Python for Data Analysis book notes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:28:24.520146Z",
     "start_time": "2018-06-04T22:28:24.259959Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.random.randint(0,100,(200,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.random.randint(0,100,(10,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[23695, 19107, 18662, ..., 30691, 16708, 10735],\n",
       "       [29749, 29865, 18006, ..., 42400, 15739, 21220],\n",
       "       [10439,  8839,  7212, ..., 18052,  6201,  8847],\n",
       "       ...,\n",
       "       [16411, 16180, 15376, ..., 25028,  8693, 15779],\n",
       "       [28125, 28314, 15971, ..., 30293, 18480, 14090],\n",
       "       [24797, 22857, 21444, ..., 27131, 13099, 13919]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%prun c = np.dot(a,b)\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Transposing Arrays and Swapping Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "arr = np.arange(16).reshape((2,2,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7]],\n",
       "\n",
       "       [[ 8,  9, 10, 11],\n",
       "        [12, 13, 14, 15]]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  8],\n",
       "        [ 4, 12]],\n",
       "\n",
       "       [[ 1,  9],\n",
       "        [ 5, 13]],\n",
       "\n",
       "       [[ 2, 10],\n",
       "        [ 6, 14]],\n",
       "\n",
       "       [[ 3, 11],\n",
       "        [ 7, 15]]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# special case of swapaxes\n",
    "arr.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  4],\n",
       "        [ 1,  5],\n",
       "        [ 2,  6],\n",
       "        [ 3,  7]],\n",
       "\n",
       "       [[ 8, 12],\n",
       "        [ 9, 13],\n",
       "        [10, 14],\n",
       "        [11, 15]]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.swapaxes(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[ 4,  5,  6,  7],\n",
       "        [12, 13, 14, 15]]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transpose can take a tuple of axis numbers to permute the axes\n",
    "# 2nd axis first, followed by the 1st, then the last...\n",
    "arr.transpose((1,0,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Universal Functions and Array Oriented programming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Vectorization** -- replacing explicit loops with array expressions.  Orders of magnitude faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(10); arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  1.        ,  1.41421356,  1.73205081,  2.        ,\n",
       "        2.23606798,  2.44948974,  2.64575131,  2.82842712,  3.        ])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of an unary ufunc - take an array return an array\n",
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.60567523,   3.27100315, -11.47365244,   0.52619633,\n",
       "         3.01175275,  12.18572824,  -3.72992482,  -7.41252386,\n",
       "        -6.49112988,  -2.00875997])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.random.randn(10)*5; arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   3., -11.,   0.,   3.,  12.,  -3.,  -7.,  -6.,  -2.]),\n",
       " array([ 0.60567523,  0.27100315, -0.47365244,  0.52619633,  0.01175275,\n",
       "         0.18572824, -0.72992482, -0.41252386, -0.49112988, -0.00875997]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraction, integral = np.modf(arr2)\n",
    "integral, fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.60567523,   3.27100315,   2.        ,   3.        ,\n",
       "         4.        ,  12.18572824,   6.        ,   7.        ,\n",
       "         8.        ,   9.        ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# binary ufunc - take 2 arrays return one array\n",
    "np.maximum(arr, arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "points = np.arange(-5, 5, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# np.meshgrid -> takes 2 1d arrays and produces 2 2d arrays corresponding to all pairs (x, y) in the arrays\n",
    "xs, ys = np.meshgrid(points, points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
       "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
       "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
       "       ..., \n",
       "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
       "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
       "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "z = np.sqrt(xs**2 + ys**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.07106781,  7.06400028,  7.05693985, ...,  7.04988652,\n",
       "         7.05693985,  7.06400028],\n",
       "       [ 7.06400028,  7.05692568,  7.04985815, ...,  7.04279774,\n",
       "         7.04985815,  7.05692568],\n",
       "       [ 7.05693985,  7.04985815,  7.04278354, ...,  7.03571603,\n",
       "         7.04278354,  7.04985815],\n",
       "       ..., \n",
       "       [ 7.04988652,  7.04279774,  7.03571603, ...,  7.0286414 ,\n",
       "         7.03571603,  7.04279774],\n",
       "       [ 7.05693985,  7.04985815,  7.04278354, ...,  7.03571603,\n",
       "         7.04278354,  7.04985815],\n",
       "       [ 7.06400028,  7.05692568,  7.04985815, ...,  7.04279774,\n",
       "         7.04985815,  7.05692568]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hZPO1tCDVCzHvFxyYztbdAlgt4wHIpnX99gWItfPylobXCjAirUnNbFpgWwJG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\nZW/l/wda1TWSxzW2fAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a4d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(z, cmap=plt.cm.gray)\n",
    "plt.title(\"Image plot of $\\sqrt{x^2 + y^2}$ for a grid of values\"); plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Conditional Logic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1.2,  1.4,  1.6,  1.8])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xarr = np.arange(1,2,.2); xarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2. ,  2.4,  2.8,  3.2,  3.6])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yarr = np.arange(2,4,.4); yarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cond = np.array([True, True, False, True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 1.2, 2.7999999999999998, 1.5999999999999999, 3.5999999999999996]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as a list comprehension -> slow, won't function with multidimensional arrays\n",
    "[(x if c else y) for x,y,c in zip(xarr, yarr, cond)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1.2,  2.8,  1.6,  3.6])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same using np.where\n",
    "# vectorized version of ternary expression: x if condition else y\n",
    "np.where(cond, xarr, yarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.75861624, -0.67417816, -1.98370089, -0.95671057],\n",
       "       [-0.10972787,  0.30311052,  1.77978788,  0.5280245 ],\n",
       "       [ 0.43177658,  0.99957938,  0.04464381,  0.5077298 ],\n",
       "       [-2.13224788,  1.26792701,  0.27561895,  1.95402795]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# arguments can be scalars instead of arrays\n",
    "# we have a random matrix we want to replace all positive values with 2 and negatives with -2\n",
    "arr = np.random.randn(4,4); arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2, -2, -2, -2],\n",
       "       [-2,  2,  2,  2],\n",
       "       [ 2,  2,  2,  2],\n",
       "       [-2,  2,  2,  2]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(arr > 0, 2, -2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Math and Statistical Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.63461572,  1.27577262, -1.04449038, -0.05022326],\n",
       "       [ 0.01910591, -0.23498075,  0.30623102,  0.09705901],\n",
       "       [-1.18191972,  0.66321773, -0.90113059,  0.01469055],\n",
       "       [-0.19144736,  1.08638564, -0.32204615,  0.44420387],\n",
       "       [-0.90874457,  1.50529216, -0.0235597 , -1.12367686]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(5,4); arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.053217744421186743"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.mean()\n",
    "# np.mean(arr) - same thing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.62839002,  4.29568741, -1.98499581, -0.61794669])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can take optional axis argument\n",
    "arr.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(1,7,1); arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1,  3,  6, 10, 15, 21]), array([  1,   2,   6,  24, 120, 720]))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accumulation functions, can also specify axis if operating on arrays\n",
    "arr.cumsum(), arr.cumprod()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "heading_collapsed": true
   },
   "source": [
    "### Set Logic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:17:23.515686Z",
     "start_time": "2018-06-04T22:17:23.512360Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "values = np.array([6,0,0,3,2,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:17:33.829845Z",
     "start_time": "2018-06-04T22:17:33.823160Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 3, 5, 6])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorted unique values\n",
    "np.unique(values)\n",
    "# sorted(set(values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:18:34.336115Z",
     "start_time": "2018-06-04T22:18:34.331065Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.intersect1d(values, [4,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:18:58.324800Z",
     "start_time": "2018-06-04T22:18:58.319656Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.union1d(values, [4,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Linear algebra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:21:47.252878Z",
     "start_time": "2018-06-04T22:21:47.249042Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "x = np.array([[1.,2.,3.],[4.,5.,6.]])\n",
    "y = np.array([[6.,23.],[-1,7],[8,9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:21:50.732911Z",
     "start_time": "2018-06-04T22:21:50.728028Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 1.,  2.,  3.],\n",
       "        [ 4.,  5.,  6.]]), array([[  6.,  23.],\n",
       "        [ -1.,   7.],\n",
       "        [  8.,   9.]]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:22:00.733088Z",
     "start_time": "2018-06-04T22:22:00.727795Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  28.,   64.],\n",
       "       [  67.,  181.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:22:34.356592Z",
     "start_time": "2018-06-04T22:22:34.351326Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6.,  15.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(x, np.ones(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:23:26.744976Z",
     "start_time": "2018-06-04T22:23:26.739740Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  28.,   64.],\n",
       "       [  67.,  181.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x@y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:23:32.008981Z",
     "start_time": "2018-06-04T22:23:32.003467Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  98.,  127.,  156.],\n",
       "       [  27.,   33.,   39.],\n",
       "       [  44.,   61.,   78.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y@x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:55:37.157074Z",
     "start_time": "2018-06-04T22:55:37.153053Z"
    }
   },
   "outputs": [],
   "source": [
    "data = {'state': ['Ohio', 'Ohio', 'Vermont', 'Vermont', 'Nevada', 'Nevada'],\n",
    "        'year': [2000, 2001, 2002, 2003, 2004, 2005],\n",
    "        'pop': [1.5, 1.7, 3.6, 3.5, 2.8, 3.5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:55:55.441402Z",
     "start_time": "2018-06-04T22:55:55.431309Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2003</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2004</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2005</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state  year  pop\n",
       "0     Ohio  2000  1.5\n",
       "1     Ohio  2001  1.7\n",
       "2  Vermont  2002  3.6\n",
       "3  Vermont  2003  3.5\n",
       "4   Nevada  2004  2.8\n",
       "5   Nevada  2005  3.5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(data); frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:56:20.905270Z",
     "start_time": "2018-06-04T22:56:20.893983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    state  pop\n",
       "0  2000     Ohio  1.5\n",
       "1  2001     Ohio  1.7\n",
       "2  2002  Vermont  3.6\n",
       "3  2003  Vermont  3.5\n",
       "4  2004   Nevada  2.8\n",
       "5  2005   Nevada  3.5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reorder columns\n",
    "frame = pd.DataFrame(data, columns=['year','state','pop']); frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:57:27.801916Z",
     "start_time": "2018-06-04T22:57:27.798143Z"
    }
   },
   "outputs": [],
   "source": [
    "debt = pd.Series([10, 12, 23, 34, 34, 11])\n",
    "# or simply: debt = [10, 12, 23, 34, 34, 11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:57:57.373407Z",
     "start_time": "2018-06-04T22:57:57.368237Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'pop'], dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:58:05.211952Z",
     "start_time": "2018-06-04T22:58:05.208384Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>3.5</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.8</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    state  pop  debt\n",
       "0  2000     Ohio  1.5    10\n",
       "1  2001     Ohio  1.7    12\n",
       "2  2002  Vermont  3.6    23\n",
       "3  2003  Vermont  3.5    34\n",
       "4  2004   Nevada  2.8    34\n",
       "5  2005   Nevada  3.5    11"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['debt'] = debt\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:58:38.489625Z",
     "start_time": "2018-06-04T22:58:38.483280Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    12\n",
       "2    23\n",
       "3    34\n",
       "4    34\n",
       "5    11\n",
       "Name: debt, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access a column w/ df.name or df[name]\n",
    "frame.debt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T22:58:46.808809Z",
     "start_time": "2018-06-04T22:58:46.802192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year        2003\n",
       "state    Vermont\n",
       "pop          3.5\n",
       "debt          34\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access a row w/ .loc\n",
    "frame.loc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    state  pop  debt\n",
       "0  2000     Ohio  1.5   0.0\n",
       "1  2001     Ohio  1.7   1.0\n",
       "2  2002  Vermont  3.6   2.0\n",
       "3  2003  Vermont  3.5   3.0\n",
       "4  2004   Nevada  2.8   4.0\n",
       "5  2005   Nevada  3.5   5.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.debt = np.arange(6.)\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-04T23:03:24.206018Z",
     "start_time": "2018-06-04T23:03:24.202219Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>Nevada</td>\n",
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       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    state  pop  debt\n",
       "0  2000     Ohio  1.5  -1.2\n",
       "1  2001     Ohio  1.7  -1.2\n",
       "2  2002  Vermont  3.6  -2.2\n",
       "3  2003  Vermont  3.5  -2.2\n",
       "4  2004   Nevada  2.8  -1.0\n",
       "5  2005   Nevada  3.5  -1.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = pd.Series([-1.2, -2.2, -1], index=[0,2,4])\n",
    "val = val.reindex(range(6), method='ffill')  # ffil: interpolates nan values forward\n",
    "frame['debt'] = val\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2000, 'Ohio', 1.5, -1.2],\n",
       "       [2001, 'Ohio', 1.7, -1.2],\n",
       "       [2002, 'Vermont', 3.6, -2.2],\n",
       "       [2003, 'Vermont', 3.5, -2.2],\n",
       "       [2004, 'Nevada', 2.8, -1.0],\n",
       "       [2005, 'Nevada', 3.5, -1.0]], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.values  # convert back to numpy ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=6, step=1)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'pop', 'debt'], dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Nevada</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state  pop  year  debt\n",
       "0     Ohio  1.5  2000  -1.2\n",
       "1     Ohio  1.7  2001  -1.2\n",
       "2  Vermont  3.6  2002  -2.2\n",
       "3  Vermont  3.5  2003  -2.2\n",
       "4   Nevada  2.8  2004  -1.0\n",
       "5   Nevada  3.5  2005  -1.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.reindex(columns=['state','pop','year','debt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "   year    state  pop  debt\n",
       "0  2000     Ohio  1.5  -1.2\n",
       "1  2001     Ohio  1.7  -1.2\n",
       "3  2003  Vermont  3.5  -2.2\n",
       "5  2005   Nevada  3.5  -1.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.drop([2,4]) # dropping rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   year    state\n",
       "0  2000     Ohio\n",
       "1  2001     Ohio\n",
       "2  2002  Vermont\n",
       "3  2003  Vermont\n",
       "4  2004   Nevada\n",
       "5  2005   Nevada"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.drop(['pop','debt'], axis=1)  # dropping columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Ohio</td>\n",
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       "      <th>2</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>-2.2</td>\n",
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       "      <th>3</th>\n",
       "      <td>Vermont</td>\n",
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      ],
      "text/plain": [
       "     state  debt\n",
       "0     Ohio  -1.2\n",
       "1     Ohio  -1.2\n",
       "2  Vermont  -2.2\n",
       "3  Vermont  -2.2\n",
       "4   Nevada  -1.0\n",
       "5   Nevada  -1.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame[['state','debt']]  # indexing w/ list selects columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Vermont</td>\n",
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      ],
      "text/plain": [
       "   year    state  pop  debt\n",
       "0  2000     Ohio  1.5  -1.2\n",
       "1  2001     Ohio  1.7  -1.2\n",
       "2  2002  Vermont  3.6  -2.2"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame[:3] # indexing w/ slice selects rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state    Ohio\n",
       "pop       1.7\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.loc[1, ['state','pop']]  # selects row: 1 and columns: ['state','pop'] using axis labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state    Ohio\n",
       "pop       1.7\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.iloc[1, [1,2]]  # same as above using integers (indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Vermont'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.at[2,'state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year        2003\n",
       "state    Vermont\n",
       "pop          3.5\n",
       "debt        -2.2\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.loc[3]  # indexing a row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
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       "      <th>pop</th>\n",
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       "      <td>Ohio</td>\n",
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       "      <td>-1.2</td>\n",
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       "    <tr>\n",
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       "      <td>2004</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.8</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>2003</td>\n",
       "      <td>Vermont</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   year    state  pop  debt\n",
       "0  2000     Ohio  1.5  -1.2\n",
       "1  2001     Ohio  1.7  -1.2\n",
       "4  2004   Nevada  2.8  -1.0\n",
       "3  2003  Vermont  3.5  -2.2\n",
       "5  2005   Nevada  3.5  -1.0\n",
       "2  2002  Vermont  3.6  -2.2"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "frame.sort_values(by='pop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame['debt/pop'] = frame['debt'] / frame['pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Nevada</td>\n",
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       "      <td>Nevada</td>\n",
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       "      <td>-1.0</td>\n",
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      "text/plain": [
       "   year    state  pop  debt  debt/pop\n",
       "0  2000     Ohio  1.5  -1.2 -0.800000\n",
       "1  2001     Ohio  1.7  -1.2 -0.705882\n",
       "2  2002  Vermont  3.6  -2.2 -0.611111\n",
       "3  2003  Vermont  3.5  -2.2 -0.628571\n",
       "4  2004   Nevada  2.8  -1.0 -0.357143\n",
       "5  2005   Nevada  3.5  -1.0 -0.285714"
      ]
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   "execution_count": null,
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   "outputs": [],
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