Email Analyticas.ipynb

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   "source": [
    "## The Engineering WOrld - A Place For Learning And Exploring\n",
    "\n",
    "# Email Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard imports"
   ]
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   "cell_type": "code",
   "execution_count": 12,
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as pp\n",
    "\n",
    "import pandas.plotting\n",
    "\n",
    "from IPython import display\n",
    "from ipywidgets import interact, widgets\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import mailbox\n",
    "import csv"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### How I converted my mailbox."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mbox = mailbox.mbox('Sent.mbox')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting object is array-like, with one entry per message. Each entry is dictionary like, with keys corresponding to metadata and data for each message."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['User-Agent',\n",
       " 'Date',\n",
       " 'Subject',\n",
       " 'From',\n",
       " 'To',\n",
       " 'CC',\n",
       " 'Message-ID',\n",
       " 'Thread-Topic',\n",
       " 'References',\n",
       " 'In-Reply-To',\n",
       " 'Content-Type',\n",
       " 'Content-Transfer-Encoding',\n",
       " 'MIME-Version']"
      ]
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     "execution_count": 4,
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     "output_type": "execute_result"
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   "source": [
    "mbox[0].keys()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The easiest way to get these data into Pandas is to build a CSV file from them. We use the module `csv` to write out the CSV file as we loop over the mailbox object. We save only subject, from, to, and date, and we write a simple header at the top with the names of columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('mbox.csv', 'w') as outfile:\n",
    "    writer = csv.writer(outfile)\n",
    "    writer.writerow(['subject','from','to','date'])\n",
    "    \n",
    "    for message in mbox:\n",
    "        writer.writerow([message['subject'], message['from'], message['to'], message['date']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All done! Thanks to Justin Ellis for inspiration with https://jellis18.github.io/post/2018-01-17-mail-analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving on!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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