Numpy and matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.   2.   3.5  4.8]\n"
     ]
    }
   ],
   "source": [
    "x=[1,2,3.5,4.8]\n",
    "\n",
    "a=np.array(x)\n",
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.   4.   7.   9.6]\n"
     ]
    }
   ],
   "source": [
    "print a*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "x=np.arange(1,10)\n",
    "print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  1.  2.  3.]\n",
      "[ 0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  0.9  1.   1.1]\n"
     ]
    }
   ],
   "source": [
    "x=np.arange(3.2)\n",
    "y=np.arange(0.1,1.2,0.1)\n",
    "print x\n",
    "print y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n",
      "1\n",
      "[[1 1]\n",
      " [2 2]]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# Numpy arrays\n",
    "\n",
    "a=np.array([1,2,3,4])\n",
    "b=np.array([[1,1],[2,2]])\n",
    "\n",
    "print a\n",
    "print a.ndim\n",
    "print b\n",
    "print b.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2L, 2L)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4L,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1]\n",
      "1\n",
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "print b[0]\n",
    "print b[0][1]\n",
    "print a[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.54761867 -0.12639415  0.95461255  1.45867417]\n",
      " [-0.4547461   0.52838102  0.04832289  1.19995036]\n",
      " [-0.75746837  0.36974273  0.14413901  0.1299774 ]\n",
      " [ 1.16836748 -0.37684581 -0.39101197 -0.85448566]\n",
      " [ 0.86537775  0.07510988 -0.69369005  1.18043086]\n",
      " [-0.59595599 -2.45454665 -0.42772218 -0.51561273]]\n"
     ]
    }
   ],
   "source": [
    "#generating random number\n",
    "a=np.random.randn(6,4)\n",
    "print a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wXEiS1FxtHzgMF5Ikla+tA8dHPgJPPpl/N1xIklSetg4ckybBDjsYLiRJKltb\nB46jjsoNtiRJUrlsPyVJkgpn4JAkSYUzcEiSpMIZOCRJUuEMHJIkqXAGDkmSVDgDhyRJKpyBQ5Ik\nFc7AIUmSCmfgkCRJhTNwSJKkwhk4JElS4QwckiSpcAYOSZJUOAOHJEkqnIFDkiQVzsAhSZIKZ+CQ\nJEmFM3BIkqTCGTgkSVLhDBySJKlwlQocEXFERDwUEa9ExG0R8aGya2o3vb29ZZdQOY5ZfRy30XPM\n6uO4tYbKBI6I2Bs4GfgqsD5wF/CriHhHqYW1Gf/FHD3HrD6O2+g5ZvVx3FpDZQIHMAX4fkrp3JTS\nfcAngdnAgeWWJUmShlOJwBERSwPdwNULtqWUEnAVsFFZdUmSpJGpROAA3gEsCTy10PangJWaX44k\nSRqNpcouoCBjAO69996y66icWbNm0d/fX3YZleKY1cdxGz3HrD6O2+gM+Ns5ppHvG/nMRGurnVKZ\nDeyRUpoxYPvZwLiU0m4L7b8vcEFTi5Qkqb18NKV0YaPerBIzHCml1yOiD9gKmAEQEVF7/N1BXvIr\n4KPAw8CcJpUpSVI7GAOsRv5b2jCVmOEAiIi9gLPJd6f8hnzXyn8Ca6WUni6xNEmSNIxKzHAApJQu\nrvXc+AawInAnsK1hQ5Kk1leZGQ5JklRdVbktVpIkVZiBQ5IkFa6ygWO0C7lFxOYR0RcRcyLigYjY\nv1m1tpLRjFtEbBYR8xf6mRcRKzSz5jJFxMSImBERf6l9/51H8JqOPtZGO2YeZxARx0TEbyLixYh4\nKiIuj4g1R/C6Tj/WRj1unX68RcQnI+KuiJhV+7klIrYb5jUNOc4qGThGu5BbRKwG/JzcGv2DwHeA\nMyJim2bU2yrqXAAvAe8ld3RdCXhXSumvRdfaQsaSL1A+nDwWi+SxBoxyzGo6/TibCJwKfBjYGlga\nmBkRbx3qBR5rQB3jVtPJx9ufgS8AXeQlQ64BpkfE2oPt3NDjLKVUuR/gNuA7Ax4H8Bhw9BD7nwTc\nvdC2XuCKsr9Li4/bZsA8YLmya2+FH2A+sPMw+3isjX7MPM7ePCbvqI3dJovYx2OtvnHzeHvzmDwL\nfHyI5xp2nFVuhqPOhdz+vfb8QL9axP5tZzEWwAvgzoh4PCJmRsTGxVZaeR1/rNXJ4+yNxpP/L/y5\nRezjsfZmIxk38HgDICKWiIh9gGWBW4fYrWHHWeUCB/Ut5LbSEPsvFxHLNLa8llXPuD0BHArsAexO\nnoq7LiLymo62AAAFMUlEQVTWK6rINuCxNnoeZwPUuiifAtyUUrpnEbt6rA0winHr+OMtIj4QES8B\nrwLTgN1SSvcNsXvDjrPKNP5S86WUHgAeGLDptoiYQO7y2lEXp6k4HmdvMg1YB/iPsgupmBGNm8cb\nAPeRr8cYR+7YfW5EbLqI0NEQVZzheIZ8/m3FhbavCDw5xGueHGL/F1NKrza2vJZVz7gN5jfAexpV\nVBvyWGuMjjzOIuI0YAdg85TSE8Ps7rFWM8pxG0xHHW8ppbkppQdTSr9LKR1LvoHgqCF2b9hxVrnA\nkVJ6HViwkBvwhoXcbhniZbcO3L9mEkOfs2o7dY7bYNYjT0lqcB1/rDVIxx1ntT+auwBbpJQeHcFL\nPNaoa9wG03HH20KWAIY6PdK446zsq2PrvKJ2L/Jy9ZOBtYDvk6+yfWft+ROAcwbsvxrwEvlq2/eR\nb9d7Ddi67O/S4uN2FLAzMAF4P/n86Ovk/4so/fs0aczGkqce1yNf/f7p2uN/9Vhr2Jh5nOXTAc+T\nb/NcccDPmAH7HO+x1pBx6+jjrTYeE4FVgQ/U/n2cC2xZe76w/6aV/uUXY9AOJy8//wo5aW0w4Lmz\ngGsW2n9T8v/hvwL8EfhY2d+h1ccN+HxtrF4Gnibf4bJp2d+hyeO1We2P5ryFfs70WGvMmHmc/f32\n4YXHax4wecA+HmsNGLdOP96AM4AHa8fMk8DMBWGj6OPMxdskSVLhKncNhyRJqh4DhyRJKpyBQ5Ik\nFc7AIUmSCmfgkCRJhTNwSJKkwhk4JElS4QwckiSpcAYOSZJUOAOHpJYXEfMjYuey65BUPwOHpGFF\nxBIRcXNE/GSh7ctFxKMRcVzBJawE/LLgz5BUINdSkTQiEfFe4HfAwSml3tq2c4F/Az6UUppbZn2S\nWpszHJJGJKX0R+AY4LSIWDEidgH2Iq8cOWTYiIj9IuK3EfFiRDwRERdExDsHPP+ViPhLRPzzgG2/\niIirBzz++ymViFg6Ik6LiMcj4pWIeCgivlDEd5bUOAYOSSOWUjoVuBM4H/g+8PWU0u+HedlSwJeB\ndYFdgFXJS2Av8E3gIfKy2UTEEcC/A5OHeL+jgB2B/wTWBD4KPDz6byOpmTylImlUIuJ9wL3A3UBX\nSmn+KF+/AXA78E8ppdm1bauTT9f8L/Ap4MCU0o8HvGY+sGtKaUZEfAdYJ6W0TUO+kKSmcIZD0mgd\nBLwMrA6sPNzOEdEdETMi4pGIeBG4rvbUKgv2SSk9BHwe+AIwfWDYGMTZwPoRcX9EfCciDB5SBRg4\nJI1YRGzMP05p/AY4c5j9lwWuBF4A9gU2AHarPf2WhXbfDJgLrBYRQ/63KaX0O2A18mmaMcDFEXHx\naL+LpOYycEgakYh4K/nai2kppeuBTwAfiohDF/GytYDlgWNSSjenlB4AVhzkvfcGdgU2J1/j8V+L\nqiWl9LeU0iUppUOBvYE9ImJ8HV9LUpMYOCSN1Im1fx4DkFJ6hHwa5H8iYpUhXvMo8BrwqYhYvXan\nyZcH7hARKwPTgKNTSrcAHwe+FBEfHuwNI2JKROwTEe+LiDXJd8o8mVJ6YTG/n6QCGTgkDSsiNgUO\nAw5IKc1ZsD2l9APgZuBHg70upfQMcAD5jpI/AEcDn11ot7OA21JK02qvmUkOIOfVTskADLy6/aXa\n+/yWfPHpKsAOi/H1JDWBd6lIkqTCOcMhSZIKZ+CQJEmFM3BIkqTCGTgkSVLhDBySJKlwBg5JklQ4\nA4ckSSqcgUOSJBXOwCFJkgpn4JAkSYUzcEiSpML9f7UXK2X5e1VJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7c0fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=[1.1,2.3,4.5,10.11]\n",
    "plt.plot(k)\n",
    "plt.ylabel(\"Y axis\")\n",
    "plt.xlabel(\"X axis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x71792b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n",
    "plt.axis([0, 5, 0, 16])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7a47da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(a,a**2,'ro',a,a**3,'b^')\n",
    "plt.axis([0,5,0,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.54761867, -0.12639415,  0.95461255,  1.45867417],\n",
       "       [-0.4547461 ,  0.52838102,  0.04832289,  1.19995036],\n",
       "       [-0.75746837,  0.36974273,  0.14413901,  0.1299774 ],\n",
       "       [ 1.16836748, -0.37684581, -0.39101197, -0.85448566],\n",
       "       [ 0.86537775,  0.07510988, -0.69369005,  1.18043086],\n",
       "       [-0.59595599, -2.45454665, -0.42772218, -0.51561273]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}