{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Random Variables in Sympy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This script follows pages 231 and 232 of Mastering SciPy.\n",
    "Illustrates some probability computations in sympy.\n",
    "We are rolling three 6-sided dice and one 100-sided dice.\n",
    "Then we compute ten samples and compute some probabilities,\n",
    "variance, standard deviation, expected value, moments.\n",
    "The cumulative distribution function, cdf, is not well defined,\n",
    "but the probability density function works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import var, Eq\n",
    "from sympy.stats import Die, sample_iter, P\n",
    "from sympy.stats import variance, std, E\n",
    "from sympy.stats import moment, cdf, density\n",
    "from sympy.stats import Exponential, skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ten samples : 69 40 13 75 81 26 26 67 78 39 \n"
     ]
    }
   ],
   "source": [
    "D6_1 = Die('D6_1', 6)\n",
    "D6_2 = Die('D6_2', 6)\n",
    "D6_3 = Die('D6_3', 6)\n",
    "D100 = Die('D100', 100)\n",
    "X = D6_1 + D6_2 + D6_3 + D100\n",
    "print('Ten samples : ', end='')\n",
    "for item in sample_iter(X, numsamples=10):\n",
    "    print(item, end=' ')\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of sum of dice : 179/200\n"
     ]
    }
   ],
   "source": [
    "print('Probability of sum of dice : ', end='')\n",
    "print(P(D6_1 + D6_2 + D6_3 < D100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Given that the first throw equals 5,\n",
      "probability that sum of first two throws is > 9 : 1/3\n"
     ]
    }
   ],
   "source": [
    "print('Given that the first throw equals 5,')\n",
    "print('probability that sum of first ', end='')\n",
    "print('two throws is > 9 : ', end='')\n",
    "print(P(D6_1 + D6_2 > 9, Eq(D6_1, 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variance of X :  842\n",
      "standard deviation of X : sqrt(842)\n",
      "expected value of X : 61\n"
     ]
    }
   ],
   "source": [
    "print('variance of X : ', variance(X))\n",
    "print('standard deviation of X :', std(X))\n",
    "print('expected value of X :', E(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the higher order moments of X around 0 :\n",
      "mu_2 = 4563\n",
      "mu_3 = 381067\n",
      "mu_4 = 339378593/10\n",
      "mu_5 = 6300603685/2\n",
      "mu_6 = 1805931466069/6\n",
      "mu_7 = 176259875749813/6\n",
      "mu_8 = 29146927913035853/10\n",
      "mu_9 = 586011570997109973/2\n"
     ]
    }
   ],
   "source": [
    "print('the higher order moments of X around 0 :')\n",
    "for n in range(2, 10):\n",
    "    m = moment(X, n, 0)\n",
    "    print('mu_{0} = {1}'.format(n, m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cumulative distribution function of D100 :\n",
      "{1: 1/100, 2: 1/50, 3: 3/100, 4: 1/25, 5: 1/20, 6: 3/50, 7: 7/100, 8: 2/25, 9: 9/100, 10: 1/10, 11: 11/100, 12: 3/25, 13: 13/100, 14: 7/50, 15: 3/20, 16: 4/25, 17: 17/100, 18: 9/50, 19: 19/100, 20: 1/5, 21: 21/100, 22: 11/50, 23: 23/100, 24: 6/25, 25: 1/4, 26: 13/50, 27: 27/100, 28: 7/25, 29: 29/100, 30: 3/10, 31: 31/100, 32: 8/25, 33: 33/100, 34: 17/50, 35: 7/20, 36: 9/25, 37: 37/100, 38: 19/50, 39: 39/100, 40: 2/5, 41: 41/100, 42: 21/50, 43: 43/100, 44: 11/25, 45: 9/20, 46: 23/50, 47: 47/100, 48: 12/25, 49: 49/100, 50: 1/2, 51: 51/100, 52: 13/25, 53: 53/100, 54: 27/50, 55: 11/20, 56: 14/25, 57: 57/100, 58: 29/50, 59: 59/100, 60: 3/5, 61: 61/100, 62: 31/50, 63: 63/100, 64: 16/25, 65: 13/20, 66: 33/50, 67: 67/100, 68: 17/25, 69: 69/100, 70: 7/10, 71: 71/100, 72: 18/25, 73: 73/100, 74: 37/50, 75: 3/4, 76: 19/25, 77: 77/100, 78: 39/50, 79: 79/100, 80: 4/5, 81: 81/100, 82: 41/50, 83: 83/100, 84: 21/25, 85: 17/20, 86: 43/50, 87: 87/100, 88: 22/25, 89: 89/100, 90: 9/10, 91: 91/100, 92: 23/25, 93: 93/100, 94: 47/50, 95: 19/20, 96: 24/25, 97: 97/100, 98: 49/50, 99: 99/100, 100: 1}\n"
     ]
    }
   ],
   "source": [
    "print('cumulative distribution function of D100 :')\n",
    "print(cdf(D100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probability density function of D100 :\n",
      "DieDistribution(100)\n"
     ]
    }
   ],
   "source": [
    "print('probability density function of D100 :')\n",
    "print(density(D100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probability density function of X :\n",
      "{4: 1/21600, 5: 1/5400, 6: 1/2160, 7: 1/1080, 8: 7/4320, 9: 7/2700, 10: 3/800, 11: 1/200, 12: 1/160, 13: 1/135, 14: 181/21600, 15: 49/5400, 16: 103/10800, 17: 53/5400, 18: 43/4320, 19: 1/100, 20: 1/100, 21: 1/100, 22: 1/100, 23: 1/100, 24: 1/100, 25: 1/100, 26: 1/100, 27: 1/100, 28: 1/100, 29: 1/100, 30: 1/100, 31: 1/100, 32: 1/100, 33: 1/100, 34: 1/100, 35: 1/100, 36: 1/100, 37: 1/100, 38: 1/100, 39: 1/100, 40: 1/100, 41: 1/100, 42: 1/100, 43: 1/100, 44: 1/100, 45: 1/100, 46: 1/100, 47: 1/100, 48: 1/100, 49: 1/100, 50: 1/100, 51: 1/100, 52: 1/100, 53: 1/100, 54: 1/100, 55: 1/100, 56: 1/100, 57: 1/100, 58: 1/100, 59: 1/100, 60: 1/100, 61: 1/100, 62: 1/100, 63: 1/100, 64: 1/100, 65: 1/100, 66: 1/100, 67: 1/100, 68: 1/100, 69: 1/100, 70: 1/100, 71: 1/100, 72: 1/100, 73: 1/100, 74: 1/100, 75: 1/100, 76: 1/100, 77: 1/100, 78: 1/100, 79: 1/100, 80: 1/100, 81: 1/100, 82: 1/100, 83: 1/100, 84: 1/100, 85: 1/100, 86: 1/100, 87: 1/100, 88: 1/100, 89: 1/100, 90: 1/100, 91: 1/100, 92: 1/100, 93: 1/100, 94: 1/100, 95: 1/100, 96: 1/100, 97: 1/100, 98: 1/100, 99: 1/100, 100: 1/100, 101: 1/100, 102: 1/100, 103: 1/100, 104: 43/4320, 105: 53/5400, 106: 103/10800, 107: 49/5400, 108: 181/21600, 109: 1/135, 110: 1/160, 111: 1/200, 112: 3/800, 113: 7/2700, 114: 7/4320, 115: 1/1080, 116: 1/2160, 117: 1/5400, 118: 1/21600}\n"
     ]
    }
   ],
   "source": [
    "print('probability density function of X :')\n",
    "print(density(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining a continuous random variable Y\n",
      "cumulative distribution function of Y :\n",
      "mu*exp(-mu*t)\n"
     ]
    }
   ],
   "source": [
    "print('defining a continuous random variable Y')\n",
    "mu = var('mu', positive=True)\n",
    "t = var('t')\n",
    "Y = Exponential('Y', mu)\n",
    "print('cumulative distribution function of Y :')\n",
    "print(density(Y)(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probability density function of Y :\n",
      "Piecewise((1 - exp(-mu*t), t >= 0), (0, True))\n"
     ]
    }
   ],
   "source": [
    "print('probability density function of Y :')\n",
    "print(cdf(Y)(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variance of Y :  mu**(-2)\n",
      "skewness of Y :  2\n"
     ]
    }
   ],
   "source": [
    "print('variance of Y : ', variance(Y))\n",
    "print('skewness of Y : ', skewness(Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "higher order moments of Y around 0 : \n",
      "mu_2 = 4563\n",
      "mu_3 = 381067\n",
      "mu_4 = 339378593/10\n",
      "mu_5 = 6300603685/2\n",
      "mu_6 = 1805931466069/6\n",
      "mu_7 = 176259875749813/6\n",
      "mu_8 = 29146927913035853/10\n",
      "mu_9 = 586011570997109973/2\n"
     ]
    }
   ],
   "source": [
    "print('higher order moments of Y around 0 : ')\n",
    "for n in range(2, 10):\n",
    "    m = moment(X, n, 0)\n",
    "    print('mu_{0} = {1}'.format(n, m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below makes a plot of the probability density function\n",
    "of the sum of dice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, matplotlib.pyplot as plt\n",
    "from sympy.stats import Die, density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "D6_1 = Die('D6_1', 6)\n",
    "D6_2 = Die('D6_2', 6)\n",
    "D6_3 = Die('D6_3', 6)\n",
    "D100 = Die('D100', 100)\n",
    "X = D6_1 + D6_2 + D6_3 + D100\n",
    "pdfX = density(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probability density function of X :\n",
      "{4: 1/21600, 5: 1/5400, 6: 1/2160, 7: 1/1080, 8: 7/4320, 9: 7/2700, 10: 3/800, 11: 1/200, 12: 1/160, 13: 1/135, 14: 181/21600, 15: 49/5400, 16: 103/10800, 17: 53/5400, 18: 43/4320, 19: 1/100, 20: 1/100, 21: 1/100, 22: 1/100, 23: 1/100, 24: 1/100, 25: 1/100, 26: 1/100, 27: 1/100, 28: 1/100, 29: 1/100, 30: 1/100, 31: 1/100, 32: 1/100, 33: 1/100, 34: 1/100, 35: 1/100, 36: 1/100, 37: 1/100, 38: 1/100, 39: 1/100, 40: 1/100, 41: 1/100, 42: 1/100, 43: 1/100, 44: 1/100, 45: 1/100, 46: 1/100, 47: 1/100, 48: 1/100, 49: 1/100, 50: 1/100, 51: 1/100, 52: 1/100, 53: 1/100, 54: 1/100, 55: 1/100, 56: 1/100, 57: 1/100, 58: 1/100, 59: 1/100, 60: 1/100, 61: 1/100, 62: 1/100, 63: 1/100, 64: 1/100, 65: 1/100, 66: 1/100, 67: 1/100, 68: 1/100, 69: 1/100, 70: 1/100, 71: 1/100, 72: 1/100, 73: 1/100, 74: 1/100, 75: 1/100, 76: 1/100, 77: 1/100, 78: 1/100, 79: 1/100, 80: 1/100, 81: 1/100, 82: 1/100, 83: 1/100, 84: 1/100, 85: 1/100, 86: 1/100, 87: 1/100, 88: 1/100, 89: 1/100, 90: 1/100, 91: 1/100, 92: 1/100, 93: 1/100, 94: 1/100, 95: 1/100, 96: 1/100, 97: 1/100, 98: 1/100, 99: 1/100, 100: 1/100, 101: 1/100, 102: 1/100, 103: 1/100, 104: 43/4320, 105: 53/5400, 106: 103/10800, 107: 49/5400, 108: 181/21600, 109: 1/135, 110: 1/160, 111: 1/200, 112: 3/800, 113: 7/2700, 114: 7/4320, 115: 1/1080, 116: 1/2160, 117: 1/5400, 118: 1/21600}\n"
     ]
    }
   ],
   "source": [
    "print('probability density function of X :')\n",
    "print(pdfX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcomes = list(pdfX.keys())\n",
    "probabilities = list(pdfX.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The outcomes : [4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29\n",
      " 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n",
      " 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77\n",
      " 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101\n",
      " 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118]\n"
     ]
    }
   ],
   "source": [
    "xnp = np.array(outcomes)\n",
    "print('The outcomes :', xnp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the probabilities : [1/21600 1/5400 1/2160 1/1080 7/4320 7/2700 3/800 1/200 1/160 1/135\n",
      " 181/21600 49/5400 103/10800 53/5400 43/4320 1/100 1/100 1/100 1/100 1/100\n",
      " 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100\n",
      " 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100\n",
      " 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100\n",
      " 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100\n",
      " 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100\n",
      " 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100\n",
      " 1/100 1/100 1/100 1/100 1/100 1/100 1/100 1/100 43/4320 53/5400 103/10800\n",
      " 49/5400 181/21600 1/135 1/160 1/200 3/800 7/2700 7/4320 1/1080 1/2160\n",
      " 1/5400 1/21600]\n"
     ]
    }
   ],
   "source": [
    "ynp = np.array(probabilities)\n",
    "print('the probabilities :', ynp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('probabilities of sum of three 6-sided dice and 100-sided dice')\n",
    "plt.plot(xnp, ynp, 'b.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look into a continuous random variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import var, lambdify\n",
    "from sympy.stats import variance, std, E\n",
    "from sympy.stats import moment, cdf, density\n",
    "from sympy.stats import Exponential, skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining a continuous random variable Y\n",
      "cumulative distribution function of Y :\n",
      "mu*exp(-mu*t)\n"
     ]
    }
   ],
   "source": [
    "print('defining a continuous random variable Y')\n",
    "mu = var('mu', positive=True)\n",
    "t = var('t')\n",
    "Y = Exponential('Y', mu)\n",
    "print('cumulative distribution function of Y :')\n",
    "print(density(Y)(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probability density function of Y :\n",
      "Piecewise((1 - exp(-mu*t), t >= 0), (0, True))\n"
     ]
    }
   ],
   "source": [
    "print('probability density function of Y :')\n",
    "pdfY = cdf(Y)(t)\n",
    "print(pdfY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Piecewise((1 - exp(-t), t >= 0), (0, True))\n"
     ]
    }
   ],
   "source": [
    "pdfYmu1 = pdfY.subs(mu, 1)\n",
    "print(pdfYmu1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fun = lambdify(t, pdfYmu1, 'numpy')\n",
    "xnp = np.linspace(0, 6, 1000)\n",
    "ynp = fun(xnp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('probabilities of an exponential random variable')\n",
    "plt.plot(xnp, ynp, 'b.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variance of Y :  mu**(-2)\n",
      "skewness of Y :  2\n",
      "higher order moments of Y around 0 : \n",
      "mu_2 = 2/mu**2\n",
      "mu_3 = 6/mu**3\n",
      "mu_4 = 24/mu**4\n",
      "mu_5 = 120/mu**5\n",
      "mu_6 = 720/mu**6\n",
      "mu_7 = 5040/mu**7\n",
      "mu_8 = 40320/mu**8\n",
      "mu_9 = 362880/mu**9\n"
     ]
    }
   ],
   "source": [
    "print('variance of Y : ', variance(Y))\n",
    "print('skewness of Y : ', skewness(Y))\n",
    "print('higher order moments of Y around 0 : ')\n",
    "for n in range(2, 10):\n",
    "    m = moment(Y, n, 0)\n",
    "    print('mu_{0} = {1}'.format(n, m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Random Variables in scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first approach to numerically redo the sympy dice experiment is to work\n",
    "with randint, but that does not lead to a uniform random variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "D6 = randint(1, 7)\n",
    "D100 = randint(1, 101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symbolically, it was very simple to construct the sum of these \n",
    "four independent random variables. \n",
    "Numerically, we address the situation in a different way. \n",
    "Assume for a second that we do not know the kind of random variable \n",
    "we are to obtain. Our first step is usually to create a big sample\n",
    "-- 10,000 throws in this case, and produce a histogram with the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = D6.rvs(10000) + D6.rvs(10000) \\\n",
    "        + D6.rvs(10000) + D100.rvs(10000)\n",
    "plt.hist(samples, bins=118-4)\n",
    "plt.xlabel('Sum of dice')\n",
    "plt.ylabel('Frequency of each sum')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows that our new random variable is not uniform."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below uses a Gaussian kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import randint, gaussian_kde, rv_discrete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "D6 = randint(1, 7)\n",
    "D100 = randint(1, 101)\n",
    "samples = D6.rvs(10000) + D6.rvs(10000) \\\n",
    "        + D6.rvs(10000) + D100.rvs(10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to approach this problem is to approximate the distribution \n",
    "of the variable from this data, and for that task, we use from \n",
    "the scipy.stats module the function gaussian_kde, which performs \n",
    "a kernel-density estimate using Gaussian kernels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = gaussian_kde(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This ``gaussian_kde`` object has methods similar to those of an actual \n",
    "random variable. To estimate the value of the corresponding probability \n",
    "of getting a 50, and the probability of obtaining a number greater \n",
    "than 100 in a throw of these four dice,\n",
    "we would issue, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00977907]\n",
      "0.8859136683771431\n"
     ]
    }
   ],
   "source": [
    "print(kernel(50))                     # The actual answer is 1/100\n",
    "print(kernel.integrate_box_1d(0,100)) # The actual answer is 177/200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of estimating this sum of random variables, \n",
    "and again assuming we are not familiar with the actual result,\n",
    "we could create an actual random variable by defining its probability\n",
    "mass function in terms of the probability mass functions of the summands. \n",
    "The key? Convolution, of course, since the random variables for these dice\n",
    "are independent. The sample space is the set of numbers from 4 to 118 \n",
    "(space_sum in the following command), and the probabilities associated \n",
    "with each element (probs_sum) are computed as the convolution of the \n",
    "corresponding probabilities for each dice on their sample spaces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n",
      "0.8950000000000006\n"
     ]
    }
   ],
   "source": [
    "probs_6dice = D6.pmf(np.linspace(1,6,6))\n",
    "probs_100dice = D100.pmf(np.linspace(1,100,100))\n",
    "probs_sum = np.convolve(np.convolve(probs_6dice,probs_6dice),\n",
    "                        np.convolve(probs_6dice,probs_100dice))\n",
    "space_sum = np.linspace(4, 118, 115)\n",
    "sum_of_dice = rv_discrete(name=\"sod\",\n",
    "                          values=(space_sum, probs_sum))\n",
    "print(sum_of_dice.pmf(50))\n",
    "print(sum_of_dice.cdf(100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following Mastering SciPy, this script makes a bar plot of the data\n",
    "in the Consumer Complaints Database, available at\n",
    "`http://catalog.data.gov/dataset/consumer-complaint-database.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Date received                                            Product  \\\n",
      "0    08/09/2015                                   Credit reporting   \n",
      "1    01/29/2019  Credit reporting, credit repair services, or o...   \n",
      "2    08/19/2015                                           Mortgage   \n",
      "3    03/04/2016                                        Credit card   \n",
      "4    03/18/2013                                           Mortgage   \n",
      "\n",
      "                              Sub-product  \\\n",
      "0                                     NaN   \n",
      "1                        Credit reporting   \n",
      "2  Conventional adjustable mortgage (ARM)   \n",
      "3                                     NaN   \n",
      "4                          Other mortgage   \n",
      "\n",
      "                                               Issue  \\\n",
      "0             Incorrect information on credit report   \n",
      "1  Problem with a credit reporting company's inve...   \n",
      "2           Loan servicing, payments, escrow account   \n",
      "3                                   Billing disputes   \n",
      "4           Loan modification,collection,foreclosure   \n",
      "\n",
      "                                           Sub-issue  \\\n",
      "0                            Information is not mine   \n",
      "1  Was not notified of investigation status or re...   \n",
      "2                                                NaN   \n",
      "3                                                NaN   \n",
      "4                                                NaN   \n",
      "\n",
      "                        Consumer complaint narrative  \\\n",
      "0                                                NaN   \n",
      "1                                                NaN   \n",
      "2                                                NaN   \n",
      "3  I am dissatisfied with the current outcome of ...   \n",
      "4                                                NaN   \n",
      "\n",
      "                            Company public response  \\\n",
      "0  Company chooses not to provide a public response   \n",
      "1                                               NaN   \n",
      "2  Company chooses not to provide a public response   \n",
      "3                                               NaN   \n",
      "4                                               NaN   \n",
      "\n",
      "                                 Company State ZIP code Tags  \\\n",
      "0    Experian Information Solutions Inc.    NJ    08872  NaN   \n",
      "1                          EQUIFAX, INC.    NY    10801  NaN   \n",
      "2                  WELLS FARGO & COMPANY    CA    94526  NaN   \n",
      "3                          DISCOVER BANK    NV    891XX  NaN   \n",
      "4  BANK OF AMERICA, NATIONAL ASSOCIATION    PA    175XX  NaN   \n",
      "\n",
      "  Consumer consent provided? Submitted via Date sent to company  \\\n",
      "0       Consent not provided           Web           08/09/2015   \n",
      "1       Consent not provided           Web           01/30/2019   \n",
      "2       Consent not provided           Web           08/19/2015   \n",
      "3           Consent provided           Web           03/04/2016   \n",
      "4                        NaN      Referral           03/19/2013   \n",
      "\n",
      "      Company response to consumer Timely response? Consumer disputed?  \\\n",
      "0  Closed with non-monetary relief              Yes                 No   \n",
      "1  Closed with non-monetary relief              Yes                NaN   \n",
      "2          Closed with explanation              Yes                 No   \n",
      "3          Closed with explanation              Yes                Yes   \n",
      "4          Closed with explanation              Yes                 No   \n",
      "\n",
      "   Complaint ID  \n",
      "0       1509954  \n",
      "1       3136759  \n",
      "2       1527601  \n",
      "3       1816726  \n",
      "4        358304  \n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"Consumer_Complaints.csv\",\n",
    "                   low_memory=False) # , parse_dates=[8,9])\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date received', 'Product', 'Sub-product', 'Issue', 'Sub-issue',\n",
      "       'Consumer complaint narrative', 'Company public response', 'Company',\n",
      "       'State', 'ZIP code', 'Tags', 'Consumer consent provided?',\n",
      "       'Submitted via', 'Date sent to company', 'Company response to consumer',\n",
      "       'Timely response?', 'Consumer disputed?', 'Complaint ID'],\n",
      "      dtype='object')\n",
      "the first column :  Date received\n"
     ]
    }
   ],
   "source": [
    "C = data.columns\n",
    "print(C)\n",
    "print('the first column : ', C[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compare dates,\n",
    "we have to convert to the datetime Timestamp type,\n",
    "just doing `min(dates)` and `max(dates)` gives wrong results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-12-01 00:00:00\n",
      "2019-11-03 00:00:00\n"
     ]
    }
   ],
   "source": [
    "received = C[0]\n",
    "dates = data[received]\n",
    "dates = pd.to_datetime(dates)\n",
    "print(dates.min()) # Timestamp('2011-12-01 00:00:00')\n",
    "print(dates.max()) # Timestamp('2019-11-01 00:00:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first date :  01/01/2012\n",
      "last date :  12/31/2018\n"
     ]
    }
   ],
   "source": [
    "dates = data[C[0]]\n",
    "print('first date : ', min(dates))\n",
    "print('last date : ', max(dates))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product\n",
      "Bank account or service                                                          86206\n",
      "Checking or savings account                                                      51743\n",
      "Consumer Loan                                                                    31604\n",
      "Credit card                                                                      89190\n",
      "Credit card or prepaid card                                                      60377\n",
      "Credit reporting                                                                140432\n",
      "Credit reporting, credit repair services, or other personal consumer reports    297318\n",
      "Debt collection                                                                 268052\n",
      "Money transfer, virtual currency, or money service                               12828\n",
      "Money transfers                                                                   5354\n",
      "Mortgage                                                                        289329\n",
      "Other financial service                                                           1059\n",
      "Payday loan                                                                       5544\n",
      "Payday loan, title loan, or personal loan                                        10809\n",
      "Prepaid card                                                                      3819\n",
      "Student loan                                                                     54820\n",
      "Vehicle loan or lease                                                            14116\n",
      "Virtual currency                                                                    18\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "groups = data.groupby('Product').size()\n",
    "print(groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups.plot(kind='barh')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another informative bar plot is achieved by stacking the bars properly. \n",
    "For instance, if we focus on complaints about mortgages in the Midwest \n",
    "during the years 2012 and 2013, we could issue the following commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year   2012  2013\n",
      "State            \n",
      "ND       14    20\n",
      "SD       33    35\n",
      "NE      108   119\n",
      "KS      146   169\n",
      "MN      478   623\n",
      "IA       99   125\n",
      "MO      519   629\n",
      "IL     1178  1611\n",
      "IN      308   416\n",
      "OH     1045  1355\n",
      "WI      417   523\n",
      "MI     1459  1774\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jan\\AppData\\Local\\Temp\\ipykernel_50400\\646900769.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Year'] = yrdata.map(lambda t: t.year)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "midwest = ['ND', 'SD', 'NE', 'KS', 'MN', 'IA', \\\n",
    "           'MO', 'IL', 'IN', 'OH', 'WI', 'MI']\n",
    "df = data[data.Product == 'Mortgage'];\n",
    "yrdata = pd.to_datetime(df['Date received'])\n",
    "df['Year'] = yrdata.map(lambda t: t.year)\n",
    "df = df.groupby(['State','Year']).size()\n",
    "barplotdata = df[midwest].unstack().loc[:, 2012:2013]\n",
    "print(barplotdata)\n",
    "barplotdata.plot(kind=\"bar\", stacked=True) #, colormap='Greys')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following Mastering SciPy, this script makes a bar plot of the data\n",
    "in the Consumer Complaints Database, available at\n",
    "http://catalog.data.gov/dataset/consumer-complaint-database.\n",
    "A pie chart showing the volumes of complaints per product \n",
    "in Puerto Rico alone, from November 2011 until September 2014, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Date received                      Product                 Sub-product  \\\n",
      "10      09/12/2016      Bank account or service  Other bank product/service   \n",
      "578     06/19/2015                     Mortgage              Other mortgage   \n",
      "648     08/19/2018                     Mortgage      Other type of mortgage   \n",
      "1340    02/24/2015              Debt collection    Non-federal student loan   \n",
      "1416    08/01/2017  Credit card or prepaid card           Store credit card   \n",
      "\n",
      "                                         Issue  \\\n",
      "10     Account opening, closing, or management   \n",
      "578   Application, originator, mortgage broker   \n",
      "648             Trouble during payment process   \n",
      "1340     Cont'd attempts collect debt not owed   \n",
      "1416              Problem when making payments   \n",
      "\n",
      "                                              Sub-issue  \\\n",
      "10                                                  NaN   \n",
      "578                                                 NaN   \n",
      "648                                                 NaN   \n",
      "1340                  Debt was discharged in bankruptcy   \n",
      "1416  You never received your bill or did not know a...   \n",
      "\n",
      "                           Consumer complaint narrative  \\\n",
      "10                                                  NaN   \n",
      "578                                                 NaN   \n",
      "648   On XX/XX/XXXX, I arrived at my residence lette...   \n",
      "1340                                                NaN   \n",
      "1416                                                NaN   \n",
      "\n",
      "                                Company public response  \\\n",
      "10    Company has responded to the consumer and the ...   \n",
      "578   Company believes it acted appropriately as aut...   \n",
      "648                                                 NaN   \n",
      "1340                                                NaN   \n",
      "1416  Company has responded to the consumer and the ...   \n",
      "\n",
      "                                Company State ZIP code            Tags  \\\n",
      "10         BANCO POPULAR DE PUERTO RICO    PR    00735   Servicemember   \n",
      "578               FIRSTBANK PUERTO RICO    PR    00623  Older American   \n",
      "648        BANCO POPULAR DE PUERTO RICO    PR    00957             NaN   \n",
      "1340  Financial Recovery Services, Inc.    PR    00961             NaN   \n",
      "1416                     CITIBANK, N.A.    PR    00602             NaN   \n",
      "\n",
      "     Consumer consent provided? Submitted via Date sent to company  \\\n",
      "10                          NaN         Phone           09/12/2016   \n",
      "578                         NaN         Phone           07/10/2015   \n",
      "648            Consent provided           Web           08/19/2018   \n",
      "1340                        NaN           Web           03/02/2015   \n",
      "1416                        NaN         Phone           08/01/2017   \n",
      "\n",
      "     Company response to consumer Timely response? Consumer disputed?  \\\n",
      "10        Closed with explanation              Yes                 No   \n",
      "578       Closed with explanation              Yes                 No   \n",
      "648       Closed with explanation              Yes                NaN   \n",
      "1340      Closed with explanation              Yes                 No   \n",
      "1416  Closed with monetary relief              Yes                NaN   \n",
      "\n",
      "      Complaint ID  \n",
      "10         2107177  \n",
      "578        1429402  \n",
      "648        2995585  \n",
      "1340       1253972  \n",
      "1416       2591318  \n",
      "size of prdata :  (3526, 18)\n",
      "     Date received          Product                     Sub-product  \\\n",
      "578     06/19/2015         Mortgage                  Other mortgage   \n",
      "648     08/19/2018         Mortgage          Other type of mortgage   \n",
      "1340    02/24/2015  Debt collection        Non-federal student loan   \n",
      "2450    05/03/2018     Student loan  Federal student loan servicing   \n",
      "4471    09/16/2012         Mortgage                     VA mortgage   \n",
      "\n",
      "                                         Issue  \\\n",
      "578   Application, originator, mortgage broker   \n",
      "648             Trouble during payment process   \n",
      "1340     Cont'd attempts collect debt not owed   \n",
      "2450      Dealing with your lender or servicer   \n",
      "4471  Loan servicing, payments, escrow account   \n",
      "\n",
      "                              Sub-issue  \\\n",
      "578                                 NaN   \n",
      "648                                 NaN   \n",
      "1340  Debt was discharged in bankruptcy   \n",
      "2450      Problem with customer service   \n",
      "4471                                NaN   \n",
      "\n",
      "                           Consumer complaint narrative  \\\n",
      "578                                                 NaN   \n",
      "648   On XX/XX/XXXX, I arrived at my residence lette...   \n",
      "1340                                                NaN   \n",
      "2450  I was using the suggested deferment options an...   \n",
      "4471                                                NaN   \n",
      "\n",
      "                                Company public response  \\\n",
      "578   Company believes it acted appropriately as aut...   \n",
      "648                                                 NaN   \n",
      "1340                                                NaN   \n",
      "2450                                                NaN   \n",
      "4471                                                NaN   \n",
      "\n",
      "                                Company State ZIP code            Tags  \\\n",
      "578               FIRSTBANK PUERTO RICO    PR    00623  Older American   \n",
      "648        BANCO POPULAR DE PUERTO RICO    PR    00957             NaN   \n",
      "1340  Financial Recovery Services, Inc.    PR    00961             NaN   \n",
      "2450            Navient Solutions, LLC.    PR    00926             NaN   \n",
      "4471              FIRSTBANK PUERTO RICO    PR    00745   Servicemember   \n",
      "\n",
      "     Consumer consent provided? Submitted via Date sent to company  \\\n",
      "578                         NaN         Phone           07/10/2015   \n",
      "648            Consent provided           Web           08/19/2018   \n",
      "1340                        NaN           Web           03/02/2015   \n",
      "2450           Consent provided           Web           05/03/2018   \n",
      "4471                        NaN           Web           09/17/2012   \n",
      "\n",
      "     Company response to consumer Timely response? Consumer disputed?  \\\n",
      "578       Closed with explanation              Yes                 No   \n",
      "648       Closed with explanation              Yes                NaN   \n",
      "1340      Closed with explanation              Yes                 No   \n",
      "2450      Closed with explanation              Yes                NaN   \n",
      "4471      Closed with explanation              Yes                Yes   \n",
      "\n",
      "      Complaint ID  \n",
      "578        1429402  \n",
      "648        2995585  \n",
      "1340       1253972  \n",
      "2450       2896125  \n",
      "4471        153494  \n",
      "size of complaints :  (2164, 18)\n",
      "Product\n",
      "Credit card         329\n",
      "Credit reporting    625\n",
      "Debt collection     565\n",
      "Mortgage            543\n",
      "Student loan        102\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prdata = data[data['State'] == 'PR']\n",
    "print(prdata.head())\n",
    "print('size of prdata : ', prdata.shape)\n",
    "somevalues = ['Mortgage', 'Credit card', 'Student loan', \\\n",
    "              'Credit reporting', 'Debt collection']\n",
    "complaints = prdata.loc[prdata['Product'].isin(somevalues)]\n",
    "print(complaints.head())\n",
    "print('size of complaints : ', complaints.shape)\n",
    "\n",
    "piedata = complaints.groupby('Product').size()\n",
    "print(piedata)\n",
    "piedata.plot(kind='pie', autopct=\"%1.1f%%\")\n",
    "plt.ylabel(\"\") # remove the None\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following Mastering SciPy, this script makes a histogram\n",
    "of the data in the Consumer Complaints Database, available at\n",
    "`http://catalog.data.gov/dataset/consumer-complaint-database`,\n",
    "to analyze the ratio of daily complaints about credit cards\n",
    "against the daily complaints on mortgages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data.groupby(['Date received',\n",
    "                   'Product']).size()\n",
    "df = df.unstack()\n",
    "ratios = df['Mortgage'] / df['Credit card']\n",
    "ratios.hist(bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting graph indicates, for instance, that there are a few days\n",
    "in which the number of complaints on mortgages is about 12 times the\n",
    "number of complaints on credit cards. It also shows that the most frequent\n",
    "situation is that of days in which the number of complaints on mortgages\n",
    "roughly triplicates the number of credit card complaints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For variables measured at intervals over time, we employ a time plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Date received'] = pd.to_datetime(data['Date received'])\n",
    "\n",
    "ts = data.groupby('Date received').size()\n",
    "ts.plot()\n",
    "plt.ylabel('Daily complaints')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note both the oscillating nature of the graph,\n",
    "as well as the slight upward trend in complaints during this period.\n",
    "We also observe what looks like a few outliers - one in March 2012,\n",
    "a few more in between May and June of the same year,\n",
    "and a few more in January and February 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For variables measured at intervals over time, we employ a time plot.\n",
    "The library pandas handles these beautifully. For instance,\n",
    "to observe the amount of daily complaints received from January 1, 2012\n",
    "to December 31, 2013, we issue the following command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date received\n",
      "2011-12-01    120\n",
      "2011-12-02    138\n",
      "2011-12-03     26\n",
      "2011-12-04     22\n",
      "2011-12-05    164\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Date received'] = pd.to_datetime(data['Date received'])\n",
    "\n",
    "ts = data.groupby('Date received').size()\n",
    "print(ts.head())\n",
    "begintime = pd.Timestamp('2012-01-01 00:00:00')\n",
    "endtime = pd.Timestamp('2014-01-01 00:00:00')\n",
    "ts = ts[begintime: endtime]\n",
    "ts.plot()\n",
    "plt.ylabel('Daily complaints')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note both the oscillating nature of the graph,\n",
    "as well as the slight upward trend in complaints during this period.\n",
    "We also observe what looks like a few outliers - one in March 2012,\n",
    "a few more in between May and June of the same year,\n",
    "and a few more in January and February 2013."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.10"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}