{
 "metadata": {
  "name": "chapter1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Will It Python\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://slendrmeans.wordpress.com/2012/04/14/will-it-python-machine-learning-for-hackers-chapter-1-part-1-loading-the-data/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This script is a Python port of the R code in Chapter 1 of _Machine Learning\n",
      "for Hackers_ by D. Conway and J.M. White. It is mainly intended to be run via\n",
      "the interactive shell, though that's not necessary.\n",
      "\n",
      "The script will produce (1) a cleaned tab-separated file of the UFO data;\n",
      "(2) a series of 4 PNG figures.\n",
      "\n",
      "The UFO dataset (approx. 75MB tab-separated file) should be located in a\n",
      "/data/ufo subfolder of the working directory. Otherwise, change the `inpath`\n",
      "and `outpath` variables at the start of the file.\n",
      "\n",
      "For a detailed description of the analysis and the process of porting it\n",
      "to Python, see: slendrmeans.wordpress.com/will-it-python.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "''''\n",
      "-------------------------------------------------------------------------------\n",
      "Filename   : ch1.py\n",
      "Date       : 2012-04-16\n",
      "Author     : C. Vogel\n",
      "Purpose    : Replicate analysis of UFO data in Chapter 1 of _Machine Learning\n",
      "           : for Hackers_.\n",
      "Input Data : ufo_awesome.csv is available at the book's github repository at\n",
      "           : https://github.com/johnmyleswhite/ML_for_Hackers.git\n",
      "Libraries  : Numpy 1.6.1, Matplotlib 1.1.0, Pandas 0.7.3\n",
      "-------------------------------------------------------------------------------\n",
      "\n",
      "This script is a Python port of the R code in Chapter 1 of _Machine Learning\n",
      "for Hackers_ by D. Conway and J.M. White. It is mainly intended to be run via\n",
      "the interactive shell, though that's not necessary.\n",
      "\n",
      "The script will produce (1) a cleaned tab-separated file of the UFO data;\n",
      "(2) a series of 4 PNG figures.\n",
      "\n",
      "The UFO dataset (approx. 75MB tab-separated file) should be located in a\n",
      "/data/ufo subfolder of the working directory. Otherwise, change the `inpath`\n",
      "and `outpath` variables at the start of the file.\n",
      "\n",
      "For a detailed description of the analysis and the process of porting it\n",
      "to Python, see: slendrmeans.wordpress.com/will-it-python.\n",
      "'''\n",
      "\n",
      "\n",
      "import numpy as np\n",
      "from pandas import *\n",
      "import matplotlib.pyplot as plt\n",
      "import datetime as dt\n",
      "import time\n",
      "import re"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####The location of the UFO raw data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inpath = '/Users/antigen/dev/ML_for_Hackers/slides/01-Slides/ls/ufo_awesome.tsv'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Fixing extra columns in the raw data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Pandas' read_table function gives an error reading the raw data file\n",
      "# `ufo_awesome.tsv`. It turns out there are extra tabs in some of the fields,\n",
      "# generating extra (>6) columns.\n",
      "\n",
      "# A test: read lines from the file until we reach a line with more than 6\n",
      "# tab-separated columns. Then print the line. I use enumerate() to identify\n",
      "# the bad line and its columns.\n",
      "# This 7th column of this bad line corresponds to the first bad date\n",
      "# column in the text. (Indicating R pushes the extra columns to new lines\n",
      "# to a new row).\n",
      "\n",
      "inf = open(inpath, 'r')\n",
      "for i, line in enumerate(inf):\n",
      "    splitline = line.split('\\t')\n",
      "    if len(splitline) != 6:\n",
      "        first_bad_line = splitline\n",
      "        print \"First bad row:\", i\n",
      "        for j, col in enumerate(first_bad_line):\n",
      "            print j, col\n",
      "        break\n",
      "inf.close()\n",
      "\n",
      "# The location of a cleaned version of the data, where the extra\n",
      "# columns are eliminated. Output of the function `ufo_tab_to_sixcols` below.\n",
      "outpath = '/Users/antigen/Desktop/ufo_awesome_6col.tsv'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "First bad row: 754\n",
        "0 19950704\n",
        "1 19950706\n",
        "2  Orlando, FL\n",
        "3 \n",
        "4 4-5 min\n",
        "5 I would like to report three yellow oval lights which passed over Orlando,Florida on July 4, 1995 at aproximately 21:30 (9:30 pm). These were the sizeof Venus (which they passed close by). Two of them traveled one after the otherat exactly the same speed and path heading south-southeast. The third oneappeared about a minute later following the same path as the other two. Thewhole sighting lasted about 4-5 minutes. There were 4 other witnesses oldenough to report the sighting. My 4 year old and 5 year old children were theones who called my attention to the "moving stars". These objects moved fasterthan an airplane and did not resemble an aircraft, and were moving much slowerthan a shooting star. As for them being fireworks, their path was too regularand coordinated. If anybody else saw this phenomenon, please contact me at:\n",
        "6 ler@gnv.ifas.ufl.edu\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ufotab_to_sixcols(inpath, outpath):\n",
      "    '''\n",
      "    Keep only the first 6 columns of data from messy UFO TSV file.\n",
      "\n",
      "    The UFO data set is only supposed to have six columns. But...\n",
      "\n",
      "    The sixth column is a long written description of the UFO sighting, and\n",
      "    sometimes is broken by tab characters which create extra columns.\n",
      "\n",
      "    For these records, we only keep the first six columns. This typically cuts\n",
      "    off some of the long description.\n",
      "\n",
      "    Sometimes a line has less than six columns. These are not written to\n",
      "    the output file (i.e., they're dropped from the data). These records are\n",
      "    usually so comprimised as to be uncleanable anyway.\n",
      "\n",
      "    This function has (is) a side effect on the `outpath` file, to which it\n",
      "    writes output.\n",
      "    '''\n",
      "\n",
      "    inf = open(inpath, 'r')\n",
      "    outf = open(outpath, 'w')\n",
      "\n",
      "    for line in inf:\n",
      "        splitline = line.split('\\t')\n",
      "        # Skip short lines, which are dirty beyond repair, anyway.\n",
      "        if len(splitline) < 6:\n",
      "            continue\n",
      "\n",
      "        newline = ('\\t').join(splitline[ :6])\n",
      "        # Records that have been truncated won't end in a newline character\n",
      "        # so add one.\n",
      "        if newline[-1: ] != '\\n':\n",
      "            newline += '\\n'       \n",
      "\n",
      "        outf.write(newline)\n",
      "\n",
      "    inf.close()\n",
      "    outf.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Run the data cleaning function to create the cleaned file. No need to do this more than once"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ufotab_to_sixcols(inpath, outpath)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####With the new clean file, we can use Pandas' to import the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ufo = read_table(outpath, sep = '\\t', na_values = '',\n",
      "                 header = None, names = ['date_occurred',\n",
      "                                         'date_reported',\n",
      "                                         'location',\n",
      "                                         'short_desc',\n",
      "                                         'duration',\n",
      "                                         'long_desc'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Print the beginning of the data; compare to table on p. 14"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print ufo.head(6).to_string(formatters = {'long_desc' : lambda x : x[ :21]})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   date_occurred  date_reported               location short_desc duration              long_desc\n",
        "0       19951009       19951009          Iowa City, IA        NaN      NaN  Man repts. witnessing\n",
        "1       19951010       19951011          Milwaukee, WI        NaN   2 min.  Man  on Hwy 43 SW of \n",
        "2       19950101       19950103            Shelton, WA        NaN      NaN  Telephoned Report:CA \n",
        "3       19950510       19950510           Columbia, MO        NaN   2 min.  Man repts. son&apos;s\n",
        "4       19950611       19950614            Seattle, WA        NaN      NaN  Anonymous caller rept\n",
        "5       19951025       19951024   Brunswick County, ND        NaN  30 min.  Sheriff&apos;s office\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Converting and cleaning up date data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Unlike the R import, Pandas' read_table pulled the dates in as integers\n",
      "# in YYYYMMDD format. We'll use the function below and map() it to the\n",
      "# date columns in the data.\n",
      "\n",
      "def ymd_convert(x):\n",
      "    '''\n",
      "    Convert dates in the imported UFO data.\n",
      "    Clean entries will look like YYYMMDD. If they're not clean, return NA.\n",
      "    '''\n",
      "    try:\n",
      "        cnv_dt = dt.datetime.strptime(str(x), '%Y%m%d')\n",
      "    except ValueError:\n",
      "        cnv_dt = np.nan\n",
      "        \n",
      "    return cnv_dt\n",
      "\n",
      "ufo['date_occurred'] = ufo['date_occurred'].map(ymd_convert)\n",
      "ufo['date_reported'] = ufo['date_reported'].map(ymd_convert)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Get rid of the rows that couldn't be conformed to datetime"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ufo = ufo[(notnull(ufo['date_reported'])) & (notnull(ufo['date_occurred']))]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Organizing location data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Note on p. 16 the authors claim strsplit() throws an error if there is no\n",
      "# comma in the entry. This doesn't appear to be true.\n",
      "\n",
      "def get_location(l):\n",
      "    '''\n",
      "    Divide the `location` variable in the data into two new variables.\n",
      "    The first is the city, the second the state (or province). The function\n",
      "    returns a two-element list of the form [city, state].\n",
      "\n",
      "    This function is a fairly direct translation of the one in the text.\n",
      "    But, by assuming legitimate U.S. locations have only one comma in them\n",
      "    (e.g. `Baltimore, MD`), the authors miss a number of data points where\n",
      "    the `city` entry has a detailed description with several commas: e.g.,\n",
      "    `Baltimore, near U.S. Rte 59, MD`.\n",
      "    '''\n",
      "    split_location = l.split(',')\n",
      "    clean_location = [x.strip() for x in split_location]\n",
      "    if len(split_location) != 2:\n",
      "        clean_location = ['', '']\n",
      "\n",
      "    return clean_location"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# As an alternative to the one-comma method for finding U.S. locations,\n",
      "# we try using a regular expression that looks for entries that end in a\n",
      "# comma and two letters (e.g., `, MD`) after stripping extra white space.\n",
      "\n",
      "# Since the regexp is going to be mapped along a Series of data, we'll \n",
      "# compile it first.\n",
      "us_state_pattern = re.compile(', [A-Z][A-Z]$', re.IGNORECASE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_location2(l):\n",
      "    '''\n",
      "    Divide the `location` variable in the data into two new variables.\n",
      "    The first is the city, the second the state (or province). The function\n",
      "    returns a two-element list of the form [city, state].\n",
      "\n",
      "    This function assumes legitimate U.S. locations have location data\n",
      "    that end in a comma plus the two-letter state abbreviation. It will\n",
      "    miss any rows where, for instance, the state is spelled out.\n",
      "\n",
      "    Note that the regexp pattern `us_state_pattern` is defined outside\n",
      "    the function, and not called as an extra argument. (Since this\n",
      "    function will be used with Pandas' map(), it's more convenient to\n",
      "    define it with a single argument.\n",
      "    '''\n",
      "    strip_location = l.strip()\n",
      "    us_state_search = us_state_pattern.search(strip_location)\n",
      "    if us_state_search == None:\n",
      "        clean_location = ['', '']\n",
      "    else: \n",
      "        us_city = strip_location[ :us_state_search.start()]\n",
      "        us_state = strip_location[us_state_search.start() + 2: ]\n",
      "        clean_location = [us_city, us_state]\n",
      "    return clean_location"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Get a series of [city, state] lists, then unpack them into new variables in the data frame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "location_lists = ufo['location'].map(get_location2)\n",
      "ufo['us_city'] = [city for city, st in location_lists]\n",
      "ufo['us_state'] = [st.lower() for city, st in location_lists]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####State list from p. 18. Note they forget DC. There seem to be 12 DC entries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "us_states = ['ak', 'al', 'ar', 'az', 'ca', 'co', 'ct', 'dc', 'de', 'fl',\n",
      "             'ga', 'hi', 'ia', 'id', 'il', 'in', 'ks', 'ky', 'la', 'ma',\n",
      "             'md', 'me', 'mi', 'mn', 'mo', 'ms', 'mt', 'nc', 'nd', 'ne',\n",
      "             'nh', 'nj', 'nm', 'nv', 'ny', 'oh', 'ok', 'or', 'pa', 'ri',\n",
      "             'sc', 'sd', 'tn', 'tx', 'ut', 'va', 'vt', 'wa', 'wi', 'wv',\n",
      "             'wy']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# If the `us_state` variable doesn't match the states in the list, set it to\n",
      "# a missing string. Then nix the rows without valid U.S. states.\n",
      "ufo['us_state'][-np.in1d(ufo['us_state'].tolist(), us_states)] = ''\n",
      "ufo['us_city'][-np.in1d(ufo['us_state'].tolist(), us_states)] = ''\n",
      "\n",
      "ufo_us = ufo[ufo['us_state'] != '']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get a data Series of years in the data. Note that Pandas' describe()\n",
      "# won't give quantiles of the datetime objects in the date variables.\n",
      "# (it requires interpolating between dates, which is tricky with\n",
      "# datetimes. We can call describe() on the years to get quantiles, though\n",
      "# since they're just integers.\n",
      "years = ufo_us['date_occurred'].map(lambda x: x.year)\n",
      "years.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "count    52134.000000\n",
        "mean      2000.572237\n",
        "std         10.889045\n",
        "min       1400.000000\n",
        "25%       1999.000000\n",
        "50%       2003.000000\n",
        "75%       2007.000000\n",
        "max       2010.000000"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Plot distribution of sigthings over time and subset to recent sigthings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Figure 1-5 of the text. Note it's over years, and not the original\n",
      "# `date_occured` variable. Matplotlib apparently can't draw histograms\n",
      "# of datetimes.\n",
      "plt.figure()\n",
      "years.hist(bins = (years.max() - years.min())/30., fc = 'steelblue')\n",
      "plt.title('Histogram of years with U.S. UFO sightings\\nAll years in data')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0x10c7da210>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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ajQgODsa5c+e6+hgVp/Z5WzXnV3N2gPmVxp5Fsw6LRc+ePbF//3588803+M9/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\nFosFERHJ+n+nufUCwwJo3AAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Restrict the dates in the data to 1990 and after"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ufo_us = ufo_us[ufo_us['date_occurred'] >= dt.datetime(1990, 1, 1)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years_post90 = ufo_us['date_occurred'].map(lambda x: x.year)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####How much data do we have now, compare to p. 22 of the text"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ufo_us.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(46780, 8)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Check how many sightings we saved with the regex version of the location-cleaning function\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "city_commas = ufo['us_city'].map(lambda x: x.count(','))\n",
      "print 'Cities with commas = ', sum(city_commas > 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cities with commas =  482\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Figure 1-6 in the text.\n",
      "plt.figure()\n",
      "years_post90.hist(bins = 20, fc = 'steelblue')\n",
      "plt.title('Histogram of years with U.S. UFO sightings\\n1990 through 2010')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.text.Text at 0x10c9a5710>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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D2KdARERWi0nBRrHN1rIYT8tiPK0XkwIREUnYp0BE1IPYp0BERFaLScFGsc3W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\n"
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# It's a little strange to histogram over dates. Let's just make a line\n",
      "# plot with the time series of no. of sigthings by date. Aggregated at the\n",
      "# national level, it looks like there's some seasonality in the data,\n",
      "# and a clear `millenium` effect.\n",
      "post90_count = ufo_us.groupby('date_occurred')['date_occurred'].count()\n",
      "plt.figure()\n",
      "post90_count.plot()\n",
      "plt.title('Number of U.S. UFO sightings\\nJanuary 1990 through August 2010')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<matplotlib.text.Text at 0x10d229b50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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s2DB89NFH6NGjh7og3+xkRIRvdjK2hNUeCHrjjTcgk8kgk8kwbNgwZfrE09MT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\n"
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Get monthly sightings by state"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Aggregate data to the state/month level with Pandas' groupby() method.\n",
      "ufo_us['year_month'] = ufo_us['date_occurred'].map(lambda x:\n",
      "                                                   dt.date(x.year, x.month, 1))\n",
      "\n",
      "sightings_counts = ufo_us.groupby(['us_state',\n",
      "                                   'year_month'])['year_month'].count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Check out Alaska to compare with p. 22. Note we get an extra row, which results from the improved location cleaning"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'First few AK sightings in data:'\n",
      "print sightings_counts.ix['ak'].head(6)\n",
      "\n",
      "print 'Extra AK sighting, no on p. 22:'\n",
      "print ufo_us[(ufo_us['us_state'] == 'ak') &\n",
      "             (ufo_us['year_month'] == dt.date(1994, 2, 1))] \\\n",
      "             [['year_month','location']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "First few AK sightings in data:\n",
        "year_month\n",
        "1990-01-01    1\n",
        "1990-03-01    1\n",
        "1990-05-01    1\n",
        "1993-11-01    1\n",
        "1994-02-01    1\n",
        "1994-11-01    1\n",
        "Extra AK sighting, no on p. 22:\n",
        "      year_month                           location\n",
        "5508  1994-02-01   Savoonga,St. Lawrence Island, AK\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Since groupby drops state-month levels for which there are no sightings,\n",
      "# we'll create a 2-level MultiIndex with the full range of state-month pairs.\n",
      "# Then, we'll re-index the data, filling in 0's where data is missing.\n",
      "ym_list = [dt.date(y, m, 1) for y in range(1990, 2011)\n",
      "                            for m in range(1, 13)\n",
      "                            if dt.date(y, m, 1) <= dt.date(2010, 8, 1)]\n",
      "\n",
      "full_index = zip(np.sort(us_states * len(ym_list)), ym_list * len(us_states))\n",
      "full_index = MultiIndex.from_tuples(full_index, names =\n",
      "                                    ['states', 'year_month'])\n",
      "\n",
      "sightings_counts = sightings_counts.reindex(full_index, fill_value = 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Plot monthly sightings by state in lattice/facet-wrap plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Subplot parameters. We set up a figures with MxN subplots, where MxN >= 51\n",
      "# (no. of states to plot). When MxN > 51, the `hangover` variable counts how\n",
      "# many extra subplot remain in the last row of figure. We'll need this to\n",
      "# to put tick labels in nice places.\n",
      "nrow = 13; ncol = 4; hangover = len(us_states) % ncol\n",
      "\n",
      "fig, axes = plt.subplots(nrow, ncol, sharey = True, figsize = (9, 11))\n",
      "\n",
      "fig.suptitle('Monthly UFO Sightings by U.S. State\\nJanuary 1990 through August 2010',\n",
      "             size = 12)\n",
      "plt.subplots_adjust(wspace = .05, hspace = .05)\n",
      "\n",
      "num_state = 0\n",
      "for i in range(nrow):\n",
      "    for j in range(ncol):\n",
      "        xs = axes[i, j]\n",
      "\n",
      "        xs.grid(linestyle = '-', linewidth = .25, color = 'gray')\n",
      "\n",
      "        if num_state < 51:\n",
      "            st = us_states[num_state]\n",
      "            sightings_counts.ix[st, ].plot(ax = xs, linewidth = .75)\n",
      "            xs.text(0.05, .95, st.upper(), transform = axes[i, j].transAxes, \n",
      "                    verticalalignment = 'top')\n",
      "            num_state += 1 \n",
      "        else:\n",
      "            # Make extra subplots invisible\n",
      "            plt.setp(xs, visible = False)\n",
      "            \n",
      "        xtl = xs.get_xticklabels()\n",
      "        ytl = xs.get_yticklabels()\n",
      "\n",
      "        # X-axis tick labels:\n",
      "        # Turn off tick labels for all the the bottom-most\n",
      "        # subplots. This includes the plots on the last row, and\n",
      "        # if the last row doesn't have a subplot in every column\n",
      "        # put tick labels on the next row up for those last\n",
      "        # columns.\n",
      "        #\n",
      "        # Y-axis tick labels:\n",
      "        # Put left-axis labels on the first column of subplots,\n",
      "        # odd rows. Put right-axis labels on the last column\n",
      "        # of subplots, even rows.\n",
      "        if i < nrow - 2 or (i < nrow - 1 and (hangover == 0 or\n",
      "                            j <= hangover - 1)):\n",
      "            plt.setp(xtl, visible = False)\n",
      "        if j > 0 or i % 2 == 1:\n",
      "            plt.setp(ytl, visible = False)\n",
      "        if j == ncol - 1 and i % 2 == 1:\n",
      "            xs.yaxis.tick_right()\n",
      "          \n",
      "        plt.setp(xtl, rotation=90.) \n",
      "    plt.savefig('ufo_ts_bystate.png', dpi = 300)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Gmtx/YMn5pyT5x217AJg2bRoiIiIQGxuL5cuXl+r+bADgyy+/1O4MDQ3VPjx7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AgAgJOTE5YuXYpffvkFSUlJZj1Y094DVjxBYMKECdi4caPZRX7u7u6oWbMmrl69ih07dmDgwIEA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\nv/6qGyVkZ9OoITxcp9u3D2jRA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      }
     ],
     "prompt_number": 27
    }
   ],
   "metadata": {}
  }
 ]
}