{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Section 2 - numpy, scipy, and pandas #\n",
    "\n",
    "**Jonathan Woodring** *Los Alamos National Laboratory*\n",
    "\n",
    "### modules ###\n",
    "\n",
    "- *numpy*: provides fast matrix and array operations\n",
    "- *scipy*: a set of specific analysis tools\n",
    "- *pandas*: a tabular data model for numpy arrays\n",
    "\n",
    "### data input/output in this section ###\n",
    "\n",
    "- *sqlite3*: to and from SQLite data\n",
    "- *numpy*: to and from binary data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# the standard name for numpy is np\n",
    "import numpy \n",
    "\n",
    "# of course you can do:\n",
    "# from numpy import *\n",
    "# but, lets not\n",
    "\n",
    "# also, we'll need pyplot\n",
    "%pylab inline --no-import-all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# comparing numpy arrays to Python lists #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done in 0.257647 seconds\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "# two million long lists\n",
    "A = range(0, 1000000)\n",
    "B = range(0, 1000000)\n",
    "# let me preallocate Cl, rather than Cl.append\n",
    "Cl = [0] * 1000000\n",
    "\n",
    "# lets time C = A + B (adding two vectors)\n",
    "start = time.time()\n",
    "for i in range(0, len(A)):\n",
    "    Cl[i] = A[i] + B[i]\n",
    "list_time = time.time() - start\n",
    "\n",
    "print 'done in %f seconds' % list_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done in 0.002438 seconds\n"
     ]
    }
   ],
   "source": [
    "# C = A + B, with numpy arrays\n",
    "\n",
    "# initializing A and B from the lists\n",
    "A = numpy.array(A, numpy.int64)  \n",
    "B = numpy.array(B, numpy.int64)\n",
    "\n",
    "# lets time C = A + B, again\n",
    "start = time.time()\n",
    "Ca = A + B\n",
    "array_time = time.time() - start\n",
    "\n",
    "print 'done in %f seconds' % array_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C = A + B with lists: 0.257647037506\n",
      "C = A + B with numpy: 0.00243806838989\n",
      "speedup: 105.676706435 times faster\n",
      "does Cl == Ca? True\n"
     ]
    }
   ],
   "source": [
    "# how much faster is numpy?\n",
    "print 'C = A + B with lists:', list_time\n",
    "print 'C = A + B with numpy:', array_time\n",
    "print 'speedup: ' + str(list_time / array_time) + ' times faster'\n",
    "# just to show they are equal\n",
    "print 'does Cl == Ca?', numpy.all(Cl == Ca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Numpy is fast\n",
    "    1. Uses machine native code, rather than intepreted\n",
    "    2. Data elements are typed, rather than PyObjects\n",
    "    3. Vectorization (SIMD)\n",
    "- Multi-dimensional indexing is more natural: [i,j] vs. [i][j]\n",
    "- Operations over collections (vectors/arrays) like R and Matlab (implicit vectorization):\n",
    "    - There's a reason we use Matlab, Fortran, R and Numpy for numerical computing\n",
    "- Critical mass from the community\n",
    "    - It's the non-standard \"standard\" library for all things numerical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A simple heat transfer simulation #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A[20,20]: 1e+06\n"
     ]
    },
    {
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       "ZhkOjmZmGQ6OZmYZDo5mZhkOjmZmGQ6OZmYZ/x+AEpNBc+ynowAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18a23fd650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initialize our 2D array to be 40x40\n",
    "A = numpy.zeros((40, 40), numpy.float32)\n",
    "\n",
    "# put a hot spot in the middle\n",
    "A[20,20] = 1000000.0\n",
    "\n",
    "# we index with tuples for multi-dimensional arrays\n",
    "print 'A[20,20]:', A[20,20]\n",
    "\n",
    "# show initial condition\n",
    "def show_temp(array):\n",
    "    plt.figure()\n",
    "    plt.imshow(array)\n",
    "    plt.set_cmap('hot')\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "    \n",
    "# the colors will rescale each time we plot\n",
    "show_temp(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# our little simulation\n",
    "def iterate(A, steps):\n",
    "    scale = .5 / 4.0\n",
    "    # iterate for a bit\n",
    "    for i in range(0, steps):\n",
    "        # roll shifts an array on an axis\n",
    "        left = numpy.roll(A, -1, 0)\n",
    "        right = numpy.roll(A, 1, 0)\n",
    "        up = numpy.roll(A, 1, 1)\n",
    "        down = numpy.roll(A, -1, 1)\n",
    "        \n",
    "        # vectorized math\n",
    "        delta = left + right + up + down - 4 * A\n",
    "        \n",
    "        # update A with dh/dt\n",
    "        A = delta * scale + A\n",
    "\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887faa410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run it\n",
    "A = iterate(A, 200)\n",
    "\n",
    "# show where we ended\n",
    "show_temp(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3183.14\n",
      "9.93174e+06\n",
      "625.0\n",
      "50000.0\n",
      "[[  16.38537598   18.84791756   27.44467163 ...,   46.08278656\n",
      "    27.44467545   18.84791756]\n",
      " [  18.84791756   21.67605591   31.54674721 ...,   52.93860626\n",
      "    31.54674721   21.67605591]\n",
      " [  27.44467163   31.54674721   45.85525513 ...,   76.8356781    45.85526276\n",
      "    31.54674721]\n",
      " ..., \n",
      " [  46.08278656   52.93860245   76.8356781  ...,  128.51898193   76.8356781\n",
      "    52.93860626]\n",
      " [  27.44467163   31.54674721   45.85525513 ...,   76.8356781    45.85526276\n",
      "    31.54674721]\n",
      " [  18.84791756   21.67605591   31.54674721 ...,   52.93860626\n",
      "    31.54674721   21.67605591]]\n",
      "[[ 173429.734375  185506.84375   222377.28125  ...,  285888.3125\n",
      "   222377.28125   185506.84375 ]\n",
      " [ 185506.875     198424.984375  237862.953125 ...,  305796.8125\n",
      "   237862.96875   198424.984375]\n",
      " [ 222377.265625  237862.9375    285139.625    ...,  366575.9375\n",
      "   285139.65625   237862.96875 ]\n",
      " ..., \n",
      " [ 285888.3125    305796.8125    366575.9375   ...,  471270.875\n",
      "   366575.9375    305796.8125  ]\n",
      " [ 222377.265625  237862.9375    285139.625    ...,  366575.9375\n",
      "   285139.65625   237862.96875 ]\n",
      " [ 185506.875     198424.984375  237862.953125 ...,  305796.8125\n",
      "   237862.96875   198424.984375]]\n"
     ]
    }
   ],
   "source": [
    "print A[20, 20] # scalars\n",
    "\n",
    "print A[20, 20] * A[21, 21]\n",
    "\n",
    "print numpy.mean(A) # reductions\n",
    "\n",
    "print numpy.sum(A - .95 * A)\n",
    "\n",
    "print A*A # element-wise multiply\n",
    "\n",
    "print A.dot(A) # matrix multiply (numpy.dot(A, A), too)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading and Writing Binary Data #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887c64fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18a23fd890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# store the state/checkpoint as a \"raw\" binary file\n",
    "A.tofile('A.bin')\n",
    "\n",
    "# just to show we actual alter B\n",
    "B = numpy.zeros((40, 40), numpy.float32)\n",
    "show_temp(B)\n",
    "\n",
    "# to read A back, have to specify the data type\n",
    "# and possibily endianess (byte-order)\n",
    "B = numpy.fromfile('A.bin', numpy.float32)\n",
    "\n",
    "# we also have to reshape it to the same shape as A\n",
    "# otherwise B will be a 1D array\n",
    "B = numpy.reshape(B, (40, 40))\n",
    "\n",
    "show_temp(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
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       "JpvsgrEJaJNNNtkFYxPQJptssgvGJqBNNtlkF4xNQJtssskuGJuANtlkk10w9v8DOotmz/ZuAskA\n",
       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887c55790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# though if we use numpy's binary format,\n",
    "# we don't have to worry about shape, type, and endianess\n",
    "# when we reload it\n",
    "numpy.save('A.npy', A)\n",
    "C = numpy.load('A.npy')\n",
    "\n",
    "# but this does mean if you are sharing binary data\n",
    "# with another program it has to know how to parse\n",
    "# .npy -- \"raw\" binary is easier to share data in the\n",
    "# short term\n",
    "\n",
    "print numpy.all((A == B) & (A == C))\n",
    "show_temp(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4.047885417938232, 4.341418743133545, 5.238766193389893, 6.7884297370910645)\n"
     ]
    }
   ],
   "source": [
    "# struct is one way to read binary if you want\n",
    "# more control over reading the binary data\n",
    "# say for instance, reading binary headers\n",
    "import struct\n",
    "\n",
    "f = open('A.bin', 'r')\n",
    "\n",
    "# read 16 bytes and unpack them into 4 floats\n",
    "print struct.unpack('ffff', f.read(16))\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indices and Slices #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "0JlZ9hzozCx7/w/BjUx0rjtTmgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18875459d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# clear it for now\n",
    "A = numpy.zeros((40, 40), numpy.float32)\n",
    "\n",
    "# \":\" means all indices in that dimension\n",
    "# row, column indexing\n",
    "A[10,:] = 10000.0\n",
    "\n",
    "show_temp(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "FZ4DnZkVngOdmRWeA52ZFd7/B0zqgD24CqLAAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887a2fad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# syntax for a slice is\n",
    "# start:end:skip where end is non-inclusive\n",
    "# (i.e., C-style)\n",
    "A[30,20:40:2] = A[10,10:30:2] * 1.5\n",
    "\n",
    "show_temp(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887dae290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numpy will \"broadcast\", i.e., replicate the\n",
    "# the input, if it is size 1 on a dimension\n",
    "A[15:25,:] = A[30,:] * 1.5\n",
    "\n",
    "show_temp(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "G4axI7C9sYZh7AhM7AzD2BGY2BmGsSMwsTMMY0dgYmcYxo7AxM4wjB2BiZ1hGDuC/x95N7N0T1GM\n",
       "dQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887e5e4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transpose the input to the output\n",
    "A[:,15:25] = numpy.transpose(A[15:25,:]) * 1.5\n",
    "\n",
    "show_temp(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selections #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "Rb8TwLW7Oj5nfz+8w+N7KoA/Svv7BICfT/G7Oj5vfzs5vsnx9C7nZJNNduHY9KbAZJNNdsHYBLTJ\n",
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887a728d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: 625.0\n"
     ]
    }
   ],
   "source": [
    "# let's load our saved data\n",
    "A = numpy.load('A.npy')\n",
    "\n",
    "show_temp(A)\n",
    "\n",
    "# and get the mean\n",
    "mean_v = numpy.mean(A)\n",
    "print 'mean:', mean_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "A1IRzTqKkfZ5AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887cf8050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .5 * mean(A) < A < mean(A)\n",
    "B = (A < mean_v) & (A > .5 * mean_v)\n",
    "\n",
    "# this is a boolean array where\n",
    "# each element is True or False based\n",
    "# on the condition\n",
    "show_temp(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": [
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       "tnAAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18874863d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# negate A where B is true\n",
    "# i.e., B = (A < mean_v) & (A > .5 * mean_v)\n",
    "A[B] = -A[B]\n",
    "\n",
    "# we could have also put the expression\n",
    "# in the brackets, i.e.,\n",
    "# A = A[(A < mean_v) & (A > .5 * mean_v)]\n",
    "\n",
    "show_temp(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16 16 16 16 16 17 17 17 17 17 17 17 18 18 18 18 18 18 18 18 18 19 19 19 19\n",
      " 19 19 19 19 19 20 20 20 20 20 20 20 20 20 21 21 21 21 21 21 21 21 21 22 22\n",
      " 22 22 22 22 22 22 22 23 23 23 23 23 23 23 24 24 24 24 24]\n",
      "[18 19 20 21 22 17 18 19 20 21 22 23 16 17 18 19 20 21 22 23 24 16 17 18 19\n",
      " 20 21 22 23 24 16 17 18 19 20 21 22 23 24 16 17 18 19 20 21 22 23 24 16 17\n",
      " 18 19 20 21 22 23 24 17 18 19 20 21 22 23 18 19 20 21 22]\n",
      "16 18\n"
     ]
    },
    {
     "data": {
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       "vfXW28pZD7beeutt5awHW2+99bZy1oOtt956WznrwdZbb72tnP0/e5xOeGmPYBMAAAAASUVORK5C\n",
       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887bed590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# where is the temp greater than 2500?\n",
    "I = numpy.where(A > 2500.0)\n",
    "\n",
    "# I is the indices where it is True\n",
    "# where I[0] are the is, and I[1] are the js\n",
    "print I[0]\n",
    "print I[1]\n",
    "print I[0][0], I[1][0] # i_0,j_0\n",
    "\n",
    "# we can modify A with the index array\n",
    "A[I] = 0.0\n",
    "\n",
    "# this is equivalent: A[I[0], I[1]] = 0.0\n",
    "\n",
    "show_temp(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "WOHUYUVsK6ywwqnDithWWGGFU4f/D39fNw7bVcOWAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887a80f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# since I are indices, we can adjust their positions\n",
    "# and use it to copy that center area around\n",
    "A[I[0], I[1] + 10] = A[I]\n",
    "A[I[0], I[1] - 10] = A[I]\n",
    "A[I[0] + 10, I[1]] = A[I]\n",
    "A[I[0] - 10, I[1]] = A[I]\n",
    "\n",
    "show_temp(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Followup numpy resources #\n",
    "\n",
    "[Link to Numpy Reference](http://docs.scipy.org/doc/numpy/reference/index.html)\n",
    "\n",
    "- Linear algebra\n",
    "- Most mathematical functions\n",
    "- Simple statistics and analysis functions\n",
    "- Reductions and scans (any, all, sum, prod, etc.)\n",
    "- Advanced indexing, selection, and set operations\n",
    "- Masked arrays\n",
    "- Interfacing with C/Cython\n",
    "- Creating your own vectorized functions and reductions\n",
    "- Many other features, check out the [reference](http://docs.scipy.org/doc/numpy/reference/index.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading SQLite Data #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GOOG\n",
      "CREATE TABLE GOOG (adj_close real, close real, date datetime, high real, low real, open real, volume integer)\n"
     ]
    }
   ],
   "source": [
    "# for next section, we'll need some sqlite this time around\n",
    "import sqlite3\n",
    "\n",
    "# get a database connection and a \"cursor\"\n",
    "conn = sqlite3.connect('GOOG.sqlite3')\n",
    "cursor = conn.cursor()\n",
    "\n",
    "# now we can execute SQL on the database\n",
    "cursor.execute('SELECT name, sql FROM sqlite_master WHERE type=\"table\"')\n",
    "\n",
    "# and get data from the cursor\n",
    "i = cursor.next()\n",
    "print i[0]\n",
    "print i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(u'2004-08-20 00:00:00', 7.299999999999997)\n",
      "(u'2004-11-11 00:00:00', 13.890000000000015)\n",
      "(u'2008-10-13 00:00:00', 25.22999999999996)\n",
      "(u'2008-10-28 00:00:00', 29.69999999999999)\n"
     ]
    }
   ],
   "source": [
    "# a useful module is sqlalchemy for interacting with many\n",
    "# databases, but here I am just going to use raw SQL code\n",
    "cursor.execute('SELECT date, close - open FROM GOOG WHERE close > open * 1.07')\n",
    "\n",
    "# the cursor is iterable\n",
    "# dates where Google closed 7% higher and by how much\n",
    "for i in cursor:\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "WMEAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887a802d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a helper function to convert SQL data to numpy arrays\n",
    "def query(cursor, command, dtype):\n",
    "    cursor.execute(command)\n",
    "    return numpy.array([i[0] for i in cursor], dtype)\n",
    "\n",
    "# get our data vectors\n",
    "X = query(cursor, 'SELECT date FROM GOOG', numpy.datetime64)\n",
    "Y = query(cursor, 'SELECT close FROM GOOG', numpy.float32)\n",
    "\n",
    "# plot the data\n",
    "plt.figure() \n",
    "plt.xlabel('date') \n",
    "plt.ylabel('close')\n",
    "plt.plot(X, Y) \n",
    "plt.title('Google close')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scipy #\n",
    "\n",
    "Both provide a bunch of specialized functionality that base numpy doesn't have.\n",
    "\n",
    "[Link to scipy reference](http://docs.scipy.org/doc/scipy/reference/)\n",
    "\n",
    "- Advanced linear algebra\n",
    "- Integration\n",
    "- Interpolation\n",
    "- FFT\n",
    "- Advanced statistics (doesn't beat **R**, though. You can use *rpy2* to use R in Python.)\n",
    "- Optimization and regression\n",
    "- Clustering\n",
    "- Signal and image processing\n",
    "- and more..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polynomial Fit #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model parameters: [ -1.33559029e+03   1.47552798e-06]\n",
      "covariance matrix:\n",
      "[[  9.45503434e+02  -7.67253771e-07]\n",
      " [ -7.67253771e-07   6.25103031e-16]]\n"
     ]
    }
   ],
   "source": [
    "# let's fit the Google stock to a curve\n",
    "import scipy.optimize as optimize\n",
    "\n",
    "# convert dates to integers for ease\n",
    "X = numpy.array(X, numpy.int64)\n",
    "\n",
    "# our linear model\n",
    "# scipy.optimize uses introspection to figure \n",
    "# out the number of model parameters\n",
    "def poly(x, a, b):\n",
    "    return a + b*x\n",
    "\n",
    "# do a least squares fit\n",
    "ab, cov = optimize.curve_fit(poly, X, Y) \n",
    "\n",
    "# ab are the model parameters, and cov is covariance matrix\n",
    "print 'model parameters:', ab\n",
    "print 'covariance matrix:\\n', cov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<functools.partial object at 0x7f1887622d60>\n",
      "model at x_0: 277.007156926\n"
     ]
    }
   ],
   "source": [
    "# to plot the curve, we need to create \n",
    "# a fixed function from our parameters\n",
    "import functools\n",
    "\n",
    "# this will freeze the model with our parameters\n",
    "# (this is a partial application or a \"closure\")\n",
    "fixed = functools.partial(poly, a=ab[0], b=ab[1])\n",
    "\n",
    "# fixed is now a function of one free parameter\n",
    "# whereas poly was originally 3 free parameters\n",
    "print fixed\n",
    "print 'model at x_0:', fixed(X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "r4BvKauSAtwEfC/pAOU26Mu9iiNXGBFzmwD2SNpGuSV1O7ATGAR+lnQmsJF/Vy/7k9LV94Rnggu+\n",
       "F9Fatu+ZZdOtXZ67l9LGvOcy6B3RgKRngPspA40/UMYxjgJPUdZJ/hQYsP2ApBsoix1NARsoVxs7\n",
       "gFWUgvOW7a0LvhMRpygFIyIiGskYRkRENJKCERERjaRgREREIykYERHRSApGREQ0koIRERGNpGBE\n",
       "REQjKRgREdHIP08dg3NNBmMCAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18875f8b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the linear fit with the data\n",
    "plt.figure() \n",
    "plt.plot(X, Y) \n",
    "\n",
    "# this is the model curve\n",
    "plt.plot(X, fixed(X)) \n",
    "\n",
    "# labels\n",
    "plt.xlabel('date') \n",
    "plt.ylabel('close')\n",
    "plt.legend(['original', 'fit'])\n",
    "plt.title('linear fit to Google close data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887c41b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can even do cubic fitting\n",
    "# just add more parameters to your model curve\n",
    "def poly(x, a, b, c, d):\n",
    "    return a + b * x + c * x * x + d * x * x * x\n",
    "\n",
    "# do the optimization and \"freezing\" the function\n",
    "ab, cov = optimize.curve_fit(poly, X, Y) \n",
    "fixed = functools.partial(poly, a=ab[0], b=ab[1], c=ab[2], d=ab[3])\n",
    "\n",
    "# plot everything\n",
    "plt.figure() \n",
    "plt.plot(X, Y) \n",
    "plt.plot(X, fixed(X)) \n",
    "plt.xlabel('date') \n",
    "plt.ylabel('close')\n",
    "plt.legend(['fit', 'original'])\n",
    "plt.title('cubic fit to Google close data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Power Spectrum #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f187f96a490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# time samples\n",
    "delta = numpy.pi / 8\n",
    "time = numpy.arange(0, 16 * numpy.pi, delta)\n",
    "\n",
    "# a made up signal\n",
    "raw = numpy.sin(time * 2 + numpy.pi) +\\\n",
    "    numpy.sin(time - numpy.pi * .5) * 2 +\\\n",
    "    numpy.sin(time * .25) * 3\n",
    "\n",
    "# some noise\n",
    "noise = (numpy.random.rand(time.size) - .5)\n",
    "\n",
    "# our noisy signal \n",
    "signal = raw + noise\n",
    "    \n",
    "# let's plot it\n",
    "plt.figure()\n",
    "plt.plot(time, raw)\n",
    "plt.plot(time, signal) \n",
    "plt.plot(time, noise)\n",
    "plt.title('signal')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('amplitude')\n",
    "plt.legend(['raw', 'signal', 'noise'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "Jv4/e3OcOmVmRpoAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f188706e950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's do some Fourier analysis\n",
    "from scipy import fftpack\n",
    "\n",
    "# standard definition of power spectrum\n",
    "power = numpy.abs(fftpack.fft(signal)) ** 2\n",
    "\n",
    "# frequencies from sampling spacing\n",
    "freqs = fftpack.fftfreq(time.size, delta)\n",
    "\n",
    "# sorted indices of the frequencies\n",
    "i = numpy.argsort(freqs)\n",
    "i = i[freqs[i] >= 0]\n",
    "\n",
    "power = power[i]\n",
    "freqs = freqs[i]\n",
    "\n",
    "# let's plot it\n",
    "plt.figure()\n",
    "plt.plot(freqs, power) \n",
    "plt.title('power spectrum')\n",
    "plt.xlabel('frequencies')\n",
    "plt.ylabel('power')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "frD+/AhwDlXXZ6u5H7jf9g317bOoCn6r2gK4sf6Ztpp3AH+2/ZjtF4Czqf6/zlPJAv/fSVT1X8wd\n",
       "qSZNxQBIEnACcIft75fOMy+SlpG0VP31osBmwE1lU72S7a/YHm37f6jeql9ue9fSueYmaZSkJeqv\n",
       "FwM2B1putJfth4D7JK1e3zUOuL1gpEYmUP1hb0XTgPUkLVr/zo8Deu3mbDSTdci0y0QoSWcA7wVe\n",
       "Lek+4GDbJxWONS8bADsDt0qaXTQPsn1hwUxzey1wcj1CYQHgFNuXFc7UjFbtTlwOOKf6PWckcJrt\n",
       "i8tG6tVngNPqxtw91BMiW039h3Ic0JLXM2zfUr+bnEJ1fegv0Pva8JnoFBHRoVpi0+2IiBh8KfAR\n",
       "ER0qBT4iokOlwEdEdKgU+IiIDpUCHxHRoVLgIyI6VAp8RESH+v+Hjg1u1m7GAQAAAABJRU5ErkJg\n",
       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18870b1f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's smooth the signal\n",
    "from scipy.signal import gaussian\n",
    "\n",
    "# gaussian kernel of 9 width, stddev of 1.5\n",
    "kernel = gaussian(9, 1.5)\n",
    "area = numpy.trapz(kernel)\n",
    "kernel = kernel / area\n",
    "\n",
    "# plot the kernel\n",
    "plt.figure() \n",
    "plt.plot(kernel) \n",
    "plt.title('Gaussian kernel')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887ced0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convolution\n",
    "from scipy.signal import convolve\n",
    "\n",
    "# pad the signal on both ends\n",
    "smooth = numpy.concatenate([signal[-4:], signal, signal[:4]])\n",
    "\n",
    "# convolve it\n",
    "smooth = convolve(smooth, kernel, 'valid')\n",
    "\n",
    "# plot the data\n",
    "plt.figure() \n",
    "plt.plot(time, smooth) \n",
    "plt.plot(time, signal)\n",
    "plt.title('signal')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('amplitude')\n",
    "plt.legend(['smoothed', 'noisy'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887b710d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# power spectrum again\n",
    "smooth_power = numpy.abs(fftpack.fft(smooth)) ** 2\n",
    "smooth_power = smooth_power[i]\n",
    "\n",
    "# comparison of the two power spectra\n",
    "plt.figure()\n",
    "plt.plot(freqs, smooth_power)\n",
    "plt.plot(freqs, power) \n",
    "plt.title('power spectrum')\n",
    "plt.xlabel('frequencies')\n",
    "plt.ylabel('power')\n",
    "plt.yscale('log')\n",
    "plt.legend(['smoothed', 'original'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Writing data to SQLite #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# we'll create a new database\n",
    "conn = sqlite3.connect('signals.sqlite3')\n",
    "cursor = conn.cursor()\n",
    "\n",
    "# and a table to store our data in it\n",
    "cursor.execute('CREATE TABLE signal (time real, raw real, smooth real)')\n",
    "\n",
    "# now we just have to insert all of the data into it\n",
    "for i, j, k in zip(time, signal, smooth):\n",
    "    cursor.execute('INSERT INTO signal VALUES (?, ?, ?)', (i, j, k))\n",
    "    \n",
    "conn.commit() # commit the transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.67588438888\n",
      "38.4845100065\n"
     ]
    }
   ],
   "source": [
    "# now we can query the data\n",
    "cursor.execute('SELECT time FROM signal WHERE abs(raw - smooth) > 1')\n",
    "\n",
    "# the times where the smooth signal\n",
    "# deviates from the original by 1\n",
    "for i in cursor:\n",
    "    print i[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas #\n",
    "\n",
    "[Link to pandas reference](http://pandas.pydata.org/pandas-docs/stable/)\n",
    "\n",
    "- Provides \"data frames,\" which are similar to **R** data frames\n",
    "    - Has functionality similar to plyr/dplyr\n",
    "    - i.e., some DSL for SQL-like queries without SQL\n",
    "- Provides tabular representations of your numpy, dict, and list data\n",
    "- A useful syntax addition on top of numpy for doing complex data slicing and selection\n",
    "- Easy input and output to tabular/columnar data formats\n",
    "    - Direct to and from CSV, HDF, SQLite, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'index', u'Unnamed: 0', u'SepalLength', u'SepalWidth', u'PetalLength', u'PetalWidth', u'Species'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 5.1</td>\n",
       "      <td> 3.5</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2</td>\n",
       "      <td> 4.9</td>\n",
       "      <td> 3.0</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2</td>\n",
       "      <td> 3</td>\n",
       "      <td> 4.7</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 1.3</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 3</td>\n",
       "      <td> 4</td>\n",
       "      <td> 4.6</td>\n",
       "      <td> 3.1</td>\n",
       "      <td> 1.5</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 4</td>\n",
       "      <td> 5</td>\n",
       "      <td> 5.0</td>\n",
       "      <td> 3.6</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Unnamed: 0  SepalLength  SepalWidth  PetalLength  PetalWidth Species\n",
       "0      0           1          5.1         3.5          1.4         0.2  setosa\n",
       "1      1           2          4.9         3.0          1.4         0.2  setosa\n",
       "2      2           3          4.7         3.2          1.3         0.2  setosa\n",
       "3      3           4          4.6         3.1          1.5         0.2  setosa\n",
       "4      4           5          5.0         3.6          1.4         0.2  setosa"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we'll sqlachemy this time around\n",
    "# which can also connect to other databases\n",
    "import sqlalchemy\n",
    "engine = sqlalchemy.create_engine(\"sqlite:///iris.sqlite3\")\n",
    "\n",
    "# pandas knows how to read a table using sqlachemy\n",
    "import pandas\n",
    "import pandas as pd # this is typical\n",
    "\n",
    "iris = pandas.read_sql_table(\"iris\", engine)\n",
    "print iris.columns\n",
    "\n",
    "# this is a table/pandas Data Frame)\n",
    "iris.head() # there is also .tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1878438890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can plot the data\n",
    "def show_iris(iris, x, y):\n",
    "    plt.figure()\n",
    "    # select two columns (a Series in pandas parlance)\n",
    "    plt.plot(iris[x], iris[y], 'o')\n",
    "    plt.xlabel(x)\n",
    "    plt.ylabel(y)\n",
    "    \n",
    "show_iris(iris, 'SepalLength', 'PetalLength')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 5.1</td>\n",
       "      <td> 3.5</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 4.9</td>\n",
       "      <td> 3.0</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 4.7</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 1.3</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 4.6</td>\n",
       "      <td> 3.1</td>\n",
       "      <td> 1.5</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 5.0</td>\n",
       "      <td> 3.6</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLength  SepalWidth  PetalLength  PetalWidth Species\n",
       "0          5.1         3.5          1.4         0.2  setosa\n",
       "1          4.9         3.0          1.4         0.2  setosa\n",
       "2          4.7         3.2          1.3         0.2  setosa\n",
       "3          4.6         3.1          1.5         0.2  setosa\n",
       "4          5.0         3.6          1.4         0.2  setosa"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting multiple columns by a list of column names\n",
    "iris = iris[['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']]\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td> 5.4</td>\n",
       "      <td> 3.7</td>\n",
       "      <td> 1.5</td>\n",
       "      <td> 0.2</td>\n",
       "      <td>     setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td> 5.4</td>\n",
       "      <td> 3.4</td>\n",
       "      <td> 1.7</td>\n",
       "      <td> 0.2</td>\n",
       "      <td>     setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> 4.8</td>\n",
       "      <td> 3.1</td>\n",
       "      <td> 1.6</td>\n",
       "      <td> 0.2</td>\n",
       "      <td>     setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td> 5.0</td>\n",
       "      <td> 3.5</td>\n",
       "      <td> 1.3</td>\n",
       "      <td> 0.3</td>\n",
       "      <td>     setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td> 7.0</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 4.7</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SepalLength  SepalWidth  PetalLength  PetalWidth     Species\n",
       "10          5.4         3.7          1.5         0.2      setosa\n",
       "20          5.4         3.4          1.7         0.2      setosa\n",
       "30          4.8         3.1          1.6         0.2      setosa\n",
       "40          5.0         3.5          1.3         0.3      setosa\n",
       "50          7.0         3.2          4.7         1.4  versicolor"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "BoMkqcVgkCS1GAySpBaDQZLU8v8BZdiAHBfLFBcAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18870913d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [] is overloaded to also do index selection\n",
    "# i.e., row selection\n",
    "sel = iris[10:51:10]\n",
    "\n",
    "show_iris(sel, 'SepalLength', 'PetalLength')\n",
    "sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td> 7.0</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 4.7</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td> 6.4</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 4.5</td>\n",
       "      <td> 1.5</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td> 6.9</td>\n",
       "      <td> 3.1</td>\n",
       "      <td> 4.9</td>\n",
       "      <td> 1.5</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td> 5.5</td>\n",
       "      <td> 2.3</td>\n",
       "      <td> 4.0</td>\n",
       "      <td> 1.3</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td> 6.5</td>\n",
       "      <td> 2.8</td>\n",
       "      <td> 4.6</td>\n",
       "      <td> 1.5</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SepalLength  SepalWidth  PetalLength  PetalWidth     Species\n",
       "50          7.0         3.2          4.7         1.4  versicolor\n",
       "51          6.4         3.2          4.5         1.5  versicolor\n",
       "52          6.9         3.1          4.9         1.5  versicolor\n",
       "53          5.5         2.3          4.0         1.3  versicolor\n",
       "54          6.5         2.8          4.6         1.5  versicolor"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887403b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with [] you can also boolean select\n",
    "sel = iris[iris.Species == 'versicolor']\n",
    "\n",
    "show_iris(sel, 'SepalLength', 'PetalLength')\n",
    "sel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td> 4.9</td>\n",
       "      <td> 2.4</td>\n",
       "      <td> 3.3</td>\n",
       "      <td> 1.0</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td> 5.0</td>\n",
       "      <td> 2.0</td>\n",
       "      <td> 3.5</td>\n",
       "      <td> 1.0</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td> 5.6</td>\n",
       "      <td> 2.9</td>\n",
       "      <td> 3.6</td>\n",
       "      <td> 1.3</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td> 5.7</td>\n",
       "      <td> 2.6</td>\n",
       "      <td> 3.5</td>\n",
       "      <td> 1.0</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td> 5.5</td>\n",
       "      <td> 2.4</td>\n",
       "      <td> 3.8</td>\n",
       "      <td> 1.1</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td> 5.5</td>\n",
       "      <td> 2.4</td>\n",
       "      <td> 3.7</td>\n",
       "      <td> 1.0</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td> 5.0</td>\n",
       "      <td> 2.3</td>\n",
       "      <td> 3.3</td>\n",
       "      <td> 1.0</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td> 5.1</td>\n",
       "      <td> 2.5</td>\n",
       "      <td> 3.0</td>\n",
       "      <td> 1.1</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SepalLength  SepalWidth  PetalLength  PetalWidth     Species\n",
       "57          4.9         2.4          3.3         1.0  versicolor\n",
       "60          5.0         2.0          3.5         1.0  versicolor\n",
       "64          5.6         2.9          3.6         1.3  versicolor\n",
       "79          5.7         2.6          3.5         1.0  versicolor\n",
       "80          5.5         2.4          3.8         1.1  versicolor\n",
       "81          5.5         2.4          3.7         1.0  versicolor\n",
       "93          5.0         2.3          3.3         1.0  versicolor\n",
       "98          5.1         2.5          3.0         1.1  versicolor"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1878422850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and use multiple columns in your query\n",
    "sel = iris[(iris.Species == 'versicolor') & (iris.PetalLength < 3.9)]\n",
    "\n",
    "show_iris(sel, 'SepalLength', 'PetalLength')\n",
    "sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 5.1</td>\n",
       "      <td> 3.5</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 4.9</td>\n",
       "      <td> 3.0</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 4.7</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 1.3</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 4.6</td>\n",
       "      <td> 3.1</td>\n",
       "      <td> 1.5</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 5.0</td>\n",
       "      <td> 3.6</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> 0.2</td>\n",
       "      <td> setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SepalLength  SepalWidth  PetalLength  PetalWidth Species\n",
       "Species                                                          \n",
       "setosa           5.1         3.5          1.4         0.2  setosa\n",
       "setosa           4.9         3.0          1.4         0.2  setosa\n",
       "setosa           4.7         3.2          1.3         0.2  setosa\n",
       "setosa           4.6         3.1          1.5         0.2  setosa\n",
       "setosa           5.0         3.6          1.4         0.2  setosa"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can change any column to be an index\n",
    "iris.index = iris.Species\n",
    "iris.head()\n",
    "\n",
    "# there is also multi-dimensional indexing,\n",
    "# for N-D indexing, but we won't go into that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 5.1</td>\n",
       "      <td> 3.5</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> 0.2</td>\n",
       "      <td>     setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 5.0</td>\n",
       "      <td> 3.0</td>\n",
       "      <td> 1.6</td>\n",
       "      <td> 0.2</td>\n",
       "      <td>     setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td> 7.0</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 4.7</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td> 6.6</td>\n",
       "      <td> 3.0</td>\n",
       "      <td> 4.4</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td> 6.3</td>\n",
       "      <td> 3.3</td>\n",
       "      <td> 6.0</td>\n",
       "      <td> 2.5</td>\n",
       "      <td>  virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td> 7.2</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 6.0</td>\n",
       "      <td> 1.8</td>\n",
       "      <td>  virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SepalLength  SepalWidth  PetalLength  PetalWidth     Species\n",
       "Species                                                                 \n",
       "setosa              5.1         3.5          1.4         0.2      setosa\n",
       "setosa              5.0         3.0          1.6         0.2      setosa\n",
       "versicolor          7.0         3.2          4.7         1.4  versicolor\n",
       "versicolor          6.6         3.0          4.4         1.4  versicolor\n",
       "virginica           6.3         3.3          6.0         2.5   virginica\n",
       "virginica           7.2         3.2          6.0         1.8   virginica"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "UhP/Hw+zMZ+VefXIAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18783a4850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# then that means you can do indexing by labels\n",
    "# label indexing in pandas is right-inclusive\n",
    "# i.e., different from list/array slicing\n",
    "sel = iris['setosa':'virginica']\n",
    "\n",
    "show_iris(sel, 'SepalLength', 'PetalLength')\n",
    "sel[::25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "RDpGiV9EpGP+A6zxY0xUljnDAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1887b4a410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to do more explicit selection, use \n",
    "# .loc, .iloc, and .ix (labels, integers, mixed)\n",
    "\n",
    "# this selects a column (a Series in pandas parlance)\n",
    "# i.e., datapanel.loc[row(s), column(s)]\n",
    "sel = iris.loc[iris.PetalWidth < 1.8, 'PetalLength']\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(sel, 'o')\n",
    "plt.ylabel('PetalLength')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>SepalLength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 1.4</td>\n",
       "      <td> 5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 1.4</td>\n",
       "      <td> 4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 1.3</td>\n",
       "      <td> 4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 1.5</td>\n",
       "      <td> 4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 1.4</td>\n",
       "      <td> 5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PetalLength  SepalLength\n",
       "Species                          \n",
       "setosa           1.4          5.1\n",
       "setosa           1.4          4.9\n",
       "setosa           1.3          4.7\n",
       "setosa           1.5          4.6\n",
       "setosa           1.4          5.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "rkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f187823d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# you can also do multiple rows and columns\n",
    "sel = iris.loc[:,['PetalLength', 'SepalLength']]\n",
    "\n",
    "show_iris(sel, 'PetalLength', 'SepalLength')\n",
    "sel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td> 5.006</td>\n",
       "      <td> 3.428</td>\n",
       "      <td> 1.462</td>\n",
       "      <td> 0.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td> 5.936</td>\n",
       "      <td> 2.770</td>\n",
       "      <td> 4.260</td>\n",
       "      <td> 1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td> 6.588</td>\n",
       "      <td> 2.974</td>\n",
       "      <td> 5.552</td>\n",
       "      <td> 2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SepalLength  SepalWidth  PetalLength  PetalWidth\n",
       "Species                                                     \n",
       "setosa            5.006       3.428        1.462       0.246\n",
       "versicolor        5.936       2.770        4.260       1.326\n",
       "virginica         6.588       2.974        5.552       2.026"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as you can see, pandas is very SQL-like/R-like\n",
    "# and you can even do aggregation\n",
    "sel = iris.groupby('Species')\n",
    "sel.aggregate(numpy.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td> 1.5</td>\n",
       "      <td> 4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td> 2.3</td>\n",
       "      <td> 6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PetalWidth  PetalLength\n",
       "Species                            \n",
       "versicolor         1.5          4.9\n",
       "virginica          2.3          6.9"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18874661d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and do something like\n",
    "B = (iris.SepalLength > 6.0) & (iris.SepalWidth < 2.7)\n",
    "sel = iris.loc[B,['Species', 'PetalWidth', 'PetalLength']]\n",
    "\n",
    "show_iris(iris[B], 'PetalWidth', 'PetalLength')\n",
    "sel.groupby('Species').aggregate(numpy.max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "      <th>researcher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td> 7.0</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 4.7</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> versicolor</td>\n",
       "      <td> Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td> 6.7</td>\n",
       "      <td> 3.1</td>\n",
       "      <td> 4.4</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> versicolor</td>\n",
       "      <td> Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> 5.5</td>\n",
       "      <td> 2.4</td>\n",
       "      <td> 3.8</td>\n",
       "      <td> 1.1</td>\n",
       "      <td> versicolor</td>\n",
       "      <td> Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td> 5.7</td>\n",
       "      <td> 3.0</td>\n",
       "      <td> 4.2</td>\n",
       "      <td> 1.2</td>\n",
       "      <td> versicolor</td>\n",
       "      <td> Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td> 6.5</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 5.1</td>\n",
       "      <td> 2.0</td>\n",
       "      <td>  virginica</td>\n",
       "      <td>  Jane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td> 7.2</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 6.0</td>\n",
       "      <td> 1.8</td>\n",
       "      <td>  virginica</td>\n",
       "      <td>  Jane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td> 6.7</td>\n",
       "      <td> 3.1</td>\n",
       "      <td> 5.6</td>\n",
       "      <td> 2.4</td>\n",
       "      <td>  virginica</td>\n",
       "      <td>  Jane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SepalLength  SepalWidth  PetalLength  PetalWidth     Species researcher\n",
       "0           7.0         3.2          4.7         1.4  versicolor      Roger\n",
       "15          6.7         3.1          4.4         1.4  versicolor      Roger\n",
       "30          5.5         2.4          3.8         1.1  versicolor      Roger\n",
       "45          5.7         3.0          4.2         1.2  versicolor      Roger\n",
       "60          6.5         3.2          5.1         2.0   virginica       Jane\n",
       "75          7.2         3.2          6.0         1.8   virginica       Jane\n",
       "90          6.7         3.1          5.6         2.4   virginica       Jane"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can even do joins\n",
    "df = pandas.DataFrame({'Species': ['versicolor', 'virginica'], 'researcher': ['Roger', 'Jane']})\n",
    "\n",
    "sel = pandas.merge(iris, df, on='Species')\n",
    "sel[::15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Species</th>\n",
       "      <th>researcher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>  0</td>\n",
       "      <td> 7.0</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 4.7</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> versicolor</td>\n",
       "      <td> Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td> 15</td>\n",
       "      <td> 6.7</td>\n",
       "      <td> 3.1</td>\n",
       "      <td> 4.4</td>\n",
       "      <td> 1.4</td>\n",
       "      <td> versicolor</td>\n",
       "      <td> Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> 30</td>\n",
       "      <td> 5.5</td>\n",
       "      <td> 2.4</td>\n",
       "      <td> 3.8</td>\n",
       "      <td> 1.1</td>\n",
       "      <td> versicolor</td>\n",
       "      <td> Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td> 45</td>\n",
       "      <td> 5.7</td>\n",
       "      <td> 3.0</td>\n",
       "      <td> 4.2</td>\n",
       "      <td> 1.2</td>\n",
       "      <td> versicolor</td>\n",
       "      <td> Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td> 60</td>\n",
       "      <td> 6.5</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 5.1</td>\n",
       "      <td> 2.0</td>\n",
       "      <td>  virginica</td>\n",
       "      <td>  Jane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td> 75</td>\n",
       "      <td> 7.2</td>\n",
       "      <td> 3.2</td>\n",
       "      <td> 6.0</td>\n",
       "      <td> 1.8</td>\n",
       "      <td>  virginica</td>\n",
       "      <td>  Jane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td> 90</td>\n",
       "      <td> 6.7</td>\n",
       "      <td> 3.1</td>\n",
       "      <td> 5.6</td>\n",
       "      <td> 2.4</td>\n",
       "      <td>  virginica</td>\n",
       "      <td>  Jane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  SepalLength  SepalWidth  PetalLength  PetalWidth     Species  \\\n",
       "0       0          7.0         3.2          4.7         1.4  versicolor   \n",
       "15     15          6.7         3.1          4.4         1.4  versicolor   \n",
       "30     30          5.5         2.4          3.8         1.1  versicolor   \n",
       "45     45          5.7         3.0          4.2         1.2  versicolor   \n",
       "60     60          6.5         3.2          5.1         2.0   virginica   \n",
       "75     75          7.2         3.2          6.0         1.8   virginica   \n",
       "90     90          6.7         3.1          5.6         2.4   virginica   \n",
       "\n",
       "   researcher  \n",
       "0       Roger  \n",
       "15      Roger  \n",
       "30      Roger  \n",
       "45      Roger  \n",
       "60       Jane  \n",
       "75       Jane  \n",
       "90       Jane  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and when you are all done, output the data back to\n",
    "# SQLite (or any other database with sqlalchemy)\n",
    "\n",
    "engine = sqlalchemy.create_engine('sqlite:///select.sqlite3')\n",
    "sel.to_sql('select', engine)\n",
    "\n",
    "copy = pandas.read_sql_table('select', engine)\n",
    "copy[::15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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