matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "x=np.linspace(0,10,10)# starting point,ending point and no.of points in between"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y=np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc7183ef2e8>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(x,y) #to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1dXbxxu46Fuek2nrmPjQ5NY6xSbEUl4dYYhCRMOA3wFJgKnCniEw9Z7evAidU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc71954cb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show() #to show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc7182da2b0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc718355908>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.xlabel(\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc718385a90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.ylabel(\"f(t)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc71832e470>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.title(\"my cool chart\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TIbtqVjaHG1rYVtXodihmkNaU1zAvJ5nMpDi3Qwl6CyelkBQfHRKTHC2hDEN7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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc71839efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show() #to show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gcaq8YE55b9TRaVi3o5Krxg8nNNDP6jiqD3MmxBLogcvoOl0YHNcMHgDeBYqA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\nD1oYlFJK9aCFQSmlVA//H43xUken+Tw7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc70915f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(1,10,100)# starting point,ending point and no.of points in between\n",
    "y=np.sin(x)\n",
    "plt.plot(x,y) #to plot\n",
    "plt.show() #to show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc7091fd860>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=np.exp(x)\n",
    "\n",
    "plt.scatter(x,m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K4SCpdubSjQROWV1IhoOk2pht0LnMrqSFZThIqoW5Xi0EjcFnu5IWluEgqRZmG3QGu5H6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\nktTGcJAktfl/G5ADHp2Et/YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc71810c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.linspace(0,100,100)\n",
    "b=2*a+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc70914e4a8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc709151a20>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.linspace(1,100,100)# starting point,ending point and no.of points in between\n",
    "\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc708e6bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 14.,   7.,   6.,   8.,   7.,   6.,   6.,   9.,   9.,  28.]),\n",
       " array([ -9.99889239e-01,  -7.99932424e-01,  -5.99975608e-01,\n",
       "         -4.00018793e-01,  -2.00061978e-01,  -1.05162420e-04,\n",
       "          1.99851653e-01,   3.99808468e-01,   5.99765284e-01,\n",
       "          7.99722099e-01,   9.99678914e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.hist(y)  #histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8Pvmd5Ad4Lb1/tR4GHgA+37U/A7i+r9+FwDfo/Qv73r72s+j9hTsEfAo4eUZ1PR24ATjY\nPZ7Sta8Af9nXbxn4FvCEo7a/EbiDXjD9LfCURdUF/EJ37K91j5ceD+MFvAH4AXBb38/2eYzXoPcL\nvSmc13TLT+7+/Ie68Tirb9v3dtvdBbxqxu/3YXV9oft7sD4+e4e9pguq64+AO7vj3wT8bN+2v96N\n4yHg1xZZV/f8fcBlR2037/G6mt5VWj+gl1+XAm8B3tKtD73/DOju7vgrfdvOdLz8pqgkNeJEnHKR\nJA1goEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1Ij/A7C6TgRwS5DZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7091a59e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  92.,  103.,  104.,   98.,  115.,  101.,   84.,   93.,  104.,  106.]),\n",
       " array([ 0.00184812,  0.10165436,  0.2014606 ,  0.30126684,  0.40107309,\n",
       "         0.50087933,  0.60068557,  0.70049181,  0.80029805,  0.9001043 ,\n",
       "         0.99991054]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R= np.random.random(1000)\n",
    "plt.hist(R)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pnAfWtcrJGmXMR4DtAElexCDol9e1yvW1H3hD9+2bC4BHq+rhSb35TEzd1HEeqZDk14DF\nqtoPXMXg17vDDM7kd25cxf2NOOb3Ac8C/ri77nykqi7esKJ7GnHMTRlxzJ8GfjzJPcC3gF+qqn/b\nuKr7GXHM7wR+P8nbGUxhvHGWT9ySXMtg6m1zd93hSuCpAFX1EQbXIS4CDgOPAW+a6PFn+L+dJGkE\nszJ1I0kak0EvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1Lj/he67KlQP9GqzgAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc718028128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 45.,  47.,  47.,  56.,  54.,  50.,  46.,  52.,  60.,  55.,  53.,\n",
       "         48.,  46.,  38.,  43.,  50.,  50.,  54.,  51.,  55.]),\n",
       " array([ 0.00184812,  0.05175124,  0.10165436,  0.15155748,  0.2014606 ,\n",
       "         0.25136372,  0.30126684,  0.35116997,  0.40107309,  0.45097621,\n",
       "         0.50087933,  0.55078245,  0.60068557,  0.65058869,  0.70049181,\n",
       "         0.75039493,  0.80029805,  0.85020117,  0.9001043 ,  0.95000742,\n",
       "         0.99991054]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.hist(R,bins=20)#no of bins increased from 10 to 20\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o86eAg8D3GIzGtwHvBd57xPt8c/dv8si4P9teKSpJjTiWplwkSatgoEtSIwx0SWqEgS5J\njTDQJakRBrokNcJAl6RGGOiS1Ij/B8PU2CRRqZk7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc708a9da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}