lab3.ipynb

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   "source": [
    "# Lab 3: Introduction to NumPy and Pandas\n",
    "\n",
    "**Tomas Beuzen, September 2020**"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you'll complete various exercises using your new best friends NumPy and Pandas! These two packages are the foundation of data science in Python, providing you with the tools to create, load, operate on and generally wrangle data in practically any way imaginable! Broadly speaking, NumPy focuses on array-like data, while Pandas focuses on tabular data - you'll be using plenty of both throughout MDS!\n",
    "\n",
    "![](img/fun.png)"
   ]
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   "source": [
    "<h2>Table of Contents<span class=\"tocSkip\"></span></h2>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Instructions\" data-toc-modified-id=\"Instructions-1\">Instructions</a></span></li><li><span><a href=\"#Exercise-1:-NumPy-Warm-Up\" data-toc-modified-id=\"Exercise-1:-NumPy-Warm-Up-2\">Exercise 1: NumPy Warm Up</a></span></li><li><span><a href=\"#Exercise-2:-Vectorizing-Geometric-Brownian-Motion\" data-toc-modified-id=\"Exercise-2:-Vectorizing-Geometric-Brownian-Motion-7\">Exercise 2: Vectorizing Geometric Brownian Motion</a></span></li><li><span><a href=\"#Exercise-3:-Advanced-NumPy-Wrangling\" data-toc-modified-id=\"Exercise-3:-Advanced-NumPy-Wrangling-8\">Exercise 3: Advanced NumPy Wrangling</a></span></li><li><span><a href=\"#Exercise-4:-Reading-Data-with-Pandas\" data-toc-modified-id=\"Exercise-4:-Reading-Data-with-Pandas-13\">Exercise 4: Reading Data with Pandas</a></span></li><li><span><a href=\"#Exercise-5:-Treasure-Hunt-with-Pandas\" data-toc-modified-id=\"Exercise-5:-Treasure-Hunt-with-Pandas-14\">Exercise 5: Treasure Hunt with Pandas</a></span></li><li><span><a href=\"#Exercise-6:-Mind-the-Gap\" data-toc-modified-id=\"Exercise-6:-Mind-the-Gap-16\">Exercise 6: Mind the Gap</a></span></li></ul></div>"
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   "source": [
    "## Instructions\n",
    "<hr>\n",
    "\n",
    "rubric={mechanics:4}"
   ]
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   "source": [
    "You will receive marks for correctly submitting this assignment. To submit this assignment you should:\n",
    "\n",
    "1. Push your assignment to your GitHub repository!\n",
    "2. Provide a link to your repository here. **Link to your GitHub repository:**\n",
    "2. Upload a HTML render of your assignment to Canvas. The last cell of this notebook will help you do that.\n",
    "3. Be sure to follow the [General Lab Instructions](https://ubc-mds.github.io/resources_pages/general_lab_instructions/).\n",
    "\n",
    "You can view a description of the different rubrics used for grading in MDS [here](https://github.com/UBC-MDS/public/tree/master/rubric)."
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   "source": [
    "# Import for this lab\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from random import gauss\n",
    "from canvasutils.submit import submit, convert_notebook\n",
    "from IPython.utils import io\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams.update({'font.size': 16,\n",
    "                     'axes.labelweight': 'bold',\n",
    "                     'figure.figsize': (8,6),\n",
    "                     'axes.grid': False})\n",
    "import lab3_tests"
   ]
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   "source": [
    "## Exercise 1: NumPy Warm Up\n",
    "<hr>\n",
    "\n",
    ">**I will demonstrate the solutions to these exercises in lab class and post the video to [Canvas](https://canvas.ubc.ca/courses/53659/external_tools/187).**"
   ]
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   "source": [
    "Just a few quick exercises to get you in the NumPy mind-set!"
   ]
  },
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   "source": [
    "### 1.1\n",
    "rubric={autograde:1}"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Create the following array and save it as a variable `ex1_1`:\n",
    "\n",
    "```python\n",
    "array([[1, 2],\n",
    "       [3, 4],\n",
    "       [5, 6]])\n",
    "```"
   ]
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   "source": [
    "ex1_1 = None\n",
    "ex1_1 = np.array([[1, 2], [3, 4], [5, 6]])"
   ]
  },
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   "cell_type": "code",
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   "outputs": [
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     "text": [
      "PASSED: type(ex1_1) == np.ndarray\n",
      "PASSED: ex1_1.shape == (3, 2)\n",
      "PASSED: ex1_1[0][0] == 1\n",
      "PASSED: ex1_1[0][1] == 2\n",
      "PASSED: ex1_1[2][0] == 5\n",
      "PASSED: ex1_1[2][1] == 6\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex1_1(ex1_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2\n",
    "rubric={autograde:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reshape the following `arr` into shape `(6, 1)` and save the result as `ex1_2`."
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([[1, 2], [3, 4], [5, 6]])\n",
    "ex1_2 = None\n",
    "ex1_2 = arr.reshape(6, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "PASSED: type(ex1_2) == np.ndarray\n",
      "PASSED: ex1_2.shape == (6, 1)\n",
      "PASSED: ex1_2[0] == 1\n",
      "PASSED: ex1_2[-1] == 6\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex1_2(ex1_2)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### 1.3\n",
    "rubric={autograde:1}"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Create a `(6, 6)` matrix by multiplying the following `arr` by itself (hint: leverage broadcasting here). The first element (top left corner) should be 1, and the last element (bottom right corner) should be 36."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(1, 7)[None, :]\n",
    "ex1_3 = None\n",
    "ex1_3 = arr.reshape(6, 1)*arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASSED: type(ex1_3) == np.ndarray\n",
      "PASSED: ex1_3.shape == (6, 6)\n",
      "PASSED: ex1_3[0, 0] == 1\n",
      "PASSED: ex1_3[-1, -1] == 36\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex1_3(ex1_3)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4\n",
    "rubric={autograde:1}"
   ]
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   "cell_type": "markdown",
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   "source": [
    "\"Center\" the following `arr` by subtracting the mean from every element, then flatten it to shape `(20,)` and sort it in descending order."
   ]
  },
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   "cell_type": "code",
   "execution_count": 40,
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     "text": [
      "[ 9.5  8.5  7.5  6.5  5.5  4.5  3.5  2.5  1.5  0.5 -0.5 -1.5 -2.5 -3.5\n",
      " -4.5 -5.5 -6.5 -7.5 -8.5 -9.5]\n"
     ]
    }
   ],
   "source": [
    "arr = np.arange(1, 21).reshape(4, 5)\n",
    "ex1_4 = None\n",
    "ex1_4 = (arr - arr.mean()).flatten()[::-1]\n",
    "print(ex1_4)"
   ]
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   "cell_type": "code",
   "execution_count": 41,
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   "outputs": [
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     "text": [
      "PASSED: type(ex1_4) == np.ndarray\n",
      "PASSED: ex1_4.shape == (20,)\n",
      "PASSED: ex1_4[0] == 9.5\n",
      "PASSED: ex1_4[-1] == -9.5\n",
      "PASSED: ex1_4.sum() == 0\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex1_4(ex1_4)"
   ]
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   "source": [
    "## Exercise 2: Vectorizing Geometric Brownian Motion\n",
    "<hr>\n",
    "\n",
    ">**I will F the solutions to these exercises in lab class and post the video to [Canvas](https://canvas.ubc.ca/courses/53659/external_tools/187).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1\n",
    "rubric={accuracy:2,quality:2}"
   ]
  },
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   "metadata": {},
   "source": [
    "In Lab 2 you created a function `simulate(S0, σ, T)` that simulates a stock price using geometric Brownian motion. In the model, the current price $S_t$ depends on the previous price $S_{t-1}$ as follows:\n",
    "\n",
    "$$S_t = S_{t-1} \\exp \\left( -0.5\\sigma^2 + \\sigma Z \\right)$$\n",
    "\n",
    "where $Z$ is a random number drawn from a standard [Gaussian distribution](https://en.wikipedia.org/wiki/Normal_distribution) and $\\sigma$ is the _volatility_ of the stock (i.e., if $\\sigma=0$ then the stock price never changes, whereas if $\\sigma$ is large the stock price can change wildly).\n",
    "\n",
    "The function we came up with in Lab 2 is shown below:"
   ]
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   "source": [
    "def simulate(S0=1, σ=0.1, T=1000):\n",
    "    \"\"\"\n",
    "    Simulates a stock price using geometric Brownian motion, \n",
    "    and returns a list containing the results.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    S0 : float \n",
    "        the initial stock price\n",
    "    σ : float \n",
    "        the volatility, should be non-negative\n",
    "    T : int\n",
    "        the number of time steps to simulate\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    list\n",
    "        The stock price of the stock at each time step \n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "    >>> simulate(3.9, 0.5, 4)\n",
    "    [3.9, 3.16886711, 1.06006414, 0.79470699]\n",
    "    \"\"\"\n",
    "    \n",
    "    S = [S0]\n",
    "    # append to list for each time T\n",
    "    for t in range(1, T):\n",
    "        Z = gauss(0, 1)\n",
    "        S.append(S[t - 1] * math.exp(-0.5 * σ ** 2 + σ * Z))\n",
    "\n",
    "    return S"
   ]
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   "source": [
    "Your task here is to re-write this function as `simulate_np(S0, σ, T)` using NumPy and **without using a for loop**. If you consider the equation above, you'll notice we don't really need a for loop to simulate **T** time steps of geometric Brownian motion. Let's do some math! Let's denote $\\theta = \\exp \\left( -0.5\\sigma^2 + \\sigma Z \\right)$, then:\n",
    "\n",
    "$$S_{t+1} = S_{t} \\theta_t$$\n",
    "\n",
    "$$S_{t+2} = S_{t+1} \\theta_{t+1} = S_{t} \\theta_t \\theta_{t+1}$$\n",
    "\n",
    "$$S_{t+3} = S_{t+2} \\theta_{t+2} = S_{t} \\theta_t \\theta_{t+1} \\theta_{t+2}$$\n",
    "\n",
    "$$\\textrm{...}$$\n",
    "\n",
    "$$S_{T} = S_{0} \\prod_{t=1}^{T} \\theta_{t}$$\n",
    "\n",
    "With the above in mind, use NumPy to create `simulate_np(S0, σ, T)` that calculate geometric Brownian motion **without a for loop**.\n",
    "\n",
    "Hints:\n",
    "- 𝜃 does not depend on the stock price at time `t`, so you can create $\\prod_{t=1}^{T} \\theta_{t}$ without using a loop.\n",
    "- You can create a `Z` with `np.random.randn()`.\n",
    "- You can calculate the cumulative product of an array using `.cumprod()`.\n",
    "- Your function can return an `ndarray` instead of a `list`.\n",
    "- You are given some plotting code to test out your function."
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_np(S0=1, σ=0.1, T=1000):\n",
    "    pass  # remove if providing a solution\n",
    "# Your answer goes here."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Code you can use for plotting your function \n",
    "try:\n",
    "    trace = simulate_np(S0=1, σ=0.1, T=1000)\n",
    "    plt.plot(trace)\n",
    "    plt.grid()\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Stock price\")\n",
    "except Exception as e:\n",
    "    print(f\"Plotting failed, here's the traceback:\\n{e}.\")"
   ]
  },
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   "source": [
    "### 2.2\n",
    "rubric={accuracy:2}"
   ]
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   "metadata": {},
   "source": [
    "This question is optional but I highly recommend it if you have time (pun intended) - it will really help drive home the power of the NumPy library vs base Python when operating on array-like data! You can use the `time` module to help calculate the time it takes (in [real time](https://communities.sas.com/t5/SAS-Programming/Real-Time-vs-CPU-time/td-p/287743#:~:text=Real%20Time%20is%20the%20actual,the%20step%20utilises%20CPU%20resources.)) for your code to execute. Write code to record how long it takes for `simulate()` and your new-and-improved `simulate_np()` to run 10,000 times (using their default values of `(S0=1, σ=0.1, T=1000)`).\n",
    "\n",
    "Hints:\n",
    "- The `time` module has already been imported for you at the start of the lab.\n",
    "- `time.time()` gives you the current clock time (so you can save a variable `start = time.time()` before you code and `end = time.time()` after your code and compare the difference)."
   ]
  },
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   "cell_type": "code",
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   "source": [
    "# Your answer goes here."
   ]
  },
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   "source": [
    "## Exercise 3: NumPy Wrangling\n",
    "<hr>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1\n",
    "rubric={accuracy:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a 400 x 400 pixel image of me, imported and displayed with `matplotlib` (a plotting library that you'll learn about in DSCI 531)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape: (400, 400)\n",
      "Max. pixel value: 1.0\n",
      "Min. pixel value: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = plt.imread('img/tomas_beuzen.png')[:,:,0]\n",
    "plt.imshow(image, cmap='gray')\n",
    "print(f\"Image shape: {image.shape}\")\n",
    "print(f\"Max. pixel value: {image.max()}\")\n",
    "print(f\"Min. pixel value: {image.min()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that `image` is just a numpy array of size `(400, 400)`, with values ranging from -1 to 1.\n",
    "\n",
    "Your task is to **rotate and flip the image** so that it looks like this:\n",
    "\n",
    "![](img/tomas_beuzen_flipped.png)\n",
    "\n",
    "Hints:\n",
    "- Plot the image using `plt.imshow(<ndarray>, cmap='gray')`, as I've done above.\n",
    "- Do not overwrite the `image` variable, we will use it in the next few questions.\n",
    "- NumPy has helpful functions for \"flipping\" arrays! In Jupyter, you can try typing `np.flip` and then press tab to see what the auto-complete reveals..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape: (400, 400)\n",
      "Max. pixel value: 1.0\n",
      "Min. pixel value: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = plt.imread('img/tomas_beuzen.png')[:,:,0]\n",
    "flip_image = np.flip(image)\n",
    "plt.imshow(flip_image, cmap='gray')\n",
    "print(f\"Image shape: {image.shape}\")\n",
    "print(f\"Max. pixel value: {image.max()}\")\n",
    "print(f\"Min. pixel value: {image.min()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2\n",
    "rubric={accuracy:2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to prepare this image to post on Instagram and I think a 20 pixel black border will really make my eyes pop. Add a 20 pixel wide border of black pixels to my image so that it looks like this:\n",
    "\n",
    "![](img/tomas_beuzen_border.png)\n",
    "\n",
    "Hints:\n",
    "- The image including the border should remain 400 x 400 pixels (i.e., you are adding the border within the image), **do not** add it to the outside and make the image 440 x 440 pixels.\n",
    "- Black pixels have a value of `0` (i.e., they have 0 brightness).\n",
    "- There are many ways to solve this question so do it however you like, but `np.pad()` might be helpful..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape: (400, 400)\n",
      "Max. pixel value: 1.0\n",
      "Min. pixel value: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = plt.imread('img/tomas_beuzen.png')[:,:,0]\n",
    "border_image = np.pad(image[20:-20,20:-20], ((20,20),(20,20)))\n",
    "plt.imshow(border_image, cmap='gray')\n",
    "print(f\"Image shape: {image.shape}\")\n",
    "print(f\"Max. pixel value: {image.max()}\")\n",
    "print(f\"Min. pixel value: {image.min()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (OPTIONAL) 3.3\n",
    "rubric={accuracy:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In DSCI 572 you'll learn about [Convolutional Neural Networks](https://en.wikipedia.org/wiki/Convolutional_neural_network) (CNN), which are often used when working with image data, e.g., to build image classification models. A common operation in a CNN architecture is [pooling](https://cs231n.github.io/convolutional-networks/#pool), in which you reduce the size of an image by looking at a small window of pixels, say a 4 x 4 window of pixels, and representing that window using e.g., the max/min/mean value of the pixels in the window. Below is an example of mean pooling, transforming a 6 x 6 image into a 3 x 3 image by taking the mean of 2 x 2 pixel windows:\n",
    "\n",
    "![](https://stanford.edu/~shervine/teaching/cs-230/illustrations/average-pooling-a.png?58f9ab6d61248c3ec8d526ef65763d2f)\n",
    "\n",
    "Source: [stanford.edu](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks)\n",
    "\n",
    "You'll learn more about CNN architecure and operations in DSCI 572, but for some foreshadowing, let's implement pooling in NumPy here. We can do it by reshaping our image into `n x n` windows and then calculating the `.max()` of each window. Your task here is to implement **mean** pooling on the image of me using a `10 x 10` window, the result will look like this:\n",
    "\n",
    "![](img/tomas_beuzen_mean_pool.png)\n",
    "\n",
    "Hint:\n",
    "- There are plenty of ways you could solve this question but I started by reshaping both axes of the image into shape `(40, 10)`, to end up with a 4D array of shape `(40, 10, 40, 10)`.\n",
    "- Then apply `.mean()` to the reshaped data on both the axes of size 10 (you can specify multiple axes to operate on simultaneously by passing a tuple to the `axes=` argument of `.mean()`)\n",
    "- See [here](https://stackoverflow.com/a/42463514) for more help.\n",
    "- Play around with you code to get a feel for what pooling does. What happens if you increase/decrease the pooling window size? Also feel free to try using `.min()` or `.max()` for a more \"horror-movie\" effect, for example: \n",
    "\n",
    "![](img/tomas_beuzen_min_pool.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your answer goes here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (OPTIONAL) 3.4\n",
    "rubric={accuracy:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the name suggests, another common operation in CNNs is convolution. A convolutional layer applies so called \"filters\" to an image. This is kind of like the pooling we saw in the previous question, but instead of representing a window of pixels by some summary statistic, a filter is a window of \"weights\", which is passed over an image and used to create \"new pixels\" by multiplying the original pixels by weights and summing them all together. The idea in CNN's is that these filters can be used to extract useful information from the data; for example, they might highlight the edges in an image, or colour boundaries, which could help a machine learning model classify what that image contains.  \n",
    "\n",
    "In the example below, a 3 x 3 filter with weights of 0's, 1's, 2's and 3's is being passed over an image. [Here is a fun interactive exploration](https://setosa.io/ev/image-kernels/) of convolutional filters if you want to learn more, but you'll learn about CNN's in DSCI 572 so don't worry too much about them here.\n",
    "\n",
    "![](http://deeplearning.net/software/theano/_images/numerical_no_padding_no_strides.gif)\n",
    "\n",
    "Source: [deeplearning.net](http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html#refresher-discrete-convolutions)\n",
    "\n",
    "Your task here is to implement a convolutional layer in NumPy. Apply the filter `f` I've defined in the code below to my image. The result will look like this:\n",
    "\n",
    "![](img/tomas_beuzen_emboss.png)\n",
    "\n",
    "You should pad the image with 0's so that you can apply the filter to the edges and corners, like this:\n",
    "\n",
    "![](http://deeplearning.net/software/theano/_images/numerical_padding_strides.gif)\n",
    "\n",
    "Source: [deeplearning.net](http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html#refresher-discrete-convolutions)\n",
    "\n",
    "There are many ways to solve this problem, but my solution involved leverage strides, here's a hint to get you started: https://jessicastringham.net/2017/12/31/stride-tricks/, and see [here](https://stackoverflow.com/questions/53097952/how-to-understand-numpy-strides-for-layman) for more information in \"strides\" in NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = np.array([[-2, -1, 0],\n",
    "              [-1, 1, 1],\n",
    "              [0, 1, 2]])  # filter to apply\n",
    "# Your answer goes here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-cbb88b5728dbc768",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "## Exercise 4: Reading Data with Pandas \n",
    "<hr>\n",
    "\n",
    "rubric={autograde:5}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading data from external sources will be one of the most common things you do in Pandas, so it's good to get some practice doing this. We will use Pandas to read in variants of the Palmer penguins data set that you've already seen in DSCI 523. In this data set there are measures on the three species of penguins shown in this illustration below. You can learn more about the penguins and this data set [here](https://allisonhorst.github.io/palmerpenguins/).\n",
    "\n",
    "![](img/penguins.png)\n",
    "\n",
    "Source: [allisonhorst.github.io](https://github.com/allisonhorst/palmerpenguins/blob/master/man/figures/lter_penguins.png?raw=true)\n",
    "\n",
    "In the data directory of this repository there are 5 different versions of the same data set in different file formats. Your task is to read each of these into a Pandas dataframe. A test has been provided to make sure you've correctly read each file in.\n",
    "\n",
    "Hint: you can compare which rows of two dataframes are not equal using something like `penguins_csv[~penguins_csv.eq(penguins_tsv).any(axis=1)]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Missing optional dependency 'xlrd'. Install xlrd >= 1.0.0 for Excel support Use pip or conda to install xlrd.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-157-3afecfa71989>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;31m# read in file named penguins.xlsx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[0mpenguins_excel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m \u001b[0mpenguins_excel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data/penguins.xls'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msheet_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'penguins'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m \u001b[1;31m# Your answer goes here.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpenguins_csv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    294\u001b[0m                 )\n\u001b[0;32m    295\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\excel\\_base.py\u001b[0m in \u001b[0;36mread_excel\u001b[1;34m(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols)\u001b[0m\n\u001b[0;32m    302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    303\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 304\u001b[1;33m         \u001b[0mio\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    305\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m         raise ValueError(\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\excel\\_base.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, path_or_buffer, engine)\u001b[0m\n\u001b[0;32m    865\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstringify_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    866\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 867\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    868\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    869\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__fspath__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\excel\\_xlrd.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[0;32m     19\u001b[0m         \"\"\"\n\u001b[0;32m     20\u001b[0m         \u001b[0merr_msg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Install xlrd >= 1.0.0 for Excel support\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m         \u001b[0mimport_optional_dependency\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"xlrd\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextra\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\compat\\_optional.py\u001b[0m in \u001b[0;36mimport_optional_dependency\u001b[1;34m(name, extra, raise_on_missing, on_version)\u001b[0m\n\u001b[0;32m     91\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     92\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mraise_on_missing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 93\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     94\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: Missing optional dependency 'xlrd'. Install xlrd >= 1.0.0 for Excel support Use pip or conda to install xlrd."
     ]
    }
   ],
   "source": [
    "# read in file named penguins.csv\n",
    "penguins_csv = None\n",
    "penguins_csv = pd.read_csv('data/penguins.csv')\n",
    "# read in file named penguins.tsv\n",
    "penguins_tsv = None\n",
    "penguins_tsv = pd.read_csv('data/penguins.tsv', delimiter= '\\s+')\n",
    "# read in file named penguins-meta-data.csv\n",
    "penguins_meta_data = None\n",
    "penguins_meta_data = pd.read_csv('data/penguins-meta-data.csv', skiprows =  9, nrows = 344, names = [\"species\",\n",
    "                                                                                                       \"island\",\n",
    "                                                                                                       \"bill_length_mm\",\n",
    "                                                                                                       \"bill_depth_mm\",\n",
    "                                                                                                       \"flipper_length_mm\",\n",
    "                                                                                                       \"body_mass_g\",\n",
    "                                                                                                       \"sex\",\n",
    "                                                                                                       \"year\"])\n",
    "# read in file named penguins2.csv\n",
    "penguins2 = None\n",
    "penguins2 = pd.read_csv('data/penguins2.csv', sep = ';', decimal = ',', header = None, names = [\"species\",\n",
    "                                                                                                \"island\",\n",
    "                                                                                                \"bill_length_mm\",\n",
    "                                                                                                \"bill_depth_mm\",\n",
    "                                                                                                \"flipper_length_mm\",\n",
    "                                                                                                \"body_mass_g\",\n",
    "                                                                                                \"sex\",\n",
    "                                                                                                \"year\"]) \n",
    "# read in file named penguins.xlsx\n",
    "penguins_excel = None\n",
    "penguins_excel = pd.read_excel('data/penguins.xls', sheet_name = 'penguins')\n",
    "# Your answer goes here.\n",
    "print(penguins_csv)\n",
    "print(penguins2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex4([penguins_csv, penguins_tsv, penguins_meta_data, penguins2, penguins_excel])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-cbb88b5728dbc768",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "## Exercise 5: Treasure Hunt with Pandas\n",
    "<hr>\n",
    "\n",
    ">**I will demonstrate solutions to some of these exercises in lab class and post the video to [Canvas](https://canvas.ubc.ca/courses/53659/external_tools/187).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learning how to index with Pandas is kind of like having to eat your vegetables: it's not the funnest thing, but you need to know it! In this exercise, I'll provide you with clues that you need to use to index a dataframe and find the \"Treasure\". For example, if the clue is \"The first ten rows\" then you might try:\n",
    "\n",
    "```python\n",
    "df.iloc[:10]\n",
    "'TREASURE'\n",
    "```\n",
    "\n",
    "The above is just an example, but the result of your indexing should always be the string `'TREASURE'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1\n",
    "rubric={autograde:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load in the file `treasure_hunt.csv` as a dataframe called `df`. Read in the first column as the dataframe index column and the first row as the dataframe columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "treasure_df = None\n",
    "treasure_df = pd.read_csv('data/treasure_hunt.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASSED: type(treasure_df) == pd.DataFrame\n",
      "PASSED: ''.join(treasure_df.columns.values) == 'C1C2C3C4C5C6C7C8C9C10C11C12C13C14C15C16C17C18C19C20C21C22C23C24C25C26C27C28C29C30'\n",
      "PASSED: ''.join(treasure_df.index.values) == 'R1R2R3R4R5R6R7R8R9R10R11R12R13R14R15R16R17R18R19R20R21R22R23R24R25R26R27R28R29R30'\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex5_1(treasure_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2\n",
    "rubric={autograde:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLUE:** C20, R1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex5_2 = None\n",
    "ex5_2 = treasure_df.loc['R1','C20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASSED: ex5_2 == 'TREASURE'\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex5_2(ex5_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3\n",
    "rubric={autograde:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLUE:** thirteenth column, thirteenth row (*hint*: recall that indexing starts at 0!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex5_3 = None\n",
    "ex5_3 = treasure_df.iloc[12,12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASSED: ex5_3 == 'TREASURE'\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex5_3(ex5_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4\n",
    "rubric={autograde:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLUE:** R28, tenth column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex5_4 = None\n",
    "ex5_4 = treasure_df.loc['R28', treasure_df.columns[9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASSED: ex5_4 == 'TREASURE'\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex5_4(ex5_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5\n",
    "rubric={autograde:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLUE:** C26, R19 to R26\n",
    "\n",
    "**HINT:** You might need to do a bit of extra wrangling to get a result of \"TREASURE\" here..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex5_5 = None\n",
    "ex5_5 = treasure_df.loc['R19':'R26', 'C26'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASSED: ex5_5 == 'TREASURE'\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex5_5(ex5_5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.6\n",
    "rubric={autograde:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLUE:** The first rows of C29 *plus* the first row of C30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex5_6 = None\n",
    "ex5_6 = treasure_df.loc['R1', 'C29':'C30'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASSED: ex5_6 == 'TREASURE'\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex5_6(ex5_6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.7\n",
    "rubric={autograde:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLUE:** The first row of all columns that **do not** have a \"Z\" in the last row.\n",
    "\n",
    "**HINT:** You might need to do a bit of extra wrangling to get a result of \"TREASURE\" here..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-134-89d222593c5e>:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  treasure_df[treasure_df.loc['R30'] != 'Z']\n"
     ]
    },
    {
     "ename": "IndexingError",
     "evalue": "Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexingError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-134-89d222593c5e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mex5_7\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtreasure_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtreasure_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'R30'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'Z'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m#treasure_df.loc['R30']\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2891\u001b[0m         \u001b[1;31m# Do we have a (boolean) 1d indexer?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2892\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2893\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_bool_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2895\u001b[0m         \u001b[1;31m# We are left with two options: a single key, and a collection of keys,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_bool_array\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2943\u001b[0m         \u001b[1;31m# check_bool_indexer will throw exception if Series key cannot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2944\u001b[0m         \u001b[1;31m# be reindexed to match DataFrame rows\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2945\u001b[1;33m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2946\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2947\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_take_with_is_copy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36mcheck_bool_indexer\u001b[1;34m(index, key)\u001b[0m\n\u001b[0;32m   2182\u001b[0m         \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2183\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2184\u001b[1;33m             raise IndexingError(\n\u001b[0m\u001b[0;32m   2185\u001b[0m                 \u001b[1;34m\"Unalignable boolean Series provided as \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2186\u001b[0m                 \u001b[1;34m\"indexer (index of the boolean Series and of \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexingError\u001b[0m: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match)."
     ]
    }
   ],
   "source": [
    "ex5_7 = None\n",
    "#treasure_df[treasure_df.loc['R30'] != 'Z']\n",
    "#treasure_df.loc['R30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex5_7(ex5_7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.8\n",
    "rubric={autograde:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CLUE:** C1 for C1 < C2 (remember, in Python, comparison operators work on string too, e.g., `\"a\" < \"b\" = True` because `a` occurs before `b` alphabetically. Also, don't forget about `.query()`!)\n",
    "\n",
    "**HINT:** You might need to do a bit of extra wrangling to get a result of \"TREASURE\" here..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex5_8 = None\n",
    "ex5_8 = treasure_df.query(\"C1 < C2\").sum()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASSED: ex5_8 == 'TREASURE'\n",
      "\n",
      "\u001b[92m============\u001b[0m\n",
      "PASSED TESTS\n",
      "\u001b[92m============\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Autograding\n",
    "lab3_tests.ex5_8(ex5_8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-cbb88b5728dbc768",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "## Exercise 6: Mind the Gap\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this exercise, we'll practice some basic Pandas operations on the [Gapminder dataset](https://www.gapminder.org/about-gapminder/) which you are already familiar with from your DSCI 523 labs. Gapminder is an educational foundation that aims to use data to unbiasedly describe trends in health and socioeconomics; it is a great source of geographical, socioeconomic, and health data - a subset of which we'll be exploring here. In particular, we'll be exploring a dataframe of the following features in this exercise:\n",
    "\n",
    "|     | country     | continent | year | lifeExp | pop      | gdpPercap  |\n",
    "|:---:|:-----------:|:---------:|:----:|:-------:|:--------:|:----------:|\n",
    "|  0  | Afghanistan | Asia      | 1952 | 28.801  | 8425333  | 779.445314 |\n",
    "|  1  | Afghanistan | Asia      | 1957 | 30.332  | 9240934  | 820.853030 |\n",
    "|  2  | Afghanistan | Asia      | 1962 | 31.997  | 10267083 | 853.100710 |\n",
    "|  3  | Afghanistan | Asia      | 1967 | 34.020  | 11537966 | 836.197138 |\n",
    "|  4  | Afghanistan | Asia      | 1972 | 36.088  | 13079460 | 739.981106 |\n",
    "| ... |     ...     |    ...    | ...  |   ...   |   ...    |    ...     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1\n",
    "rubric={accuracy:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the gapminder dataset into a dataframe called `df` from this url: <https://raw.githubusercontent.com/jstaf/gapminder/master/gapminder/gapminder.csv>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year  lifeExp       pop   gdpPercap\n",
       "0     Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
       "1     Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
       "2     Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
       "3     Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
       "4     Afghanistan      Asia  1972   36.088  13079460  739.981106\n",
       "...           ...       ...   ...      ...       ...         ...\n",
       "1699     Zimbabwe    Africa  1987   62.351   9216418  706.157306\n",
       "1700     Zimbabwe    Africa  1992   60.377  10704340  693.420786\n",
       "1701     Zimbabwe    Africa  1997   46.809  11404948  792.449960\n",
       "1702     Zimbabwe    Africa  2002   39.989  11926563  672.038623\n",
       "1703     Zimbabwe    Africa  2007   43.487  12311143  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/jstaf/gapminder/master/gapminder/gapminder.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2\n",
    "rubric={accuracy:1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which continent has the most observations in the gapminder dataset? You can leave your answer as the output of a dataframe operation (*hint*: `.value_counts()`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'value_count'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-149-0fe2aa84b512>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'continent'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5128\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5129\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5130\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5132\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'value_count'"
     ]
    }
   ],
   "source": [
    "df.value_count('continent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3\n",
    "rubric={accuracy:2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the minimum and maximum life expectancies in the dataset, and what are the corresponding countries and the years? (*hint*: `.argmin()`/`.argmax()`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your answer goes here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4\n",
    "rubric={accuracy:2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much larger is the total population in this dataset in 2007 compared to 1952? You can give you answer as a float, e.g., \"the population is 1.8 times larger in 2007 than in 1952.\" (*hint*: I would use `.query()` to subset the dataframe for 1952 and then calculate the `.sum()` of the population, then repeat for 2007)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your answer goes here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.5\n",
    "rubric={accuracy:2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the mean life expectancy of countries with the highest 50% of `gdpPercap` and countries with the lowest 50% of `gdpPercap`? (*hint*: try combining `.query()` and `.median()`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your answer goes here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-f7d15fa1e22d412c",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "## Submission to Canvas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PLEASE READ:** When you are ready to submit your assignment do the following:\n",
    "1. Run all cells in your notebook to make sure there are no errors by doing `Kernel -> Restart Kernel and Run All Cells...`\n",
    "2. Convert your notebook to `.html` format using the `convert_notebook()` function below or by `File -> Export Notebook As... -> Export Notebook to HTML`\n",
    "3. Run the code `submit()` below to go through an interactive submission process to Canvas.\n",
    "4. After submission, be sure to do a final push of all your work to GitHub (including the rendered html file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "cell-cd03c1d25ce1d32e",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# convert_notebook(\"lab3.ipynb\", \"html\")  # uncomment and run when you want to try convert your notebook (or you can convert manually from the File menu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# submit(course_code=53659, token=False)  # uncomment and run when ready to submit to Canvas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/super.png)"
   ]
  }
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