{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\\rightarrow$Run All).\n",
    "\n",
    "Make sure you fill in any place that says `YOUR CODE HERE` or \"YOUR ANSWER HERE\", as well as your name and collaborators below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NAME = \"\"\n",
    "COLLABORATORS = \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Frame: Creation and Basic Access\n",
    "\n",
    "## Creation from Native Data Structure"
   ]
  },
  {
   "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** Fill in the remaing code in the following cell, to build List of Dictionaries representation of the data in `madison_temp.csv` in the data directory, assigning to `LoD`.  Your code is a single assigment to add exactly one field to a dictionary row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "0ec767b73748338ec8bd9cbbe4a5be25",
     "grade": true,
     "grade_id": "cell-68d9f41c9730a7f8",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "path = os.path.join(datadir, \"madison_temp.csv\")\n",
    "\n",
    "LoD = []\n",
    "with open(path, 'r') as f:\n",
    "    columns = f.readline().strip().split(',')\n",
    "    for line in f:\n",
    "        D = {}\n",
    "        fields = line.strip().split(',')\n",
    "        for column_number, value in enumerate(fields):\n",
    "            # YOUR CODE HERE\n",
    "            raise NotImplementedError()\n",
    "        LoD.append(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2** Using the same incantation as the book in section 6.3.1 on data frame creation, create a pandas data frame named `temps` using the `LoD` created in **Q1**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "7f54747a6d62951bd2b975f981c79264",
     "grade": true,
     "grade_id": "cell-969255558e24c607",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q3** Using the methods shown in 6.3.2 to obtain the prefix and suffix of the data, do so for the `temps` data frame in the following two cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "b6b1b810900854b0beca9b0dfa9b69e6",
     "grade": true,
     "grade_id": "cell-851c7d99e842827a",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "b7c529c54164abf3498eba14fd732361",
     "grade": true,
     "grade_id": "cell-ccd18a93fe1bcef5",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q4** In a single assignment line, using an **attribute** of the data frame object, assign to `nrows` and `ncols` the number of rows and columns in the temperatures data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "87cb23aa2e28953f880e1a8b090254c4",
     "grade": true,
     "grade_id": "cell-95a884efb4c95c06",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "print(nrows, ncols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q5** In the following code cell, invoke the data frame method that gives detailed information and the dataframe (its `Index`, columns, and data types for each of the columns.  The, in the markdown cell below that, write your observations on the `Index` and the data types.  Given the data in the data set, what data types are **not** correct if we want to work with the data set?  (Hint: On page 168, in the first paragraph, we see a similar set of observations for the `topnames` data set.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "956b6b31f0618770fcd704a184678f4e",
     "grade": true,
     "grade_id": "cell-0876863e052a5f0f",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "d14d75e6fce4f97b0508a5745ff7975a",
     "grade": true,
     "grade_id": "cell-1fccd52d920f2d3c",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "source": [
    "YOUR ANSWER HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q6** Print the `columns` attribute of the data frame and then the `index` attribute of the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "dbb14fce0a91567457123353de89decd",
     "grade": true,
     "grade_id": "cell-4c8b9a4532d9dbfa",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q7** Carefully consider the data set.  In the cell that follows, identify the independent variable(s) and the dependent variable(s)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "4c77c004cc402f857b56440e2fb53199",
     "grade": true,
     "grade_id": "cell-7ec5857915d8c023",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "source": [
    "YOUR ANSWER HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q8** As done in the book (pg 169) set the `Index` (the logical row identifiers) to be the variable or variables you have identified as the independent variable(s).  It you identified more than one independent variable, you specify a list. If there is just a single independent variable, you can either specify a list with one element, or can specify just the string column name.  Assign the result to `temps2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "3d1ece632031ef1f57b56578c69c183a",
     "grade": true,
     "grade_id": "cell-d37e2e5a9d62fd48",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q9** Using the method that gives a prefix of the data, invoke the method to show the first **10** rows of `temps2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "cd437e254a7c0abeb8a40c1e81546c35",
     "grade": true,
     "grade_id": "cell-e502fa41572e8145",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q10** Repeat the method to find information about a data frame, this time using the `temps2` data frame.  What are the differences between the earlier information gathering about `temps`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "4da84b71cc526f681afe796c70dff8ac",
     "grade": true,
     "grade_id": "cell-d522148291b3ee4a",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "9834db579a012edf57ca7b29e3b4a0db",
     "grade": true,
     "grade_id": "cell-94f730b01fb6ba73",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
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   },
   "source": [
    "YOUR ANSWER HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q11** The last DataFrame method discussed in Section 6.3 allows us to transform an existing data frame and convert data types associated with columns.  Invoke this method on either the `temps` or `temps2` data frames and convert the `EMXT` and `EMNT` columns to be integer numbers.  Assign the result to `temps3`, and show the last 10 rows of the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "350b04351ddc942ffd498e81c0a3ca8b",
     "grade": true,
     "grade_id": "cell-e02b0f846b2d0f0d",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q12** At the end of section 6.3.1, the book demonstrates how to construct a data frame directly from a CSV file.  Do so now to create a data frame named `earthquakes` from the CSV file named `\"earthquake_all_month.csv\"` in the data directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "741309540c4002452b9d37c98e39558e",
     "grade": true,
     "grade_id": "cell-cd78774fba01a30b",
     "locked": false,
     "points": 1,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# YOUR CODE HERE\n",
    "raise NotImplementedError()\n",
    "earthquakes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q_n** In any time remaining, explore the `earthquakes` data frame performing as many of the methods from **Q3** through **Q11** as are applicable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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