{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making a subset of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we create a subset of the NYC taxi dataset. This subset only contains 0.5% of all rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instructions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* It is recommended to have **100GB of free space** on your hard drive.\n",
    "* This notebook should be in the `minibook-2nd-code/chapter2/cleaning/` directory.\n",
    "* The original data files should be in the `minibook-2nd-code/chapter2/data/` directory.\n",
    "* The 24 data files are named `trip_data_1.csv.zip`, ..., `trip_fare_1.csv.zip`. **Make sure to download these files and put them in the `data` directory**.\n",
    "* This notebook will create two `trip_data_subset.csv` and `trip_fare_subset.csv` files in the `data` directory.\n",
    "* Once the 24 data files have been put in `data/`, click on `Cell > Run all` above to launch the extraction process. **It will take several minutes**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The procedure implemented here is memory-efficient in that the full ZIP files are not first extracted on disk. Unzipping occurs on the fly. We make heavy use of Python generators and iterators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Relative path of the directory containing the data.\n",
    "data_dir = '../data'\n",
    "# Only keep one out of 'step' lines ('200' = 0.5%).\n",
    "step = 200\n",
    "# In every file, stop after 'stop' lines (None=until the end).\n",
    "stop = None  # type 400 to test the script and generate tiny subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path as op\n",
    "import re\n",
    "import zipfile\n",
    "import glob\n",
    "from itertools import chain, islice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _csv_filename(zip_filename):\n",
    "    \"\"\"Return the filename of the CSV in a ZIP file.\"\"\"\n",
    "    return op.splitext(op.basename(zip_filename))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def _iter_lines(zip_filename):\n",
    "    \"\"\"Iterate over all rows from a zipped CSV file.\"\"\"\n",
    "    print(\"Processing file {file}...\".format(file=zip_filename))\n",
    "    csv_filename = _csv_filename(zip_filename)\n",
    "    with zipfile.ZipFile(zip_filename) as z:\n",
    "        with z.open(csv_filename) as f:\n",
    "            for line in f:\n",
    "                yield line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _iter_all_lines(files, step=None, stop=None):\n",
    "    \"\"\"Iterate over rows from several zipped CSV files.\"\"\"\n",
    "    # Keep the header row in the first file, but not in the other files.\n",
    "    return chain(*[islice(_iter_lines(f), min(1, i), stop, step)\n",
    "                   for i, f in enumerate(files)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _extract_number(filename):\n",
    "    \"\"\"Return the month number appearing in a ZIP file.\"\"\"\n",
    "    r = re.search(r'([\\d]+)', filename)\n",
    "    if r:\n",
    "        return int(r.group(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _zip_filenames(name):\n",
    "    \"\"\"Return the ordered list of ZIP filenames.\"\"\"\n",
    "    return sorted(glob.glob(op.join(data_dir, 'trip_{name}_*.zip'.format(name=name))), \n",
    "                  key=_extract_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def _make_extract(step=None, stop=None):\n",
    "    \"\"\"Create the subset data.\"\"\"\n",
    "    for name in ('fare', 'data'):\n",
    "        # Output CSV filename (with the subset data).\n",
    "        filename = op.join(data_dir, 'trip_{name}_subset.csv'.format(name=name))\n",
    "        # List of zipped CSV files.\"\n",
    "        files = _zip_filenames(name)\n",
    "        with open(filename, 'wb') as f:\n",
    "            # Iterate over a subset of the rows from all files.\n",
    "            for line in _iter_all_lines(files, step=step, stop=stop):\n",
    "                f.write(line)\n",
    "        print(\"*** Done! {0} has been successfully created. ***\\n\".format(filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the subset data (**this will take a while**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "_make_extract(step=step, stop=stop)"
   ]
  }
 ],
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