/t3RMUU6tJPxKL3W7HHXfcgSVLluCee+5BQ0MDHn/8caxcuRK1tbVCxhi08vLyoDBFAwq1IOd36WJx8JD0+8PnfvMVxpuF678eZ3bgwDdD52nz7NmzcDmdgrR0uyRXQmaKleQh5auvvoJRzQxoG+v+uCTKC4/Hg+++k/73hfQjsfztb3+D1+vFnj17oFa33UgfeeQR6HQ6/PWvfxUswGD27cFDcGoHXx+sO35jPL47nCdpq7Cmpgal56pwSZQwM3oAYFKUB4cOfRdU63aEdPz4cShNUYI9kHTFpYnB4bx80a7XbuvmTzEz2j6oath9oZYDP4rxYNuWz4S9EOmTPieW3bt344EHHkBc3A/dPklJSVizZg32798vSHDBzO/3Iz8/H35TomDXCBgTYW+xSVpkb//+/UgyKxCnE2bgFQDGRvjg9/uGTHmXghMn4eKhEnZ/BPTROFUobk2tiooKHD1+EnMSxNkRdU68E/u+/gbNzc2iXI90r8+Jxel0QqO5uOwEy7JgWeFuOsHqxIkT8Pl8bdM5BcIpNVCYY/Htt9LN0d+350tMMgu7kZJKDoyP8mPfvq8EvU6wKDhxEn6hZ4NdIKCPRqutWdTSJxs+Xo8x0UCiXpz7w2iLH7E6YPPmzaJcLxgsWLAA77zzjiDnvvvuu/Hggw8O6L19Tixz5szBSy+91GlwrLGxEU8++SRmzpw5oIuHsgMHDoAxJwxqG9m+cOji8dXX3wh6je60tLQg/+hxTIsVrhus3dRIJ/Z+uSvsH1L8fj9qqirBaiNEvS6nMkCmVKG0tFSU6zkcDmzdsgUL48Xb3ZFhgJx4Gzas/3DIDOKvW7cO11xzjdRhXKTPieWRRx5BVVUVsrKy4Ha7ceutt2LevHmw2+14+OGHhYwxKO35ah+cBuFn9fgtyThZcBx2u3DTNbvzzTffwKxheCmT35vJ0V40NreEfc2nmpoaBAJ+BLQWcS/MMJDpIkTbFOvTTz+FUeHH1BjhH0rONyfBA4/Tjl27dol6XalERkZCq9VKHcZF+pxYoqOj8f777+Of//wn/vjHP+Lmm2/GP//5T6xfv77TuEtvnE4nrrjiCrz22msA2grx3XPPPZg2bRpmzZqFV155pf+fQmTV1dWoPFcOvyVZ8Gux+mgwKq0kK6d37diOH0U5BB94BQCDksP4aBa7dn0h/MUkVFlZCblKAyj4r2bcG6/SgIqKCsGv4/F48NH77+LKJJso353zqeXAwoRW/O+tNyRt/X7wwQe47LLLMH78eFxxxRXYuHEjAGDUqFH48ssvO47Lzc3FqFGjOkrujBo1Cn//+98xc+ZMLF++HKtWrcITTzzR6dyPPfYYbr/9dgA/dIXt378f48aN67S/UUNDA8aOHdtRMmnPnj1YsWIFJk6ciCVLlmD9+vWdzrtx40ZcdtlluOSSS/CHP/wBXu/AHwr6vTJh+vTpuPHGG/HTn/4Us2fPBtPPBV5/+ctfOj01rV27FkDbtMS33noL7733HrZs2dLfsES1Z88eKEwxg9+fvC8YGTymZOz6crfw1zpPc3MzvsvLx/RYYdcfnC872oEvd+4I6+6wqqoqyLRmSa7tUxpQWi58Ytm8eTPgc2J2vHjfnfNdNsyNutpa7N27V5LrnzhxAn/6059w3333Yfv27bjlllvw4IMP9rkbcvPmzXjrrbfw5JNPYtmyZdi+fXvHzFCWZbF9+3YsXbq003uys7MRGRmJnTt3dry2bds2JCUl4ZJLLkFRURHuvvturFq1Cp999hl+9atf4cknn+wYjzpw4AAefvhh3HrrrdiwYQOMRmOnBNhffS5wNXr06G6TiFKpRFxcHBYvXoy7774bcnnXlQq3bt2K0tJSTJkyBUBb62X79u345JNPoNVqkZ6ejptuugnr1q3D4sWLB/BxxPH5F7vgMArfWmnntaQgN/dLOJ1O6HQ6Ua65Z88eROlkSDOJ11c9NcaLN0+34siRI5g8ebJo1xVTbW0tfEq9JNfm1AbU1ApbycHj8eB/b72JpcNsvGwINxAGJYfLkhx447VXMGfOnG7vR0KprKwEwzBISEhAUlISVq1ahZSUFERG9m0m4LXXXouRI0cCAOLj4/H4448jLy8PU6ZMwbfffovW1lZcdtllnd4jk8mwePFibN26Fddeey2AtvvtkiVLAACvvvoqli9fjuuvvx4AMHz4cJSXl+P111/HkiVL8P777yMnJwerVq0CAPzhD3/AV18NfDJNn1ssjz76KCIjI7F27Vps2LABGzZswKOPPoro6GjccccduO+++/DFF1/ghRde6PL9VVVVePrpp/HUU09BJmu7bFlZGViWxYgRPxTiS0tLQ1FR0YA/kNAqKipQUnQavsg00a4ZMCWAkyvx9ddfi3bNbZs/xczoVjEqjnTQKThMifFj+7bgbrEORlV1DbxyafrEWZUBDVaroNfYuHEj4HVgbqI0rZV2Vya7YK2tHdRT90DNmTMHEydOxHXXXYcrr7wSTz75JGJiYmAy9a2HIzn5h4fWiIgIzJ49G1u3bgUAbNmyBfPnz4def/HDybJly5Cbm4vGxkbU1tbiu+++w7JlywAARUVF2LBhAyZPntzx30svvYSSkpKOn48bN67jXAzDYMKECQP+N+hzi+W1117D448/jnnz5nW8Nnr06I6Mum3bNsTExODee+/Fb3/7207vDQQCuP/++3HPPfdg2LBhHa87HA6oVKpOTxQajQYul2vAH0hoO3fuhNwc1+9uMMbrgLqsrdgi43OCU/Wj5cHI4Da37atx4ZOKEM6dO4eTp4tx23Txbw6zYh341+7duOc39wbloORgVVZVg1VZJLk2p9LD43bB4XB0eWMaLIfDgf+99V9cO9wmSPmf/tArOSwe1orX/vMy5s6dC6VS2Nmb59NoNHjvvfeQl5eHPXv2YNeuXXjnnXfw8ssvX3RsV/XbLlzWsXTpUjz11FN44IEHsGPHDjz22GNdXnf8+PFITk7Gjh074PF4MGrUKKSnp3dc5+abb8YNN9zQ5XsZhrloIbZSqRxwfbk+/99fV1fXKZO2i4+PR3V1dcefbTbbRcf861//QlxcHFasWNHpdZ1OB5/P16lP3e12i9bd018sy2LT5i1wWPrfWlGfOwRlc9vYkqq6/+XwvVEjkX/4O9TX1/f7vf21detWZEYygi6K7M74SB/UMnZQzfBgVl9v7d9DBY9YVVsysQrUann//fdgkLklG1u50OXDXHC2NIm+riUvLw8vvvgipkyZgt/+9rfYtGkTxo0bh88//xxKpbLT3jh9KTKbk5OD1tZWvPXWWwgEArj00ku7PXbp0qXYtWsXduzY0WkcJj09HWVlZUhJSen4b//+/R1rYDIzMy/aF+nEiRP9/egd+pxYpkyZgqeffrrTqtbm5mY888wzHWMmO3bsQGpq6kXv/eyzz7B7925MmzYN06ZNw3fffYfnn38eb7/9NhiG6TSoVVJS0tG/GGzy8/PR1Ng4oG4wmbv5vD/3f8dEVhcFmSEK27Zt6/d7+8Pv92P7ls9waax46w/Op5ABs2Ls2PzpRkmuLyS/34+W5iawKoM0AciVkKs0gtT2a2xsxEcffIBrUmyQB0nNdI0CWJFsw39ff1XUvVq0Wi3+/e9/46233kJFRQX27duH4uJiTJw4ERMmTMC7776L4uJi7N+/H6+//nqfzpeTk4N//OMfuPzyy6FSqbo9dtmyZThw4ADy8vI6JZbbbrsNu3fvxssvv4yysjJs3boVTz75ZMeM3ltuuQV79+7FG2+8gbNnz+K5554b1NT/Pn8FHnvsMVRUVODSSy/FlVdeiUWLFmHOnDmorq7GY489hr179+LZZ5/Fb37zm4veu23bNhw+fBiHDh3CoUOHMHXqVNxzzz144oknsHDhQjzzzDOw2+0oLi7GO++8g5UrVw74AwlpwyefwB+RKmqNpw4MA0fESGz45FNBZ00dPHgQDocdWSLOBrvQnAQ3jhw/IcrUWDHV19eD4zhwamkG7wFApjUKklje+u+bSNL7MW2Q61YaPTK8dbrt36fZM/gBvnmJHqhYF9at+2jQ5+qr0aNH4+mnn8aHH36IK6+8smO21TXXXIM1a9bA6/VixYoVePLJJ3Hffff16ZzLli2D0+m8aDbYhVJSUpCZmYlJkyYhPv6HqiDjx4/HCy+8gC1btmDJkiV46qmncOedd+LnP/85AGDSpEl4/vnn8eGHH2LFihUoLi7uGPgfiD6PsSQmJuKTTz7BgQMHUFhYCKVSiYyMDEyZMgUFBQUYP348du/e3eeZD+0effRRPProo8jJyYFSqcTNN9+M5cuX9/uDCK2xsRFf79sHd8YiyWLwRaWj6dh3OHjwILKzswW5xqZPNiA71g0Nvxti9kuinkVmJIMtWzZj9epfSBcIz6qrqyFTKMEppBs78ikNHV3XfKmoqMCmTZvw+0uaBz3Z48MzOnxnbXtw21Smw90TBrcwWCEDrklpxhvv/g/Ll6+AxWIZXIB9tHjx4i5nto4ZMwbr1q3r9FrheTXcCrup5zZ37twuf9bVQtCPPuo6iS5cuBALFy7sNubeft4ffb59HDlyBGvXrkVRUdFFT8wMw/SrP+7tt9/u+LPZbMYzzzzT5/dK5bPPPgOji0DAwP/eK32mUMMXmYYP160XJLE0NDTgwLcH8cgU6SdPzI1rxbrPNuG2234OhULCLMejyspKyHVmUTb36o5XoUdpWTmv53zjtVcxLorF6IjBT02vcv4wkafGyc804axYL7ZU+vC/d97Gr379/3g5J+lZn7vCHn/8cVgsFjz77LPQaDR4+umnce+990Kn04VEYhgMn8+Hj9Z/DEfUKElvCgDgjhmD7w4eFKSbaOvWrUgyMkgToYRLb7JiPXC7nWFVObu0tBQelQiLansQ0FhQwmO9sOLiYny5ew9+nHrxpJ1gIWOAH6fYsHHjxiG3U6lU+pxYTp06hQcffBCLFi3C2LFjERkZiTvuuAMPP/ww/vvf/woZo+T27NkDp8sDX2S61KGA1UVCZknEunXrez+4P+dlWWz+dCPmxoq7dqU7ajkwI9aFz8JoEP9U0Rn4VNKsum/Hai2w1lTD4+FnDO31V1/BtDg/Uo3SbHvcVxMifUgzB/DOW+F9rwoWfU4scrkcBkPbbJbU1NSO/r7s7GycOXNGmOiCAMdxePt/78EZlQnIg6NLxh4zBpu3bEFrK38zt44cOYL6hgbMHMRUUd4HXhNcOHjocFg8ZXIchzNFRQjoxS2XfyFWGwmOQ8fCuMEoKirC/gO5WDlcmhmE/cEwwFXDbdiydStqamqkDifs9TmxXHLJJXj33XfBsixGjx7dUYenqKhI1MVHYsvPz0d5WSl8sWOkDqVDwJwMVqnDp59+yts5t2zehKkxPhiUA9+t8sKB18FKNQaQbGbw+eefD/pcUquoqIDb6QCrj5E2ELkCcmPUoNYotHv7v29gWlwAwwzB3VppNzbCj5EWFu+/967UoYS9PieWe++9Fx9//DHeeOMNrFixAiUlJcjJycFvfvObXqfAhbK3//cu/FHpki1q6xLDwBE9Bu9/+NGgKpC2czgc2LtnL+bEDW6uvxADr7NjWrFt86eSbs/MhyNHjkBhiASnlL6agFMbg0PfHR7UOcrKyrDv6/1Ymhz8rZXzLR3Wgi1bNqOxsVHqUMJanxPLxIkT8cUXX2DlypUwmUz46KOPcNNNN+Gvf/0rHnroISFjlExJSQkOHzoEd9y43g8WmS96JJweX6dqpgO1d+9e6JUcxkUG357zM+I8qK618vKELaXcbw/CpYuVOgwAQMCYgLz8/EFthvXRhx9gXDQb9GMrF5oQ6UOCnusoY0+E0a81snq9HlFRbX3EsbGxuPXWW7F48eJ+l84PFe++9x4QOVz03f76RKaAM2oU3v7fu4NeMLlz+1ZkR9tF3zujL0wqDhOiWXzxxeATqFR8Ph++/fZb+MzDej9YBH5TIjxuNwoKCgb0fpvNhs8/345FiaHVWgHaxlouT2jBJxvW8zaBgVwsOEajg1BdXR12ffEFnJlXSB1Kt7yxY1B77DgOHDgw4O2hm5qakHfkGNZMDd5fsuxoOz78Yid+9atfi14CnQ95eXnw+f0ImBKlDqWNXAlYkrBnzx5ccskl/X77tm3bEK1te/oPRdPjPPjgrBt79+4VpajrI48/iSprs+DXAYDEGAsee/j3olyrJ5RYuvHRunWAMUbaBZG9Uajhic7Af99+Z8CJZd++fYjWyzAiiLs0pkR78XqhHQUFBZg4caLU4fTb9s8/h9+SAsiC59fNaU7Fjp278Ktf/apfyZrjOGza+DHmxrYExbT0gVDKgFmxdmz65GNREkuVtRn5SpEm/1hP9uvwwsJCrF27FqdOnUJ8fDz+8Ic/YO7cuYMOI0jKxQUXu92OTz75FI7ocZIviOyNJ3YcTp86NeBujW++2oPJEY6g/phaBTA6ksM333wjdSj95nQ6sXfvV3BHjOj94B60bbvQtliU8Q2+oKI/IgUOpwOHDh3q1/tOnDiB6tq6oKlgPFDzEtw4evwkqqqqpA5FMl6vF7/85S+xaNEiHDx4EPfffz9+85vfoLKyctDnpsTShU2bNoFVaOCPGC51KL3i1AYEokbgf+++1+/3ejweHM7Lx6So4L9JTIpw4Ju9u6UOo9927twJTqFGwJw0qPMMdtuFi8iV8EWMwIZP+jdlfeeOzzExmoVRFdqz9BL0LNIiZPjiiy+kDkUyubm5cLvd+NnPfgalUomcnBxkZWVh06ZNgz43JZYL+P1+vP/hR7BHjwGY0PjnccWOw/5vvu7309eJEyfAsSxGWaQv4dKbCZFelFfVoKGhQepQ+ozjuLZSQJb0QX+XBrvtQlfc0aOQe2B/nxegsiyL3V9+gezowRWGDBbZUa34cud2qcOQTHFxMdLT0ztNvuJrB9/QuHOKaM+ePbA7XfBFBeeeMF1h9dFgzAn48MP+lQY/duwY0iPaKsAGuzgtC7NWjmPHjkkdSp/l5+ej4tw5+GJHSx1Kl1hDDGTGGKz/+OM+HX/y5Em0tDpwSVRoDtpfaEq0FyVlFbxXew4VTqfzot0q+drBNwRuKeJ69/0P4IzIaJs5E0Ic0WOwZevWTrvT9eZo3nfI0IfG0yfDAJlmL44dOyp1KH32zrvvtS2uDYJFkd2xR4/Bxk8+7dP3Zv/+/RgdyUE/iOoMwSROx2KYWY4DBw5IHYokdDod3G53p9f42sGXEst5Tpw4gZIzZ4L2CbMnfksyWLkaW7du7fN7zp4tQUoQzwa7UIrOg5KirverCDbFxcU4fOgg3HHjpQ6lR/6IVARkqj6VBzqU+w3Gm/v+4BIKxpkcOJgbPhW0+yM9PR1nz57t9BpfO/hSYjnPuvUfg41KBaeWaOvYwWBksEdm4sN1H/ep/ElraysamluRpA/+8ZV2ifoASsvKpA6jT/771tvgIlPAai1Sh9IzRgZ79Fi8+94HPS4YdDgcOH3mLMZGhEc3WLtxEV7k5x9BIBA6D1h8yc7OhlwuxyuvvAKv14tdu3YhNzd3UDtHtgueifUSs9ls2LNnN1zpws9rF4ovJgP1R/Nw+PBhTJ06tcdjKysrIWOAWK1w2xzzLUEXQJPNDqfTyUtzXSjl5eX4au8eOMYskzqUPvFFZ8BVdwybN2/G1Vdf3eUxJ06cgErBYHiIFJzsqwyzHy63B6WlpUhPF2ZbjMQYS7/XlwzqWn2kUqnwn//8B3/605/w8ssvIy4uDs899xySk5MHHQcllu9t374djMaEgDG+94ODlUIDX+QIfLzxk14TS1NTEwxqeUgM3LczqdqSYHNzc1Anlv++9Ta4iGSw+mipQ+kbmRz2mLF46+3/YenSpVCpVBcdUlBQgHQLB3kIfV/6Qq/kMMyiaPt8AiWWYFgJ353MzEy8+y7/1Z7D7GsyMBzHYcMnn8JuSQ/6BZG98URlYP83X6O5ubnH45qbm2FSh9Zn1Ss4yJi2pBisqqqqsGvXF3DEhVaFAF/MKLS63Ni+vevpt6dOHMMI3eAXZgajVJ0ThYWnpA4jrFBiQVtZg5qqKvijRdohUsAS8AFDHGQaY68LvzweD9Ty0JrdI2MAtUJ20UyWYPL2O+8A5kSwhuCoZNxnMgUc0WPx5ltvd1n1uKioCCmG0BmP648UvQ+nTx6XOoywQokFwLbtnwMRw8ApBexe4TgYDAYMHz4cJhUjXHJhGDjMI7BlW88LvxiGAYfQarEAAAdAJgvOr63VasX27dvhiJ0gdSgD4osdjWZbC7788stOr7e2tqKhqSVkNvTqr2EGP8rPVQ26Sjj5QXD+horI7/dj5xdfwGkZXC2n3nGIjIzEW2+9hUiLCQgMfoOu7vii0lBcdBoVFRXdHsMwjJANJ8GwQRzzhx9+CBiiQ3ecTq6EM3o03nzr7U4zC8vKyqCQMYjThmli0Qfg8fmH7EJJIQz5xHL8+HE4HQ74LULXBWPQ2NiIW265BY3NLYD84gFSvnAaMxTmWHz11VfdHmM0GmEXcOaoEEnLGwC8fhYmk4n/kw+Sw+HAp5s+C4nCpT3xxY5BdVVVp+KUFRUViDWKP9FDrAcfo5KDXi3npfgiaTPkE8vevXvBmpOEX2nPMLDb7SgvL0eLlxP85uMwDMPOXbu7/XlERARsrgCvv7wch47uPrfSzPuNocXb9nWNjIzk98Q82L59OwIyVUgULu0Jp9TCG5WO9z74sOO1yspKxKrFGV8R+jvUFYYBYvXosYVP+ocSy76v4TaJvLOfCE+0fksKiosKu51BFR0dDW+Ag93HXyws0NHdZ7REwuHn93M2emRQyOVB12JpKza5AY7IjJApXNoTT8xoHD50qKNrqKa6CtFKcSZMCP0d6k60ytfnYpykd6H/WzAIlZWVqK+rhd80uJLmwYjVWqDQGnD48OEuf56QkAC1UoEKB387MsqAju6+1uZG6BX8Pm5WOOQYnhQfdLtInjhxAjXVlfBFZ0gdCi9YXRTk5jhs27YNAFBXU4VIjTjjK0J/h7oTqfSgtmbo7s3CtyG9QPLw4cNQGCJDs4RLbxgGHkMCcr89iJycnIt+LJfLMTw5EefsNoyJ4Kebg2EAe6sddrsdSTo/7w2zcrsCaaNH8XtSHny+Ywc4S3JQF5vsL4c5FVu2fY6f/exnaGpshNkszg1e6O9Qd8wqFqcb6gU59/N/fQT2enHGbwzRSbjnocf6/b6jR4/i9ttvx7fffstLHEM6sRw5egxubYisjh4Anz4WR452X2Z+9NgJKD5UAoD/bg4hbgjFDj2WjRnL/4kHgeM47PpyN1yRobUgsjf+yBGw5ueiuLgYzS2tMEWLPxVXzDkQJiWHZoEW3trrK3Gz5aAg577Q2wPIjVu2bMGaNWt4rZc2pLvCjh0vgF8fI3UYggnoY1FbXYnW1tYufz5l6jScsKlDYtpxq49BWXMAU6ZMkTqUToqKitDaYoPfIvI4ncA4pRZySxwOHDgAp8sNXZiUyu+OVsHB4QzPygI9ee655/Dqq6/irrvu4vW8QzaxeL1e1NVUIaANvhlGfGG1ZjAyOcq6qQg8adIk2FwBVPI4ziKUk01KmAw6pKamSh1KJ4cPH4bCHAcoNL0fHGKcunh8vf8A/AE25Ko09JdWwcHlCv4tuvm2atUqfPzxxxg3bhyv5x2yiaWqqgocx4HVmqUORTiMDAq9GefOnevyxxERERg9Mg2H64VbU8OXww1azJgxM+hW3R85egxOdZS4FxWpiRkwxOHM99vUKpjwTixyhoN/CJbOj4uLE+S8wfVbKiKr1Qq5SiPoQsVgEFAZepxGOTfnMhxqNIoYUf/5WCCvQYW58xdIHcpFCk8XISBGFWOxSgKdJ6CPgs/b9hQfuks++4YB+rSPEembIZtYWlpaIFOGX/fFhfyMstsxFgCYO3cuSpsDqHYG71fhWIMSjEzZ61YAYvP5fGhqsILViNHqFa8kUAeFBjJV2++Inwvv1OLnGCgVQ3ouE6+C924iMLfbDShCa1/7gfAzcjidrm5/npiYiPFjMrGvWi1iVP3zVZ0B8xcsgFodXDHW19eD4zhwKr0IVxOvJND5ZBoTZAzgDoR3YvEEAI0mvHsvxDRkE4tSqQTY8O9TlXMc1Oqef2EWL1uJr63GoCzw2OJlkG+V48rFg98ulW92ux0AwMlFSHgilwRqxynU0KiVvFZoCEYtXhlMxuDuEg4lQ7btp9VqwfnDa//ursg5PzSanrv85s2bhxdf+Dvy6pWYGhNc/yZ7qtRISojjfdYKH7xeLxiZDBB7QoGICzxYRga9VotGd3g/gzZ5ZIiOFWYPHUN00oDWlwz0WgORnZ2NvLw83uIYsoklMTERflcrEPAJX4BSQkqfHUlJPX/ZdDodFi26Aru++RRTYxpEiqx3LAd8WWvCqp9fByYIKwbL5XJwwdjM4xEDDgaTGbWuWqlDEVStR4WkYSmCnHsgK+FDXXg/hvRg2LBhYBgGMlez1KEIh2Xhtzdi+PDeK+6uvOpqHLcC1Y7g+Urk1yth98mwaNEiqUPpUtuYD9f2cBKmZGwA0bHxOOcWcBO8IFDh0iB95EipwwgbwXMXEZlarcaIkRlQtITvHgxyew3kcjkyMzN7PTY1NRWTJ03A55XBcwP5vNqEKxcvhl4vxuB4/7WX72d83U+OCHWMz4n09HSU2BgEwnSDRaefQaXN36ffE9I3oiaWo0ePYtWqVZg2bVpbv/6LL4LjOHi9XqxduxbZ2dnIzs7GE088wWvdmu7kzJsLnV2cxMJqLOf9WZyy78rmckyZOrXXMZZ2191wI/bVaINioLasVY6TDcA1P75W6lC6ZTKZoFSpIfPapQ5FGBwLv6sVU6ZMgcfPodQenj3np5oU0Gk1GEktFt6IllgcDgd+8YtfYPHixcjNzcWbb76JjRs34t1338WLL76IM2fOYPv27fjkk0+Qm5uL1157TfCY5s2bB7alDjKH8OMKnuRp8Fna+nC9CZcIfj0EfNA0lWDRZQv7/JasrCzExMbgy0rpp/Vur9RjRvaPMGxY8NbgYhgGicOGQeYSpnih1BhPK7iAH6NHj8b4MaOQVx+eY5F5jRpMmzYt6Ko6hDLR/iWrq6sxZcoU3HTTTZDL5UhNTcVll12Gw4cPY8OGDVi9ejUsFgvi4+Nx5513Yv369YLHlJSUhJmzZkNX130FYL5wKj08KTPa/qwUvrtJaS2E2WTE3Llz+/wemUyGa69fhZ01Jvgl7PZo9jA4UKPCdTfcKF0QfTR21CgoXY1ShyEIuaMe5ogomEwmzF+4CN82GEOiYGl/+FngUL0WCxZeLnUoYUW0xDJy5Ei89NJLHX/3er3Yu3cvRo8eDavVivT09I6fpaWloaysDF6v8KuLf3rLzZA1lEJmtwp+LdH4PdBbC/DTm2+Cop+riRctWoQAo0JunXSLxXZWajAidTguuUSElt0gXXLJRKidYfTdOY/CXodLJk4A0Na6r3dyOG0Lr+6ww/UqMDIlsrOzpQ4lrEjS9vN6vbj33nuhUqmwePFiAG3rStpptVpwHNe2Ol5gmZmZuGLxlTBW7AfY8Bid1FUeQnJiPJYs6f+iQo1Gg+VXXY3Pq8XZb/xC3gDwZY0R195wY1BOMb7QpEmT4Hc0g3G3SB0K77TOGkyb2rZNQUREBObNm4vPq8JrU7wd1SYsWbYs6Ko6hDrRE4vVasUtt9yC+vp6vPHGGzAY2r6o5ycRl6ttlo1OJ84MpbvuvBN6mR+qmiOiXE9IclsFFNYiPPTg7/vdWmm3cuVVKLdxKJLg6XR/rRoKtQ7z588X/doDER8fj4SkZChsFVKHwivGbYPf3oTp06d3vHbtdTfgu1p5SGyz0BeFzQqcaWqbak/4JWpiOX36NK655hqkpqbirbfeQkREBMxmM2JiYlBSUtJxXElJCVJTUwd8Y+wvo9GItY/8EZrqI5CH8A2C8bTCWLoXt91266CmTkZHR2PupZdih8hPpxzX9gS58uoft5XcCREL5l0KXWvXWxOEKmVTKVLS0hF73mr0UaNGYXp2FjaWhX7pE44D1pWZceWVVyIhIUHqcMKOaImlqakJt912G5YuXYq//e1vUKl+6MNfvnw5XnrpJTQ0NKC2thYvv/wyVq5cKVZoAIBp06bhtltvhbH0q9Ds1gj4YDq7G9OmTMaNNw5+0PvqH1+LQ3UKNHvE644qsilQ2cph2bJlol2TDwsWLADbVAXGGz47EOpbynHl5Zdd9Pptt6/GwTqFYK3ZRN0PywzidcItOThcr8TZFjlu+enPBLvGUCZaYtm4cSOsViveffddTJ48ueO/u+++G/fccw/Gjh2L5cuXY/ny5cjKysLq1avFCq3DjTfeiJkzsmAu3gHGF0I3CTYA49ndSIzQ4ZE/PszLtMlx48Zh+LBE7K0Wb2uBL6v1mDN7VsfCw1CRlpaGxGHDoGgoljoUXsicDfC31HfZHTly5EgsXbIEbxdbBClaet1IJ6bGtO0BsyxFmN9BTwD4X0kEVt14c6cWGeGPaInl1ltvRWFhIfLz85GXl9fx3wsvvAC1Wo01a9bg66+/Rm5uLh566CHI5eL348pkMjzy8MMYmzECpuKdgD8EtirlWOjL9iFK7sHfn32mY8xqsBiGwYqrr8Weur5XPR7M06bDx+DbOiWWrbiqX+8LBgzDYOXyZTA0F4u2u6OQVPVnMHHSpG53F7z9jtVoCuiw/Rz/Dx2Raha3ZDoAABa1MP+WH5/VQ2WIxE9+8hNBzk+GcEmX7qhUKjz5xF+RGhcBc7AnF46FruwbmDxW/P25ZxAdze9OhgsWLECTq++D+IN52vy2ToUIixmTJ0/ud5zB4PLLLwfrskFuD/FijawfmqZirFzefXekyWTCb++7H+tLjagKotpyfVFkU+DzCi1+/9AfaSaYgELrWyESnU6H5597FqkxxuBNLt8nFbOnFv948QUkJyfzfgmTyYSZ07Oxr0bb+8EY3NPmN/VGXH7lkpBd/WyxWDBnzqXQ1BdKHcqgKBrPQqtRY86cOT0eN2/ePMyaPRv/LoyUdDFtf7j8DF4pjMBVV10VEmukQllo/haLwGg04vm/P4fUWNP3Yy7Cr6nps++7v8yeWvzjheeRmpoq2KUWLroS3zVoBS1A2OiRobCBw8KFFw8Wh5Jrrr4K8sbS0BqfOx/HwdBQiBXLlvVpVt69990Ph9yCj0qCs0jo+TgOeLPIBH1UEu5Y/Qupwwl7lFh6YDQa8cLfn0NGUjTMZ7YHxw2DDcBwdjci/U3410v/EDSpAMCPfvQjeFmgUMA1LYetSgyLj0VKijD7YYhlwoQJGJacDGUd/60WMYqYyhxWsPZ6rFixvE/HG41GrPnzY/i8Qhv0dcT2VqtxuEGDtY/+hbrARECJpRcGgwF/f/YZjBs5HOaibWA8ElayZf0wluxCjNyFf/3zH4J0f11Iq9Vi2tQpOGwV7pcxr8mA2fMWhMRK+54wDIOfXH8d9I2ned/2WowiplrrKcycOatfM6XGjx+PO1avxiunLKhzBeftpLRVjreKjLjvdw+E/MNLqAjOb0KQ0Wq1ePrJv2HKuFEwn9kmzTqXgA+m4p2I17L410v/EHVRV/aM2TjRKsxiSR8LFDbKwqZWU05ODlRyBoqmUl7PK3QRU8brhLyxBNdf1/9tCq6//gZMnpaFf5yMhFf43S76xe5j8I+TkbjyysW4/HIqNCkWSix9pFar8dfH/4LpUye1tVxcNvEu7vfCXLwDw8wq/OulfyAmJka8a6OtHlaFzY8WL/8timKbAhzDYOzYsbyfWwpqtRorViyHoeGU1KH0i9J6CskpqZgwYUK/38swDB586I/wa2PxZpEpaGZcsxzw71MWRCaMwK/vvkfqcIYUSiz9oFQq8ee1a3DpzOy2MRe3CMkl4IW5ZAeGR+nx0osvICIiQvhrXmD48OGwGPUobOa/H/20TYnRmSPDqt/7qpUrwbZaQ6diNhuArrEIP7nu2gF3RxoMBjz217/hUL0OX1YFx/+XG8/qcNZlwKOPP9Gp0gcRHiWWflIoFPjjww9hzowsmIsETi4BH8zFO5EcqccLf38OJpM4O09eiGEYZGZmoszO/6LVMocao8dN5P28UoqNjcX06TOgqQ+NVouiqRQqOYMFCxYM6jwjRozA/b9/EO+cMaK4Rdry+kcalNhUrsOfHv0Lra6XACWWAZDL5fjjww9hVvY0mIt3ClMjig3AePZLJFk0kiaVdhmjx6Lc2bf1LP1R7lQjIyOD9/NK7cfXXA1l41nAH0TT1LuhbzyNZUuX8NJqzMnJwfLlK/CPE5GSbXFd75bh5VMW3Pbzn2PKlCmSxDDUUWIZIIVCgUf++DDGj0qDqWQn4OdxUzKOg77sa0QpvPj7s8/AbDbzd+4BSk1NRY2L364wHwvUtfrDcqbOlClTEB0TC2X9GalD6ZHM1QS2uRrLeSz8+cu7foWYpFS8WmgRfbzFzwL/OhWB8RMn4yc/WSXuxUkHSiyDoFQq8cTjj2N4jBnG0j0Ax88qQnVVHgzuWvz92WcQFRXFyzkHKz4+HvWOAK+FBxvdMnBAWJYtZxgG11y1AoamM0FdP0xVfxoTLpmEYcOG8XZOpVKJtY/+BYWtWuysFK+IKQBsLNWhkTXhDw8/ErJVHMIB/csPkk6nw1N/ewIGvw3qqsFvFCZvLoe65igef+xRUdap9FVcXBz8LIdmL39fmXq3DFq1EkZj6O/v0ZVFixaBdTZDZq+TOpSusQFomkpwVR8XRPZHQkIC7v3dA/igxIhqpzi3mWKbApvLdXjokbWwWCyiXJN0jRILD2JjY/HYo3+GumZwG4UxHjuMpV/hjtvvCLpijO2/qA4e+83tPhnMRkPIL4zsjsViQdb0GdA0Bmd3mKL5HJQyBrNmzRLk/AsWLMD06dPx6ukIQUrsn8/HAv85HYHly5Zj6tSpwl6M9IoSC08mT56Mm266CcZz+4GAr/8n4DgYzu3HxAnj8ZOf3MB/gIOkUqmgUirg9POXBJx+BgZD8NeZGoyli6+EqqkUYP1Sh3IRbXMJFubkCDbVm2EY/Pa++1HlUmNftbBTkLeVa+BVmnDHL6gOWDCgxMKjm2+6CfFREdBWftfv9yoaiqFy1uH3D9wftE/wWo0Kbh4TizvAQKvlfxV5MMnOzoZKqYCiOci2LvZ7wDSdw6JFwq5Gj4iIwG23r8aHpWZeH0rO1+iR4dNyI35992+g04X39ylUUGLhkUqlwoO/vx/KulP9W9/C+mGoycPPb7sN8fHxwgU4SDJGBj6LHLMcJNnQTUxKpRLz58+DprlU4kg6UzaVwhIRgfHjxwt+rRUrVsAUEYUdAmwMBgCflekwcuRIzJ07V5Dzk/6jxMKzCRMmYOq0H0FXk9/n9yjrCmHUqLBy5UrB4uKDTMbwOsGJA4AgbZ3xaWFODmTN5wbWRSoQra0Mly3MEaV1rFAocMutt2NblREunnsEmzwMdldrcOvtq4O2pT8UUWIRwOo7fg6ZtQSMp7X3gzkWhvoTuPVntwR9WROWZXnNAzIGYHmuAhyMLrnkEui0uqDpDmN8LqC5Cgu62NNeKDk5OdDpjThQy+93fE+VBiNShtOAfZChxCKAzMxMjMzMhNJ6utdj5bYKyFgvLrss+De5cnt9UMv4a7KoZBw87iDcnZNncrkc8+ZdCo2tTOpQAACK5nJYIiMxatQo0a4pl8uxZPlK7K7jr4IEywF76oxYftWPqbUSZCixCOSqFcuhby7pdXGctrEYCxbkBP2gI8dx8Hh9UPI4JKKSAW538Jc84cO8uXPbpqIHwewwbcs5LJg/T/Sb8aJFi3C2iYWVp31bSloUaHZxmC9iy4v0DSUWgcyePRt+tx0yV2P3B7EByFsqMX9e8A86ut1usCwHnYK/FotWwcHpcvF2vmA2adIkKJVKyG1V0gYS8ALNlZh76aWiXzohIQHDk+KRV89PpeH8eiUmjh8Dg0GYvYLIwFFiEYjZbMbIjFFQNHe/YFJur4UMbTedYGe3t+2cqVfwNy9Mr2BhdwTBds8iUCqVmDljBtS2cknjUNgqoNPpMW7cOEmu/6PpM1HYys/ssEK7AdOyZ/JyLsIvSiwCypo2BRp3fbc/l9vrkDlqdNAP2gNAa2vbRAQ+Wyw6JQeXxwefL3hmSwlp7qVzoGqp4K2m3ECobecwZ/YsyaZ5jx49BqX2wX/fAyxQagPGjBnDQ1SEb5RYBDRmzBjIHfXdjrOo3Y2YOD40dk602WzQKGVQ8Xg/Mirb/l1aWiTY6lkCWVlZQMALuVS1w9gAFLYKzJ0rfjdYu5EjR8Jq9w962nG9WwaPn0V6ejo/gRFeUWIR0MiRI+F3O8B0syeH0t0UMnuR2Gw2GNX8fl0MSrbj3EOBTqfDpMlToGySZnaYvLUacgaS7lGSmJgIALC6BveEUuuSQ69VS75PEekaJRYBxcbGQi5XQObu4omcDcDnbOG1XLmQWlpaoOd5Z2K1HFApZEMmsQBAzvx50Laek6SUvqq5DFnZ2ZJ2varValiMejR4BnfrafTIEBsTRdOMgxQlFgHJ5XJExcSC8VycWBiPHeC4jie4YNfa2trRwuCTQS3rmBgwFMyaNQsBtx0yR/djb4LgWKht57AgCGYgRlhMaBnk9gstXhkiIyJ5iojwjRKLwGJiYiDrYutimc8BlVoTMnuRtLS0QCfjf5DdoBw6YyxA22zB8RMmQtlUKup15a01QMCHGTNmiHrdrpjNFrQOcvuFVh8DEyWWoEWJRWDxcbFgfBcnFsbrhNkSIUFEA+NwOKBl+E8sGgUHp3NoTDlut+iyhdC1lInaHaZqKkVWVlZQLMQ1mExwDbLSsdMvg9Fk4ScgwjtKLAKLjoqEkr24bAnjdyEiMnQSi9vlgFrO/41QI2PhGiKLJNvNmTMHrMcBmcMqzgXZANS2MlwhcIn8vjKaIuDwD+7W42RVIdPaH4oosQjMYrFAwXovep3xuREZEUKJxekUJLGoGBYeT/jXCzuf2WzG1KnToGooFuV68pZKyMFh+vTpolyvN0aTCc6AYlDncLFySixBjBKLwMxmM2RdTDeWBTyIDqEWC8vxW9m4HcNwYFnpFgxKZcniK6GxlQIiVHfWNhZj/vz5QbMQ12AwwMEOboqh3SejUi5BjBKLwEwmEzjfxYlFyfpgNpsliGhgOJaFUBM7OQmm3kptxowZUMoYKJoFLvHid0PWVI4li68U9jr9YLFY0Oob3DqWVm/beUhwosQiMLPZjIDHddFArYL1htTiLrlcgQDHf2phOQYy2dD7GqrVaiy6/HLoms4Ieh1lfTGiY2IxYcIEQa/TH5GRkWgeRO9ngAVsLj8iQqgreagZer/RIjOZTODYwEXl0pmAO6RaLFq9AR4Bem08nDwoZipJYfnyZeAaK/q2IdxAcBz0TWdw1YplQbWQMC4uDs1OP7wD/D41eWVgOQT1Nt5DHSUWgbUnD8bXeeYT53WFVGLR6Q1wB/gvXOgOMNBqtbyfNxSkp6d/vyFcoSDnl9trwbmaceWVwdMNBrSVzwcAq3tg36c6lwxqpQKRkbSOJVhRYhGYyWQCI5N1rhfGsfC7XSHVlI+IiEBLgJ99NM5n8zBD+gZx3Y+vga7xjCCD+Nr6QsyZc2nQfc8MBgOiIkyodAwssVQ6FBienBhUrTDSGSUWgclkMugNRsjOa7EwAQ8ALqQGH2NiYtDkHdwU0QuxHNDkDCAmJobX84aSefPmQaNUQNF4ltfzMl4HZI1ncd21P+b1vHxJT0tHeevAEku5XYn0DPG2VSb9R4lFBJaIiE5dYe1/DqUn9bi4ONQ7WV4Xi9u8DPwsh9jYWP5OGmJUKhVWrlgOQ8OpXlficzI5OLkSnKz3G7Ky7hRSUtMwdmxwbsswdsIlKHbqB/TeYocOY8cFz2QEcrGgSSyFhYW44YYbMGnSJFxxxRXYs2eP1CHxJiY6plNZF8bnglZvgErFf9eSUFJSUuD0smgaZFXa81U6FNBqVEO6xQIAV121EpyjAXJ7bc8HKtSwT74RUPSyHoX1Q9d4GqtuuC5ou4vGjx+P4mYG/n4uYbL7GFTY/Bg/frwwgRFeBEVi8Xq9+OUvf4lFixbh4MGDuP/++/Gb3/wGlZWVUofGi/i4mM5dYT43IkKsgF50dDQMWg3ODbBfvCsVdjlGDE8O2pufWKKjozFv3nzorCd7P5jp/VdWWX8Geq0G8+fP5yE6YYwfPx5+lkFxS/+6V080KRFhMiA1NVWYwAgvgiKx5Obmwu1242c/+xmUSiVycnKQlZWFTZs2SR0aL+Lj4qBm3R1dGYzfjbi4OKnD6heGYZCePgJn+3kj6MlZuxojRwdnV43YfnLD9UBjKZiu9u7pD46DvuEkrr/2x1Aqed5Ah0cajQYTxo/B0YaLY1TJAK2chaqLu9PRRjWmTvvRkFz7FEqC4v+d4uJipKend3pyTUtLQ1FRkYRR8ScmJgZyn6OjK0Me8CAxPvTGFSZOnoYz9p7XnPR0U7hQUasGEyZM5Cm60JaRkYFx4ydAXVcwqPPIbefAeBxYtmwZT5EJZ9acechvvrjel17J4Z9zmqBXdh5zYjngSJMWM2fPEStEMkBBkVicTic0Gk2n1zQaTdhUvY2NjQXram0bnGVkUAdcIbm4a/z48ShqliHQQ794dzeFC9W7Zah3UF/5+W5a9ROoG84A/oEvSzdYT2LJ4sUhUdVh1qxZOGcLoMZ58W1I3sWdqcimgMPLITs7W4ToyGAERWLR6XRwuzvX03K73WGzIjs+Ph4BnwcItFU5lvkcIdcVBgATJkyAj0Wv/eJd3RQudLxRiYSYqJBMsELJzs5GbGwsVHWnBvR+maMenK0K1113Lc+RCSMhIQGZ6ak4WNe3SSwH69SYNnUK9PqBzSYj4gmKxJKeno6zZzvP4y8pKcHIkSMliohf7bOeZN627YgDzpaQTCw6nQ4Txo3tsl+8v4416ZA9c/aQH7g/n0wmw40/uQG6hsIBLZjUWk9g+oyZSEpKEiA6YSy47Ap829h764rlgIONeixYGBx7ypCeBUViyc7OhlwuxyuvvAKv14tdu3YhNzcXS5YskTo0XqjVahiMZjAeBxi/G2zAH5KJBQBmzL4UR2yD2wfDxwIFTQpkB8n+IMHk8ssvh1rB9HvBJON1Qt5wtm0SQAiZN28eypoDqHL0fCsqbFbA7m3rPiPBLygSi0qlwn/+8x/s3r0b06dPx9NPP43nnnsOycnJUofGm+jYWMi8djAeOxiGQXR0tNQhDcicOXNQ1hxAbRf94n1V0KgEJ1Ng6tSpPEYWHtRqNa5asRyGxsJ+bV2srDuJ4SmpQVXFuC/i4+MxdlQGDtT2vDbnQJ0G07OzqBssRARFYgGAzMxMvPvuuzh8+DC2bt0a1HPwByIxIR4yrwMyrwMmSwQUCn7Lo4glISEBI1OTccg68MWdB+u1mDF9RkgtEBXTypUrwbZa+751MRuArrEIP7n+2pDsWly46Ep822jsNo/6WeBQgw45ly0SNzAyYEGTWMJdYnwcFH4HGK8DUSHaWmk3b+EifNs4sO4wHwscbtBg3oIcnqMKHzExMZgxYyY09X2reqxoKoVKIQvZh7G5c+eippVFub3rxbcnmpTwsbKg2VqZ9I4Si0hiYmKgCrgh8zmQEOIzoRYsWICzTSyqB9AddqxBCY5R0JTRXlxz9VVQNp7t09RjfVMRli1ZHDRbD/dXVFQUxo8d3e3ssINWDWbMmH7RkgQSvCixiCQ6OhqM1wmF34WEuNBbHHm+xMREjMlMx/6a/t/I9lt1mHPppSF7ExTL5MmTEREZBWVDcY/HMW4b2KYqLF26VKTIhDEv5zIcarq4FRxggcONWsydTy3cUEKJRSRRUVEIuB1Qsp6QqmrcncuuWIIDDd33i3fF5WeQZ1Xhssupr7w3MpkMK5cvhb65pMfjlPVnkDl6DIYPHy5SZMKYPXs2qlouXix5pkUBl49DVlaWRJGRgaDEIpLIyEiwAR/gbA66jZcGYsGCBahzsChp7fskhENWFQwGPSZPnixgZOFj4cKFCLTUQeZq7voAjoPeVoplSxaLGpcQYmNjkTY8Cfn1nbvD8utVuGTihLBZLD1UUGIRSfumXgG3PSwSi8ViwY+mTsE3NX3v995vNSDn8kWQy/nf4jgcJSQkIHP0GCgau261yBxWsO5WzJ07V+TIhJE961IUtHROIAWtBkyfOVuiiMhAUWIRicFgAL6fChpKe9335PIrFuPbBl2PtcPaNXsYnGgALqdusH65fGEO9K3nuvyZsuksJk6aEhJ1wfpi6tSpKGySd+zR0uplUNoUoPVOIYgSi0hkMhnaVxiEy41g5syZ8ARkKGjqvcTLgTo1khLiwqZMj1jmzJkDf2vDxeX0OQ661krkzA+P1grQVuTUFwDK7G3dq6dtChj1WowYMULiyEh/UWKRgMFgkDoEXmi1WsyaNQu5Vm2vx37bYETO5VeG5AI+KcXFxSEpeTgUts6tFsbdAr+jOazWdmg0GqSlDsMZW1tiOWNTYtzYsfSdCUGUWETV9gsSTmUp5i3IweEGTY9bzNa7ZDjTyIbsAj6pzZk1E1p7dafXFC0VSEoeHnbbOo8ZPwml9rap6KUuHcaMp/16QhElFhG1P3iFajmXrmRlZYGFHAWN3XeHHbSqMGJ4ElJSUkSMLHxMnjwZstZagPshe2scdcj+0TQJoxJGRkYGyl0acBxQ3iqjrtMQRYlFROHYpFer1cjOzsbhhu4XPB5uMuDS+QtFjCq8TJgwAWzAB5mzse0FjoPcXhuW07bT0tJQ1RJAs5dBqztA4yshihKLiLj+rCYMIbMvnYv8Jh3YLj5eq5dBUSNH5c4HQafTISk5BXJ7HQCA8bQi4HFi7NixEkfGv2HDhiHAcjjeqIJCLg/Z7SWGOkosIhoxIk3qEASRnZ0Nm5tFaevF61OONioRYTEhIyNDgsjCxyXjx0HpbAAAyB31MEdEISoqSuKo+Gc2m2HQanC8UYn42Eha8xSiwqezPwQ8/vhf0NLS0vuBIcZoNGL0yDQUNB5FmqnzzofHmzT4Udb0sOwGFFNGxkio9+XCCUDmbERGRviOPcRER6LI6kDKqNAu1jqUUYtFRPHx8cjMzJQ6DEFMmz4LBS2dp1FzHHDCpsGPsqiS8WClpaUh0NoIcCw0PhtGZ4ZvCzAmNhb1bjli4hOkDoUMECUWwospU6agqKltv5V2NS4ZmpyBsBxkFltycjI4NgDGY4fC0xJWu6teKCKqbQp1ZFRo71s0lFFiIbwYPXo0WI5B6XlFKYualUiMiw6Las5Ss1gsUGu1kLlt8DttGDZsmNQhCWbM2HFgGCZsW/dDAY2xEF5oNBqMTEvBGVsrMsx+AEBRiwoTJk6SNrAwwTAMoqNjYbeeBseyiA/xzeJ6snLlSqxcuVLqMMggUIuF8GbMuIkdq6aBtpXTo8eOkzCi8HL5ZTnIiFTiR1nZYVEhm4QvarEQ3mRkZuLgbg0AG/wsUGkL0DRjHv30llvw01tukToMQnpFiYXwZuTIkahu8cMbAGpccvhZDmlp4bl2hxDSPeoKI7xJTk4GywG1LjmqHXLERJpp5z9ChiBKLIQ3Op0O0REmVDvlqHbKMTw5tPdhJ4QMDCUWwqukpCTUOmWocyuQlEIFBAkZiiixEF7FJw5Dg0eORr8mrKfEEkK6R4mF8Co+IREHrDqcaQJVpiVkiKJZYYRXy5cvR0xMDORyOWbOnCl1OIQQCTBciG8SMmrUKBQWFkodBiGEkO9RVxghhBBeUWIhhBDCK0oshBBCeEWJhRBCCK8osRBCCOEVJRZCCCG8osRCCCGEV5RYCCGE8IoSCyGEEF5RYiGEEMIrSiyEEEJ4FRZFKEeNGiV1CISQIYzqFXYW8kUoCSGEBBfqCiOEEMIrSiyEEEJ4RYmFEEIIryixEEII4RUlFkIIIbyixEIIIYRXlFiGoJtvvhmvvfaa1GEQAf3rX//CtGnTkJ2djZaWFsGv9+CDD+LRRx8V/DokNFBiISQMrV+/Hr/97W+Rm5sLk8kkdThkiKHEEkIqKiowefJkvPPOO5g1axaysrLw5ptv4n//+x9mz56NrKwsvP766wCAbdu24ZprrkFWVhamTZuGBx54AD6f76JzejwePPHEE5g7dy5mzZqFRx55BA6HQ+yPRni0cOFCVFRU4Mknn8SDDz6IL774AsuXL8e0adNwww034NixYx3Hjho1Cu+99x7mz5+PyZMn46mnnsLnn3+OnJwcTJ06FX/96187js3NzcWqVaswY8YMTJ48GXfeeWeXraFAIICXX34ZOTk5yM7Oxt133436+npRPjsJEhwJGefOneMyMzO5Rx55hPN4PNymTZu4MWPGcA8++CDn8Xi4nTt3cmPGjOFOnjzJTZw4kTt06BDHcRxXWlrKZWVlcZs3b+Y4juNuuukm7tVXX+U4juMee+wxbtWqVZzVauVaWlq4u+66i3vwwQcl+4yEH/Pnz+e2bt3KHT16lJs0aRL3zTffcD6fj9u4cSOXlZXFNTc3cxzHcZmZmdwvfvELzm63c4cPH+YyMzO522+/nWttbeUKCgq4MWPGcKdPn+YcDgc3derUju9QbW0tt2jRIu7f//43x3Ec9/vf/57785//zHEcx73++uvcFVdcwZWVlXEul4v705/+xN18883S/EMQSVCLJQT97Gc/g0qlwvTp0xEIBHDLLbdApVJhzpw5CAQCAIDPPvsMU6dORXNzMxoaGmCxWFBbW9vpPBzH4aOPPsJ9992H6OhoGI1G3Hfffdi4cSM8Ho8UH43wbN26dVi6dClmzJgBhUKBFStWIDU1Fdu3b+845sYbb4Rer8fkyZOhVqtx/fXXw2AwYOzYsYiMjERVVRXUajXWr1+PxYsXw263o66uDhEREairq7vomh9++CHuuusuDB8+HBqNBg888AAOHTqE0tJSET85kVJYFKEcaiwWCwBAJmt7LjAajZ3+zjAM1q9fj3Xr1kGj0WDs2LHw+XxgWbbTeRobG+F2u3HHHXeAYZiO1xUKBaqqqjBixAgRPg0RUlVVFXJzc7F169aO1/x+P6qqqjr+bjabO/4sl8s7vk9A23eKZVnI5XLs3bsXb7zxBgKBAMaMGQO73X7Rd6r9mmvWrMGf//znjtcUCgUqKyuRmprK8yckwYgSSxjasWMHPvnkE6xfvx5xcXEAgGXLll10nMVigVKpxPvvv4+MjAwAgNfrRUVFBYYPHy5qzEQYsbGxuPHGG/H73/++47Vz584hIiKi4+/nP1R0Jy8vD88++yzWrVuH9PR0AMCdd97Z7TUffvhhzJs3r+O14uJiJCcnD/BTkFBDXWFhyGKxQC6XQ6VSwefz4e2338bp06fh9/s7HSeXy7FixQo888wzaGpqgtfrxVNPPYXVq1dLFDnh21VXXYWPP/4Y+fn54DgO+/fvx9KlS3H8+PF+nae1tRUymQxqtRosy2Lr1q3Yt2/fRd+p9mv+85//RHV1NQKBAP7zn//g+uuvh9vt5utjkSBHLZYwlJWVhUOHDiEnJwdqtRpTpkzBsmXLUFxcfNGxDz/8MJ599lmsXLkSDocDEyZMwCuvvAK5XC5B5IRv06ZNw5/+9Cc88sgjqKysRExMDNasWYPp06f36zxz5szBihUrcNVVV0Emk2H06NG4/vrrcerUqYuOveOOO8CyLG688UY0NzcjIyMDr776Kk17HkJoPxZCCCG8oq4wQgghvKLEQgghhFeUWAghhPCKEgshhBBeUWIhhBDCK0oshBBCeEWJhRBCCK8osRBCCOEVJRZCCCG8osRCwtYHH3yAyy67DOPHj8cVV1yBjRs3AgDsdjseeeQRZGVldWxE1b6lwMGDBzF69Gjs2LEDAODz+bBy5UrcfffdUn0MQkIOlXQhYenEiRO45ppr8Nxzz2HChAnYs2cPHn30UWzbtg0vvPACrFYrHnjgAajVarz00ksoKSnBhg0boFAo8Oc//xm7d+/G5s2b8frrr+O9997Dpk2bEBkZKfXHIiQkUBFKEpYqKyvBMAwSEhKQlJSEVatWISUlBQ6HA5s3b8bevXs7thR4+umnkZ2djX379mHevHn43e9+hz179uDhhx/Gjh078MILL1BSIaQfKLGQsDRnzhxMnDgR1113HdLS0jBv3jxcddVVqKysBABcccUVnY53uVwoKSnBvHnzoNfrsXbtWqxevRpLlizBggULpPgIhIQsSiwkLGk0Grz33nvIy8vDnj17sGvXLrzzzjt46qmnoFQqO8Zbznf+ToonT56EXC7HkSNH4HQ6odPpRIyekNBGg/ckLOXl5eHFF1/ElClT8Nvf/habNm3CuHHjsH79evh8PjidTqSkpCAlJQXR0dF44oknOvZkLy4uxksvvYT/+7//g0KhwLPPPivthyEkxFCLhYQlrVaLf//737BYLFiwYAFKS0tRXFyMBx98EEqlEg888ADWrl2LiIgIPPPMMzh+/DjS09PBsiwefvhhzJ49G4sXL4bZbMbtt9+OxYsXY8qUKVJ/LEJCAs0KI2Fry5Yt+Oc//4mysjJERkbi+uuvx1133YWWlhY88cQT2LVrF7xeLyZPnoyHHnoII0eOxJtvvonnn38emzdvRmJiIgDg3nvvxcmTJ7Fx40ao1WqJPxUhwY8SCyGEEF7RGAshhBBeUWIhhBDCK0oshBBCeEWJhRBCCK8osRBCCOEVJRZCCCG8osRCCCGEV5RYCCGE8IoSCyGEEF79fwV4EMfLghDgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 424.185x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex', y='age', kind='violin', data = df_titanic, hue='survived'); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55388cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x ='sex', y='age', kind='violin', data = df_titanic, hue='survived', col='survived'); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b684030d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80d1adb6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9374a812",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4801f074",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b679c6fe",
   "metadata": {},
   "source": [
    "**Example: Strip Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "96aa4b61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y ='age', kind='strip', data = df_titanic);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01582db2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e20ddfe1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3f83dac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3ae3039",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecfbe124",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x ='sex', y='age', kind='strip', data = df_titanic);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1abfe35b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd63c2f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b69833d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "646aa5f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "01e6be0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 424.185x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex', y='age', kind='strip', data = df_titanic, hue='survived');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c867588",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a68a915",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d3de288",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbfb6962",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02950f7d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "890d3229",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb54251c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a68c2eb4",
   "metadata": {},
   "source": [
    "**Example: Swarm Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e0d4376a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 424.185x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex', y='age', kind='swarm', data = df_titanic, hue='survived');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e60089d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "266f7774",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "162618b0",
   "metadata": {},
   "source": [
    "**Example: Sub-Plots using FacetGrid**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7e683624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex', y='age', kind='box', data = df_titanic, hue='survived', col='survived');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8a02f58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a83013ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd943c4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d88e4cf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x ='sex', y='age', kind='box', data = df_titanic, col='survived');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52b92f5e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9585bbeb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb819477",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66856a6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e12f1e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "698b0a8a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e465ccf0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2ac0807",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3c99e2d6",
   "metadata": {},
   "source": [
    "### c. The `sns.displot()` Method\n",
    "- Categorical estimate plots:\n",
    "    - `histplot` (with ``kind=\"hist\"``)\n",
    "    - `kdeplot` (with ``kind=\"kde\"``)\n",
    "    - `ecdfplot` (with ``kind=\"ecdf\"``)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576caf96",
   "metadata": {},
   "source": [
    "**Example: Histogram**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6fccc05b",
   "metadata": {},
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\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\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71b0b7c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tips.total_bill.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb217ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tips.total_bill.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc7e10ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tips.total_bill.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2837490",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ff2af1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfd59a06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3a95265",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b6832b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b6ab37fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='hist');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5e2a64e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45c653a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7b81271",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b90da1f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c639d918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='hist', bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60141699",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ed7749ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o+n09zrEpSEH9Hy503BEgBWZle1yHI0BFriPE4xoAlDdel1Xt2rW1f/9+/e53vyuwfN++fapRo8YND1YROF3Sk8vXeJyzbOw47f5xTxFranzjQpWAy5Imbn7f45xFj/TV4o1TCh0fPThR55avLHRckkJGDC5yHQBuLl6X1aBBgzRt2jRlZGSoRYsWki6/I3nx4sWKj48vtYAAAFxXWWVnZ2vx4sU6f/68bDabbr/9do0ZM0aDBg0qzYwAgAquyLJyOBzauHGjunXrppEjR2rkyJE6e/as1qxZoxo1auiRRx4pi5wAgArM4wfZZmVlKTY2VtOnT9eRI0fcy2vUqKGsrCzNnDlTcXFxys72fEEcAICS8FhWCxYsUE5Ojt5//31FRkYWGJs8ebI2b96sjIwMLVmypFRDAgAqNo9ltX37dk2aNEl33HHHNcd/97vfacKECdq2bVuphAOAm1beJemXn0v/X94lX+/pDeHxmlVmZqbq16/vcQVNmzZVRkbGDQ0FADc9V7705Wulv537Hit6zq906tRJmZmZstsLvmn58ccfV1xcXIFlixYt0v79+8vknoYey6pWrVo6duyY6tSpU+ic77//vsQf/Q4AMMesWbPUrVu3IueNGjWqDNJc5rGsunXrpnnz5qlVq1YKDAy8atzhcCgxMVEdO3YsrXwAAEM0btxYAwcO1DvvvKP+/fsrKChIqampBT4jsLR4LKsRI0boH//4h/r06aPY2FhFREToX/7lX3ThwgWlpKQoOTlZTqdTCQkJpR4UAOB7WVlZ+uKLL+RwOLR8+fIy267HsqpcubLefPNNzZ49W7NmzVJOTo4kybIsVa1aVT179tSjjz6q6tWrl0lYAEDpmzx5sqZM+f+PNIuIiHAXU7du3RQYGHjNs22lqcg3BVepUkV/+9vf9Je//EU//PCDLl68qOrVq+vOO++Un5/HFxMCAMqhmTNnFnrNylevUfD645YCAwMVHh5emlkAAIYr6e3pi4tDIwCA8a7rtvYAgBvEz/+63wNV7O3cBG6OvQCA8ibAzFuEfvjhh4WO/fa29mPGjCntOG6cBgQAGI+yAgAYj7ICABiPsgIAGI+yAgAYj7ICABjPuLLKyclRt27dtGzZMl9HAQAYwriyev7553Xs2DFfxwAAFNMPP/xww9dp1JuCt27dqqNHjyoqKsrXUQCgVOXm58ppOUt9O3abXcH+wdf1mLNnz2rOnDn65JNPlJOToxo1aqh79+4aM2aMgoKCPD72xRdf1C+//KKpU6eWJPZVjCmrkydPavbs2Xr99df19NNP+zoOAJQqp+XUyn0rS307g5sNvu7HjBs3TnXq1NHWrVtVtWpVfffddxo/frwuXryo5557zuNjz58/r0qVKhU3bqGMKCun06kJEyZo7Nixqlu3rq/jGCeieiNfRyhXbDY/Zedle5xTnL82gYpiz549io+PV9WqVSVJ9evX1zPPPKPPPvtMkvTGG29o7dq1OnnypAICAvTwww9r8uTJSkpK0jvvvCObzabvv/9eS5cuvWGZjCirhQsXKjQ0VL169fJ1FANZOndom+cpnVuUTZRywmW5tGr/Ko9zivPXJlBR9OjRQ5MnT1Z0dLRat26tyMhI3XPPPbrnnnv0zTffaN68eVq9erXCw8OVkpKif//3f1f37t01fPhwHTlyRJUqVbo5TwO+++67+vHHH9WqVStJl18RuGfPHv3www+aNm2ab8MBQAXzwgsvaNOmTXrvvff01ltvKScnR5GRkZoyZYqaNm2qjRs36o477tDZs2eVk5OjypUrKyMjo1QzGVFW27YVPHKIjY1Vx44dFR8f76NEAFBx+fn5qXfv3urdu7ecTqfS0tKUlJSkoUOH6h//+IcWLVqkbdu26dZbb1XTpk1lWZZcLlfpZirVtQMAypVPP/1UrVu31qVLlyRJdrtdzZo10wsvvKCffvpJycnJSk1N1fvvv6+tW7dqzpw5siyr1HMZcWT1W6tWeb7eAAAoHffcc4+qVKmiKVOmaNy4capbt64yMzO1dOlSNWzYUBcvXlRAQIACAgJ06dIlLViwQD///LPy8/MlSYGBgcrKyrrhuTiyAgC4hYSE6I033lBgYKBiYmJ09913Kzo6Wj/99JP+8z//U8OHD1flypXVrl07denSRZmZmWrfvr2OHDkiSerevbs++eQTDRgw4IbmMvLICgBudnabvUxelWq32a/7MXfccYdmzJhR6Linj8Nr27atdu7ced3bLAplBQA+wPv8rg+nAQEAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADG403BAOADrtxcWfmlf1t7m79dfsFld1v70kJZAYAPWPlOnVu+vNS3c2tc3HU/piS3tS8tnAYEABSwZ88edevW7arb2lerVk07d+5UZGRkgfmdOnVy35ewU6dOWrJkibp3766oqCjFxsbq1KlTJc5EWQEACrhyW/sZM2Zox44dOnfunO655x6NHz/eq8dv2bJFK1as0EcffaTc3FwtXLiwxJk4DQgAKMDTbe290a9fP4WGhkqSHnjgAX3xxRclzsSRFQCggCu3tU9KStLXX3+tdevWKTQ0VEOHDnXfQdiTGjVquL/29/e/IXcSpqwAAG5F3da+SpUq7rsCS5JlWbpw4UKp56KsAABuv76t/fHjxyVJmZmZSkxMVMOGDVWvXj3l5+frgw8+kMvl0sqVK5WdnV3quSgrAIBbUbe1Dw0N1aRJk/Qf//Efatu2rY4fP66oqKhSz8ULLADAB2z+9mK9B6o427leRd3WfsiQIRoyZMg1xz788MMC38fHxys+Pv66M/wWZVWKLjmccro8XVi0lUkOP5tN/XtN9zjHJpsim3QokzxlweVwFDEhQBdzPc+x26TgvLxCx21+Nlken9+i53i1jmJ8AgHMx3N6fSirUuR0WUr6LL3Q8fj29cskh8uSnly+xuOcZWOfUOaeDYVP6NryBqcqXZd27/Y47oyQRr39lsc5ix7p6/ETBm4dMljnVqz0uI6i5ni1jjL46xswHdesAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGM6asUlJSFBMTo1atWqljx45KTEyUZVm+jgUAMIARZZWdna2RI0eqR48e2rlzp1asWKGNGzdq9erVvo4GADCAEWV16tQpRUVFadCgQbLb7QoLC1OXLl307bff+joaAMAARpRVw4YNNX/+fPf3DodDn376qZo0aeLDVAAAU/j7OsBvORwOjR8/XoGBgRo4cKCv4xQq25Enp6uoa2q2Mski2RTZpEMZbcszy+ks0ePtClLIsOEe59jkp/69pnsYDypRhl+tSEH9Hy50+JcgP4/jkuQI9Pz3oMM/SIF9+xcxJ0AhHmcULTc/V07L83Njt9kV7B9cwi2VjaL2pzztC7xjVFmdOXNGY8aMkSQtX75clSpV8nGiwjldlka99ZbHOYse6VdGaSxl7tlQ+HDXlmWUQ8o/fbpEj3e6LE2d9azHOdMmPq8nl68pdHzZ2CdKlMHNkhZvnFLo8KjBiR7HJWn04ESP405Jry3b7nHO2PEl/zlyWk6t3LfS45zBzQaXeDtlpaj9KU/7Au8YcRpQkg4ePKg//elPCgsL0+uvv67q1av7OhIAwBBGHFmdP39eQ4cOVXR0tCZOnOjrOAAAwxhxZLVx40adOXNGq1evVmRkpPvf448/7utoAAADGHFkFRcXp7i4OF/HAAAYyogjKwAAPKGsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxqOsAADGo6wAAMajrAAAxvP3dQBfyM3PldNyepwTYkl+lqvQcZsVJMvpeR2SpPxfPA5HNulQ5Cosl1X0djyyebWdsuBns2nEwAWFjluylUkOm6SI6o08zrEXkdXfi/99bApS0ND4QsddXuyvJSnrl589zvGz+cnl4efV8uI/q58rQFmXCv95tfvZFBIU6HEd2Y48OT38vNptUnBensd12Pzt8gsO9jjHcllyORweJkjZedke12G32RXs73k7MEeFLCun5dTKfSs9zhnVZJD01fxCx233jlf+6dNFbMlS3s6NhQ93nKTMPRs8r6JrS7mysorYTlEsr7ZTFlyWpb/NmVbo+LSJz5dJDpssnTu0zeMcZ+cWHrM+N3F6kdtxuqT4xMRCx5eNHVfkOixJi1aO8Thn1OBEj3NGDS48wxVOl6UFL84qdDxh0kSv1jHqrbcKHV/0SF+dW77c4zpujYsrcjuSpUu7dxc66mrh0qr9qzyuYXCzwV5sB6bgNCAAwHiUFQDAeJQVAMB4lBUAwHiUFQDAeJQVAMB4lBUAwHiUFQDAeJQVAMB4lBUAwHiUFQDAeJQVAMB4lBUAwHiUFQDAeJQVAMB4lBUAwHiUFQDAeJQVAMB4lBUAwHiUFQDAeJQVAMB4xpTVgQMHNGDAAN19993q1q2bPvnkE19HAgAYwoiycjgcGj16tLp27aqvv/5aEyZM0BNPPKETJ074OhoAwABGlNXOnTuVm5urIUOGKCAgQJ07d1br1q31zjvv+DoaAMAARpTVkSNHFB4eLpvN5l7WoEEDHTp0yIepAACmsFmWZfk6xIIFC7Rr1y4lJSW5l82dO1cHDhzQggULPD62cePGpR0PANwOHDjg6wgVkr+vA0hSpUqVlJubW2BZbm6uKlWqVORj+cEBgJufEacBw8PD9d133xVYlp6eroYNG/ooEQDAJEaUVZs2bWS327VkyRI5HA59+OGH2rlzpx588EFfRwMAGMCIa1aSdPDgQU2bNk1paWkKDQ3VxIkT9a//+q++jgUAMIAxZQUAQGGMOA0IAIAnlBUAwHiUFQDAeJSVD6WkpKh169bu7x0Oh/7617+qTZs2atOmjWbMmCGn0+nDhNcnJSVFMTExatWqlTp27KjExERZllXu9+ujjz5Sz549FRkZqS5dumjt2rWSyv/zJUk5OTnq1q2bli1bJkn6+eefNXbsWLVq1Urt2rXTkiVLfJzw+v39739Xs2bNFBkZ6f63YcOGm+L5qtAs+MSWLVusli1bWnfffbd72Zw5c6yYmBjr/Pnz1qlTp6xevXpZixcv9mFK72VlZVn33nuvtWrVKis/P9/67rvvrE6dOlnJycnler9OnDhhNW/e3Prss88sy7Ks//mf/7EiIiKsPXv2lOv9uuLpp5+2mjRpYi1dutSyLMsaN26c9fjjj1s5OTnW4cOHrY4dO1pbtmzxccrr8+yzz1ovv/zyVctvhuerIuPIygdeeeUVLV26VAkJCQWWb9iwQSNGjFC1atVUq1YtjRo1SuvWrfNRyutz6tQpRUVFadCgQbLb7QoLC1OXLl307bffluv9ql27tr766iu1b99eLpdL58+fl91uV5UqVcr1fknS1q1bdfToUUVFRUm6fJS1fft2jRkzRiEhIQoPD9egQYP09ttv+zjp9dm3b5/uuuuuq5aX9+eroqOsfCAmJkbr169Xs2bN3MsuXryoM2fOKDw83L2sQYMGOnbsmBwOhy9iXpeGDRtq/vz57u8dDoc+/fRTNWnSpFzvlyRVqVJFWVlZat68uYYMGaLY2FjVrFmzXO/XyZMnNXv2bM2aNUt+fpd/DRw7dkwul0v169d3zytvHyidl5engwcPav369Wrfvr26dOmiJUuW6MKFC+X6+YIhnw1Y0YSGhl61LCcnR5IUEhLiXhYSEiLLspSbm6vAwMAyy1dSDodD48ePV2BgoHr06KE5c+aU+/0KCQnR7t27lZaWpuHDhys4ONi9/NdzysN+OZ1OTZgwQWPHjlXdunXdy7OzsxUYGCi73e5eFhwcrEuXLvkiZrGcO3dOf/jDH9SnTx+99tprOnz4sBISEpSXlyepfD5fuIyyMsSV/4l+/YG+V35JePOBvqY4c+aMxowZI0lavny5+6/28r5fdrtddrtdf/jDH/TII48oJSVFUvncr4ULFyo0NFS9evUqsLxSpUrKy8uTy+Uq8LyZvj+/FhoaqjfeeMP9fdOmTRUbG6tNmzZJKp/PFy7jNKAhqlatqttuu03p6enuZenp6QoLC5O/f/n4m+LgwYP605/+pLCwML3++uuqXr16ud+vr776Sv369SuwLC8vT7fccku53a93331XH3/8sVq1aqVWrVrpm2++0dy5c7Vq1SrZbDYdPXrUPbe8faD0gQMH9NprrxVY9ssvv+i2224rt88XLuNZMkh0dLTmz5+vpk2bKj8/X4sWLdLDDz/s61heOX/+vIYOHaro6GhNnDixwFh53q+77rpL33//vV5//XUNHDhQu3bt0oYNGzR//nzVrFmzXO7Xtm3bCnwfGxurjh07Kj4+Xjk5OXrppZf04osvKiMjQ8nJyRo3bpyPkl6/ypUra8mSJbrjjjvUu3dvpaamKjk5Wc8++6z27t1bLp8vXMZnA/rQzp07NWrUKO3atUvS5b8AX3zxRW3fvl35+fnq1auXJk2aVOAagqmWL1+umTNnKiQkpMAdnzt06KDZs2eX2/2SpL179+qFF17QoUOHVLt2bY0ZM0b/9m//Vq6fr1/7dVlduHBBzz33nD7//HMFBAQoNjZWI0eO9HXE6/L555/r5Zdf1nfffafq1asrPj5eAwcOvGmer4qKsgIAGI9rVgAA41FWAADjUVYAAONRVgAA41FWAADjUVYAAONRVjBOWlqa/vnPf97wucePH1fjxo118OBBr+Z36tRJycnJhY7HxsbqxRdflCRNnjxZjz/+uCQpMTFRffr08WobALxDWcE4CQkJOnz48A2fe6MlJibq0Ucf9cm2gYqGj1sCiqlatWq+jgBUGBxZwSixsbE6ceKEpk+frtjYWGVmZmrSpElq27atoqKiNHbsWP3444/XnCtJKSkpGjx4sCIjIxUREaG+fftq9+7dxc5z7NgxDRgwQBEREerTp4/7o7GubP/KaUAApYuyglESExNVq1YtjR8/Xq+++qqGDBmiEydOKCkpSStXrlRGRoYeffRRWZZVYG5iYqKysrI0fPhw3XXXXdq0aZP+/ve/q1KlSpo6dWqx86xZs0a9e/fWxo0b1bRpU40aNUpZWVk3cI8BeIOyglGqVasmu92uypUrKyUlRUePHtVLL72k5s2bKyIiQq+++qr279+vL7/8ssDcatWqKTc3V8OGDdNTTz2lO++8U3fddZdiYmJKdKfbPn36qH///goPD9fUqVMVEBCgrVu33sA9BuANrlnBWIcPH1bt2rUL3Fm5Vq1aqlOnjg4dOqR27doVmF+zZk3169dPq1evVlpamo4ePar9+/fL5XIVO0OLFi3cXwcGBqpRo0Zev5oQwI1DWcFYQUFB11zucrmuWUAZGRnq27ev6tevrz/+8Y/q0aOHzp8/r6eeeqrYGX57+wiXy6WAgIBirw9A8VBWMFZ4eLhOnjypjIwM99HV6dOnderUKYWHh181f8uWLfL399eKFSvc99RKSkqSJBX3TjhpaWnur3Nzc5WWlqbo6OhirQtA8XHNCsapXLmyjhw5osaNG6tx48Z68sknlZqaqr1792r8+PEKCwtT27ZtC8w9e/asQkNDlZmZqY8//ljHjx/X+vXrtXDhQkmSw+EoVpY1a9Zo48aNOnLkiP7yl78oJCREDz300A3bVwDeoaxgnD//+c/avHmzhg0bpgULFujWW29VbGys4uLiVKtWLa1YsUKBgYEF5sbHx6t79+7q16+fJk+erOjoaL355pt6/vnnZbPZlJqaWqwsw4cP18qVK9WrVy+dOnVKSUlJ7m0DKDvcKRgAYDyOrAAAxuMFFqhwzp49qwceeMDjnPnz5+u+++4ro0QAisJpQFQ4TqdTx48f9zjn9ttvV0hISBklAlAUygoAYDyuWQEAjEdZAQCMR1kBAIxHWQEAjEdZAQCM978biEQcHm/N3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 434.77x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='hist', bins=30, hue='day');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f314d78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3daed90e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f33d24df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad2c2604",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01caf3da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a04b6c3a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67bf70d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b80e7b3b",
   "metadata": {},
   "source": [
    "**Example: KDE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "135cb094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17deb532",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "124e6a7d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6664a26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "37d93f7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd1443938b0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='kde', fill=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2049a2c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8828b769",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b08928f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0687f721",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "471fcb1c",
   "metadata": {},
   "source": [
    "**Example: Histogram + KDE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99d30d98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25a2585b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='hist', kde=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72df971a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6162f0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, hue='day');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7fe0c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, hue='day', col='day');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00cad4c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "51d87ad6",
   "metadata": {},
   "source": [
    "**Example: Adding hue**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "897203bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='kde', fill=True, hue='day');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93a2c257",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99bf3f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='kde', fill=True, hue='day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb108852",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e506b830",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9f304b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='kde', fill=True, hue='day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc5d34fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9934b5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x= 'tip', data=df_tips, kind='kde', fill=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3fb1013",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='kde', fill=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc71054f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "316b1dca",
   "metadata": {},
   "source": [
    "**Example: ECDF**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77466a8b",
   "metadata": {},
   "source": [
    ">**Binning Bias** is a pitfall of histograms where you will get different representations of the same data as you change the number of bins of a histogram plot. Note the values along the y-axis changes as you change the number of bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ff6f695",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax = plt.subplots(2,2)\n",
    "ax[0][0].hist(df_tips['total_bill'],bins=5);\n",
    "ax[0][1].hist(df_tips['total_bill'],bins=25);\n",
    "ax[1][0].hist(df_tips['total_bill'],bins=50);\n",
    "ax[1][1].hist(df_tips['total_bill'],bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b4557fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f7de17f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c45b9bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9288e94f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "552f3901",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67747875",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0483722a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x='total_bill', data=df_tips, kind='ecdf');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5c0fe9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x='tip', data=df_tips, kind='ecdf');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f04c46b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83750785",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a07cae32",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x='tip', data=df_tips, kind='ecdf', hue='time');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39b87f7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tips.tip.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67ae60bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f27954d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41b436f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd879a34",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bcf8259",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "845eccc6",
   "metadata": {},
   "source": [
    "**Example: Bivariate Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c09a93e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x='total_bill', y='tip', data=df_tips, kind='hist', cbar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87c1e392",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c33fde10",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d329fbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65e7e1eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x='total_bill', y='tip', data=df_tips, kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "679cb94c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bfbc3db",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x='total_bill', y='tip', data=df_tips, kind='hist', hue='day', col='day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b8290a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a843afb9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46f4ed2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "386afb38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36e259ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1679dc20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "662722bf",
   "metadata": {},
   "source": [
    "**Example: Sub-Plots using FacetGrid**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c75f55b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.displot(x= 'total_bill', data=df_tips, kind='hist', hue='day', col='day');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e475c8e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65ead653",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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