introduction/survey.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = {'Python':           '00132203133321',\n",
    "        'C/C++':            '32212322314333',\n",
    "        'Java':             '04413321433144',\n",
    "        'Matlab':           '32330020210000',\n",
    "        'Other':            '00004004400003',\n",
    "        'Linear Algebra':   '44433343324443',\n",
    "        'Computer Vision':  '11200132111213',\n",
    "        'State Machine':    '40234243230203',\n",
    "        'PID':              '04400003000003',\n",
    "        'Path Planning':    '30232042133103',\n",
    "        'Machine Learning': '20312231114211',\n",
    "        'SLAM':             '00300002001000',\n",
    "        'git':              '44444443331443',\n",
    "        'svn':              '44442030320220',\n",
    "        'test driven':      '33124340322430',\n",
    "        'pair programming': '00104440122440',\n",
    "        'scrum':            '00024030431440'\n",
    "       }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for k, v in data.iteritems():\n",
    "    data[k] = [int(c) * 1.25 for c in data[k]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C/C++</th>\n",
       "      <th>Computer Vision</th>\n",
       "      <th>Java</th>\n",
       "      <th>Linear Algebra</th>\n",
       "      <th>Machine Learning</th>\n",
       "      <th>Matlab</th>\n",
       "      <th>Other</th>\n",
       "      <th>PID</th>\n",
       "      <th>Path Planning</th>\n",
       "      <th>Python</th>\n",
       "      <th>SLAM</th>\n",
       "      <th>State Machine</th>\n",
       "      <th>git</th>\n",
       "      <th>pair programming</th>\n",
       "      <th>scrum</th>\n",
       "      <th>svn</th>\n",
       "      <th>test driven</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.50</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    C/C++  Computer Vision  Java  Linear Algebra  Machine Learning  Matlab  \\\n",
       "0    3.75             1.25  0.00            5.00              2.50    3.75   \n",
       "1    2.50             1.25  5.00            5.00              0.00    2.50   \n",
       "2    2.50             2.50  5.00            5.00              3.75    3.75   \n",
       "3    1.25             0.00  1.25            3.75              1.25    3.75   \n",
       "4    2.50             0.00  3.75            3.75              2.50    0.00   \n",
       "5    3.75             1.25  3.75            3.75              2.50    0.00   \n",
       "6    2.50             3.75  2.50            5.00              3.75    2.50   \n",
       "7    2.50             2.50  1.25            3.75              1.25    0.00   \n",
       "8    3.75             1.25  5.00            3.75              1.25    2.50   \n",
       "9    1.25             1.25  3.75            2.50              1.25    1.25   \n",
       "10   5.00             1.25  3.75            5.00              5.00    0.00   \n",
       "11   3.75             2.50  1.25            5.00              2.50    0.00   \n",
       "12   3.75             1.25  5.00            5.00              1.25    0.00   \n",
       "13   3.75             3.75  5.00            3.75              1.25    0.00   \n",
       "\n",
       "    Other   PID  Path Planning  Python  SLAM  State Machine   git  \\\n",
       "0    0.00  0.00           3.75    0.00  0.00           5.00  5.00   \n",
       "1    0.00  5.00           0.00    0.00  0.00           0.00  5.00   \n",
       "2    0.00  5.00           2.50    1.25  3.75           2.50  5.00   \n",
       "3    0.00  0.00           3.75    3.75  0.00           3.75  5.00   \n",
       "4    5.00  0.00           2.50    2.50  0.00           5.00  5.00   \n",
       "5    0.00  0.00           0.00    2.50  0.00           2.50  5.00   \n",
       "6    0.00  0.00           5.00    0.00  0.00           5.00  5.00   \n",
       "7    5.00  3.75           2.50    3.75  2.50           3.75  3.75   \n",
       "8    5.00  0.00           1.25    1.25  0.00           2.50  3.75   \n",
       "9    0.00  0.00           3.75    3.75  0.00           3.75  3.75   \n",
       "10   0.00  0.00           3.75    3.75  1.25           0.00  1.25   \n",
       "11   0.00  0.00           1.25    3.75  0.00           2.50  5.00   \n",
       "12   0.00  0.00           0.00    2.50  0.00           0.00  5.00   \n",
       "13   3.75  3.75           3.75    1.25  0.00           3.75  3.75   \n",
       "\n",
       "    pair programming  scrum   svn  test driven  \n",
       "0               0.00   0.00  5.00         3.75  \n",
       "1               0.00   0.00  5.00         3.75  \n",
       "2               1.25   0.00  5.00         1.25  \n",
       "3               0.00   2.50  5.00         2.50  \n",
       "4               5.00   5.00  2.50         5.00  \n",
       "5               5.00   0.00  0.00         3.75  \n",
       "6               5.00   3.75  3.75         5.00  \n",
       "7               0.00   0.00  0.00         0.00  \n",
       "8               1.25   5.00  3.75         3.75  \n",
       "9               2.50   3.75  2.50         2.50  \n",
       "10              2.50   1.25  0.00         2.50  \n",
       "11              5.00   5.00  2.50         5.00  \n",
       "12              5.00   5.00  2.50         3.75  \n",
       "13              0.00   0.00  0.00         0.00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pandas.DataFrame(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "programming = ['Python', 'C/C++', 'Java', 'Matlab', 'Other']\n",
    "theory = ['Linear Algebra', 'Computer Vision', 'State Machine', 'PID', 'Path Planning', 'Machine Learning', 'SLAM']\n",
    "tools = ['git', 'svn', 'test driven', 'pair programming', 'scrum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d74b85b90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEbCAYAAAAibQiyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npuYhIiIbpuYhIiIbpuYhIiIb9v8BomflHHf1wPkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d74b6add0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(programming, return_type='axes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d729bc2d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAFLCAYAAAAu4wBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d729abe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(theory, return_type='axes', rot=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d72852550>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAFMCAYAAAAz5jD9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d7281fd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(tools, return_type='axes', rot=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "group_data = {k:  (data[v].sum(axis=1) / len(v)).values  for k, v in {'tools': tools, 'programming': programming, 'theory': theory}.iteritems()}\n",
    "group_data = pandas.DataFrame(group_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8d72658dd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8d725f3bd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8d72583210>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8d724e7150>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8d724685d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8d72436e50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8d723cd310>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8d72352190>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8d722a6bd0>]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9QAAAPUCAYAAABM1HGEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nADoQqAEAAKCD/x94TyCzuGamJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d72660d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas.tools.plotting.scatter_matrix(group_data, figsize=(15, 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.75\n",
       "1     2.75\n",
       "2     2.50\n",
       "3     3.00\n",
       "4     4.50\n",
       "5     2.75\n",
       "6     4.50\n",
       "7     0.75\n",
       "8     3.50\n",
       "9     3.00\n",
       "10    1.50\n",
       "11    4.50\n",
       "12    4.25\n",
       "13    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[tools].sum(axis=1)/ len(tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>git</th>\n",
       "      <th>svn</th>\n",
       "      <th>test driven</th>\n",
       "      <th>pair programming</th>\n",
       "      <th>scrum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     git   svn  test driven  pair programming  scrum\n",
       "0   5.00  5.00         3.75              0.00   0.00\n",
       "1   5.00  5.00         3.75              0.00   0.00\n",
       "2   5.00  5.00         1.25              1.25   0.00\n",
       "3   5.00  5.00         2.50              0.00   2.50\n",
       "4   5.00  2.50         5.00              5.00   5.00\n",
       "5   5.00  0.00         3.75              5.00   0.00\n",
       "6   5.00  3.75         5.00              5.00   3.75\n",
       "7   3.75  0.00         0.00              0.00   0.00\n",
       "8   3.75  3.75         3.75              1.25   5.00\n",
       "9   3.75  2.50         2.50              2.50   3.75\n",
       "10  1.25  0.00         2.50              2.50   1.25\n",
       "11  5.00  2.50         5.00              5.00   5.00\n",
       "12  5.00  2.50         3.75              5.00   5.00\n",
       "13  3.75  0.00         0.00              0.00   0.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[tools]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}