data_set_cleanup.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## This notebook is used for cleanup and formatting of NTSB data.\n",
    "\n",
    "### Source: https://www.ntsb.gov/_layouts/ntsb.aviation/index.aspx\n",
    "### File Format: Pipe Delimited '|'"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the NTSB dataset"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Pandas library\n",
    "import pandas as pd \n",
    "filename = 'data/AviationData.txt' \n",
    "data = pd.read_csv(filename, delimiter='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print shape of the dataframe\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print first rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the last column"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data.columns[:-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print out columns of the df\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split out the Location field into 'City' and 'State'."
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.concat([data, data[' Location '].str.partition(',')[[0, 2]]], axis=1)\n",
    "splitLocation = data[' Location '].str.split(\", \", expand=True,)\n",
    "splitLocation = splitLocation.drop(columns=[2, 3, 4], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add split fields to DataFrame"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.concat([data, splitLocation])\n",
    "data[\"City\"] = splitLocation[0]\n",
    "data[\"State\"] = splitLocation[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the new columns."
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " data = data.rename(index=str, columns={0: \"City\", 1: \"State\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the Location column"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(columns=[' Location '])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the dates format to %Y-%m-%d (to match what Splunk expects)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert Event Date\n",
    "data[' Event Date '] = pd.to_datetime(data[' Event Date '])\n",
    "data[' Event Date '] = data[' Event Date '].dt.strftime('%Y-%m-%d')\n",
    "\n",
    "# convert Publication Date\n",
    "data[' Publication Date '] = pd.to_datetime(data[' Publication Date '], errors='coerce')\n",
    "data[' Publication Date '] = data[' Publication Date '].dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the updated DataFrame as CSV"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"output/AviationData.csv\", sep=',', index=False)"
   ]
  }
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