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   "source": [
    "# Denison CS181/DA210 Homework\n",
    "\n",
    "Before you turn this problem in, make sure everything runs as expected. This is a combination of **restarting the kernel** and then **running all cells** (in the menubar, select Kernel$\\rightarrow$Restart And Run All).\n",
    "\n",
    "Make sure you fill in any place that says `YOUR CODE HERE` or \"YOUR ANSWER HERE\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path\n",
    "\n",
    "datadir = \"publicdata\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1** Assume a CSV file with the same format used in our variations of `topnames` with a header line containing the four column names, and then $x$ rows of comman-separated field values.  Write a function\n",
    "\n",
    "    readTopNamesLoD(path)\n",
    "\n",
    "that reads the file and creates a LoD representation and returns the structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "e25f39844d9d2748bff03c53cbc941a4",
     "grade": false,
     "grade_id": "cell-3ac611d5fbae072d",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "tn10data = readTopNamesLoD(os.path.join(datadir, \"tn10.csv\"))\n",
    "print(tn10data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "4e11b79c99ee82d12b348abc23ebb02e",
     "grade": true,
     "grade_id": "cell-ab5935ca57c293ca",
     "locked": true,
     "points": 2,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "tn10data = readTopNamesLoD(os.path.join(datadir, \"tn10.csv\"))\n",
    "assert isinstance(tn10data, list)\n",
    "assert len(tn10data) == 10\n",
    "assert len(tn10data[0]) == 4\n",
    "assert isinstance(tn10data[0], dict)\n",
    "assert 'year' in tn10data[0]\n",
    "assert tn10data[0]['sex'] == 'Female'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "576595eabedcb299d2517a2a60baf438",
     "grade": true,
     "grade_id": "cell-0367eb87726497a0",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# hiddent tests here\n",
    "assert True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2** Write a function \n",
    "\n",
    "    addCatColumnLoD(tnLoD, threshold1, threshold2)\n",
    "   \n",
    "that adds a categorical column to a LoL representation in parameter `tnDoL` with the new column named `category` whose values are the strings `\"small\"` when count is below `threshold1`, is `\"medium\"` when count is greater than or equal to `threshold1` and less than `threshold2`, and `\"large\"` when count is greater than or equal to `threshold2`.  The function should perform its modifications in place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "e2e468bb34e3efd8d81862ac6851f059",
     "grade": false,
     "grade_id": "cell-0d9878d3fcbb29fa",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "38069b13b60b1e9d8fcf2bfa00cb9228",
     "grade": false,
     "grade_id": "cell-234c176a7a087ee7",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "LoD = [{'year': 2014, 'sex': 'Female', 'name': 'Emma', 'count': 20936}, \n",
    "       {'year': 2014, 'sex': 'Male', 'name': 'Noah', 'count': 19305}, \n",
    "       {'year': 2015, 'sex': 'Female', 'name': 'Emma', 'count': 20455}, \n",
    "       {'year': 2015, 'sex': 'Male', 'name': 'Noah', 'count': 19635}, \n",
    "       {'year': 2016, 'sex': 'Female', 'name': 'Emma', 'count': 19496}, \n",
    "       {'year': 2016, 'sex': 'Male', 'name': 'Noah', 'count': 19117}, \n",
    "       {'year': 2017, 'sex': 'Female', 'name': 'Emma', 'count': 19800}, \n",
    "       {'year': 2017, 'sex': 'Male', 'name': 'Liam', 'count': 18798}, \n",
    "       {'year': 2018, 'sex': 'Female', 'name': 'Emma', 'count': 18688}, \n",
    "       {'year': 2018, 'sex': 'Male', 'name': 'Liam', 'count': 19837}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "0688c41a24079d2cb3edb6306c9a4bea",
     "grade": true,
     "grade_id": "cell-1da7403c4d9cd8aa",
     "locked": true,
     "points": 3,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "addCatColumnLoD(LoD, 19000, 19500)\n",
    "assert len(LoD) == 10\n",
    "assert isinstance(LoD[0], dict)\n",
    "assert len(LoD[0]) == 5\n",
    "assert 'category' in LoD[0]\n",
    "assert [ rowD['category'] for rowD in LoD ] == ['large', 'medium', 'large',\n",
    " 'large', 'medium', 'medium', 'large', 'small', 'small', 'large']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q3** Write a function\n",
    "\n",
    "    dropColumnLoD(LoD, columnname)\n",
    "    \n",
    "that drops the column specified by `columnname` from the list of dictionary representation given in `LoD`.  This should be done \"in place\" and should **not** assume any particular columns.  If the specified `columnname` is not present, there should be no error, nor effect in calling this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "3f74489e6c9f3186aac30ebd738cd729",
     "grade": false,
     "grade_id": "cell-f4c135a24d6f1851",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LoD = [{'year': 2014, 'sex': 'Female', 'name': 'Emma', 'count': 20936}, \n",
    "       {'year': 2014, 'sex': 'Male', 'name': 'Noah', 'count': 19305}, \n",
    "       {'year': 2015, 'sex': 'Female', 'name': 'Emma', 'count': 20455}, \n",
    "       {'year': 2015, 'sex': 'Male', 'name': 'Noah', 'count': 19635}, \n",
    "       {'year': 2016, 'sex': 'Female', 'name': 'Emma', 'count': 19496}, \n",
    "       {'year': 2016, 'sex': 'Male', 'name': 'Noah', 'count': 19117}, \n",
    "       {'year': 2017, 'sex': 'Female', 'name': 'Emma', 'count': 19800}, \n",
    "       {'year': 2017, 'sex': 'Male', 'name': 'Liam', 'count': 18798}, \n",
    "       {'year': 2018, 'sex': 'Female', 'name': 'Emma', 'count': 18688}, \n",
    "       {'year': 2018, 'sex': 'Male', 'name': 'Liam', 'count': 19837}]\n",
    "dropColumnLoD(LoD, 'sex')\n",
    "LoD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "56fd72f2531a5e2252b033f8ac96b27a",
     "grade": true,
     "grade_id": "cell-0fbea25dc33f6bdc",
     "locked": true,
     "points": 3,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "LoD = [{'year': 2014, 'sex': 'Female', 'name': 'Emma', 'count': 20936}, \n",
    "       {'year': 2014, 'sex': 'Male', 'name': 'Noah', 'count': 19305}, \n",
    "       {'year': 2015, 'sex': 'Female', 'name': 'Emma', 'count': 20455}, \n",
    "       {'year': 2015, 'sex': 'Male', 'name': 'Noah', 'count': 19635}, \n",
    "       {'year': 2016, 'sex': 'Female', 'name': 'Emma', 'count': 19496}, \n",
    "       {'year': 2016, 'sex': 'Male', 'name': 'Noah', 'count': 19117}, \n",
    "       {'year': 2017, 'sex': 'Female', 'name': 'Emma', 'count': 19800}, \n",
    "       {'year': 2017, 'sex': 'Male', 'name': 'Liam', 'count': 18798}, \n",
    "       {'year': 2018, 'sex': 'Female', 'name': 'Emma', 'count': 18688}, \n",
    "       {'year': 2018, 'sex': 'Male', 'name': 'Liam', 'count': 19837}]\n",
    "\n",
    "dropColumnLoD(LoD, 'count')\n",
    "assert len(LoD) == 10\n",
    "assert isinstance(LoD[0], dict)\n",
    "assert len(LoD[0]) == 3\n",
    "assert 'count' not in LoD[0]"
   ]
  }
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