Data Visualization(2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  4  9 16 25 36 49 64 81]\n"
     ]
    }
   ],
   "source": [
    "x=np.arange(10)\n",
    "x=x**2\n",
    "print(x)"
   ]
  },
  {
   "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"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Working hours')"
      ]
     },
     "execution_count": 38,
     "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": [
    "plt.plot(x)\n",
    "#plt.show()\n",
    "a=x*x\n",
    "plt.show()\n",
    "plt.scatter(x,a,marker=\"*\",label=\"DD\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Days\")\n",
    "plt.title(\"Routine\")\n",
    "plt.ylabel(\"Working hours\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## num=np.random.randint(0,5,5)\n",
    "n=np.arange(5)\n",
    "print(num)\n",
    "print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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.bar(n,num,0.5,label=\"Caffiene Daily\",color=\"Silver\")\n",
    "plt.bar(n+0.5,num*2,0.5,label=\"Fruits\",color=\"Orange\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label=['dd','ee','ff','gg']\n",
    "values=[20,30,40,50]\n",
    "plt.pie(values,labels=label,radius=1,shadow=True,explode=(0,1,0,0),colors=[\"green\",\"black\",\"pink\",\"blue\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#data x as acidity y density of milk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readData(filename)\n",
    "    df=pd.read_csv(filename)         #it reads files\n",
    "    return df.values                #it converts into an array\n",
    "x=readData()\n",
    "x=x.reshape(99,)\n",
    "y=y.reshape(99,)\n",
    "print(x)\n",
    "print(y)\n",
    "plt.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hypothesis(theta,x):\n",
    "        return theta[0]+theta[1]*x\n",
    "\n",
    "def error(X,Y,theta):\n",
    "    total_error=0\n",
    "    m=X.shape[0]\n",
    "    \n",
    "    for i in range(m):\n",
    "        total_error+=(Y[i]- hypothesis(theta,X[i]))**2\n",
    "        \n",
    "    return 0.5*total_error\n",
    "def gradientDecent(X,Y,learning_rate,maxItr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}