Adding lab3 and lab5 solutions.

amplab · Apr 9, 2014 · e4410b3 · e4410b3
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diff --git a/lab3/soln.ipynb b/lab3/soln.ipynb
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+{
+ "metadata": {
+  "name": "Untitled0"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "%matplotlib inline\nimport pandas as pd\ndata = pd.read_csv(\"emdata-tsv (1).csv\")\n    ",
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "data.describe()\ndata.shape\ndata.Country.describe()\ndata.Type.describe()",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 18,
+       "text": "count                  17828\nunique                    15\ntop       Transport Accident\nfreq                    4351\nName: Type, dtype: object"
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "data.describe()",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "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>Start</th>\n      <th>End</th>\n      <th>Duration</th>\n      <th>Killed</th>\n      <th>Cost</th>\n      <th>Affected</th>\n      <th>Column 12</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 17828.000000</td>\n      <td> 17828.000000</td>\n      <td> 17828.000000</td>\n      <td>   13996.000000</td>\n      <td> 1.108500e+04</td>\n      <td>   3772.000000</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>  1990.883049</td>\n      <td>  1990.918387</td>\n      <td>     0.035338</td>\n      <td>    2718.355173</td>\n      <td> 5.586713e+05</td>\n      <td>    488.759659</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>    17.851370</td>\n      <td>    17.836943</td>\n      <td>     0.302913</td>\n      <td>   75162.832728</td>\n      <td> 6.951056e+06</td>\n      <td>   3384.235083</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>  1900.000000</td>\n      <td>  1900.000000</td>\n      <td>     0.000000</td>\n      <td>       1.000000</td>\n      <td> 1.000000e+00</td>\n      <td>      0.003000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>  1986.000000</td>\n      <td>  1986.000000</td>\n      <td>     0.000000</td>\n      <td>      12.000000</td>\n      <td> 6.000000e+01</td>\n      <td>      5.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>  1996.000000</td>\n      <td>  1996.000000</td>\n      <td>     0.000000</td>\n      <td>      24.000000</td>\n      <td> 1.000000e+03</td>\n      <td>     35.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>  2003.000000</td>\n      <td>  2003.000000</td>\n      <td>     0.000000</td>\n      <td>      57.000000</td>\n      <td> 1.975000e+04</td>\n      <td>    200.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>  2008.000000</td>\n      <td>  2009.000000</td>\n      <td>     9.000000</td>\n      <td> 5000000.000000</td>\n      <td> 3.000000e+08</td>\n      <td> 125000.000000</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows \u00d7 7 columns</p>\n</div>",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 19,
+       "text": "              Start           End      Duration          Killed          Cost  \\\ncount  17828.000000  17828.000000  17828.000000    13996.000000  1.108500e+04   \nmean    1990.883049   1990.918387      0.035338     2718.355173  5.586713e+05   \nstd       17.851370     17.836943      0.302913    75162.832728  6.951056e+06   \nmin     1900.000000   1900.000000      0.000000        1.000000  1.000000e+00   \n25%     1986.000000   1986.000000      0.000000       12.000000  6.000000e+01   \n50%     1996.000000   1996.000000      0.000000       24.000000  1.000000e+03   \n75%     2003.000000   2003.000000      0.000000       57.000000  1.975000e+04   \nmax     2008.000000   2009.000000      9.000000  5000000.000000  3.000000e+08   \n\n            Affected  Column 12  \ncount    3772.000000          0  \nmean      488.759659        NaN  \nstd      3384.235083        NaN  \nmin         0.003000        NaN  \n25%         5.000000        NaN  \n50%        35.000000        NaN  \n75%       200.000000        NaN  \nmax    125000.000000        NaN  \n\n[8 rows x 7 columns]"
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": "print data.Killed.groupby(data.Type).sum().order()",
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": "Type\nWildfire                             3287\nMass movement dry                    4919\nIndustrial Accident                 49797\nMass movement wet                   55040\nMiscellaneous accident              58121\nVolcano                             95979\nExtreme temperature                108938\nTransport Accident                 201053\nStorm                             1373104\nEarthquake (seismic activity)     2311491\nComplex Disasters                 5610000\nFlood                             6911040\nEpidemic                          9555059\nDrought                          11708271\nInsect infestation                    NaN\ndtype: float64\n"
+      }
+     ],
+     "prompt_number": 23
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}