{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://apssdc.in\"><img src=\"https://raw.githubusercontent.com/AP-State-Skill-Development-Corporation/APSSDC-Registration/main/apssdc-logo.png\" width=\"900\" align=\"center\"></a>\n",
    "\n",
    "<h1><center>Data Visualization using Matplotlib</center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recap\n",
    "\n",
    "- Pandas\n",
    "    - Series\n",
    "    - DataFrame\n",
    "- Importing the Data/ Export the data\n",
    "- Cleaning of Data\n",
    "    - Handling Missing Data\n",
    "        - isna, dropna, fillna\n",
    "    - Duplicates\n",
    "        - isduplicated, drop_duplicates\n",
    "    - Outliers\n",
    "- Statisticals/mathematical analysis\n",
    "- Data Visualization\n",
    "\n",
    "\n",
    "### Day Objectives\n",
    "\n",
    "- Data viz. using Python\n",
    "- Tools for Data Viz.\n",
    "- History of Matplotlib\n",
    "- Different plots \n",
    "    - Line plot\n",
    "    - Scatter Plot\n",
    "    - Histogram\n",
    "    - Bar Graph\n",
    "    - Box plot\n",
    "    - Pie Chart\n",
    "    \n",
    "    \n",
    "### Data viz.\n",
    "\n",
    "Finding the insights from the data\n",
    "\n",
    "### Tools for Data viz.\n",
    "\n",
    "- MS PowerBI\n",
    "- Tableau\n",
    "- MS Excel/ GSheets\n",
    "- Datalab -> GCP\n",
    "- SAS\n",
    "\n",
    "### Programming\n",
    "\n",
    "- Python -> matplotlib, seaborn, plotly (OS, Entriprize), Bokeh, geoplotlib)\n",
    "- R-Programming -> ggplot\n",
    "- JSV, \n",
    "\n",
    "\n",
    "### Data Visualization using Matplotlib\n",
    "\n",
    "- John D. Hunter -> Neurobioligist -> Matlab -> Matplotlib\n",
    "- It is classified into 3 layers\n",
    "    - Backend Layer\n",
    "    - Artist layer\n",
    "    - Scripting layer\n",
    "        - pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.2.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "`matplotlib.pyplot` is a state-based interface to matplotlib. It provides\n",
      "a MATLAB-like way of plotting.\n",
      "\n",
      "pyplot is mainly intended for interactive plots and simple cases of\n",
      "programmatic plot generation::\n",
      "\n",
      "    import numpy as np\n",
      "    import matplotlib.pyplot as plt\n",
      "\n",
      "    x = np.arange(0, 5, 0.1)\n",
      "    y = np.sin(x)\n",
      "    plt.plot(x, y)\n",
      "\n",
      "The object-oriented API is recommended for more complex plots.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(plt.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Plot\n",
    "\n",
    "it is used to identify the changes in the data W.r.to time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(1, 55)\n",
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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9KE7o3CrskgSFvojUg8VbCrn20cW0bZHM3CmjyWjfIuySJKC1d0SkTr29Lp+rZyykc1oKT3/rNAV+I6MzfRGpMy8u38H3nlpGZuc0Zl83kk5pKWGXJIdQ6ItInZi7aBt3PreCSK92TJ90Km2aJ4ddktRAoS8ix+2hf2zk7lfWcnb/Tjw0YQTNmyWFXZJ8gSOO6ZvZTDPLN7OV1dp+bma5ZrYsuF1U7bk7zGyDma0zswurtY8wsxXBc/eamdV9d0SkIbk7v3l1LXe/spaLT+7KIxMjCvxGrjYf5M4CxtTQ/gd3HxrcXgYws8HAOGBIcMwDZvbp34AHgalAZnCr6TVFJEZUVjk/fn4lD769kStH9uRP44bRrKnmhjR2R/wJufs7QGEtX28s8KS7l7n7ZmADMNLMugKt3f19d3dgDnDpsRYtIuEqLa/kpseX8MTCbdx4Tj9+/fUTSWqi/7zHguP5tXyzmS0Phn/aBW3dge3V9skJ2roH24e218jMpppZlpllFRQUHEeJIlLXikvLuebRRby6aic/vXgwt40ZiEZrY8exhv6DQD9gKJAH/C5or+kn74dpr5G7T3P3iLtHOnXSWtsijUV+SSlXPPwBWVv28KdxQ5l8Zp+wS5KjdEyzd9x916fbZvYI8GLwMAfIqLZrD2BH0N6jhnYRiRFbdu9j4sxF7N5bxoxrTtXFT2LUMZ3pB2P0n/o68OnMnheAcWaWYmZ9iH5gu8jd84ASMxsdzNqZCMw/jrpFpAGtzC3iPx/6FyWl5TwxZbQCP4Yd8UzfzOYC5wAdzSwH+BlwjpkNJTpEswW4AcDdV5nZPGA1UAHc5O6VwUvdSHQmUHPgleAmIo3c2+vy+a/Hl9CuRTNmXzdSC6fFOItOpmm8IpGIZ2VlhV2GSEKal7WdO55dwYD0NGZdeyqdW6eGXZLUkpllu3vk0HZ9I1dEPsfdue/NDfz+jY84K7MjD4wfTlqqllWIBwp9Efk3FZVV/HT+SuYu2s43hnfnN5edTHKSvnQVLxT6IvKZfWUVfHvuUt5cm8/N557ArRf01xz8OKPQFxEAdhWXct2sxazJK+auS09kwuheYZck9UChLyKsySvmulmLKT5QzoxJp3LuwM5hlyT1RKEvkuD+8VEBNz2+hFYpTZn3rdMY0q1N2CVJPVLoiySwxxdu5b/nr6J/ehozr4nQtU3zsEuSeqbQF0lAlVXRdfCnvbOJcwd04r6rhtMqRXGQCPRTFkkw+8oq+M6Ty/j7ml1cPboXP/vaYJpqSmbCUOiLJJDcTw4wedZiPtpVwi8uGcKk03uHXZI0MIW+SIJYsm0PU+dkU1ZeyaPXjtSiaQlKoS+SAOYvy+WHzyynS+tU5k4ZRWZ6WtglSUgU+iJxrKrK+cPfP+K+Nzcwsk97HpowgvYtm4VdloRIoS8Sp0pKy/neUx/y9zW7+GakB3ddepIuXC4KfZF4tGX3PqbMyWLT7n38/GuDmXR6b62hI4BCXyTuvLu+gJufWIoZzLluJGec0DHskqQRUeiLxAl3Z8Z7m/n1y2s4oXMrpk88lZ4dWoRdljQyCn2ROHDgYCU/fm4Fzy7N5YLB6fz+iqH6hq3USH8rRGLc9sL93PCXbNbsLOa752dyy3mZNGmi8XupmUJfJIa981EBtzy5lMoqZ8akCOcNTA+7JGnkFPoiMcjdefAfG/nf19aR2TmNh68eQe+OLcMuS2KAQl8kxpSUlvOjZ5bzysqdfO2UbvzmspNo0Uz/lKV29DdFJIas3VnMjY8tYVvhfn7y1UFMPrOP5t/LUVHoi8SIv2bn8OPnV9A6NZm5U0Yzsk/7sEuSGKTQF2nkSssr+cXfVjF30XZG923PvVcOo3NaathlSYw64kIcZjbTzPLNbGW1tvZm9oaZrQ/u21V77g4z22Bm68zswmrtI8xsRfDcvab/k4oc0baP93PZg/9i7qLt/Nc5/Xhs8igFvhyX2qy+NAsYc0jb7cACd88EFgSPMbPBwDhgSHDMA2aWFBzzIDAVyAxuh76miFTzyoo8vnrfu2wv3M/0iRF+NGagrnAlx+2If4Pc/R2g8JDmscDsYHs2cGm19ifdvczdNwMbgJFm1hVo7e7vu7sDc6odIyLVlJZX8t/zV3Lj40vo27ElL91yFucP1vx7qRvHOqaf7u55AO6eZ2adg/buwAfV9ssJ2sqD7UPba2RmU4n+r4CePXseY4kisWfz7n3c/MQSVu0o5voz+/CjMQO1HLLUqbr+ILemcXo/THuN3H0aMA0gEol84X4i8WT+slzufHYFyU2bMH1iRGf3Ui+ONfR3mVnX4Cy/K5AftOcAGdX26wHsCNp71NAukvD2H6zgFy+s5qms7UR6tePeK4fRrW3zsMuSOHWs/298AZgUbE8C5ldrH2dmKWbWh+gHtouCoaASMxsdzNqZWO0YkYS1MreIi+99j3nZ0dk5c6eOVuBLvTrimb6ZzQXOATqaWQ7wM+BuYJ6ZTQa2AZcDuPsqM5sHrAYqgJvcvTJ4qRuJzgRqDrwS3EQSUlVVdO37e15bS4eWKTx+/ShO76eLnUj9s+hkmsYrEol4VlZW2GWI1Jn8klJunfch767fzQWD0/nNZSfTThcrlzpmZtnuHjm0Xd/IFWlAf1+9i9v+upy9ZRXcdemJjB/VU2vnSINS6Is0gH1lFdz10mrmLtrOwC5pPDl1NJnpaWGXJQlIoS9Sz7K37uH785axrXA/N5zdl+9/uT8pTZOOfKBIPVDoi9ST8soq7l2wnvvf2kDXNs15cspoRvXtEHZZkuAU+iL14KNdJdw670NW5BZx2fAe/PySwaSlJoddlohCX6QuVVY5097ZxB/e+IhWqU15cPxwvnJS17DLEvmMQl+kjmws2MsPnv6Qpds+YcyQLtz19RPp2Col7LJE/o1CX+Q4VVY5j/5zM799bR2pyUn8adxQLjmlm6ZiSqOk0Bc5DhsL9nLbM8vJ2rqH8wd15tdfP4nOrXWRE2m8FPoix6C8sopp72ziTwvW0zw5id9dfgrfGN5dZ/fS6Cn0RY7SytwifvTMclbnFXPRSV34+SVDdAlDiRkKfZFaKi2v5N4F63n4nU20b9mMhyYMZ8yJmpkjsUWhL1IL/9ywm588v5LNu/fxzUgPfnzRYNq00Lx7iT0KfZHD+HhvGf/z8hqeXZJL7w4teGzyKM7M1BLIErsU+iI1cHeezs7h1y+vYV9ZBd8+7wRuOvcEUpO1Zo7ENoW+yCHW7yrhJ8+vZOHmQk7t3Y5ff/0krYgpcUOhLxLYV1bBvQvWM+O9zbRMacrd3ziJb0YyaNJE0zAlfij0JeG5Oy+tyOOuF9ews7iUKyIZ/GjMADpoCQWJQwp9SWgbC/bys/mreG/DboZ0a80DE4YzvGe7sMsSqTcKfUlIxaXl3LdgPY/+cwstmiXxq7FDuGpUL5I0lCNxTqEvCaWyynk6azu/fW0dhfsPckUkgx9cOECrYUrCUOhLwli0uZBf/G0Vq3YUc2rvdsz+2khO7N4m7LJEGpRCX+Leto/385vX1vLS8jy6tknl3iuH8bWTu2pxNElICn2JW0X7y7nvzfXMfn8LSU2MW76UyY1n96N5M33BShKXQl/izsGKKv7ywVbuXbCe4tJyLh/Rg+9/eQBd2mglTJHjCn0z2wKUAJVAhbtHzKw98BTQG9gCfNPd9wT73wFMDva/xd1fO573F6muqio63/5/X1/H1o/3c1ZmR+68aBCDurYOuzSRRqMuzvTPdffd1R7fDixw97vN7Pbg8W1mNhgYBwwBugF/N7P+7l5ZBzVIAnN33lm/m3teXcuqHcUM7JLG7OtGcnb/TmGXJtLo1MfwzljgnGB7NvA2cFvQ/qS7lwGbzWwDMBJ4vx5qkASxZNse7nl1LR9sKqRHu+b84YpTuOSU7ppvL/IFjjf0HXjdzBx42N2nAenungfg7nlm1jnYtzvwQbVjc4K2zzGzqcBUgJ49ex5niRKP1u4s5vevf8Trq3fRsVUzfnHJEMaNzCClqT6kFTmc4w39M9x9RxDsb5jZ2sPsW9Opl9e0Y/DLYxpAJBKpcR9JTOt3lfDHBet5aXkeaSlNufXL/bnuzD60TNGcBJHaOK5/Ke6+I7jPN7PniA7X7DKzrsFZflcgP9g9B8iodngPYMfxvL8kjo0Fe7l3wXpe+HAHLZKTuPncE7j+rD60bdEs7NJEYsoxh76ZtQSauHtJsH0B8EvgBWAScHdwPz845AXgCTP7PdEPcjOBRcdRuySAjQV7uf+tDTy/NJeUpknc8B/9mPoffWnfUmEvciyO50w/HXgu+FZjU+AJd3/VzBYD88xsMrANuBzA3VeZ2TxgNVAB3KSZO/JF1uQVc/9bG3hpRR4pTZtw3Rl9+NY5/bRGjshxMvfGPWQeiUQ8Kysr7DKkgSzb/gl/fnMDf1+zi1YpTZl4Wi+uO7OPwl7kKJlZtrtHDm3Xp18SOnfnnxs+5uF3NvLu+t20aZ7Md8/P5NrT+9CmRXLY5YnEFYW+hKaisoqXV+7k4X9sZNWOYjqlpXDbmIFMGN2TtFSFvUh9UOhLg9t/sIKns3J45N1N5Ow5QN9OLfnNZSdx6bDummcvUs8U+tJgcj85wJz3tzB34TaKSysY0asd/33xYM4flK6Lj4s0EIW+1Ct3Z8m2Pcx8bwuvrtoJwJghXbjuzN6M6NU+5OpEEo9CX+pFaXklLy3PY877W/gwp4jWqU25/qw+TDytN93bNg+7PJGEpdCXOrX14308vnAb87K288n+cvp1asmvLj2Ry4Z3p0Uz/XUTCZv+Fcpxq6is4q11BTz2wVb+8VEBTZsYFwxJZ8LoXpzWt4MuSyjSiCj05Zht2b2PeVnbeSY7h/ySMtJbp/C98/szbmQG6a11lSqRxkihL0eltLySV1fu5MnF2/hgUyFNDM4b2JlvRjI4d2BnkpOahF2iiByGQl+OqKrKWbylkGeX5PLyijxKyiro2b4FP7xwAJcN76Frz4rEEIW+fKHNu/fx3JIcnl2aS86eA7RslsSYE7ty2YjujO7TQXPrRWKQQl/+TV7RAV5ansffPtzBhzlFNDE444SO/OCCAVwwJF0zcERinP4FCwUlZbyyMhr0i7fsAeCk7m2486KBXHJKdw3fiMQRhX6Cytmzn9dW7eK1VTvJ2lJIlcOA9DR+cEF/Lj65G707tgy7RBGpBwr9BOHubMjfy+urd/Hqyp2syC0CYGCXNG4+L5OvntSVAV3SQq5SROqbQj+OlZZXsnBzIW+u2cWb6/LZXngAgGE923L7VwZy4ZAu9NEZvUhCUejHme2F+3lnfQFvryvgvfW7OVBeSWpyE87o15Fvnd2PLw1M1xi9SAJT6Me44tJy3t/4Me+uL+Dd9bvZ+vF+ALq3bc5/jujBeQM7c1q/DqQma516EVHox5zi0nKythSycFMhH2wuZGVuEZVVTstmSYzu24FrT+/NmZmd6Neppda8EZHPUeg3cvnFpSzZtoesLXtYuLmQVTuKqHJITjKGZbTjv87px5kndGRYz3Y0a6olEETk8BT6jUhZRSVr80pYum0PS7Z9QvbWPeR+Ev3wtVnTJgzNaMvN52Uyuk97hvVsR/NmGrIRkaOj0A9JWUUl63aWsCK3iJW5RSzPKeKjXSWUVzoA6a1TGNGrHdee0ZvhvdoxpFtrXT9WRI6bQr+eVVU5uZ8cYN3OEtbuLGbtzhLW7ixh8+59VFZFA751alNO7tGWyWf25eQebTgloy3d2qRqTF5E6pxCvw64O0UHytlWuJ/Nu/exsWAfGwv2sqlgH5t376W0vOqzfXu0a87ALq0ZM6QLg7q25qTubcho31wBLyINosFD38zGAH8CkoDp7n53Q9dwtCoqqyjYW8bOolJ2FZeSV1RK7p4DbN+zn22FB8gp3E9JWcVn+zcxyGjfgr4dW3J6vw7069SKAV3S6J/eirTU5BB7IiKJrkFD38ySgPuBLwM5wGIze8HdVzfE+7s7ZRVVlJRWUFJaTnFwX1JaQfGBcj7ed5DC4LZ7b9ln9wUlZQQjMZ9JadqEjPYtyGjXnJG925HRvgU92rWgb6eW9OrQQuPvItIoNfSZ/khgg7tvAjCzJ4GxQJ2H/vWzF7MhPzq0cqvzWKMAAARISURBVKC8ktLySsoqqo54XItmSXRo1Yz2LVNIb53KkG6t6dI6lfQ2qdH71ql0aZNKh5bNNCQjIjGnoUO/O7C92uMcYNShO5nZVGAqQM+ePY/pjXp1aEnLlKakNk0iNbkJqclJpCRHt9NSmpKWmkxaavS+dfPofYeWzfTNVRGJaw0d+jWdGvvnGtynAdMAIpHI556vjZ9ePPhYDhMRiWsN/RXOHCCj2uMewI4GrkFEJGE1dOgvBjLNrI+ZNQPGAS80cA0iIgmrQYd33L3CzG4GXiM6ZXOmu69qyBpERBJZg8/Td/eXgZcb+n1FRKThh3dERCRECn0RkQSi0BcRSSAKfRGRBGLux/TdpwZjZgXA1iPs1hHY3QDlhC0R+qk+xo9E6Gdj7mMvd+90aGOjD/3aMLMsd4+EXUd9S4R+qo/xIxH6GYt91PCOiEgCUeiLiCSQeAn9aWEX0EASoZ/qY/xIhH7GXB/jYkxfRERqJ17O9EVEpBYU+iIiCSTmQ9/MxpjZOjPbYGa3h11PXTCzmWaWb2Yrq7W1N7M3zGx9cN8uzBqPl5llmNlbZrbGzFaZ2XeC9njrZ6qZLTKzD4N+/iJoj6t+QvQa2Ga21MxeDB7HYx+3mNkKM1tmZllBW0z1M6ZDv9qF1r8CDAauNLN4uGTWLGDMIW23AwvcPRNYEDyOZRXAre4+CBgN3BT87OKtn2XAee5+CjAUGGNmo4m/fgJ8B1hT7XE89hHgXHcfWm1+fkz1M6ZDn2oXWnf3g8CnF1qPae7+DlB4SPNYYHawPRu4tEGLqmPunufuS4LtEqJh0Z3466e7+97gYXJwc+Ksn2bWA/gqML1ac1z18TBiqp+xHvo1XWi9e0i11Ld0d8+DaGACnUOup86YWW9gGLCQOOxnMOyxDMgH3nD3eOznH4EfAVXV2uKtjxD9hf26mWWb2dSgLab62eAXUaljtbrQujReZtYK+CvwXXcvNqvpRxrb3L0SGGpmbYHnzOzEsGuqS2Z2MZDv7tlmdk7Y9dSzM9x9h5l1Bt4ws7VhF3S0Yv1MP5EutL7LzLoCBPf5Iddz3MwsmWjgP+7uzwbNcdfPT7n7J8DbRD+viad+ngFcYmZbiA6xnmdmjxFffQTA3XcE9/nAc0SHmGOqn7Ee+ol0ofUXgEnB9iRgfoi1HDeLntLPANa4+++rPRVv/ewUnOFjZs2B84G1xFE/3f0Od+/h7r2J/ht8090nEEd9BDCzlmaW9uk2cAGwkhjrZ8x/I9fMLiI6nvjphdb/J+SSjpuZzQXOIbps6y7gZ8DzwDygJ7ANuNzdD/2wN2aY2ZnAu8AK/n8c+E6i4/rx1M+TiX64l0T0JGueu//SzDoQR/38VDC88wN3vzje+mhmfYme3UN0aPwJd/+fWOtnzIe+iIjUXqwP74iIyFFQ6IuIJBCFvohIAlHoi4gkEIW+iEgCUeiLiCQQhb6ISAL5P9JtSoTihKUpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23547b8f610>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TW/DfN8YssprzrVM2WI8FVnsOUPeSdgRwyGqPqKfdZTx8RXeqjeHtNbrIilKuZtaaA9QYePDy7nZHaZLGjN4R4B1gjzHmT3XeWgJMsZ5PARbXaZ8kIr4iEkPtBdtN1imgYhEZbn3mPXX2cQlRHf0ZP6Ar7288yLHTFXbHUUo1kyOnyvlw00EmDgwnMtjf7jhN0pgj/ZHA3cBoEdlm/dwAvAxcIyL7gWus1xhjUoCFwG5gBTDNGFNtfdbDwGxqL+6mA8ubszOO4CdXdqesqpo56zLsjqKUaibvrMugvKqGh6907qN8gAYHmRpj1lH/+XiAq8+xz0vAS/W0JwN9LySgs4nr3Jbr+3Zh3oZMHhgVS3t/b7sjKaWa4ERJBfM3ZDKuXxg9QgPtjtNkekduC3jkqjiKy6t4d4Me7Svl7Oasy+B0RTWPjo6zO0qz0KLfAuK7tuPa+M7MWZdBUVml3XGUUhfpZGkl767PZGyfLvTq0tbuOM1Ci34LeXR0HEVlVczfkGl3FKXURZq7PpPi8ioevbqH3VGajRb9FtIvoj2jLwll9roMTukC6ko5neKySuasz2BM78706dre7jjNRot+C3rs6jhOlFQyPynT7ihKqQs0b0MmJ0srecyFjvJBi36LGhgZxJW9Qpi15oAe7SvlRIrKKnl7bQZXXxJK/4ggu+M0Ky36LeyJMT05UVLJPD23r5TTmLu+9ij/iTE97Y7S7LTot7CBkUGMviSUWWsOUKwjeZRyeCdLK5m99gBjenemX4TrnMs/Q4t+K3hiTBwnS/VoXylnMHd9JkVlVTwxxjXG5Z9Ni34r6B8RxJjeoby9VsftK+XITpZWMnvdAa6N70zfcNc7ygct+q3miTE9OVlayTtr9S5dpRzV7LUHKC6r4slrXO9c/hla9FtJ3/D2XN+3C++sy+C4zsCplMM5eqqcOesyGNc/jN5h7eyO02K06LeiJ6/pyemKKt7S+faVcjhvrTlAaWU1T7roufwztOi3op6d2zJ+QFfmbciksLjc7jhKKUtBURnzkzKZODCcHqGuMcfOuWjRb2WPXx1HRXUNM75JszuKUsoy45t0KqsNj7v4UT5o0W91sSGB3DI4nPe/PUjuiVK74yjl9rKPlfD+xixuS4igW8cAu+O0OC36NnjcusvvL1/uszmJUurPX+5HRHjsatc/ygct+rYID2rDXcO78fGWHNIKTtkdRym3tT+/mE++y2HKiG6EtW9jd5xWoUXfJtOu6k4bb0/+tHKv3VGUclt//GIv/j5ePHyla82keT5a9G3SMdCX+0fFsmznYXbmnLQ7jlJuZ1v2CT5PyeeBUbEEB/jYHafVaNG30QOjYujg780rK1LtjqKUWzHG8MryVIIDfLh/VIzdcVqVFn0btfXz5pHRcaxLO8La/YV2x1HKbazeV0jSgaM8NroHgb5edsdpVVr0bXbX8CgiOrTh5eWp1NQYu+Mo5fKqawwvL08lKtifO4Z1sztOq9OibzNfL0+eurYXKYeK+PeOQ3bHUcrlLd6WS+rhYp66rhc+Xu5XAhvssYjMEZECEdlVp+0FEckVkW3Wzw113psuImkisldErqvTPkREdlrvvSEi0vzdcU7jB3QlPqwdf/h8L+VV1XbHUcpllVVW89oX++gX3p4b+4XZHccWjflnbi4wtp72140xA62fZQAiEg9MAvpY+8wQEU9r+5nAVCDO+qnvM92Sh4fwzPWXkHO8lPeSsuyOo5TLei8pi9wTpfxi7CV4eLjncWeDRd8YswY41sjPmwAsMMaUG2MygDQgUUTCgHbGmCRjjAHmAxMvNrQrurxnCKPiOvHXr9I4WaILrSjV3I6fruCvX+3nyl4hXBbXye44tmnKCa1HRGSHdfqng9UWDmTX2SbHagu3np/dXi8RmSoiySKSXFjoPqNanr2hN0Vllfz1q/12R1HK5bzx1X5OlVcx/fredkex1cUW/ZlAd2AgkAe8ZrXX9/uSOU97vYwxs4wxCcaYhJCQkIuM6Hx6h7Xjh0MimJeUycGjJXbHUcplZBw5zXtJWdw+NJJeXVx76uSGXFTRN8bkG2OqjTE1wNtAovVWDhBZZ9MI4JDVHlFPuzrLT6/phZeHB698rjdsKdVcXl2Rio+XB0+Ocd1lEBvrooq+dY7+jJuBMyN7lgCTRMRXRGKovWC7yRiTBxSLyHBr1M49wOIm5HZZXdr78cDlsXy2I48tWY29lKKUOpfNmcdYvuswUy+PJbSdn91xbNeYIZsfAklALxHJEZH7gVet4Zc7gKuAJwGMMSnAQmA3sAKYZow5MwbxYWA2tRd304Hlzd0ZV/Hg5bGEtvXlxaV79IYtpZqgpsbw4r9306WdH1Mvj7U7jkNo8P5jY8zkeprfOc/2LwEv1dOeDPS9oHRuKsDXi6fHXsJT/9zO4u253DwoouGdlFLfs+i7XHbmnuT12wfg7+Ne0y2ci/vdjuYkfjAonH7h7Xll+V5KKqrsjqOU0zldXsUfPk9lQGQQEwacc7Cg29Gi76A8PITnb4rncFEZs9YcsDuOUk7nrdXp5BeV8/yN8W57I1Z9tOg7sKHRwYzrH8abq9N1PV2lLkDO8RLeWnOAmwZ0ZUi3Dg3v4Ea06Du46ddfAsDvl+2xOYlSzuN3y/bgIfKfvz/qv7ToO7iIDv48dEV3lu7I49sDR+2Oo5TD25B2hGU7D/OTK7vTNcg91r29EFr0ncBDV3QnPKgNLyxJoaq6xu44SjmsquoaXvh3CpHBbXhAh2jWS4u+E/Dz9uRX43qTeriYDzYdtDuOUg7rH99msS//FL8aF4+ft2fDO7ghLfpOYmzfLlzavSN//HwvR0+V2x1HKYdTWFzOayv3MSquE9fGd7Y7jsPSou8kRIQXJ/ShpKJaF1JXqh4vL0+lrLKaF8b3QddoOjct+k6kR2hb7h8Vw8LkHLZkHbc7jlIOY3PmMf61NYcHRsXSPSTQ7jgOTYu+k3lsdBxd2vnx3Ke7qNZ5eZSiqrqG5z7dRdf2fjwyuofdcRyeFn0nE+DrxXM3xrM7r4h/fKtLKyo1PymL1MPFPH9TvM6v0wha9J3QDf26MCquE3/8fC/5RWV2x1HKNodPlvGnlfu4vGcI1/XpYnccp6BF3wmJCL+Z0Jfy6hp+s3S33XGUss2LS1OorK7htxP66sXbRtKi76SiOwXwyFU9WLojj9X73GcdYaXO+Dq1gGU7D/PY1XFEdfS3O47T0KLvxB68IpbYkACe+3QXZZXVDe+glIsorajmucW76BEayAOj9M7bC6FF34n5enny24l9OXishDdW7bc7jlKt5s+r9pFzvJTfTuyLj5eWsQuh/7Wc3KXdO/HDIRG8teYAuw8V2R1HqRa3K/cks9dmMGloJMNjO9odx+lo0XcBvxzXmw7+3kxftEPH7iuXVlVdwzOLdtDB34fp1/e2O45T0qLvAoL8fXj+pj5szznJ3A2ZdsdRqsW8uz6TXblF/Hp8H9r7e9sdxylp0XcRN/UP46peIfzx871kHyuxO45SzS7r6GleW7mXMb1DuaGfjsm/WFr0XYSI8Nub++HpITyzaAfG6Gke5TpqagzP/Gsn3h4e/GaijslvCi36LiQ8qA3Tb7iE9WlHWbA52+44SjWbDzYdJOnAUX45rjdh7XU1rKZosOiLyBwRKRCRXXXagkVkpYjstx471HlvuoikicheEbmuTvsQEdlpvfeG6D/VLWLy0ChGxHbkpc/2cEgXU1cuIPdEKb9ftofLenTi9qGRdsdxeo050p8LjD2r7RlglTEmDlhlvUZE4oFJQB9rnxkicmb5mpnAVCDO+jn7M1Uz8PAQXrmlP9U1hmc/2amneZRTM8YwfdFODPD7H/TT0zrNoMGib4xZAxw7q3kCMM96Pg+YWKd9gTGm3BiTAaQBiSISBrQzxiSZ2io0v84+qplFdfTnF2N78c3eQv6ZnGN3HKUu2oLN2azZV8j06y8hMlinWmgOF3tOv7MxJg/Aegy12sOBuieTc6y2cOv52e2qhdwzIpoRsR15celuco7raB7lfLKPlfDbpbsZ2aMjdw7rZnccl9HcF3Lr+93LnKe9/g8RmSoiySKSXFiok4ldDA8P4dVb+2OM4emPd1CjN20pJ1JTY3jqn9sREV69dQAeHnpap7lcbNHPt07ZYD0WWO05QN0rLRHAIas9op72ehljZhljEowxCSEhIRcZUUUG+/OrG+PZkH6U93TBFeVE5m7IZGPGMZ6/MZ7wIB2t05wutugvAaZYz6cAi+u0TxIRXxGJofaC7SbrFFCxiAy3Ru3cU2cf1YImDY3kyl4h/G7ZHtIKTtkdR6kG7c8v5pUVqYy+JJQfJkQ0vIO6II0ZsvkhkAT0EpEcEbkfeBm4RkT2A9dYrzHGpAALgd3ACmCaMebMnL8PA7OpvbibDixv5r6oeogIr97SH38fT578aBsVVTV2R1LqnCqqanh8wTYCfb145Zb+OlqnBYijD+lLSEgwycnJdsdweit2Heahf2zhkat68NR1veyOo1S9XlmRysxv0nn7ngSuie9sdxynJiJbjDEJZ7frHbluYmzfLtyWEMGMb9LYnHn2CFyl7LfxwFHeXJ3O5MRILfgtSIu+G3n+pj5EdPDniQXbOFlaaXccpf7jZEklT360jW7B/vxqXLzdcVyaFn03EujrxRuTB5FfVMazi/RuXeUYjDE8s2gHhafKeWPyIAJ8veyO5NK06LuZgZFB/PTanny2M4+FyTopm7Lfh5uyWb7rME9d24v+EUF2x3F5WvTd0EOXd+fS7h15YcluHcapbLU/v5gXl6YwKq6TLnDeSrTouyEPD+H12wfi7+PJtPe3UlpR3fBOSjWzkooqfvL+VgJ9vXjth3rXbWvRou+mOrfz4/XbB7KvoJhf/zvF7jjKDf3f4hTSCk/x+u0DCW3nZ3cct6FF341d3jOEaVf2YMHmbD75TmfjVK3n4y05/HNLDo9e1YNRcTrVSmvSou/mnhgTR2JMML/8ZBf78ovtjqPcwN7DxTz36S6Gxwbz+JiedsdxO1r03ZyXpwd/nTwIfx9PHvrHForLdPy+ajlFZZU89I8tBPp58cakQXjqefxWp0Vf0bmdH3+dPJisoyU8/bEuqq5ahjGGn/9zOwePlfD3OwbreXybaNFXAIzo3pGnr+vF8l2Hmb02w+44ygW9teYAn6fkM/36S0iMCbY7jtvSoq/+Y+rlsYzt04WXV6SyPu2I3XGUC1mzr5BXV6Qyrl8Y918WY3cct6ZFX/2HiPDH2wYQ2ymAaR9sJfuYLrOomi7r6Gke/fA74kLb8uqtOl2y3bToq/8R6OvF2/ckUFNjeGB+MiUVVXZHUk7sdHkVU+dvAWDWPUN0Xh0HoEVffU90pwD+esdg9uUX87OF23V9XXVRamoMP124jf0FxfztjkF06xhgdySFFn11Dlf0DGH69b1Zvuswf/5yn91xlBN6beVePk/J59kbeusNWA5Ef9dS5/TjUTHsLyjmja/S6B4ayISB4XZHUk5i0dYc/v517YIoeuHWseiRvjonEeG3E/uRGBPMzz/ewZas43ZHUk4gOfMYz/xrJ8Njg/n1+L564dbBaNFX5+Xj5cGbdw0hrL0fD8xPJvPIabsjKQeWceQ0D8xPpmuQHzPvHIKPl5YYR6N/IqpBwQE+vHvvUIwx3Dd3M8dOV9gdSTmgo6fKuffdTYgIc+9LpEOAj92RVD206KtGiQ0JZPaUBHJPlPLA/GTKKnUOfvVfZZXVPDA/mcMny5g9JYHoTjpSx1Fp0VeNNqRbMK/fNpCtB4/z2IffUVVdY3ck5QCqqmt45IOtfJd9gr9MGsjgqA52R1LnoUVfXZBx/cP4vxvj+WJ3Pr/6dJdOzubmjDE8+8lOvtxTwIvj+zC2b5jdkVQDmlT0RSRTRHaKyDYRSbbagkVkpYjstx471Nl+uoikicheEbmuqeGVPe4dGcMjV9UuvvLaFzqG3529+vleFibn8NjVcdw9ItruOKoRmuNI/ypjzEBjTIL1+hlglTEmDlhlvUZE4oFJQB9gLDBDRDyb4fuVDX52bU8mJ0byt6/TeHvNAbvjKBu8uTqdmd+kc8ewKJ4cE2d3HNVILXF6ZwIwz3o+D5hYpzlZtUYAAA2zSURBVH2BMabcGJMBpAGJLfD9qhWcGcM/rn8YLy3bw/sbs+yOpFrRe0mZvLw8lfEDuvKbCToW35k0tegb4AsR2SIiU622zsaYPADrMdRqDwey6+ybY7V9j4hMFZFkEUkuLCxsYkTVUjw9hNdvG8joS0L51ae7dJ1dN/GvLTk8tziFMb0789ptA3T1KyfT1KI/0hgzGLgemCYil59n2/r+z6j3KqAxZpYxJsEYkxASonN2ODIfLw9m3DmYEbEd+dnC7Szelmt3JNWCPv0ul59/vJ2RPTrytzsG4e2pY0GcTZP+xIwxh6zHAuATak/X5ItIGID1WGBtngNE1tk9AjjUlO9XjsHP25PZUxIYGh3Mkx9t49/b9Y/VFS3elstPF25jWExHZt8zFD9vvSTnjC666ItIgIi0PfMcuBbYBSwBplibTQEWW8+XAJNExFdEYoA4YNPFfr9yLP4+Xrx731ASooN54qNtLNHC71IWb8vlyY+2kRgTzDv3JtDGRwu+s2rKLJudgU+sCzhewAfGmBUishlYKCL3AweBHwIYY1JEZCGwG6gCphlj9LZOF+Lv48W79w7lvrmbeWLBd1RU1XDrkAi7Y6kmWrg5m18s2kFidDBz7h2Kv49OzuvMxNFvrklISDDJycl2x1AXoKSiigff28La/Uf4zcS+3D28m92R1EWan5TJ84tTGBXXiVl36xG+MxGRLXWG0v+HXoVRzc7fp3bJxTG9Q3nu013M/CZd79x1MsYY/v51Gs8vTuGa+M7MnqIF31Vo0Vctws/bk5l3DeGmAV15ZUUqL322R5dddBI1NYbfLN3DHz7fy4SBXZlx52B8vbTguwo9OadajLenB3+5fSAdA3yYvS6DY6creOXW/jrMz4FVVNXw9Mfb+XTbIX40MoZfjeuNh47Ddyla9FWL8vAQ/u+meDoF+vDHL/ZRUFzOjLsG087P2+5o6iwnSyt56L0tJB04ys+v68VPruyud9q6ID3kUi1ORHhkdBx//OEAvj1wlFtnbiDneIndsVQd2cdKuHXmBpKzjvGn2wYw7aoeWvBdlBZ91WpuHRLBvB8lkneyjJtnbGDrQV1z1xFsyTrGzTM2kF9UxvwfDeMHg3WYrSvToq9a1cgenVj08KW08fZk0lvf8vEWna/HTguTs5k8ayOBvp4s+smljOje0e5IqoVp0VetLq5zWxZPG0lCdAee+ud2frN0N5W6Clerqqyu4df/TuHpj3eQGBPMp9NG0iO0rd2xVCvQoq9s0SHAh/k/SuTeS6N5Z10Gd7z9LflFZXbHcguHT5Yxada3vLs+kx+NjGHufUMJ8tdFzN2FFn1lGy9PD14Y34e/TBrIrtwixr2xlvVpR+yO5dLW7T/CuDfWsieviL9OHsTzN8XjpUNo3Yr+aSvbTRgYzpJHRtK+jTd3vbORV1ek6umeZlZZXcPLy1O5e85GOgT4sOSRkdw0oKvdsZQNtOgrhxDXuS3/fvQybhsSyYxv0vnhm0lkHT1tdyyXkHHkNLe+mcSbq9OZNDSSJY/o+Xt3pkVfOQx/Hy9eubU/f7tjEOmFpxj757W8l5Sp0zdcpJoaw7wNmVz/lzVkFJ7i73cM5vc/6K+zZLo5/dNXDufG/l0Z0q0DT3+8g+cWp7Ai5TC/v7k/UR397Y7mNA4eLeGZRTvYkH6UK3qG8Mot/enS3s/uWMoB6NTKymEZY/hg00F+99keqo3hyTE9uf+yGL3weB6V1TXMXpvBX1btw8vDg1+O682koZF6d60bOtfUylr0lcM7dKKU5xen8OWefHqHtePX4/uQGBNsdyyH8+2Bo7ywJIXUw8VcG9+ZFyf01aN7N6ZFXzk1Ywwrdh3mN0t3c+hkGTcN6Mr06y+ha1Abu6PZLvdEKb9ftoelO/IID2rDczfGM7ZvF7tjKZudq+jrOX3lFESE6/uFcWWvUGauTufN1el8kXKY+0bG8PAV3Wnv736zdp4oqWDmN+m8uyETAR6/Oo6Hruiui52o89IjfeWUso+V8PrKfXyyLZd2ft5MvTyWe0Z0o60bTNlcXFbJ/KQs3lqdTnF5FTcPCuen1/QkooNe6Fb/pad3lEvak1fEHz7fy1epBbRv482PRsYw5dJuLjmtwPHTFcxLymTOugyKyqq4+pJQfj62F5d0aWd3NOWAtOgrl7Yj5wRvrErjyz35tPH25NYhEdw3MprYkEC7ozXZgcJTzFmfwcdbciirrOHa+M48OjqOfhHt7Y6mHJgWfeUWUg8X8c7aDBZvO0RFdQ0je3RkcmIU18Z3wcfLeYZ6lldV80VKPh9uOsiG9KP4eHowcVBX7r8sll5d9G5a1TAt+sqtFBSXsWBTNh9tzib3RClB/t7c0C+MCQO6MjQ62CHXfa2pMWzKPMaS7YdYtjOPEyWVhAe1YdLQSCYlRhHS1tfuiMqJaNFXbqm6xrB2fyGLtuaycnc+pZXVhLT1ZUzvUMb07syI7h1tnZagpKKKDWlH+XJPPqtSCygsLqeNtyfX9unMDwZHMKpHJ4f8B0o5Pocp+iIyFvgL4AnMNsa8fL7tteir5nK6vIov9+TzRUo+q/cVcqq8Cm9PYWBkECNiOzKoWwcGRAQRHNByF4GPna5ge84JtmYdJyn9KNuyT1BVYwj09eKKXiFcG9+Za+I76/w4qskcouiLiCewD7gGyAE2A5ONMbvPtY8WfdUSyquq2ZRxjPVpR0lKP8LO3JOcmdctMrgNPUPb0qNzILGdAuga1IauQW0IaetLW1+v805pYIyhuLyKwuJyDp0o5dCJUg4cOU1a/in25heTc7wUAA+BfhFBXNq9IyO7dyIxJtiprjkox+coN2clAmnGmANWqAXABOCcRV+pluDr5cmouBBGxYUAcKq8ip05J9mec4KduSdJLzjF2v1HqDhrXn9PD6GdnxdtvD3x9vLAy0OoqjFUVtVQWllNUVkV1WfNCurj6UFsSAADIoO4a3g3BkQE0S+iPYG+ejSvWl9r/18XDmTXeZ0DDDt7IxGZCkwFiIqKap1kyq0F+noxonvH/1kYvKq6hryTZRw6UUruiVKOna7gREklJ0orKKusobK6hqpqg7en4O3pga+3B0FtfAjy9yY4wIdw6zeEsPZ+OkmcchitXfTr+734e+eXjDGzgFlQe3qnpUMpVR8vTw8ig/2JDNY7XZXraO3Djxwgss7rCOBQK2dQSim31dpFfzMQJyIxIuIDTAKWtHIGpZRyW616escYUyUijwCfUztkc44xJqU1MyillDtr9eEDxphlwLLW/l6llFK6MLpSSrkVLfpKKeVGtOgrpZQb0aKvlFJuxOFn2RSRQuA0cMTuLC2oE9o/Z+XKfQPtnzPrZowJObvR4Ys+gIgk1zdxkKvQ/jkvV+4baP9ckZ7eUUopN6JFXyml3IizFP1ZdgdoYdo/5+XKfQPtn8txinP6SimlmoezHOkrpZRqBlr0lVLKjTh80ReRR0Vkr4ikiMirddqni0ia9d51dmZsChF5SkSMiHSq0+b0fRORP4hIqojsEJFPRCSozntO3z8AERlr9SFNRJ6xO09TiUikiHwtInusv2+PW+3BIrJSRPZbjx3sznqxRMRTRL4TkaXWa5fpW2M5dNEXkauoXUO3vzGmD/BHqz2e2rn4+wBjgRnWoutORUQiqV0k/mCdNpfoG7AS6GuM6Q/sA6aD6/TPyvx34HogHphs9c2ZVQE/M8b0BoYD06w+PQOsMsbEAaus187qcWBPndeu1LdGceiiDzwMvGyMKQcwxhRY7ROABcaYcmNMBpBG7aLrzuZ14Gn+d8lIl+ibMeYLY0yV9fJbaldJAxfpH7WZ04wxB4wxFcACavvmtIwxecaYrdbzYmqLYzi1/ZpnbTYPmGhPwqYRkQhgHDC7TrNL9O1COHrR7wmMEpGNIrJaRIZa7fUtsB7e6umaQETGA7nGmO1nveX0favHj4Dl1nNX6Z+r9KNeIhINDAI2Ap2NMXlQ+w8DEGpfsib5M7UHWTV12lylb43W6ouonE1EvgS61PPWL6nN14HaXzWHAgtFJJZGLrButwb69ixwbX271dPmcH2D8/fPGLPY2uaX1J42eP/MbvVs75D9a4Cr9ON7RCQQ+BfwhDGmSKS+rjoXEbkRKDDGbBGRK+3OYyfbi74xZsy53hORh4FFpvZmgk0iUkPtBElOscD6ufomIv2AGGC79RcqAtgqIok4Sd/g/H92ACIyBbgRuNr894YQp+lfA1ylH/9DRLypLfjvG2MWWc35IhJmjMkTkTCg4Nyf4LBGAuNF5AbAD2gnIv/ANfp2QRz99M6nwGgAEekJ+FA7I94SYJKI+IpIDBAHbLIt5QUyxuw0xoQaY6KNMdHUFpDBxpjDOHnfzhCRscAvgPHGmJI6b7lE/4DNQJyIxIiID7UXp5fYnKlJpPYI5B1gjzHmT3XeWgJMsZ5PARa3dramMsZMN8ZEWH/fJgFfGWPuwgX6dqFsP9JvwBxgjojsAiqAKdYRY4qILAR2U3vqYJoxptrGnM3GGOMqffsb4AustH6b+dYY85Cr9M8YUyUijwCfA57AHGNMis2xmmokcDewU0S2WW3PAi9Te2r1fmpHmv3QpnwtwZX7Vi+dhkEppdyIo5/eUUop1Yy06CullBvRoq+UUm5Ei75SSrkRLfpKKeVGtOgrpZQb0aKvlFJu5P8BuQiYyrTfhOAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-55, 55)\n",
    "y = x ** 2\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23547cfc490>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x = [1,2,3,4,5]\n",
    "y = [4,2,6,1,8]\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applications\n",
    "\n",
    "- Stock Market -> price w.r.to time\n",
    "- Temp\n",
    "- ECG -> Pulse\n",
    "- Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/AP-State-Skill-Development-Corporation/Datasets/master/Stock/MSFT_data.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-02-08</td>\n",
       "      <td>27.35</td>\n",
       "      <td>27.71</td>\n",
       "      <td>27.31</td>\n",
       "      <td>27.55</td>\n",
       "      <td>33318306</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-02-11</td>\n",
       "      <td>27.65</td>\n",
       "      <td>27.92</td>\n",
       "      <td>27.50</td>\n",
       "      <td>27.86</td>\n",
       "      <td>32247549</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-02-12</td>\n",
       "      <td>27.88</td>\n",
       "      <td>28.00</td>\n",
       "      <td>27.75</td>\n",
       "      <td>27.88</td>\n",
       "      <td>35990829</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-02-13</td>\n",
       "      <td>27.93</td>\n",
       "      <td>28.11</td>\n",
       "      <td>27.88</td>\n",
       "      <td>28.03</td>\n",
       "      <td>41715530</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-02-14</td>\n",
       "      <td>27.92</td>\n",
       "      <td>28.06</td>\n",
       "      <td>27.87</td>\n",
       "      <td>28.04</td>\n",
       "      <td>32663174</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close    volume  Name\n",
       "0  2013-02-08  27.35  27.71  27.31  27.55  33318306  MSFT\n",
       "1  2013-02-11  27.65  27.92  27.50  27.86  32247549  MSFT\n",
       "2  2013-02-12  27.88  28.00  27.75  27.88  35990829  MSFT\n",
       "3  2013-02-13  27.93  28.11  27.88  28.03  41715530  MSFT\n",
       "4  2013-02-14  27.92  28.06  27.87  28.04  32663174  MSFT"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(url)\n",
    "\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1259, 7)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25, 10)) #w, h\n",
    "\n",
    "df1 = df.head(60)\n",
    "\n",
    "\n",
    "plt.plot(df1['date'], df1['open'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25, 10)) #w, h\n",
    "\n",
    "df1 = df.head(60)\n",
    "\n",
    "\n",
    "plt.plot(df1['date'], df1['open'])\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel(\"Opening Price of MSFT Stock\")\n",
    "plt.title(\"Date vs Open Price\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25, 10)) #w, h\n",
    "\n",
    "df1 = df.head(60)\n",
    "\n",
    "\n",
    "plt.plot(df1['date'], df1['open'])\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel(\"Opening Price of MSFT Stock\")\n",
    "plt.title(\"Date vs Open Price\")\n",
    "\n",
    "plt.xticks(df1['date'], rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Y = F(X)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x = np.arange(-55, 55)\n",
    "\n",
    "\n",
    "plt.plot(x, x ** 2)\n",
    "plt.plot(x, x ** 3)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"Y = f(x)\")\n",
    "plt.title(\"Y = F(X)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args, scalex=True, scaley=True, data=None, **kwargs)\n",
      "    Plot y versus x as lines and/or markers.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        plot([x], y, [fmt], *, data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "    \n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "    \n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\n",
      "    \n",
      "    >>> plot(x, y)        # plot x and y using default line style and color\n",
      "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
      "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
      "    \n",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "    \n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "    ...      linewidth=2, markersize=12)\n",
      "    \n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "    \n",
      "    \n",
      "    **Plotting labelled data**\n",
      "    \n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "    \n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "    \n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFame` or a structured numpy array.\n",
      "    \n",
      "    \n",
      "    **Plotting multiple sets of data**\n",
      "    \n",
      "    There are various ways to plot multiple sets of data.\n",
      "    \n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "    \n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "    \n",
      "    - Alternatively, if your data is already a 2d array, you can pass it\n",
      "      directly to *x*, *y*. A separate data set will be drawn for every\n",
      "      column.\n",
      "    \n",
      "      Example: an array ``a`` where the first column represents the *x*\n",
      "      values and the other columns are the *y* columns::\n",
      "    \n",
      "      >>> plot(a[0], a[1:])\n",
      "    \n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "    \n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "    \n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using\n",
      "    :rc:`axes.prop_cycle`.\n",
      "    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional and default to `range(len(y))`.\n",
      "    \n",
      "        Commonly, these parameters are 1D arrays.\n",
      "    \n",
      "        They can also be scalars, or two-dimensional (in that case, the\n",
      "        columns represent separate data sets).\n",
      "    \n",
      "        These arguments cannot be passed as keywords.\n",
      "    \n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "    \n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "    \n",
      "        This argument cannot be passed as keyword.\n",
      "    \n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "    \n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. `plot('n', 'o', data=obj)`\n",
      "            could be `plt(x, y)` or `plt(y, fmt)`. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            `plot('n', 'o', '', data=obj)`.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, optional, default: True\n",
      "        These parameters determined if the view limits are adapted to\n",
      "        the data limits. The values are passed on to `autoscale_view`.\n",
      "    \n",
      "    **kwargs : `.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\n",
      "        Example::\n",
      "    \n",
      "        >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n",
      "        >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n",
      "    \n",
      "        If you make multiple lines with one plot command, the kwargs\n",
      "        apply to all those lines.\n",
      "    \n",
      "        Here is a list of available `.Line2D` properties:\n",
      "    \n",
      "        Properties:\n",
      "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "        alpha: float or None\n",
      "        animated: bool\n",
      "        antialiased or aa: bool\n",
      "        clip_box: `.Bbox`\n",
      "        clip_on: bool\n",
      "        clip_path: Patch or (Path, Transform) or None\n",
      "        color or c: color\n",
      "        contains: callable\n",
      "        dash_capstyle: {'butt', 'round', 'projecting'}\n",
      "        dash_joinstyle: {'miter', 'round', 'bevel'}\n",
      "        dashes: sequence of floats (on/off ink in points) or (None, None)\n",
      "        data: (2, N) array or two 1D arrays\n",
      "        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
      "        figure: `.Figure`\n",
      "        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
      "        gid: str\n",
      "        in_layout: bool\n",
      "        label: object\n",
      "        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "        linewidth or lw: float\n",
      "        marker: marker style\n",
      "        markeredgecolor or mec: color\n",
      "        markeredgewidth or mew: float\n",
      "        markerfacecolor or mfc: color\n",
      "        markerfacecoloralt or mfcalt: color\n",
      "        markersize or ms: float\n",
      "        markevery: None or int or (int, int) or slice or List[int] or float or (float, float)\n",
      "        path_effects: `.AbstractPathEffect`\n",
      "        picker: float or callable[[Artist, Event], Tuple[bool, dict]]\n",
      "        pickradius: float\n",
      "        rasterized: bool or None\n",
      "        sketch_params: (scale: float, length: float, randomness: float)\n",
      "        snap: bool or None\n",
      "        solid_capstyle: {'butt', 'round', 'projecting'}\n",
      "        solid_joinstyle: {'miter', 'round', 'bevel'}\n",
      "        transform: `matplotlib.transforms.Transform`\n",
      "        url: str\n",
      "        visible: bool\n",
      "        xdata: 1D array\n",
      "        ydata: 1D array\n",
      "        zorder: float\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    lines\n",
      "        A list of `.Line2D` objects representing the plotted data.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of varying size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    **Format Strings**\n",
      "    \n",
      "    A format string consists of a part for color, marker and line::\n",
      "    \n",
      "        fmt = '[marker][line][color]'\n",
      "    \n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "    \n",
      "    Other combinations such as ``[color][marker][line]`` are also\n",
      "    supported, but note that their parsing may be ambiguous.\n",
      "    \n",
      "    **Markers**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'.'``          point marker\n",
      "    ``','``          pixel marker\n",
      "    ``'o'``          circle marker\n",
      "    ``'v'``          triangle_down marker\n",
      "    ``'^'``          triangle_up marker\n",
      "    ``'<'``          triangle_left marker\n",
      "    ``'>'``          triangle_right marker\n",
      "    ``'1'``          tri_down marker\n",
      "    ``'2'``          tri_up marker\n",
      "    ``'3'``          tri_left marker\n",
      "    ``'4'``          tri_right marker\n",
      "    ``'s'``          square marker\n",
      "    ``'p'``          pentagon marker\n",
      "    ``'*'``          star marker\n",
      "    ``'h'``          hexagon1 marker\n",
      "    ``'H'``          hexagon2 marker\n",
      "    ``'+'``          plus marker\n",
      "    ``'x'``          x marker\n",
      "    ``'D'``          diamond marker\n",
      "    ``'d'``          thin_diamond marker\n",
      "    ``'|'``          vline marker\n",
      "    ``'_'``          hline marker\n",
      "    =============    ===============================\n",
      "    \n",
      "    **Line Styles**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "    \n",
      "    Example format strings::\n",
      "    \n",
      "        'b'    # blue markers with default shape\n",
      "        'or'   # red circles\n",
      "        '-g'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        '^k:'  # black triangle_up markers connected by a dotted line\n",
      "    \n",
      "    **Colors**\n",
      "    \n",
      "    The supported color abbreviations are the single letter codes\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        color\n",
      "    =============    ===============================\n",
      "    ``'b'``          blue\n",
      "    ``'g'``          green\n",
      "    ``'r'``          red\n",
      "    ``'c'``          cyan\n",
      "    ``'m'``          magenta\n",
      "    ``'y'``          yellow\n",
      "    ``'k'``          black\n",
      "    ``'w'``          white\n",
      "    =============    ===============================\n",
      "    \n",
      "    and the ``'CN'`` colors that index into the default property cycle.\n",
      "    \n",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Y = F(X)')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x = np.arange(-5, 10)\n",
    "\n",
    "\n",
    "plt.plot(x, x ** 2, c = 'black', marker = '>')\n",
    "plt.plot(x, x ** 3, c = '#00ff00', marker = '*')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"Y = f(x)\")\n",
    "plt.title(\"Y = F(X)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Y = F(X)')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x = np.arange(-5, 10)\n",
    "\n",
    "\n",
    "plt.plot(x, x ** 2, c = 'black', marker = '>', linestyle = '-.')\n",
    "plt.plot(x, x ** 3, c = '#00ff00', marker = '*')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"Y = f(x)\")\n",
    "plt.title(\"Y = F(X)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "x = np.arange(-5, 10)\n",
    "\n",
    "\n",
    "plt.plot(x, x ** 2, c = 'black', marker = '>', linestyle = '-.')\n",
    "plt.plot(x, x ** 3, c = '#00ff00', marker = '*')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"Y = f(x)\")\n",
    "plt.title(\"Y = F(X)\")\n",
    "plt.legend(['Y = X ** 2', 'Y = X ** 3'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "x = np.arange(-5, 10)\n",
    "\n",
    "\n",
    "plt.plot(x, x ** 2, c = 'black', marker = '>', linestyle = '-.')\n",
    "plt.plot(x, x ** 3, c = '#00ff00', marker = '*')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"Y = f(x)\")\n",
    "plt.title(\"Y = F(X)\")\n",
    "plt.legend(['Y = X ** 2', 'Y = X ** 3'], loc = 'upper center')\n",
    "plt.text(1,1, \"(1,1)\")\n",
    "plt.text(5, 25, \"(5, 25)\")\n",
    "plt.grid()\n",
    "#plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot\n",
    "\n",
    "identifying the relationship between two features\n",
    "\n",
    "- +ve\n",
    "- -ve \n",
    "- neutral"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1,2,3,4,5]\n",
    "y = np.array([2,4,6,8,10])\n",
    "y2 = -y\n",
    "y3 = [5,5,5,5,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": [
    "plt.scatter(x, y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": [
    "plt.scatter(x, y3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function scatter in module matplotlib.pyplot:\n",
      "\n",
      "scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=<deprecated parameter>, edgecolors=None, *, plotnonfinite=False, data=None, **kwargs)\n",
      "    A scatter plot of *y* vs. *x* with varying marker size and/or color.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : scalar or array-like, shape (n, )\n",
      "        The data positions.\n",
      "    \n",
      "    s : scalar or array-like, shape (n, ), optional\n",
      "        The marker size in points**2.\n",
      "        Default is ``rcParams['lines.markersize'] ** 2``.\n",
      "    \n",
      "    c : array-like or list of colors or color, optional\n",
      "        The marker colors. Possible values:\n",
      "    \n",
      "        - A scalar or sequence of n numbers to be mapped to colors using\n",
      "          *cmap* and *norm*.\n",
      "        - A 2-D array in which the rows are RGB or RGBA.\n",
      "        - A sequence of colors of length n.\n",
      "        - A single color format string.\n",
      "    \n",
      "        Note that *c* should not be a single numeric RGB or RGBA sequence\n",
      "        because that is indistinguishable from an array of values to be\n",
      "        colormapped. If you want to specify the same RGB or RGBA value for\n",
      "        all points, use a 2-D array with a single row.  Otherwise, value-\n",
      "        matching will have precedence in case of a size matching with *x*\n",
      "        and *y*.\n",
      "    \n",
      "        If you wish to specify a single color for all points\n",
      "        prefer the *color* keyword argument.\n",
      "    \n",
      "        Defaults to `None`. In that case the marker color is determined\n",
      "        by the value of *color*, *facecolor* or *facecolors*. In case\n",
      "        those are not specified or `None`, the marker color is determined\n",
      "        by the next color of the ``Axes``' current \"shape and fill\" color\n",
      "        cycle. This cycle defaults to :rc:`axes.prop_cycle`.\n",
      "    \n",
      "    marker : `~matplotlib.markers.MarkerStyle`, optional\n",
      "        The marker style. *marker* can be either an instance of the class\n",
      "        or the text shorthand for a particular marker.\n",
      "        Defaults to ``None``, in which case it takes the value of\n",
      "        :rc:`scatter.marker` = 'o'.\n",
      "        See `~matplotlib.markers` for more information about marker styles.\n",
      "    \n",
      "    cmap : `~matplotlib.colors.Colormap`, optional, default: None\n",
      "        A `.Colormap` instance or registered colormap name. *cmap* is only\n",
      "        used if *c* is an array of floats. If ``None``, defaults to rc\n",
      "        ``image.cmap``.\n",
      "    \n",
      "    norm : `~matplotlib.colors.Normalize`, optional, default: None\n",
      "        A `.Normalize` instance is used to scale luminance data to 0, 1.\n",
      "        *norm* is only used if *c* is an array of floats. If *None*, use\n",
      "        the default `.colors.Normalize`.\n",
      "    \n",
      "    vmin, vmax : scalar, optional, default: None\n",
      "        *vmin* and *vmax* are used in conjunction with *norm* to normalize\n",
      "        luminance data. If None, the respective min and max of the color\n",
      "        array is used. *vmin* and *vmax* are ignored if you pass a *norm*\n",
      "        instance.\n",
      "    \n",
      "    alpha : scalar, optional, default: None\n",
      "        The alpha blending value, between 0 (transparent) and 1 (opaque).\n",
      "    \n",
      "    linewidths : scalar or array-like, optional, default: None\n",
      "        The linewidth of the marker edges. Note: The default *edgecolors*\n",
      "        is 'face'. You may want to change this as well.\n",
      "        If *None*, defaults to :rc:`lines.linewidth`.\n",
      "    \n",
      "    edgecolors : {'face', 'none', *None*} or color or sequence of color, optional.\n",
      "        The edge color of the marker. Possible values:\n",
      "    \n",
      "        - 'face': The edge color will always be the same as the face color.\n",
      "        - 'none': No patch boundary will be drawn.\n",
      "        - A Matplotlib color or sequence of color.\n",
      "    \n",
      "        Defaults to ``None``, in which case it takes the value of\n",
      "        :rc:`scatter.edgecolors` = 'face'.\n",
      "    \n",
      "        For non-filled markers, the *edgecolors* kwarg is ignored and\n",
      "        forced to 'face' internally.\n",
      "    \n",
      "    plotnonfinite : boolean, optional, default: False\n",
      "        Set to plot points with nonfinite *c*, in conjunction with\n",
      "        `~matplotlib.colors.Colormap.set_bad`.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    paths : `~matplotlib.collections.PathCollection`\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    **kwargs : `~matplotlib.collections.Collection` properties\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    plot : To plot scatter plots when markers are identical in size and\n",
      "        color.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    * The `.plot` function will be faster for scatterplots where markers\n",
      "      don't vary in size or color.\n",
      "    \n",
      "    * Any or all of *x*, *y*, *s*, and *c* may be masked arrays, in which\n",
      "      case all masks will be combined and only unmasked points will be\n",
      "      plotted.\n",
      "    \n",
      "    * Fundamentally, scatter works with 1-D arrays; *x*, *y*, *s*, and *c*\n",
      "      may be input as N-D arrays, but within scatter they will be\n",
      "      flattened. The exception is *c*, which will be flattened only if its\n",
      "      size matches the size of *x* and *y*.\n",
      "    \n",
      "    .. note::\n",
      "        In addition to the above described arguments, this function can take a\n",
      "        **data** keyword argument. If such a **data** argument is given, the\n",
      "        following arguments are replaced by **data[<arg>]**:\n",
      "    \n",
      "        * All arguments with the following names: 'c', 'color', 'edgecolors', 'facecolor', 'facecolors', 'linewidths', 's', 'x', 'y'.\n",
      "    \n",
      "        Objects passed as **data** must support item access (``data[<arg>]``) and\n",
      "        membership test (``<arg> in data``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.scatter(x, y, marker = '>', c = ['b', 'r', 'g', 'black', '#ffaabb'], alpha = 1.0, s = y**3, edgecolors = 'black')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"Y = f(x)\")\n",
    "plt.title(\"Y = F(X)\")\n",
    "plt.legend(['Y = X ** 2'], loc = 'upper center')\n",
    "plt.text(1,1, \"(1,1)\")\n",
    "plt.text(5, 2, \"(5, 25)\")\n",
    "plt.grid(color='#aa0000', linestyle='-', linewidth=2)\n",
    "#plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function grid in module matplotlib.pyplot:\n",
      "\n",
      "grid(b=None, which='major', axis='both', **kwargs)\n",
      "    Configure the grid lines.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    b : bool or None, optional\n",
      "        Whether to show the grid lines. If any *kwargs* are supplied,\n",
      "        it is assumed you want the grid on and *b* will be set to True.\n",
      "    \n",
      "        If *b* is *None* and there are no *kwargs*, this toggles the\n",
      "        visibility of the lines.\n",
      "    \n",
      "    which : {'major', 'minor', 'both'}, optional\n",
      "        The grid lines to apply the changes on.\n",
      "    \n",
      "    axis : {'both', 'x', 'y'}, optional\n",
      "        The axis to apply the changes on.\n",
      "    \n",
      "    **kwargs : `.Line2D` properties\n",
      "        Define the line properties of the grid, e.g.::\n",
      "    \n",
      "            grid(color='r', linestyle='-', linewidth=2)\n",
      "    \n",
      "        Valid keyword arguments are:\n",
      "    \n",
      "        Properties:\n",
      "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "        alpha: float or None\n",
      "        animated: bool\n",
      "        antialiased or aa: bool\n",
      "        clip_box: `.Bbox`\n",
      "        clip_on: bool\n",
      "        clip_path: Patch or (Path, Transform) or None\n",
      "        color or c: color\n",
      "        contains: callable\n",
      "        dash_capstyle: {'butt', 'round', 'projecting'}\n",
      "        dash_joinstyle: {'miter', 'round', 'bevel'}\n",
      "        dashes: sequence of floats (on/off ink in points) or (None, None)\n",
      "        data: (2, N) array or two 1D arrays\n",
      "        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
      "        figure: `.Figure`\n",
      "        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
      "        gid: str\n",
      "        in_layout: bool\n",
      "        label: object\n",
      "        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "        linewidth or lw: float\n",
      "        marker: marker style\n",
      "        markeredgecolor or mec: color\n",
      "        markeredgewidth or mew: float\n",
      "        markerfacecolor or mfc: color\n",
      "        markerfacecoloralt or mfcalt: color\n",
      "        markersize or ms: float\n",
      "        markevery: None or int or (int, int) or slice or List[int] or float or (float, float)\n",
      "        path_effects: `.AbstractPathEffect`\n",
      "        picker: float or callable[[Artist, Event], Tuple[bool, dict]]\n",
      "        pickradius: float\n",
      "        rasterized: bool or None\n",
      "        sketch_params: (scale: float, length: float, randomness: float)\n",
      "        snap: bool or None\n",
      "        solid_capstyle: {'butt', 'round', 'projecting'}\n",
      "        solid_joinstyle: {'miter', 'round', 'bevel'}\n",
      "        transform: `matplotlib.transforms.Transform`\n",
      "        url: str\n",
      "        visible: bool\n",
      "        xdata: 1D array\n",
      "        ydata: 1D array\n",
      "        zorder: float\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    The axis is drawn as a unit, so the effective zorder for drawing the\n",
      "    grid is determined by the zorder of each axis, not by the zorder of the\n",
      "    `.Line2D` objects comprising the grid.  Therefore, to set grid zorder,\n",
      "    use `.set_axisbelow` or, for more control, call the\n",
      "    `~matplotlib.axis.Axis.set_zorder` method of each axis.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Graph\n",
    "\n",
    "\n",
    "count of the categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 30, '56 Boys')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x = ['m', 'f']\n",
    "y = [55, 75]\n",
    "\n",
    "plt.bar(x, y, color = ['green', 'grey'])\n",
    "plt.text(0, 30, \"56 Boys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function text in module matplotlib.pyplot:\n",
      "\n",
      "text(x, y, s, fontdict=None, withdash=<deprecated parameter>, **kwargs)\n",
      "    Add text to the axes.\n",
      "    \n",
      "    Add the text *s* to the axes at location *x*, *y* in data coordinates.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : scalars\n",
      "        The position to place the text. By default, this is in data\n",
      "        coordinates. The coordinate system can be changed using the\n",
      "        *transform* parameter.\n",
      "    \n",
      "    s : str\n",
      "        The text.\n",
      "    \n",
      "    fontdict : dictionary, optional, default: None\n",
      "        A dictionary to override the default text properties. If fontdict\n",
      "        is None, the defaults are determined by your rc parameters.\n",
      "    \n",
      "    withdash : boolean, optional, default: False\n",
      "        Creates a `~matplotlib.text.TextWithDash` instance instead of a\n",
      "        `~matplotlib.text.Text` instance.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    text : `.Text`\n",
      "        The created `.Text` instance.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    **kwargs : `~matplotlib.text.Text` properties.\n",
      "        Other miscellaneous text parameters.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    Individual keyword arguments can be used to override any given\n",
      "    parameter::\n",
      "    \n",
      "        >>> text(x, y, s, fontsize=12)\n",
      "    \n",
      "    The default transform specifies that text is in data coords,\n",
      "    alternatively, you can specify text in axis coords ((0, 0) is\n",
      "    lower-left and (1, 1) is upper-right).  The example below places\n",
      "    text in the center of the axes::\n",
      "    \n",
      "        >>> text(0.5, 0.5, 'matplotlib', horizontalalignment='center',\n",
      "        ...      verticalalignment='center', transform=ax.transAxes)\n",
      "    \n",
      "    You can put a rectangular box around the text instance (e.g., to\n",
      "    set a background color) by using the keyword *bbox*.  *bbox* is\n",
      "    a dictionary of `~matplotlib.patches.Rectangle`\n",
      "    properties.  For example::\n",
      "    \n",
      "        >>> text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
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