{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "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": "markdown",
   "metadata": {},
   "source": [
    "**Q1:** Convert the following into a data frame and name it `df`: \n",
    "\n",
    "    [[1, 2, 3],[4, 5, 6],[7, 8, 9],\n",
    "    [10, 11, 12],[13, 14, 15],[16, 17, 18]]\n",
    "\n",
    "Then display the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "ba11c34ff7129a5ad3bfcf537164848f",
     "grade": false,
     "grade_id": "cell-4936305b37b4ed31",
     "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": "cc66688370c8b79948a916306882fe58",
     "grade": true,
     "grade_id": "cell-4957ce7849139538",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "assert list(df[0])==[1, 4, 7, 10, 13, 16]\n",
    "assert list(df[2])==[3, 6, 9, 12, 15, 18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2:** With reference to the above, assign the columns the following labels: 'A', 'B', and 'C'.\n",
    "\n",
    "Assign the rows the following labels: 'U','V','W','X','Y', and 'Z'\n",
    "\n",
    "The result should still be `df`.  Then, display the data frame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "32e62405f10c140c5d1b4532558bd258",
     "grade": false,
     "grade_id": "cell-f5c50ce855088830",
     "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": "7b1b1d625d32e472009ae0ab187ca731",
     "grade": true,
     "grade_id": "cell-68b87801c6087f1b",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "assert list(df['A'])==[1, 4, 7, 10, 13, 16]\n",
    "assert list(df['C'])==[3, 6, 9, 12, 15, 18]\n",
    "assert list(df.loc['X'])==[10, 11, 12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q3:** In two steps:\n",
    "\n",
    "- Project the first two columns and assign the result to `df1`\n",
    "- From `df1` select all the rows from index 3 onward and assign the result to `df2`\n",
    "\n",
    "Then display the result.\n",
    "\n",
    "Think about how you might accomplish the same in one line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "c1b561fa8cef0b1078d7f573bb707fd0",
     "grade": false,
     "grade_id": "cell-350eadd927920816",
     "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": "cd37c53f87c18a8d85c7615f441c6913",
     "grade": true,
     "grade_id": "cell-f053c2df6f936901",
     "locked": true,
     "points": 2,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "assert list(df2['A'])==[10, 13, 16]\n",
    "assert list(df2['B'])==[11, 14, 17]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q5:** Convert the following dictionary (a DoL representation) to a data frame called `df` and then assign to variable `test1` the column vector associated with the scores of **Test1**.\n",
    "\n",
    "    {'Name' : ['Owen', 'Rachel', 'Kyle'], \n",
    "             'Test1' : [82, 85, 95], \n",
    "             'Test2' : [89, 85, 90], \n",
    "             'Test3' : [93, 85, 87]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "d6233a00a8efbb82120e59e2fe3f57ed",
     "grade": false,
     "grade_id": "cell-3ec9b06343f7d5d6",
     "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": "3f8f6f2410c8a997ceac21529f455aa1",
     "grade": true,
     "grade_id": "cell-bacb0663343d7f7c",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "assert df.shape == (3,4)\n",
    "assert isinstance(test1, pd.core.series.Series)\n",
    "assert list(test1) == [82, 85, 95]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q6:** Print the following:\n",
    "\n",
    "1. The test scores of Test1 (as a Series)\n",
    "2. Rachel's test scores (as a one-row DataFrame)\n",
    "3. Kyle's third test score (as a scalar integer)\n",
    "4. Using indexing, the scores of Owen and Rachel (as a two-row DataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "78f0c814a7656f293bf568ffddd3a0fe",
     "grade": true,
     "grade_id": "cell-15d0798550e374e5",
     "locked": false,
     "points": 4,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q7**: Compute a column Series, assigned to `final`, that equals the average of the three test scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "18fc48338bedeb9e3c944f27a55f07a8",
     "grade": false,
     "grade_id": "cell-f52532d3db1fb99c",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "006f52dfff6c96c16b457645efef6184",
     "grade": true,
     "grade_id": "cell-402ed44a99667b38",
     "locked": true,
     "points": 2,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "assert isinstance(final, pd.core.series.Series)\n",
    "assert list(final)[:2] == [88.0, 85.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q8** Use the following data to create a DataFrame named `df`:\n",
    "\n",
    "\n",
    "    data = {'animal': ['cat','cat','snake','dog',\n",
    "        'dog','cat','snake','cat','dog','dog'],\n",
    "    'age': [2.5, 3, 0.5, 7, 5, 2, 4.5, 4, 7, 3],\n",
    "    'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "    'priority': ['yes','yes','no','yes','no', \n",
    "                 'no','no','yes','no', 'no']}\n",
    "\n",
    "Using as index:\n",
    "\n",
    "    labels = ['a', 'b', 'c', 'd', 'e', \n",
    "              'f', 'g', 'h', 'i', 'j']\n",
    "\n",
    "Then, carry out a selection of rows `d` through `g`, inclusive using row labels (Index elements), not positions.  Assign the result to `df2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "e33ac59cd4ab09686fe7e256d4f93c22",
     "grade": false,
     "grade_id": "cell-61eb39ee4ca8447a",
     "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": "ad92f3d9010c0ad6ba26bbf002caa963",
     "grade": true,
     "grade_id": "cell-f2ba02aa5279c53e",
     "locked": true,
     "points": 2,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "assert df.shape == (10,4)\n",
    "assert df2.shape == (4,4)\n",
    "assert 'd' in df2.index\n",
    "assert 'g' in df2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q9:** In a single assignment statement, select the data in rows 2, 3, and 4 and project the columns 'animal' and 'age', assigning to `df3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "31bc9ad69b16727d1148831b4a3fbb5a",
     "grade": false,
     "grade_id": "cell-37270ca1f13d69c2",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "e518557eb79008df6f51979624b94cbe",
     "grade": true,
     "grade_id": "cell-638458b2a637a019",
     "locked": true,
     "points": 2,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "assert df3.shape == (3, 2)\n",
    "assert 'c' in df3.index\n",
    "assert 'e' in df3.index\n",
    "assert 'animal' in df3.columns\n",
    "assert 'age' in df3.columns"
   ]
  }
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