13_Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "__author__ = \"kyubyong. kbpark.linguist@gmail.com\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.11.3'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Order statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q1. Return the minimum value of x along the second axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=\n",
      " [[0 1]\n",
      " [2 3]]\n",
      "ans=\n",
      " [0 2]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(4).reshape((2, 2))\n",
    "print(\"x=\\n\", x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q2. Return the maximum value of x along the second axis. Reduce the second axis to the dimension with size one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=\n",
      " [[0 1]\n",
      " [2 3]]\n",
      "ans=\n",
      " [[1]\n",
      " [3]]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(4).reshape((2, 2))\n",
    "print(\"x=\\n\", x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q3. Calcuate the difference between the maximum and the minimum of x along the second axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=\n",
      " [[0 1 2 3 4]\n",
      " [5 6 7 8 9]]\n",
      "ans=\n",
      " [4 4]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(10).reshape((2, 5))\n",
    "print(\"x=\\n\", x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q4. Compute the 75th percentile of x along the second axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=\n",
      " [[ 1  2  3  4  5]\n",
      " [ 6  7  8  9 10]]\n",
      "ans=\n",
      " [ 4.  9.]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(1, 11).reshape((2, 5))\n",
    "print(\"x=\\n\", x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Averages and variances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q5. Compute the median of flattened x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=\n",
      " [[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "ans=\n",
      " 5.0\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(1, 10).reshape((3, 3))\n",
    "print(\"x=\\n\", x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q6. Compute the weighted average of x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.66666666667\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(5)\n",
    "weights = np.arange(1, 6)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q7. Compute the mean, standard deviation, and variance of x along the second axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=\n",
      " [0 1 2 3 4]\n",
      "mean=\n",
      " 2.0\n",
      "std=\n",
      " 1.41421356237\n",
      "variance=\n",
      " 2.0\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(5)\n",
    "print(\"x=\\n\",x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q8. Compute the covariance matrix of x and y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans=\n",
      " [[ 1. -1.]\n",
      " [-1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 2])\n",
    "y = np.array([2, 1, 0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q9. In the above covariance matrix, what does the -1 mean?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q10. Compute Pearson product-moment correlation coefficients of x and y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans=\n",
      " [[ 1.          0.92857143]\n",
      " [ 0.92857143  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 3])\n",
    "y = np.array([2, 4, 5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q11. Compute cross-correlation of x and y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans=\n",
      " [19]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 3])\n",
    "y = np.array([2, 4, 5])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q12. Compute the histogram of x against the bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans=\n",
      " (array([2, 3, 1], dtype=int64), array([0, 1, 2, 3]))\n"
     ]
    },
    {
     "data": {
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ZutrOJBvnlc2MFwBY72bHy1w/WPGtDAoLrbWfV9WDSc5PcluSVFWN1/94wFttyejwxBJ2\nJdk6pIsAsI4s9AX6y0kuW9GtTHIY4jNJvjgODQ9k9PX/tUm+mCRVdU2SU1prO8brVyV5PMl3kmxI\n8tEk70py4XI7DwAceoPDQmvtz8f3VPijjA4/PJTk3a2158ZVNic5dU6TYzO6L8MpGV1i+XCS81tr\n9yyn4wDAdEx0gmNr7cYkNy7y2uXz1q9Lct0k2wEAVp9nQwAAXcICANAlLAAAXcICANAlLAAAXcIC\nANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAl\nLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAA\nXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLAAAXcICANAlLBxRZle7A6wb5hrTYq6t\nBROFhar6RFU9XlU/q6r7q+o3lqh/XlU9WFUvVtWeqtoxWXfp85+KaTHXmBZzbS0YHBaq6neSXJ/k\nD5P8epJvJ7m9qk5cpP5pSb6a5K4kZyW5IcnNVXXhZF0GAKZpkj0LO5N8obV2a2vt0SRXJHkhyYcX\nqf/xJI+11j7ZWvtea+3fJ/lP4/cBANa4QWGhqo5Jsi2jvQRJktZaS3JnkrMXafa28etz3d6pDwCs\nIUcPrH9ikl9Jsnde+d4kZyzSZvMi9U+oquNaay8t0GbD6I//kuR/DOzieva/kvyH1e7EYeTlOX//\nWpJHVqsjh6EfJPnyanfiMPPfx3+aa8OYa8MdmGsHfpcu39CwMC2njf74d6vaicPTx1a7A4epq1e7\nA4ehy1a7A4cpc204c21CpyX55kq80dCw8NdJ9ifZNK98U5JnFmnzzCL1n19kr0IyOkzxwSRPJHlx\nYB8BYD3bkFFQuH2l3nBQWGit/byqHkxyfpLbkqSqarz+x4s0uy/JxfPKLhqXL7adHyX5syF9AwBe\nsSJ7FA6Y5GqIzyT5aFV9qKp+LclNSV6b5ItJUlXXVNUtc+rflOT1VXVtVZ1RVVcm+cD4fQCANW7w\nOQuttT8f31PhjzI6nPBQkne31p4bV9mc5NQ59Z+oqvck2ZXk9zM6W+UjrbX5V0gAAGtQja58BABY\nmGdDAABdwgIA0LUqYcGDqCYzZNyqantVvTxv2V9VJ02zz6upqs6tqtuq6ofjn//Sg2iz7ufa0HEz\n15Kq+lRVPVBVz1fV3qr6SlW98SDardv5NsmYmWtJVV1RVd+uqn3j5ZtV9ZtLtFn2PJt6WPAgqskM\nHbexluQNGZ10ujnJya21Zw91X9eQ4zM6AffKjMaiy1x7xaBxG1vvc+3cJJ9L8tYkFyQ5JskdVfWa\nxRqYb8PHbGy9z7Unk/xBkq0ZPX7hG0n+oqrOXKjyis2z1tpUlyT3J7lhznpldIXEJxepf22Sh+eV\nzSb52rT7vprLBOO2PaMbaJ2w2n1fC0tG93a+dIk65tpk42au/fKYnDgeu3d06phvw8fMXFt4XH6U\n5PJFXluReTbVPQseRDWZCcctGQWKh6rqqaq6o6refmh7ethb93NtGcy1V3tdRt+Af9ypY7692sGM\nWWKuvaKqjqqq383oXkeL3ehwRebZtA9D9B5EtXmRNt0HUa1s99asScbt6YweFPH+JO/LaNfV3VW1\n5VB18ghgrk3GXJtjfFfbzya5t7X23U5V821swJiZa0mq6s1V9ZMkLyW5MclvtdYeXaT6isyztfog\nKZaptbYnyZ45RfdX1elJdiZZNydRceiZa7/kxiRvSnLOanfkMHJQY2auveLRjM4/2JjRHZFvrap3\ndgLDsk17z8K0HkR1pJlk3BbyQJJfXalOHYHMtZWzLudaVX0+ySVJzmutPb1EdfMtg8dsIeturrXW\nftFae6y19q3W2r/O6IT3qxapviLzbKphobX28yQHHkSV5FUPolrsoRf3za0/1n0Q1ZFmwnFbyJaM\nduOxsHU/11bQuptr4196703yrtba9w+iybqfbxOM2ULW3VxbwFFJFjuksDLzbBXO2vztJC8k+VCS\nX0vyhYzO5Pw749evSXLLnPqnJflJRmd0npHR5Vz/N8kFq30G6hoft6uSXJrk9CT/IKPjgT/PKL2v\n+s8zpTE7PqNddVsyOsv6n4/XTzXXVnTczLXRbvT/ndHlgJvmLBvm1Pm0+bbsMTPXRmNybpK/l+TN\n4/+Pv0jyj8evH5LPtdX6Ya9M8kSSn2WUbt4y57U/TfKNefXfmdE3658l+Z9J/ulq/4Ot9XFL8q/G\nY/XTJM9ldCXFO1f7Z5jyeG0f/7LbP2/5E3Nt5cbNXHvlEtP547U/yYfm1DHfljlm5lpLkpuTPDae\nM88kueNAUDiU88yDpACALs+GAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6hAUAoEtYAAC6\nhAUAoEtYAAC6/h+sRyodSeNw6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3faa891d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([0.5, 0.7, 1.0, 1.2, 1.3, 2.1])\n",
    "bins = np.array([0, 1, 2, 3])\n",
    "print(\"ans=\\n\", ...)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.hist(x, bins=bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q13. Compute the 2d histogram of x and y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans=\n",
      " [[ 3.  0.  0.  0.]\n",
      " [ 0.  2.  0.  0.]\n",
      " [ 0.  0.  1.  1.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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wsJ/Hrbfeuij2YyE/j7vuumtR7Acs7Odx1113LYr9gPI/j263+9zfxpGRETqd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\nsrmQJEmlsrmQJEml+v8Bev3xcJRJzrUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3fbcfa7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xedges = [0, 1, 2, 3]\n",
    "yedges = [0, 1, 2, 3, 4]\n",
    "x = np.array([0, 0.1, 0.2, 1., 1.1, 2., 2.1])\n",
    "y = np.array([0, 0.1, 0.2, 1., 1.1, 2., 3.3])\n",
    "...\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q14. Count number of occurrences of 0 through 7 in x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans=\n",
      " [1 3 1 1 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 1, 3, 2, 1, 7])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q15. Return the indices of the bins to which each value in x belongs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ans=\n",
      " [1 4 3 2]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0.2, 6.4, 3.0, 1.6])\n",
    "bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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