{
 "cells": [
  {
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path\n",
    "import pandas as pd\n",
    "\n",
    "datadir = \"publicdata\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q1** In the data directory you will find `members.csv`, with (fake) information on a number of individuals in Ohio. We will use this for the next several exercises. Read this dataset into a `pandas DataFrame` using `read_csv`. Name it `members0`, and do not include an index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "0a2f8713c50f72645198651cc6fc3085",
     "grade": false,
     "grade_id": "cell-dcba7fd85d6129f6",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Solution cell\n",
    "\n",
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "members0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "2dfd04928d95b68558a0beafb38dd37a",
     "grade": true,
     "grade_id": "cell-f303315087a54035",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Testing Cell\n",
    "\n",
    "assert True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2** Repeat the above, but now do include an index, by specifying `index_col` in the constructor. Name your `DataFrame` `members`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "2264d125067033a6c20ac534d4e441d7",
     "grade": false,
     "grade_id": "cell-6920bdc40cf31840",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Solution cell\n",
    "\n",
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "members.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "0ad7a5cca82dc1c1da1bc9e20b8449ca",
     "grade": true,
     "grade_id": "cell-e841d3a08206426d",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Testing Cell\n",
    "\n",
    "assert True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q3** Write a projection from `members` that will isolate just the `'Phone'` column into a variable `phone_series`. What type are the values in this column?  Include the answer as a comment in your solution cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "a546bb12c25d9ee208db35419b4c42b0",
     "grade": false,
     "grade_id": "cell-44b6c8151853d9da",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "phone_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "9f3e48feac189a537246aec430ced978",
     "grade": true,
     "grade_id": "cell-73008dca051ae781",
     "locked": true,
     "points": 1,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Testing Cell\n",
    "\n",
    "assert True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q4** Write a selection on `members` that will isolate just those rows where the Phone number starts with a `614` area code. Selection involves picking which rows will be shown.  Name the result `cbus`.\n",
    "\n",
    "In order to complete this:\n",
    "\n",
    "1. Write a lambda function that will, given a string, isolate the first three characters and compare these to `'614'`.\n",
    "2. Apply the lambda function to the column you isolated in the last question.\n",
    "3. Use the result as an index to the `pandas DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "7d5fe644b5fae41196f8ddc86ae272a6",
     "grade": false,
     "grade_id": "cell-b886afb32e3e5ffc",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "cbus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "8f7e6eed350f8b8f7e691238a137699e",
     "grade": true,
     "grade_id": "cell-b93c6719fddc412b",
     "locked": true,
     "points": 2,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Testing Cell\n",
    "\n",
    "assert True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q5** Split up the column `Name` into two different columns, `FName` and `LName`. We follow a similar process for this example as we did for selecting rows.\n",
    "\n",
    "1. Write a lambda function that will, given a string, split on space and select only the first element in the resultant list.\n",
    "2. Apply the lambda function to the `Name` column, and save the result.\n",
    "3. Access the dataframe text using a new column name (`FName`), and assign the result from step 2.\n",
    "4. Acquire the Last name from the Name column and create a new column in the data frame called LName."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "d9125eb3f6f15085027f28b536ec07e2",
     "grade": false,
     "grade_id": "cell-ff271df66aa02ad9",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Solution cell\n",
    "\n",
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "fdbac9aafb11868d5d060941b3929e52",
     "grade": true,
     "grade_id": "cell-c0fdceafe85eadaf",
     "locked": true,
     "points": 3,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Testing Cell\n",
    "\n",
    "assert True"
   ]
  }
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