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    "# Analyzing Data from Multiple Files\n",
    "\n",
    "<br/>\n",
    "\n",
    "<section class=\"objectives panel panel-warning\">\n",
    "<div class=\"panel-heading\">\n",
    "<h2><span class=\"fa fa-certificate\"></span> Learning Objectives </h2>\n",
    "</div>\n",
    "<ul>\n",
    "<li> Use a library function to get a list of filenames that match a simple wildcard pattern. </li>\n",
    "<li> Use a for loop to process multiple files. </li>\n",
    "</ul>\n",
    "</section>\n",
    "\n",
    "We now have almost everything we need to process all our data files. The only thing that’s missing is a library with a rather unpleasant name:"
   ]
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   "cell_type": "code",
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   "source": [
    "import glob"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `glob` library contains a single function, also called `glob`, that finds files whose names match a pattern. We provide those patterns as strings: the character `*` matches zero or more characters, while `?` matches any one character. We can use this to get the names of all the HTML files in the current directory:"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "print(glob.glob('data/inflammation*.csv'))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As these examples show, glob.glob’s result is a list of strings, which means we can loop over it to do something with each filename in turn. In our case, the “something” we want to do is generate a set of plots for each file in our inflammation dataset.\n",
    "\n",
    "```python\n",
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "\n",
    "filenames = glob.glob('data/inflammation*.csv')\n",
    "filenames = filenames[0:3]\n",
    "for f in filenames:\n",
    "    print(f)\n",
    "\n",
    "    data = numpy.loadtxt(fname=f, delimiter=',')\n",
    "\n",
    "    fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
    "\n",
    "    axes1 = fig.add_subplot(1, 3, 1)\n",
    "    axes2 = fig.add_subplot(1, 3, 2)\n",
    "    axes3 = fig.add_subplot(1, 3, 3)\n",
    "\n",
    "    axes1.set_ylabel('average')\n",
    "    axes1.plot(data.mean(axis=0))\n",
    "\n",
    "    axes2.set_ylabel('max')\n",
    "    axes2.plot(data.max(axis=0))\n",
    "\n",
    "    axes3.set_ylabel('min')\n",
    "    axes3.plot(data.min(axis=0))\n",
    "\n",
    "    fig.tight_layout()\n",
    "    matplotlib.pyplot.show(fig)\n",
    "```"
   ]
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   "source": [
    "Sure enough, the maxima of the first two data sets show exactly the same ramp as the first, and their minima show the same staircase structure; a different situation has been revealed in the third dataset, where the maxima are a bit less regular, but the minima are consistently zero."
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