week04.ipynb

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  {
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   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting going with joining tables in Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drudgery w/o interest beyond the fact that data is coming from multiple sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import gzip            # can uncompress gzipped files, useful for accessing the pleiades data\n",
    "import io              # useful routines for input/output\n",
    "import pandas as pd    # for working with \"rows/columns\" oriented data\n",
    "import urllib.request  # for loading documents using http\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib # plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "# load csv files\n",
    "ramphs = pd.read_csv(\"http://sebastianheath.com/roman-amphitheaters/roman-amphitheaters.csv\")\n",
    "chronogrps = pd.read_csv(\"http://sebastianheath.com/roman-amphitheaters/chronogrps.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load gzipped PLEIADES. key is \"io.BytesIO(gzip.decompress(response.read())\", which \n",
    "# delivers (so to speak) an uncompressed csv to 'pd.read_csv'.\n",
    "response = urllib.request.urlopen(\"http://atlantides.org/downloads/pleiades/dumps/pleiades-places-latest.csv.gz\")\n",
    "pleiades = pd.read_csv(io.BytesIO(gzip.decompress(response.read())))\n",
    "\n",
    "# One note: because it takes a long time to load the pleiades data,\n",
    "# avoid running this cell again unless necessary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# metmuseum = pd.read_csv(\"https://github.com/metmuseum/openaccess/blob/master/MetObjects.csv?raw=true\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"Number of pleiades geographic entites loaded: %s\" % len(pleiades))\n",
    "print(\"Number of roman amphitheater records loaded loaded: %s\" % len(ramphs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Pleiades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# simple way to get column names\n",
    "# list(pleiades)\n",
    "\n",
    "# pleiades.dtypes\n",
    "\n",
    "# pd.options.display.max_columns = 999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# unique values\n",
    "pleiades.featureTypes.unique()\n",
    "\n",
    "# sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# substring searching\n",
    "pleiades[pleiades.featureTypes.str.contains('amphitheatre')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# renaming columns\n",
    "# https://stackoverflow.com/questions/19758364/rename-a-single-column-header-in-a-pandas-dataframe\n",
    "print(\"https://stackoverflow.com/questions/19758364/rename-a-single-column-header-in-a-pandas-dataframe\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pd.merge\n",
    "\n",
    "ramphs.merge(chronogrps, left_on = 'chronogroup', right_on = 'chronogroup')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pd.set_index\n"
   ]
  }
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