{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib tutorial 05   \n",
    "\n",
    "** Histogram Plots **   \n",
    "        \n",
    "    It's a graphical representation of a frequency distribution of some numerical data. Rectangles with equal sizes in the horizontal directions have heights with the corresponding frequencies.\n",
    "\n",
    "    If we construct a histogram, we start with distribute the range of possible x values into usually equal sized and adjacent intervals or bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# generating some data points\n",
    "X = np.random.random_integers(20, 50, 1000)\n",
    "Y = np.random.random_integers(20, 50, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Plotting Histogram **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x9266588>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x91ad3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(X)\n",
    "plt.xlabel(\"Value of X\")\n",
    "plt.ylabel(\"Freq\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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My07vZvrO568/dewoqfJirwR2pW1UYvvCTSmwGxIYMSyB0Uzf+fz1pzqBUWtKStJFwJ8D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\nJYERERG1JDAiIqKW/w8z4EYz4O7AkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x91bf5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaussian_numbers = np.random.normal(size=10000)\n",
    "plt.hist(gaussian_numbers)\n",
    "plt.title(\"Gaussian Histogram\")\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n:  [    8.    75.   460.  1367.  2628.  2800.  1834.   638.   176.    14.] 10000.0\n",
      "bins:  [-3.84331083 -3.09940042 -2.35549002 -1.61157962 -0.86766922 -0.12375882\n",
      "  0.62015159  1.36406199  2.10797239  2.85188279  3.59579319]\n",
      "patches:  <a list of 10 Patch objects>\n",
      "Rectangle(-3.84331,0;0.74391x8)\n",
      "Rectangle(-3.0994,0;0.74391x75)\n",
      "Rectangle(-2.35549,0;0.74391x460)\n",
      "Rectangle(-1.61158,0;0.74391x1367)\n",
      "Rectangle(-0.867669,0;0.74391x2628)\n",
      "Rectangle(-0.123759,0;0.74391x2800)\n",
      "Rectangle(0.620152,0;0.74391x1834)\n",
      "Rectangle(1.36406,0;0.74391x638)\n",
      "Rectangle(2.10797,0;0.74391x176)\n",
      "Rectangle(2.85188,0;0.74391x14)\n"
     ]
    },
    {
     "data": {
      "image/png": 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vSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7edbe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n, bins, patches = plt.hist(gaussian_numbers)\n",
    "print(\"n: \",n, np.sum(n))  # freq\n",
    "print(\"bins: \", bins)\n",
    "print(\"patches: \", patches)\n",
    "\n",
    "for p in patches:\n",
    "    print(p,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "             By default, hist is using 10 equal bins to plot the data, we can increase this no by using bins=n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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spc8Ck7sktZDJXZJayOQuSS1kcpekFjK5z4H+PsmS5oPJfQ44tYA0f0zuktRCJndJaqFt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\nyOQuSS30/wGeTzOaVVAu2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n, bins, patches = plt.hist(gaussian_numbers, bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Another important keyword parameter of hist is \"normed\". \"normed\" is optional and the default value is 'False'. If it is set to 'True', the first element of the return tuple will be the counts normalized to form a probability density, \n",
    "    i.e., \"n/(len(x)`dbin)\", ie the integral of the histogram will sum to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ati4//Twzs2vUjYk+S6CPCOB7LD39vLh4MUG/YYax8EhETJSloA+Li5nfqAnSo4+IaLgE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x954fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n, bins, patches = plt.hist(gaussian_numbers, bins=100, normed=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    If both the parameters 'normed' and 'stacked' are set to 'True', the sum of the histograms is normalized to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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53sG7HfoF46A3synSoe8VSovBNXozs4Jz0JuZFZxLN7NYevlh12LNiitT0EtaAtzLi0+Y\n+njV8YXA54BLgI9ExN9n7Wv5me5pUV6D3mDq1TinnXoaYy+MTR7z5KB3NAx6SX3AOuAK4AgwJGlL\nRIykmv0cuAVYdhJ9rQv5kYEGJ56YTT+TwN8XvSNLjX4RsD8iDkTEGLAJWJpuEBE/i4hvAy8029fM\nzNorS9DPAQ6mtg8l+7KYSV8zM2uBrjoZ29/fP/m6VCpRKpVyG8tsk67FmjXLJ/Y7o1wuUy6Xm+6X\nJegPA/NS23OTfVk01Tcd9NZZ6VosuP5qzfE5nc6ongAPDAxk6pcl6IeABZLmA6PAcmDFNO01g75m\n1iP8V2DvaBj0ETEuaTWwnRcvkRyWtLJyOAYlnQd8CzgbOC7pVuBNEfFcrb5t+2zMrGO8VELvyFSj\nj4htwMKqfRtSr58GXpO1r5mZdY6XQDAzKzgHvZlZwTnozcwKrquuo7f28xo2ZrOPZ/SzzMT1zulr\n5s2s2Bz0ZmYF56A3Mys4B72ZWcE56M3MCs5Bb2ZWcA56M7OCc9CbmRWcg97MrOB8Z2xB+Yk/lpf0\nOvX+3usODvoCqV7ewGuFWx68Tn33cdAXSL3HuflJQGazW6YavaQlkkYk7ZN0e502n5K0X9Jjki5O\n7X9K0l5JeyQ92qqBW3YTMyyvb2M2OzWc0UvqA9YBVwBHgCFJWyJiJNXmauCCiHi9pLcB64HLksPH\ngVJEHGv56M3MrKEsM/pFwP6IOBARY8AmYGlVm6XA5wEiYjfwsuQ5slB5WLiv7jEzy0mWAJ4DHExt\nH0r2TdfmcKpNADskDUm66WQHamZmJ6cTJ2MXR8SopHOpBP5wROys1bC/v3/ydalUolQqdWB4ZtYJ\nvuR35srlMuVyuel+WYL+MDAvtT032Vfd5jW12kTEaPL/M5I2UykFNQx6MyuWeleFWXbVE+CBgYFM\n/bIE/RCwQNJ8YBRYDqyoarMVWAV8UdJlwC8i4mlJZwB9EfGcpDOBq4BsIzOzwvJNVZ3VMOgjYlzS\namA7lZr+xogYlrSycjgGI+IhSddI+gHwP8D1SffzgM2SInmv+yNie3s+ldkj/SfwaaeextgLYzmP\nyKy2evdw+KaqzspUo4+IbcDCqn0bqrZX1+j3JHDRTAZoJ6r+E9g/MNatHOjdwXfGmlmuXMZpPwe9\nmeXKs/72c9CbWVeqXqTPs/2T56A3s66UPhcFcO/g3S7xnCQHvZn1hHSJx6HfHAe9mfUc1/Wb46Dv\nEdX1SjOzrBz0PcK3j9ts4IfktIeDvkt5Bm+zkUsy7eGg71LVVxz4m96sMa+QWZuDPgf1vhk9izeb\nGZc4a3PQ56DeN6O/Sc2a57p+Y37En5n1tIm6frrUaVN5Rp8zz0bM2m+21+4d9B1Sr/7uqwzM2i9d\nFp2Nd9VmCnpJS4B7efHBIx+v0eZTwNVUHjzywYh4LGvfIpnuoSAOdLP8zcbJVcOgl9QHrAOuAI4A\nQ5K2RMRIqs3VwAUR8XpJbwM+A1yWpW8vKZfLNR9YXj1bz/uhILvKuzr2XjOxq7yLy0qX5T2Mhvz1\nbK1OfT1nWhat9/Pei7LM6BcB+yPiAICkTcBSIB3WS4HPA0TEbkkvk3Qe8LoMfXvC2vvW8PC/PcKi\n4UtO+HOv266W2V3eXXmIY5fbXd7dE8Hkr2drderrmWXmnv5lUP0X+OM7hvmv0jfbP9AOyBL0c4CD\nqe1DVMK/UZs5GfvWNfbCGHt+9C0iAoD5r3wtr3r5q7N2zyzLiZpjzx7lDy59O7fefOuUGp+Z9a7q\nXwbpK3f+7KvXTb5u1cncvE4Kt+tkrFrxQb771B7+5T8+O7k9/5Wv5a+u+ygwfS283hew2fp5lhOo\n1X3MrBj6+vqmTOhacTI3r7/+NTFbrttAugzoj4glyfZfA5E+qSrpM8A3IuKLyfYI8IdUSjfT9k19\njOkHYmZmJ4iIhhPrLDP6IWCBpPnAKLAcWFHVZiuwCvhi8ovhFxHxtKSfZeibebBmZta8hkEfEeOS\nVgPbefESyWFJKyuHYzAiHpJ0jaQfULm88vrp+rbtszEzsxM0LN2YmVlv67q1biTdJum4pHPyHkst\nkv5O0l5JeyRtk/SqvMdUi6R7JA1LekzSVyT9Vt5jqkXSn0p6QtK4pEvyHk+apCWSRiTtk3R73uOp\nR9JGSU9L+m7eY6lH0lxJX5f0PUmPS/rLvMdUi6SXSNqd/Hw/LunOvMc0HUl9kr4jaet07boq6CXN\nBa4EDuQ9lmncExFviYiLgX8HuvUbYTtwYURcBOwH/ibn8dTzOPAnwH/mPZC01M1+7wIuBFZIekO+\no6rrc1TG2c1eAD4cERcCvw+s6savZ0T8H/CO5Of7IuBqSZkvCc/BrcD3GzXqqqAH/gFYk/cgphMR\nz6U2zwSO5zWW6UTEIxExMbZdwNw8x1NPRPx3ROynRZfkttDkjYIRMQZM3OzXdSJiJ3As73FMJyJ+\nMrEsSvIzNEzlPpuuExHPJy9fQuU8ZlfWt5OJ8TXAZxu17Zqgl3QtcDAiHs97LI1IukvSj4H3An+b\n93gyuAH4Wt6D6DH1bgK0GZL0Wiqz5d35jqS2pByyB/gJsCMihvIeUx0TE+OGv4g6unqlpB1MvflZ\nVAa5FvgIlbJN+lguphnnHRHxYESsBdYmddtbgP7Oj7LxOJM2dwBjEfGFHIZIMoaG47TZQdJZwJeB\nW6v+Ou4ayV/CFyfntR6Q9KaIaFge6SRJfww8HRGPSSrRIC87GvQRcWWt/ZJ+F3gtsFeSqJQZvi1p\nUUT8tINDBOqPs4YvAA+RU9A3GqekD1L50+6dHRlQHU18PbvJYWBeantuss9OkqRTqYT8v0bElrzH\n00hE/ErSN4AlZKiDd9hi4FpJ1wAvBc6W9PmI+ECtxl1RuomIJyLiVRFxfkS8jsqfyRfnEfKNSFqQ\n2lxGpdbYdZLlodcA1yYnmHpBN9XpJ28UlHQ6lZv9pr2yIWeiu75+tfwz8P2I+Me8B1KPpFdIelny\n+qVUqgxdtwhjRHwkIuZFxPlUvje/Xi/koUuCvoage79p75b0XUmPAX9E5ax3N/on4CxgR3L51afz\nHlAtkpZJOghcBnxVUlecS4iIcWDiZr/vAZu69WY/SV8Avgn8jqQfS7o+7zFVk7QY+HPgncmli99J\nJiPd5reBbyQ/37uBhyPioZzHNGO+YcrMrOC6dUZvZmYt4qA3Mys4B72ZWcE56M3MCs5Bb2ZWcA56\nM7OCc9CbmRWcg97MrOD+H2ytjqh9jnRiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x962eac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(gaussian_numbers, \n",
    "         bins=100, \n",
    "         normed=True, \n",
    "         stacked=True, \n",
    "         edgecolor=\"#6A9662\",\n",
    "         color=\"#DDFFDD\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    can plot it as a cumulative distribution function as well by setting the parameter 'cumulative'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HldXYdD+U9FsRsUbSz0m6v4n/nhHxf5Juy3++10q6w/a6msvq5QFJr5QZWNcM/U8k/U5N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\niSDQASARBDoAJIJAB4BE/D/XmFy6CazqYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x97f3588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(gaussian_numbers, \n",
    "         bins=100, \n",
    "         normed=True,\n",
    "         stacked=True,\n",
    "         cumulative=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Bar Plots **  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gaRPf7x/vWylV9dDRp8BV9Rng0UmedKLrSLKHjaD8UFV94jhDVqKf29W5Kv2cqOe/gc8D\nF009tBL9PGqzOlegnxcAL09yF/A3wC8luXpqzCr0cts6V6CXk7V8c/znfwIfY+O+WpPm6umQob7V\n/9zXApfCsXeiPlhV6wNeex6b1jm5TpXkPDZ+5fO/TlRhEz4AfKWq3rvJ46vSzy3rXIV+JnlyktPH\n26cCLwG+OjVs6f2cpc5l97Oqrqiqp1fVM4BXAtdX1aVTw5bey1nqXHYvJ6592vjZLkkeB/wy8OWp\nYXP1dOE3H00VdhDogDOS3AscAB7Dxm0D3ldVn05ycZJ/A74LvH6I6w5dJ/CKJG8Avg/8L/AbS6jx\nAuA1wO3j9dUCrgDOZIX6OUudrEA/gR8HPpiNW0WfAvzduH+/wwr1c5Y6WY1+/pAV7OVxrWgv9wIf\ny8btVPYAH66q6/r01DcfSVJDfKFUkhpiqEtSQwx1SWqIoS5JDTHUJakhhrokNcRQl6SGGOqS1JD/\nB+PZh4uSKHxsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x96f33c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars = plt.bar([1,2,3,4], [1,4,9,16])\n",
    "bars[0].set_color('green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yw/1d02Urpaq+c/QlcFV9HHhUksef7BxJdrBelO+vqg8fZ5WVmOdWOVdlnhvyfBu4Dbh8\n5qGVmOdRm+VcgXleArwkyVeAvwSel+T6mXVWYZZb5lyBWW7M8o3pf/8d+CDr19XaaKGZDlnqJ/qX\n+ybgKjj2SdRvVdXagPtexKY5N56nSnIR62/5/M+TFWyD9wJfqKp3bvL4qszzhDlXYZ5Jnpjk7Ont\nM4AXAF+cWW3p85wn57LnWVXXVtVTquo84GXArVV11cxqS5/lPDmXPcsN+z5z+mqXJI8FfgX43Mxq\nC82084ePZoIdAEbAE5IcBvYCj2b9sgHvrqqPJXlhkn8B/hu4eoj9Dp0TeGmS1wDfA/4X+I0lZLwE\neAVw3/T8agHXArtZoXnOk5MVmCfw48D7sn6p6NOAv57O73dYoXnOk5PVmOcPWMFZHteKznIn8MGs\nX05lB3BDVd3cZ6Z++EiSGuIvSiWpIZa6JDXEUpekhljqktQQS12SGmKpS1JDLHVJaoilLkkN+X/r\nFXYN3x0FRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9439dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=plt.figure()\n",
    "ax=f.add_subplot(1,1,1)\n",
    "ax.bar([1,2,3,4], [1,4,9,16])\n",
    "children = ax.get_children()\n",
    "children[3].set_color('g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6H5W0tnu7HhERE+nkzOEk8DnbHwA+DNwl6f3A3cCjtq8CHgM2ApR7SH8cWAbcDGyVNHan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\nfVF/phVJmRQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x94f8668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = ('2010', '2011', '2012', '2013', '2014')\n",
    "visitors = (1241, 50927, 162242, 222093, 296665 / 8 * 12)\n",
    "index = np.arange(len(visitors))\n",
    "bar_width = 1.0\n",
    "plt.bar(index, visitors, bar_width,  color=\"green\")\n",
    "plt.xticks(index + bar_width / 2, years) # labels get centered\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}