{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Session 4 Exercises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import qeds\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unemployment Data Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>state</th>\n",
       "      <th>Arizona</th>\n",
       "      <th>California</th>\n",
       "      <th>Florida</th>\n",
       "      <th>Illinois</th>\n",
       "      <th>Michigan</th>\n",
       "      <th>New York</th>\n",
       "      <th>Texas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-01</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state       Arizona  California  Florida  Illinois  Michigan  New York  Texas\n",
       "Date                                                                         \n",
       "2000-01-01      4.1         5.0      3.7       4.2       3.3       4.7    4.6\n",
       "2000-02-01      4.1         5.0      3.7       4.2       3.2       4.7    4.6\n",
       "2000-03-01      4.0         5.0      3.7       4.3       3.2       4.6    4.5\n",
       "2000-04-01      4.0         5.1      3.7       4.3       3.3       4.6    4.4\n",
       "2000-05-01      4.0         5.1      3.7       4.3       3.5       4.6    4.3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Load up the data -- this may take a couple seconds\n",
    "url = \"https://datascience.quantecon.org/assets/data/state_unemployment.csv\"\n",
    "unemp_raw = pd.read_csv(url, parse_dates=[\"Date\"])\n",
    "unemp_all = (\n",
    "    unemp_raw\n",
    "    .reset_index()\n",
    "    .pivot_table(index=\"Date\", columns=\"state\", values=\"UnemploymentRate\")\n",
    ")\n",
    "\n",
    "# Will only work with this subset of data\n",
    "states = [\n",
    "    \"Arizona\", \"California\", \"Florida\", \"Illinois\",\n",
    "    \"Michigan\", \"New York\", \"Texas\"\n",
    "]\n",
    "unemp = unemp_all[states]\n",
    "unemp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**\n",
    "\n",
    "Imagine that we want to determine whether unemployment was high (> 6.5),\n",
    "medium (4.5 < x <= 6.5), or low (<= 4.5) for each state and each month.\n",
    "\n",
    "1. Write a Python function that takes a single number as an input and\n",
    "  outputs a single string noting if that number is high, medium, or low.  \n",
    "1. Pass your function to `applymap` (quiz: why `applymap` and not\n",
    "  `agg` or `apply`?) and save the result in a new DataFrame called\n",
    "  `unemp_bins`.  \n",
    "1. (Challenging) This exercise has multiple parts:  \n",
    "  1. Use another transform on `unemp_bins` to count how many\n",
    "    times each state had each of the three classifications.  \n",
    "    - Hint 1: Will this value counting function be a Series or scalar\n",
    "      transform?  \n",
    "    - Hint 2: Try googling \"pandas count unique value\" or something\n",
    "      similar to find the right transform.  \n",
    "  1. Construct a horizontal bar chart of the number of occurrences of\n",
    "    each level with one bar per state and classification (21 total\n",
    "    bars).  \n",
    "1. (Challenging) Repeat the previous step, but count how many states had\n",
    "  each classification in each month. Which month had the most states\n",
    "  with high unemployment? What about medium and low?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 1: Write a Python function to classify unemployment levels.\n",
    "def unemployment_classifier(ur):\n",
    "    if ur > 6.5:\n",
    "        return \"high\"\n",
    "    elif ur > 4.5:\n",
    "        return \"medium\"\n",
    "    else:\n",
    "        return \"low\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'medium'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment_classifier(4.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 2: Pass your function from part 1 to applymap\n",
    "unemp_bins = unemp.applymap(unemployment_classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Arizona</th>\n",
       "      <th>California</th>\n",
       "      <th>Florida</th>\n",
       "      <th>Illinois</th>\n",
       "      <th>Michigan</th>\n",
       "      <th>New York</th>\n",
       "      <th>Texas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-01</th>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-01</th>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-01</th>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-01</th>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Arizona California Florida Illinois Michigan New York   Texas\n",
       "Date                                                                    \n",
       "2000-01-01     low     medium     low      low      low   medium  medium\n",
       "2000-02-01     low     medium     low      low      low   medium  medium\n",
       "2000-03-01     low     medium     low      low      low   medium     low\n",
       "2000-04-01     low     medium     low      low      low   medium     low\n",
       "2000-05-01     low     medium     low      low      low   medium     low\n",
       "...            ...        ...     ...      ...      ...      ...     ...\n",
       "2017-08-01  medium     medium     low   medium   medium   medium     low\n",
       "2017-09-01  medium        low     low   medium   medium   medium     low\n",
       "2017-10-01  medium        low     low   medium   medium   medium     low\n",
       "2017-11-01  medium        low     low   medium   medium   medium     low\n",
       "2017-12-01  medium        low     low   medium   medium   medium     low\n",
       "\n",
       "[216 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemp_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Arizona</th>\n",
       "      <th>California</th>\n",
       "      <th>Florida</th>\n",
       "      <th>Illinois</th>\n",
       "      <th>Michigan</th>\n",
       "      <th>New York</th>\n",
       "      <th>Texas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>75</td>\n",
       "      <td>106</td>\n",
       "      <td>68</td>\n",
       "      <td>91</td>\n",
       "      <td>142</td>\n",
       "      <td>65</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>69</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>97</td>\n",
       "      <td>106</td>\n",
       "      <td>79</td>\n",
       "      <td>106</td>\n",
       "      <td>57</td>\n",
       "      <td>129</td>\n",
       "      <td>107</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "state   Arizona  California  Florida  Illinois  Michigan  New York  Texas\n",
       "high         75         106       68        91       142        65     51\n",
       "low          44           4       69        19        17        22     58\n",
       "medium       97         106       79       106        57       129    107"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemp_bin_counts = unemp_bins.apply(pd.Series.value_counts, axis=0)\n",
    "unemp_bin_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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198fMbHh4eZiZtQD6AilAI2CumTV198PRDVuCqKRpbCeqpGlvADVr1mT//v1s2rSJnj17kpOTw4IFC/JL/s6ePZu1a9cyfPjwEz7/tm3bGDx4MDNmzChL+CIRKbHn7u4Lgb1Hrb4WOHI3x2TgugLrp7n7d+7+KfAx0DY6oYpUvF69epUpsQM0atRIiV1ipqxj7g3cfTtA+PXs8Ppzgc8KbLc1vO4YZjbQzLLMLGvXrl1lDEOkfL300kvcfffdAAwYMIDBgwfTrl07GjdunJ+43Z3f/OY3tGzZktTUVKZPnw6E6sK3bNkSgDVr1tC2bVvS0tJo1aoVGzdujM0FSdyIdvmBY6slQZG3wLr7BGAChO5QjXIcIuVi+/btLF68mPXr19OrVy9++tOf8vrrr5Odnc3KlSvZvXs3F198MR07diy033PPPcc999zDz3/+c/7zn/9w+LBGKqV8lbXnvtPMGgKEX78Ir98KnFdgu0RgW9nDEzm5XHfddVSqVIkWLVqwc+dOIFQWuF+/fiQkJNCgQQM6derEsmXLCu132WWX8eijj/L444+zefPmIksFi0RTWZP7bCAz/D4TeKPA+r5mVsXMkoEmwNLIQhQ5eVSpUiX//ZG6TKWpz3TTTTcxe/ZsqlWrRvfu3Zk3r/j6QiKRKnFYxsymAp2Bema2FRgJPAa8ama/ALYANwC4+xozexVYC+QBgzRTRoKuY8eOPP/882RmZrJ3714WLlzIE088UahW+yeffELjxo0ZPHgwn3zyCatWreKKK+KjQFxBScP/Xmz7pqoVFEgcKDG5u3u/4zR1Pc72jwCPRBKUxKfSTF08GfXu3ZslS5bQunVrzIw//vGPnHPOOYUetD19+nT+8pe/ULlyZc455xwefPDB2AUscUH13CWu7d+/H4CkpCRyckL36XXu3JnOnTsDoRkyAwYMAEIzZ4ra18x44okneOKJJwq1FzzmiBEjGDFiRDldhcixVH5ARCSAlNxFRAJIyV1EJICU3EVEAkjJXUQkgJTcRUQCSFMhy0GJpWs7/6liAjnF/OnO6N61Oei5km8SMjNuvvlmXnnlFQDy8vJo2LAhl1xySalK/hYsFXy0Bx98kI4dO/LjH/84sgsRKQMld4lrNWrUICcnh2+//ZZq1aoxZ84czj33h0KmvXr1olevXmU69ujRo6MVpsgJ07CMxL2rrrqKv/89dFv81KlT6dfvh5uyC5b83blzJ71796Z169a0bt2aDz74AIDDhw9z++23k5KSQrdu3fj222+B0A1QR8oC/+Mf/6BZs2a0b9+ewYMH07NnTwCWLl1Ku3btSE9Pp127dmzYsCH/vH369KFHjx40adKE+++/v2K+GRIYSu4S9/r27cu0adM4ePAgq1at4pJLLilyu8GDB9OpUydWrlzJihUrSElJAWDjxo0MGjSINWvWcNZZZ/Haa68V2u/gwYPccccdvP322yxevJiCzy9o1qwZCxcu5KOPPmL06NE88MAD+W3Z2dlMnz6d1atXM336dD777DNESkvDMhL3WrVqxaZNm5g6dSpXX331cbebN28eL78cetpkQkICtWrV4ssvvyQ5OZm0tDQA2rRpU6imDMD69etp3LgxycnJAPTr148JEyYAkJubS2ZmJhs3bsTMOHToUP5+Xbt2pVatWgC0aNGCzZs3c9555yFSGuq5ixAaW7/vvvsKDcmUVsEywAkJCeTl5RVqL64k8O9+9zu6dOlCTk4Ob775ZqFKkiUdV6Q4Su4iwG233caDDz5Iamrqcbfp2rUrzz77LBAaZ9+3b1+pjt2sWTM++eST/B79kcfwQajnfuQD3KMLk4lEQsMyctIozdTF8pKYmMg999xT7DZPP/00AwcOZNKkSSQkJPDss8/SsGHDEo9drVo1/ud//ocePXpQr1492rb94Znx999/P5mZmYwdOzYu67tL+bHSPEWmvGVkZHhWVlasw/jBqFoltOcW21zSPPd5Jcxzj2WSq0jr1q2jefMS7gkIiP3791OzZk3cnUGDBtGkSROGDh0a1XOcCt/Pkh/WcVPxB4jwd6+kZwY8eWPPYttvTB5WbHviYx2KbY82M1vu7hlFtWlYRqQCTJw4kbS0NFJSUsjNzeWOO+6IdUgScBqWEakAQ4cOjXpPXaQ46rmLiASQkruISABFlNzNbKiZrTGzHDObamZVzayOmc0xs43h19rRClZEREqnzMndzM4FBgMZ7t4SSAD6AsOBd929CfBueFlERCpQpB+ongZUM7NDQHVgGzAC6BxunwwsAIqfPyRCydPQTtS9098qcRsz49e//jVPPvkkAGPGjGH//v2MGjUq4vMfPHiQtLQ0/va3v+XfHPXHP/6RTz75hOeee67E/UeNGkXNmjW57777Io5F4k+Ze+7u/jkwBtgCbAdy3f0doIG7bw9vsx04u6j9zWygmWWZWVbBQkoiFalKlSq8/vrr7N69O+rHrlq1KuPGjeNXv/oV7s7nn3/O888/zx/+8IcS91WpAYlUJMMytYFrgWSgEVDDzG4u7f7uPsHdM9w9o379+mUNQyQip512GgMHDuSpp546pm3Xrl1cf/31XHzxxVx88cW8//77AKSmpvLVV1/h7tStWze/mNgtt9zC3LlzCx2jR48eNGzYkJdffpmhQ4cyatQo9u3bR9euXWnVqhVdu3Zly5YtQKhE8K9//Wu6dOnCsGGF/9mdOHEiV111VX45YZGSRDIs82PgU3ffBWBmrwPtgJ1m1tDdt5tZQ+CLKMQpUm4GDRpEq1atjqmZfs899zB06FDat2/Pli1b6N69O+vWrePyyy/n/fff5/zzz6dx48YsWrSI/v378+GHH+bXnilo3LhxtG3bliZNmnDLLbfwk5/8hP79+5OZmckLL7zA4MGDmTVrFgD//ve/mTt3LgkJCflDQ8888wzvvPMOs2bNKlRMLB6lTj5+7R+AVysojlNBJMl9C3CpmVUHvgW6AlnAN0Am8Fj49Y1IgxQpT2eeeSb9+/dn/PjxVKtWLX/93LlzWbt2bf7yvn37+Prrr+nQoQMLFy7k/PPP56677mLChAl8/vnn1KlTh5o1ax5z/EaNGnHFFVfkP6BjyZIlvP7660Cot1/wj8oNN9xAQkJC/vIrr7xCYmIis2bNonLlylG/dgmuSMbc/wXMAFYAq8PHmkAoqV9pZhuBK8PLIie1IUOGMGnSJL755pv8dd9//z1LliwhOzub7OxsPv/8c8444ww6duzIokWLWLRoEZ07d6Z+/frMmDGDDh2OX1ekUqVKVKpU9K+bmeW/r1GjRqG2li1bsmnTJrZu3RrhFUq8iWieu7uPdPdm7t7S3W9x9+/cfY+7d3X3JuHXvdEKVqS81KlTh5/97GdMmjQpf123bt145pln8pezs7MBOO+889i9ezcbN26kcePGtG/fnjFjxhSb3Atq164d06ZNA2DKlCm0b9/+uNump6fz/PPP06tXL7Zt21aGK5N4pdoyctIozdTFcj3/vfcWSubjx4/PH4/Py8ujY8eO+VMYL7nkEg4fPgxAhw4dGDFiRLFJuqDx48dz22238cQTT1C/fn1efPHFYrc/8sfjmmuuYc6cOdSrV6+MVyjxJC6Te8llRysoEIm5/fv3579v0KABBw4cyF+uV69eoQdrFPTKK6/kv2/Xrh3ff/99secp+CCOpKQk5s2bV+w2QKG59t27d6d79+7FnkOkINWWEREJICV3EZEAUnIXEQkgJXcRkQBSchcRCSAldxGRAIrLqZBycto6fFFUj1fSk+j37NlD165dAdixYwcJCQkcKWK3dOlSTj/99KjGI1KRlNwlbtWtWzf/rlPVTpeg0bCMxJ01u9cc8/XFgS/Y8c0OXn33VTp16kSbNm3o3r0727dvJzc3lwsvvJANGzYA0K9fPyZOnAjAXXfdRUZGBikpKYwcOTL/HMOHD6dFixa0atVKfzAkJtRzFwlzdx4d8Shz/j6H+vXrM336dH7729/ywgsv8MwzzzBgwADuuecevvzyS26//XYAHnnkEerUqcPhw4fp2rUrq1atIjExkZkzZ7J+/XrMjK+++iq2FyZxScm9DFRTOpgOfXeIj9d9zJVXXgnA4cOHadiwIQBXXnklf/vb3xg0aBArV67M3+fVV19lwoQJ5OXlsX37dtauXUuLFi2oWrUqv/zlL7nmmmvyS/2KVCQld5Ewx/mvZv9F9rLsY9q+//571q1bR7Vq1di7dy+JiYl8+umnjBkzhmXLllG7dm0GDBjAwYMHOe2001i6dCnvvvsu06ZN45lnnimyloxIedKYu0jY6aefzt7de1myZAkAhw4dYs2aNQA89dRTNG/enKlTp3Lbbbdx6NAh9u3bR40aNahVqxY7d+7k7bffBkLFyHJzc7n66qsZN25c/oe2IhVJPXc5aZQ0dbG8WSXjqReeYtiwYeTm5pKXl8eQIUOoXLkyf/7zn1m6dGn+wzoefvhhHnroIdLT00lJSaFx48ZcfvnlAHz99ddce+21HDx4EHcv8vmsIuVNyf0k9OSNxY/RxrrueRANun9Q/vuFCxce075u3br892PHjs1/f3SZ3iOWLl0aveBEykDDMiIiAaTkLiISQEruElPuHusQAkHfRzlaRMndzM4ysxlmtt7M1pnZZWZWx8zmmNnG8GvtaAUrwVK1alX27NmjxBQhd2fPnj1UrarnQ8oPIv1A9Wngn+7+UzM7HagOPAC86+6PmdlwYDgwLMLzSAAlJiaydetWdu3aVaHn3bF/R7HtlXadev/QVq1alcTExFiHISeRMid3MzsT6AgMAHD3/wD/MbNrgc7hzSYDC1BylyJUrlyZ5OTkCj/vzyb/rNj21ZmrKygSkfITSRelMbALeNHMPjKzP5tZDaCBu28HCL+eXdTOZjbQzLLMLKuie24iIkEXSXI/DbgIeNbd04FvCA3BlIq7T3D3DHfPOFJDW0REoiOS5L4V2Oru/wovzyCU7HeaWUOA8OsXkYUoIiInqsxj7u6+w8w+M7ML3X0D0BVYG/7KBB4Lv74RlUhFAqKkJ07FugyDBEOks2X+G5gSninzCXArof8GXjWzXwBbgBsiPIeIiJygiJK7u2cDGUU0dY3kuCIi5eFPd8ZP6eVTb0KviIiUSMldRCSAlNxFRAJIyV1EJICU3EVEAkjJXUQkgJTcRUQCSM9QPQXpDkcRKYl67iIiAaTkLiISQEruIiIBpOQuIhJASu4iIgGk5C4iEkBK7iIiAaTkLiISQEruIiIBpOQuIhJAKj8gp5yk4X8vtn3TY9dUUCQiJy/13EVEAiji5G5mCWb2kZm9FV6uY2ZzzGxj+LV25GGKiMiJiEbP/R5gXYHl4cC77t4EeDe8LCIiFSii5G5micA1wJ8LrL4WmBx+Pxm4LpJziIjIiYu05z4OuB/4vsC6Bu6+HSD8enZRO5rZQDPLMrOsXbt2RRiGiIgUVObkbmY9gS/cfXlZ9nf3Ce6e4e4Z9evXL2sYIiJShEimQl4O9DKzq4GqwJlm9hdgp5k1dPftZtYQ+CIagYqISOmVuefu7iPcPdHdk4C+wDx3vxmYDWSGN8sE3og4ShEROSHlMc/9MeBKM9sIXBleFhGRChSVO1TdfQGwIPx+D9A1GscVEZGy0R2qIiIBpOQuIhJASu4iIgGk5C4iEkBK7iIiAaTkLiISQEruIiIBpOQuIhJASu4iIgGk5C4iEkBK7iIiAaTkLiISQEruIiIBpOQuIhJASu4iIgGk5C4iEkBK7iIiAaTkLiISQEruIiIBpOQuIhJAZU7uZnaemc03s3VmtsbM7gmvr2Nmc8xsY/i1dvTCFRGR0jgtgn3zgHvdfYWZnQEsN7M5wADgXXd/zMyGA8OBYZGHKnJqePLGnsW235isXwcpf2Xuubv7dndfEX7/NbAOOBe4Fpgc3mwycF2EMYqIyAmKypi7mSUB6cC/gAbuvh1CfwCAs6NxDhERKb2Ik7uZ1QReA4a4+74T2G+gmWWZWdauXbsiDUNERAqIZMwdM6tMKLFPcffXw6t3mllDd99uZg2BL4ra190nABMAMjIyPJI4RKJpXbPmxbY3X7+ugiIRKbtIZssYMAlY5+5jCzTNBjLD7zOBN8oenoiIlEUkPffLgVuA1WaWHV73APAY8KqZ/QLYAtwQUYQiInLCypzc3X0xYMdp7lrW44qc7P5057xYhyBSIt2hKiISQEruIiIBpOQuIhJASu4iIgGk5C4iEkBK7iIiAaTkLiISQEruIiIBpOQuIhJASu4iIgGk5C4iEkBK7iIiAaTkLiISQEruIiIBpOQuIhJASu4iIgGk5C4iEkBK7iIiARTJM1RFTk6jahXfnvyjiolDJIbUcxcRCSAldxGRACq35G5mPcxsg5l9bGbDy+s8IiJyrHJJ7maWAPwJuApoAfQzsxblcS4RETlWefXc2wIfu/sn7v4fYBpwbTmdS0REjmLuHv2Dmv0U6OHuvwwv3wJc4u53F9hmIDAwvHghsCHqgZw86gG7Yx2ElJl+fqeuoP/sznf3+kU1lNdUSCtiXaG/Iu4+AZhQTuc/qZhZlrtnxDoOKRv9/E5d8fyzK69hma3AeQWWE4Ft5XQuERE5Snkl92VAEzNLNrPTgb7A7HI6l4iIHKVchmXcPc/M7gb+H5AAvODua8rjXKeIuBh+CjD9/E5dcfuzK5cPVEVEJLZ0h6qISAApuYuIBJCSu4hIACm5i4gEkOq5l6NwjZ0GFPg+u/uW2EUkpWVmo4FFwAfu/k2s45ETY2a1Cd1rU/B3b0XsIqp4mi1TTszsv4GRwE7g+/Bqd/dWsYtKSsvMbgPaA5cBXxNK9Avd/Y2YBiYlMrPfAwOA/+WHO+Pd3a+IWVAxoOReTszsY0L1dPbEOhYpOzM7B/gZcB9Q293PiHFIUgIz2wCkhosWxi2NuZefz4DcWAchZWNmfzazD4BnCf1r/1OgdmyjklLKAc6KdRCxpjH3KDOzX4fffgIsMLO/A98daXf3sTEJTE5UXUJ3V38F7AV2u3teTCOS0voD8JGZ5VD4d69X7EKqeEru0Xfk3/Yt4a/Tw19yCnH33gBm1hzoDsw3swR3T4xtZFIKk4HHgdX88HlX3NGYu0gRzKwn0AHoSGg4ZgmwyN1fiGlgUiIze8/dO8U6jlhTci8nZvYmR9WwJzQGnwU87+4HKz4qKS0z+xOwkFBCV7nqU4iZjSU0HDObwsMymgopkTOzp4H6wNTwqhuBHUA14Ex3vyVWsUnpmFkD4OLw4lJ3/yKW8UjpmNn8IlZrKqREh5ktdPeORa0zszXunhKr2KRkZnYDMAZYQOjJYh2A37j7jFjGJVJa+kC1/NQ3sx8duSPVzH5E6HmOAHE9//YU8X+Ai4/01s2sPjAXUHI/yZnZg0Wtd/fRFR1LLCm5l597gcVm9r+Een7JwK/MrAahT/Pl5FbpqGGYPei+kFNFwXIRVYGewLoYxRIzGpYpR2ZWBWhGKLmv14eopw4zewJoReHPTFa5+7DYRSVlEf49nO3u3WMdS0VSco8yM7vC3eeZWZ+i2t399YqOScrGzK4HLif0x3mhu8+McUhSBuEiYkvdvUmsY6lIGpaJvk7APOAn4eUjfz0t/F7J/RTh7q8Br8U6DjkxZraaH37vEgjNWour8XZQz73cmFlV4HogiR/+iHq8fahzqjGzrzn2/gQI/3F29zMrOCQ5QWZ2foHFPGBnPJaOUM+9/MwiVJdkBXBkrF1/SU9yqvp46jKzM919H6ESzQWdaWa4+95YxBUr6rmXEzPLcfeWsY5DJF6Y2Vvu3tPMPiXUkbICze7ujWMUWkwouZcTM5sA/F93Xx3rWEQk/ii5R1mBD3NOA5oQKv37HT+M2epJTCLlwMwuKq5dtWUkIkd9mHMMd99cUbGIxJMCNWWqAhnASkKdqlbAv9y9faxiiwV9oBplSt4iseHuXQDMbBow8MiQqJm1JPSYxLii26lFJGiaFfysy91zgLTYhRMb6rmLSNCsM7M/A38h9PnXzai2jIjIqS18A+FdhJ6iBaGHrjwbb7WdlNxFJHDMrBrwI3ffEOtYYkVj7iISKGbWC8gG/hleTjOz2TENKgaU3EUkaEYCbQmV/8DdswnVeIorSu4iEjR57p4b6yBiTbNlRCRocszsJiDBzJoAg4EPYhxThVPPXUSC5r+BFEJlP/4K5AL3xDSiGFByF5GgaRH+Oo1QKYJrgWUxjSgGNBVSRALFzDYQKjeQA3x/ZH28lQbRmLuIBM0ud38z1kHEmnruIhIoZtYV6Ae8S2jcHYi/h9Or5y4iQXMr0AyozA/DMnH3cHoldxEJmtbunhrrIGJNs2VEJGg+NLMWsQ4i1jTmLiKBYmbrgAuAT4njR1wquYtIoBzvUZfxNhVSyV1EJIA05i4iEkBK7iIiAaTkLiISQEruIiIB9P8BK7zIPLg4uFsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Part 3: Count the number of times each state had each classification.\n",
    "unemp_bin_counts.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            high  low  medium\n",
       "Date                         \n",
       "2000-01-01   0.0  4.0     3.0\n",
       "2000-02-01   0.0  4.0     3.0\n",
       "2000-03-01   0.0  5.0     2.0\n",
       "2000-04-01   0.0  5.0     2.0\n",
       "2000-05-01   0.0  5.0     2.0\n",
       "...          ...  ...     ...\n",
       "2017-08-01   0.0  2.0     5.0\n",
       "2017-09-01   0.0  3.0     4.0\n",
       "2017-10-01   0.0  3.0     4.0\n",
       "2017-11-01   0.0  3.0     4.0\n",
       "2017-12-01   0.0  3.0     4.0\n",
       "\n",
       "[216 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Part 4: Apply the same transform from part 4, but to each date instead of to each state.\n",
    "unemp_bin_counts_month = unemp_bins.apply(\n",
    "    pd.Series.value_counts, axis=\"columns\"\n",
    ").fillna(0)\n",
    "\n",
    "unemp_bin_counts_month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which month had the most states with high unemployment? What about medium and low?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2009-04-01 00:00:00\n",
      "2001-09-01 00:00:00\n",
      "2000-08-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(unemp_bin_counts_month[\"high\"].idxmax())\n",
    "print(unemp_bin_counts_month[\"medium\"].idxmax())\n",
    "print(unemp_bin_counts_month[\"low\"].idxmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high     2009-04-01\n",
       "low      2000-08-01\n",
       "medium   2001-09-01\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemp_bin_counts_month.agg(pd.Series.idxmax, axis=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python style conventions (including naming variables): https://www.python.org/dev/peps/pep-0008"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**\n",
    "\n",
    "- For a single state of your choice, determine what the mean\n",
    "  unemployment is during \"Low\", \"Medium\", and \"High\" unemployment times.  \n",
    "  - Think about how you would do this for all the\n",
    "    states in our sample and write your thoughts... We will soon\n",
    "    learn tools that will *greatly* simplify operations like\n",
    "    this that operate on distinct *groups* of data at a time.  \n",
    "- Which states in our sample performs the best during \"bad times?\" To\n",
    "  determine this, compute the mean unemployment for each state only for\n",
    "  months in which the mean unemployment rate in our sample is greater\n",
    "  than 7.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0636363636363635"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemp.loc[unemp_bins.loc[:, \"Arizona\"] == 'low', \"Arizona\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.436082474226807"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemp.loc[unemp_bins.loc[:, \"Arizona\"] == 'medium', \"Arizona\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.73333333333333"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemp.loc[unemp_bins.loc[:, \"Arizona\"] == 'high', \"Arizona\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WDI Data Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://datascience.quantecon.org/assets/data/wdi_data.csv\"\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "wdi = df.set_index([\"country\", \"year\"])\n",
    "wdiT = wdi.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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></th>\n",
       "      <th>GovExpend</th>\n",
       "      <th>Consumption</th>\n",
       "      <th>Exports</th>\n",
       "      <th>Imports</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Canada</th>\n",
       "      <th>2017</th>\n",
       "      <td>0.372665</td>\n",
       "      <td>1.095475</td>\n",
       "      <td>0.582831</td>\n",
       "      <td>0.600031</td>\n",
       "      <td>1.868164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.364899</td>\n",
       "      <td>1.058426</td>\n",
       "      <td>0.576394</td>\n",
       "      <td>0.575775</td>\n",
       "      <td>1.814016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.358303</td>\n",
       "      <td>1.035208</td>\n",
       "      <td>0.568859</td>\n",
       "      <td>0.575793</td>\n",
       "      <td>1.794270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.353485</td>\n",
       "      <td>1.011988</td>\n",
       "      <td>0.550323</td>\n",
       "      <td>0.572344</td>\n",
       "      <td>1.782252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.351541</td>\n",
       "      <td>0.986400</td>\n",
       "      <td>0.518040</td>\n",
       "      <td>0.558636</td>\n",
       "      <td>1.732714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GovExpend  Consumption   Exports   Imports       GDP\n",
       "country year                                                      \n",
       "Canada  2017   0.372665     1.095475  0.582831  0.600031  1.868164\n",
       "        2016   0.364899     1.058426  0.576394  0.575775  1.814016\n",
       "        2015   0.358303     1.035208  0.568859  0.575793  1.794270\n",
       "        2014   0.353485     1.011988  0.550323  0.572344  1.782252\n",
       "        2013   0.351541     0.986400  0.518040  0.558636  1.732714"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**\n",
    "\n",
    "Try setting `my_df` to some subset of the rows in `wdi` (use one of the\n",
    "`.loc` variations above).\n",
    "\n",
    "Then see what happens when you do `wdi / my_df` or `my_df ** wdi`.\n",
    "\n",
    "Try changing the subset of rows in `my_df` and repeat until you\n",
    "understand what is happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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></th>\n",
       "      <th>GovExpend</th>\n",
       "      <th>Consumption</th>\n",
       "      <th>Exports</th>\n",
       "      <th>Imports</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Canada</th>\n",
       "      <th>2010</th>\n",
       "      <td>0.347332</td>\n",
       "      <td>0.921952</td>\n",
       "      <td>0.469949</td>\n",
       "      <td>0.500341</td>\n",
       "      <td>1.613543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.358303</td>\n",
       "      <td>1.035208</td>\n",
       "      <td>0.568859</td>\n",
       "      <td>0.575793</td>\n",
       "      <td>1.794270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.372665</td>\n",
       "      <td>1.095475</td>\n",
       "      <td>0.582831</td>\n",
       "      <td>0.600031</td>\n",
       "      <td>1.868164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Germany</th>\n",
       "      <th>2010</th>\n",
       "      <td>0.653386</td>\n",
       "      <td>1.915481</td>\n",
       "      <td>1.443735</td>\n",
       "      <td>1.266126</td>\n",
       "      <td>3.417095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.706115</td>\n",
       "      <td>2.033666</td>\n",
       "      <td>1.803081</td>\n",
       "      <td>1.527074</td>\n",
       "      <td>3.718482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GovExpend  Consumption   Exports   Imports       GDP\n",
       "country year                                                      \n",
       "Canada  2010   0.347332     0.921952  0.469949  0.500341  1.613543\n",
       "        2015   0.358303     1.035208  0.568859  0.575793  1.794270\n",
       "        2017   0.372665     1.095475  0.582831  0.600031  1.868164\n",
       "Germany 2010   0.653386     1.915481  1.443735  1.266126  3.417095\n",
       "        2015   0.706115     2.033666  1.803081  1.527074  3.718482"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# my_df = wdi.loc[pd.IndexSlice[[\"Canada\", \"Germany\"], [2010, 2015, 2017]], :]\n",
    "# Produce the same output\n",
    "my_df = wdi.loc[([\"Canada\", \"Germany\"], [2010, 2015, 2017]), :]\n",
    "\n",
    "my_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo = wdi / my_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar = wdi.divide(my_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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></th>\n",
       "      <th>GovExpend</th>\n",
       "      <th>Consumption</th>\n",
       "      <th>Exports</th>\n",
       "      <th>Imports</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Canada</th>\n",
       "      <th>2017</th>\n",
       "      <td>0.372665</td>\n",
       "      <td>1.095475</td>\n",
       "      <td>0.582831</td>\n",
       "      <td>0.600031</td>\n",
       "      <td>1.868164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.364899</td>\n",
       "      <td>1.058426</td>\n",
       "      <td>0.576394</td>\n",
       "      <td>0.575775</td>\n",
       "      <td>1.814016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.358303</td>\n",
       "      <td>1.035208</td>\n",
       "      <td>0.568859</td>\n",
       "      <td>0.575793</td>\n",
       "      <td>1.794270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.353485</td>\n",
       "      <td>1.011988</td>\n",
       "      <td>0.550323</td>\n",
       "      <td>0.572344</td>\n",
       "      <td>1.782252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.351541</td>\n",
       "      <td>0.986400</td>\n",
       "      <td>0.518040</td>\n",
       "      <td>0.558636</td>\n",
       "      <td>1.732714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GovExpend  Consumption   Exports   Imports       GDP\n",
       "country year                                                      \n",
       "Canada  2017   0.372665     1.095475  0.582831  0.600031  1.868164\n",
       "        2016   0.364899     1.058426  0.576394  0.575775  1.814016\n",
       "        2015   0.358303     1.035208  0.568859  0.575793  1.794270\n",
       "        2014   0.353485     1.011988  0.550323  0.572344  1.782252\n",
       "        2013   0.351541     0.986400  0.518040  0.558636  1.732714"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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></th>\n",
       "      <th>GovExpend</th>\n",
       "      <th>Consumption</th>\n",
       "      <th>Exports</th>\n",
       "      <th>Imports</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Canada</th>\n",
       "      <th>2010</th>\n",
       "      <td>0.347332</td>\n",
       "      <td>0.921952</td>\n",
       "      <td>0.469949</td>\n",
       "      <td>0.500341</td>\n",
       "      <td>1.613543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.358303</td>\n",
       "      <td>1.035208</td>\n",
       "      <td>0.568859</td>\n",
       "      <td>0.575793</td>\n",
       "      <td>1.794270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.372665</td>\n",
       "      <td>1.095475</td>\n",
       "      <td>0.582831</td>\n",
       "      <td>0.600031</td>\n",
       "      <td>1.868164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Germany</th>\n",
       "      <th>2010</th>\n",
       "      <td>0.653386</td>\n",
       "      <td>1.915481</td>\n",
       "      <td>1.443735</td>\n",
       "      <td>1.266126</td>\n",
       "      <td>3.417095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.706115</td>\n",
       "      <td>2.033666</td>\n",
       "      <td>1.803081</td>\n",
       "      <td>1.527074</td>\n",
       "      <td>3.718482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GovExpend  Consumption   Exports   Imports       GDP\n",
       "country year                                                      \n",
       "Canada  2010   0.347332     0.921952  0.469949  0.500341  1.613543\n",
       "        2015   0.358303     1.035208  0.568859  0.575793  1.794270\n",
       "        2017   0.372665     1.095475  0.582831  0.600031  1.868164\n",
       "Germany 2010   0.653386     1.915481  1.443735  1.266126  3.417095\n",
       "        2015   0.706115     2.033666  1.803081  1.527074  3.718482"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>GovExpend</th>\n",
       "      <th>Consumption</th>\n",
       "      <th>Exports</th>\n",
       "      <th>Imports</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Canada</th>\n",
       "      <th>2000</th>\n",
       "      <td>0.270553</td>\n",
       "      <td>0.677713</td>\n",
       "      <td>0.499526</td>\n",
       "      <td>0.380823</td>\n",
       "      <td>1.342805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.279767</td>\n",
       "      <td>0.694230</td>\n",
       "      <td>0.484696</td>\n",
       "      <td>0.362023</td>\n",
       "      <td>1.366590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.286094</td>\n",
       "      <td>0.721974</td>\n",
       "      <td>0.490465</td>\n",
       "      <td>0.368615</td>\n",
       "      <td>1.407725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.294335</td>\n",
       "      <td>0.741796</td>\n",
       "      <td>0.481993</td>\n",
       "      <td>0.384199</td>\n",
       "      <td>1.433089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.299854</td>\n",
       "      <td>0.764357</td>\n",
       "      <td>0.508657</td>\n",
       "      <td>0.416754</td>\n",
       "      <td>1.477317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">United States</th>\n",
       "      <th>2013</th>\n",
       "      <td>2.353381</td>\n",
       "      <td>10.687214</td>\n",
       "      <td>2.118639</td>\n",
       "      <td>2.600198</td>\n",
       "      <td>15.853796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2.334071</td>\n",
       "      <td>11.000619</td>\n",
       "      <td>2.209555</td>\n",
       "      <td>2.732228</td>\n",
       "      <td>16.242526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2.373130</td>\n",
       "      <td>11.409800</td>\n",
       "      <td>2.222228</td>\n",
       "      <td>2.881337</td>\n",
       "      <td>16.710459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2.407981</td>\n",
       "      <td>11.722133</td>\n",
       "      <td>2.219937</td>\n",
       "      <td>2.936004</td>\n",
       "      <td>16.972348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2.405743</td>\n",
       "      <td>12.019266</td>\n",
       "      <td>2.287071</td>\n",
       "      <td>3.069954</td>\n",
       "      <td>17.348627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    GovExpend  Consumption   Exports   Imports        GDP\n",
       "country       year                                                       \n",
       "Canada        2000   0.270553     0.677713  0.499526  0.380823   1.342805\n",
       "              2001   0.279767     0.694230  0.484696  0.362023   1.366590\n",
       "              2002   0.286094     0.721974  0.490465  0.368615   1.407725\n",
       "              2003   0.294335     0.741796  0.481993  0.384199   1.433089\n",
       "              2004   0.299854     0.764357  0.508657  0.416754   1.477317\n",
       "...                       ...          ...       ...       ...        ...\n",
       "United States 2013   2.353381    10.687214  2.118639  2.600198  15.853796\n",
       "              2014   2.334071    11.000619  2.209555  2.732228  16.242526\n",
       "              2015   2.373130    11.409800  2.222228  2.881337  16.710459\n",
       "              2016   2.407981    11.722133  2.219937  2.936004  16.972348\n",
       "              2017   2.405743    12.019266  2.287071  3.069954  17.348627\n",
       "\n",
       "[72 rows x 5 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi.divide(my_df, fill_value=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>GovExpend</th>\n",
       "      <th>Consumption</th>\n",
       "      <th>Exports</th>\n",
       "      <th>Imports</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Canada</th>\n",
       "      <th>2000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">United States</th>\n",
       "      <th>2013</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    GovExpend  Consumption  Exports  Imports  GDP\n",
       "country       year                                               \n",
       "Canada        2000        0.0          0.0      0.0      0.0  0.0\n",
       "              2001        0.0          0.0      0.0      0.0  0.0\n",
       "              2002        0.0          0.0      0.0      0.0  0.0\n",
       "              2003        0.0          0.0      0.0      0.0  0.0\n",
       "              2004        0.0          0.0      0.0      0.0  0.0\n",
       "...                       ...          ...      ...      ...  ...\n",
       "United States 2013        0.0          0.0      0.0      0.0  0.0\n",
       "              2014        0.0          0.0      0.0      0.0  0.0\n",
       "              2015        0.0          0.0      0.0      0.0  0.0\n",
       "              2016        0.0          0.0      0.0      0.0  0.0\n",
       "              2017        0.0          0.0      0.0      0.0  0.0\n",
       "\n",
       "[72 rows x 5 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(wdi / my_df).fillna(0.0)  # Fills with just 0s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>GovExpend</th>\n",
       "      <th>Consumption</th>\n",
       "      <th>Exports</th>\n",
       "      <th>Imports</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Canada</th>\n",
       "      <th>2000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">United States</th>\n",
       "      <th>2013</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    GovExpend  Consumption  Exports  Imports  GDP\n",
       "country       year                                               \n",
       "Canada        2000        0.0          1.0      2.0      3.0  4.0\n",
       "              2001        0.0          1.0      2.0      3.0  4.0\n",
       "              2002        0.0          1.0      2.0      3.0  4.0\n",
       "              2003        0.0          1.0      2.0      3.0  4.0\n",
       "              2004        0.0          1.0      2.0      3.0  4.0\n",
       "...                       ...          ...      ...      ...  ...\n",
       "United States 2013        0.0          1.0      2.0      3.0  4.0\n",
       "              2014        0.0          1.0      2.0      3.0  4.0\n",
       "              2015        0.0          1.0      2.0      3.0  4.0\n",
       "              2016        0.0          1.0      2.0      3.0  4.0\n",
       "              2017        0.0          1.0      2.0      3.0  4.0\n",
       "\n",
       "[72 rows x 5 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_vals = {\n",
    "    \"GovExpend\": 0,\n",
    "    \"Consumption\": 1,\n",
    "    \"Exports\": 2,\n",
    "    \"Imports\": 3,\n",
    "    \"GDP\": 4\n",
    "}\n",
    "\n",
    "# Fills with value from associated dictionary\n",
    "(wdi / my_df).fillna(fill_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo.divide?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"3\" valign=\"top\">Canada</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>2015</th>\n",
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       "      <th>2017</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Germany</th>\n",
       "      <th>2010</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GovExpend  Consumption  Exports  Imports  GDP\n",
       "country year                                               \n",
       "Canada  2010        1.0          1.0      1.0      1.0  1.0\n",
       "        2015        1.0          1.0      1.0      1.0  1.0\n",
       "        2017        1.0          1.0      1.0      1.0  1.0\n",
       "Germany 2010        1.0          1.0      1.0      1.0  1.0\n",
       "        2015        1.0          1.0      1.0      1.0  1.0\n",
       "        2017        1.0          1.0      1.0      1.0  1.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foo.loc[my_df.index, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**\n",
    "\n",
    "Use `pd.IndexSlice` to extract all data from `wdiT` where the `year`\n",
    "level of the column names (the second level) is one of 2010, 2012, and 2014\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Canada</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Germany</th>\n",
       "      <th colspan=\"3\" halign=\"left\">United Kingdom</th>\n",
       "      <th colspan=\"3\" halign=\"left\">United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GovExpend</th>\n",
       "      <td>0.353485</td>\n",
       "      <td>0.354342</td>\n",
       "      <td>0.347332</td>\n",
       "      <td>0.685990</td>\n",
       "      <td>0.666454</td>\n",
       "      <td>0.653386</td>\n",
       "      <td>0.538888</td>\n",
       "      <td>0.528194</td>\n",
       "      <td>0.521146</td>\n",
       "      <td>2.334071</td>\n",
       "      <td>2.398873</td>\n",
       "      <td>2.510143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumption</th>\n",
       "      <td>1.011988</td>\n",
       "      <td>0.961226</td>\n",
       "      <td>0.921952</td>\n",
       "      <td>1.999953</td>\n",
       "      <td>1.967390</td>\n",
       "      <td>1.915481</td>\n",
       "      <td>1.675716</td>\n",
       "      <td>1.612550</td>\n",
       "      <td>1.598563</td>\n",
       "      <td>11.000619</td>\n",
       "      <td>10.534042</td>\n",
       "      <td>10.185836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exports</th>\n",
       "      <td>0.550323</td>\n",
       "      <td>0.505969</td>\n",
       "      <td>0.469949</td>\n",
       "      <td>1.712270</td>\n",
       "      <td>1.607455</td>\n",
       "      <td>1.443735</td>\n",
       "      <td>0.774022</td>\n",
       "      <td>0.745484</td>\n",
       "      <td>0.690824</td>\n",
       "      <td>2.209555</td>\n",
       "      <td>2.045509</td>\n",
       "      <td>1.846280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imports</th>\n",
       "      <td>0.572344</td>\n",
       "      <td>0.547756</td>\n",
       "      <td>0.500341</td>\n",
       "      <td>1.445409</td>\n",
       "      <td>1.354122</td>\n",
       "      <td>1.266126</td>\n",
       "      <td>0.827311</td>\n",
       "      <td>0.772692</td>\n",
       "      <td>0.745065</td>\n",
       "      <td>2.732228</td>\n",
       "      <td>2.560677</td>\n",
       "      <td>2.360183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.782252</td>\n",
       "      <td>1.693428</td>\n",
       "      <td>1.613543</td>\n",
       "      <td>3.654924</td>\n",
       "      <td>3.559587</td>\n",
       "      <td>3.417095</td>\n",
       "      <td>2.657159</td>\n",
       "      <td>2.529323</td>\n",
       "      <td>2.452900</td>\n",
       "      <td>16.242526</td>\n",
       "      <td>15.567038</td>\n",
       "      <td>14.992053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "country        Canada                       Germany                      \\\n",
       "year             2014      2012      2010      2014      2012      2010   \n",
       "GovExpend    0.353485  0.354342  0.347332  0.685990  0.666454  0.653386   \n",
       "Consumption  1.011988  0.961226  0.921952  1.999953  1.967390  1.915481   \n",
       "Exports      0.550323  0.505969  0.469949  1.712270  1.607455  1.443735   \n",
       "Imports      0.572344  0.547756  0.500341  1.445409  1.354122  1.266126   \n",
       "GDP          1.782252  1.693428  1.613543  3.654924  3.559587  3.417095   \n",
       "\n",
       "country     United Kingdom                     United States             \\\n",
       "year                  2014      2012      2010          2014       2012   \n",
       "GovExpend         0.538888  0.528194  0.521146      2.334071   2.398873   \n",
       "Consumption       1.675716  1.612550  1.598563     11.000619  10.534042   \n",
       "Exports           0.774022  0.745484  0.690824      2.209555   2.045509   \n",
       "Imports           0.827311  0.772692  0.745065      2.732228   2.560677   \n",
       "GDP               2.657159  2.529323  2.452900     16.242526  15.567038   \n",
       "\n",
       "country                 \n",
       "year              2010  \n",
       "GovExpend     2.510143  \n",
       "Consumption  10.185836  \n",
       "Exports       1.846280  \n",
       "Imports       2.360183  \n",
       "GDP          14.992053  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdiT.loc[:, pd.IndexSlice[:, [2010, 2012, 2014]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Canada</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Germany</th>\n",
       "      <th colspan=\"3\" halign=\"left\">United Kingdom</th>\n",
       "      <th colspan=\"3\" halign=\"left\">United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GovExpend</th>\n",
       "      <td>0.353485</td>\n",
       "      <td>0.354342</td>\n",
       "      <td>0.347332</td>\n",
       "      <td>0.685990</td>\n",
       "      <td>0.666454</td>\n",
       "      <td>0.653386</td>\n",
       "      <td>0.538888</td>\n",
       "      <td>0.528194</td>\n",
       "      <td>0.521146</td>\n",
       "      <td>2.334071</td>\n",
       "      <td>2.398873</td>\n",
       "      <td>2.510143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumption</th>\n",
       "      <td>1.011988</td>\n",
       "      <td>0.961226</td>\n",
       "      <td>0.921952</td>\n",
       "      <td>1.999953</td>\n",
       "      <td>1.967390</td>\n",
       "      <td>1.915481</td>\n",
       "      <td>1.675716</td>\n",
       "      <td>1.612550</td>\n",
       "      <td>1.598563</td>\n",
       "      <td>11.000619</td>\n",
       "      <td>10.534042</td>\n",
       "      <td>10.185836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exports</th>\n",
       "      <td>0.550323</td>\n",
       "      <td>0.505969</td>\n",
       "      <td>0.469949</td>\n",
       "      <td>1.712270</td>\n",
       "      <td>1.607455</td>\n",
       "      <td>1.443735</td>\n",
       "      <td>0.774022</td>\n",
       "      <td>0.745484</td>\n",
       "      <td>0.690824</td>\n",
       "      <td>2.209555</td>\n",
       "      <td>2.045509</td>\n",
       "      <td>1.846280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imports</th>\n",
       "      <td>0.572344</td>\n",
       "      <td>0.547756</td>\n",
       "      <td>0.500341</td>\n",
       "      <td>1.445409</td>\n",
       "      <td>1.354122</td>\n",
       "      <td>1.266126</td>\n",
       "      <td>0.827311</td>\n",
       "      <td>0.772692</td>\n",
       "      <td>0.745065</td>\n",
       "      <td>2.732228</td>\n",
       "      <td>2.560677</td>\n",
       "      <td>2.360183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.782252</td>\n",
       "      <td>1.693428</td>\n",
       "      <td>1.613543</td>\n",
       "      <td>3.654924</td>\n",
       "      <td>3.559587</td>\n",
       "      <td>3.417095</td>\n",
       "      <td>2.657159</td>\n",
       "      <td>2.529323</td>\n",
       "      <td>2.452900</td>\n",
       "      <td>16.242526</td>\n",
       "      <td>15.567038</td>\n",
       "      <td>14.992053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "country        Canada                       Germany                      \\\n",
       "year             2014      2012      2010      2014      2012      2010   \n",
       "GovExpend    0.353485  0.354342  0.347332  0.685990  0.666454  0.653386   \n",
       "Consumption  1.011988  0.961226  0.921952  1.999953  1.967390  1.915481   \n",
       "Exports      0.550323  0.505969  0.469949  1.712270  1.607455  1.443735   \n",
       "Imports      0.572344  0.547756  0.500341  1.445409  1.354122  1.266126   \n",
       "GDP          1.782252  1.693428  1.613543  3.654924  3.559587  3.417095   \n",
       "\n",
       "country     United Kingdom                     United States             \\\n",
       "year                  2014      2012      2010          2014       2012   \n",
       "GovExpend         0.538888  0.528194  0.521146      2.334071   2.398873   \n",
       "Consumption       1.675716  1.612550  1.598563     11.000619  10.534042   \n",
       "Exports           0.774022  0.745484  0.690824      2.209555   2.045509   \n",
       "Imports           0.827311  0.772692  0.745065      2.732228   2.560677   \n",
       "GDP               2.657159  2.529323  2.452900     16.242526  15.567038   \n",
       "\n",
       "country                 \n",
       "year              2010  \n",
       "GovExpend     2.510143  \n",
       "Consumption  10.185836  \n",
       "Exports       1.846280  \n",
       "Imports       2.360183  \n",
       "GDP          14.992053  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.IndexSlice[:, [2010, 2012, 2014]]\n",
    "wdiT.loc[:, idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>GovExpend</th>\n",
       "      <th>Consumption</th>\n",
       "      <th>Exports</th>\n",
       "      <th>Imports</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Canada</th>\n",
       "      <th>2017</th>\n",
       "      <td>0.372665</td>\n",
       "      <td>1.095475</td>\n",
       "      <td>0.582831</td>\n",
       "      <td>0.600031</td>\n",
       "      <td>1.868164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.364899</td>\n",
       "      <td>1.058426</td>\n",
       "      <td>0.576394</td>\n",
       "      <td>0.575775</td>\n",
       "      <td>1.814016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.358303</td>\n",
       "      <td>1.035208</td>\n",
       "      <td>0.568859</td>\n",
       "      <td>0.575793</td>\n",
       "      <td>1.794270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.353485</td>\n",
       "      <td>1.011988</td>\n",
       "      <td>0.550323</td>\n",
       "      <td>0.572344</td>\n",
       "      <td>1.782252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.351541</td>\n",
       "      <td>0.986400</td>\n",
       "      <td>0.518040</td>\n",
       "      <td>0.558636</td>\n",
       "      <td>1.732714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">United States</th>\n",
       "      <th>2004</th>\n",
       "      <td>2.267999</td>\n",
       "      <td>9.311431</td>\n",
       "      <td>1.335978</td>\n",
       "      <td>2.108585</td>\n",
       "      <td>13.846058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>2.233519</td>\n",
       "      <td>8.974708</td>\n",
       "      <td>1.218199</td>\n",
       "      <td>1.892825</td>\n",
       "      <td>13.339312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.193188</td>\n",
       "      <td>8.698306</td>\n",
       "      <td>1.192180</td>\n",
       "      <td>1.804105</td>\n",
       "      <td>12.968263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.112038</td>\n",
       "      <td>8.480461</td>\n",
       "      <td>1.213253</td>\n",
       "      <td>1.740797</td>\n",
       "      <td>12.746262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>2.040500</td>\n",
       "      <td>8.272097</td>\n",
       "      <td>1.287739</td>\n",
       "      <td>1.790995</td>\n",
       "      <td>12.620268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    GovExpend  Consumption   Exports   Imports        GDP\n",
       "country       year                                                       \n",
       "Canada        2017   0.372665     1.095475  0.582831  0.600031   1.868164\n",
       "              2016   0.364899     1.058426  0.576394  0.575775   1.814016\n",
       "              2015   0.358303     1.035208  0.568859  0.575793   1.794270\n",
       "              2014   0.353485     1.011988  0.550323  0.572344   1.782252\n",
       "              2013   0.351541     0.986400  0.518040  0.558636   1.732714\n",
       "...                       ...          ...       ...       ...        ...\n",
       "United States 2004   2.267999     9.311431  1.335978  2.108585  13.846058\n",
       "              2003   2.233519     8.974708  1.218199  1.892825  13.339312\n",
       "              2002   2.193188     8.698306  1.192180  1.804105  12.968263\n",
       "              2001   2.112038     8.480461  1.213253  1.740797  12.746262\n",
       "              2000   2.040500     8.272097  1.287739  1.790995  12.620268\n",
       "\n",
       "[72 rows x 5 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>year</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2017</td>\n",
       "      <td>GovExpend</td>\n",
       "      <td>0.372665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2016</td>\n",
       "      <td>GovExpend</td>\n",
       "      <td>0.364899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2015</td>\n",
       "      <td>GovExpend</td>\n",
       "      <td>0.358303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2014</td>\n",
       "      <td>GovExpend</td>\n",
       "      <td>0.353485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2013</td>\n",
       "      <td>GovExpend</td>\n",
       "      <td>0.351541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>United States</td>\n",
       "      <td>2004</td>\n",
       "      <td>GDP</td>\n",
       "      <td>13.846058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>United States</td>\n",
       "      <td>2003</td>\n",
       "      <td>GDP</td>\n",
       "      <td>13.339312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>United States</td>\n",
       "      <td>2002</td>\n",
       "      <td>GDP</td>\n",
       "      <td>12.968263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>United States</td>\n",
       "      <td>2001</td>\n",
       "      <td>GDP</td>\n",
       "      <td>12.746262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>United States</td>\n",
       "      <td>2000</td>\n",
       "      <td>GDP</td>\n",
       "      <td>12.620268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  year   variable      value\n",
       "0           Canada  2017  GovExpend   0.372665\n",
       "1           Canada  2016  GovExpend   0.364899\n",
       "2           Canada  2015  GovExpend   0.358303\n",
       "3           Canada  2014  GovExpend   0.353485\n",
       "4           Canada  2013  GovExpend   0.351541\n",
       "..             ...   ...        ...        ...\n",
       "355  United States  2004        GDP  13.846058\n",
       "356  United States  2003        GDP  13.339312\n",
       "357  United States  2002        GDP  12.968263\n",
       "358  United States  2001        GDP  12.746262\n",
       "359  United States  2000        GDP  12.620268\n",
       "\n",
       "[360 rows x 4 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi_melted = wdi.reset_index().melt(id_vars=[\"country\", \"year\"])\n",
    "wdi_melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>variable</th>\n",
       "      <th>Consumption</th>\n",
       "      <th>Exports</th>\n",
       "      <th>GDP</th>\n",
       "      <th>GovExpend</th>\n",
       "      <th>Imports</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Canada</th>\n",
       "      <th>2000</th>\n",
       "      <td>0.677713</td>\n",
       "      <td>0.499526</td>\n",
       "      <td>1.342805</td>\n",
       "      <td>0.270553</td>\n",
       "      <td>0.380823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.694230</td>\n",
       "      <td>0.484696</td>\n",
       "      <td>1.366590</td>\n",
       "      <td>0.279767</td>\n",
       "      <td>0.362023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.721974</td>\n",
       "      <td>0.490465</td>\n",
       "      <td>1.407725</td>\n",
       "      <td>0.286094</td>\n",
       "      <td>0.368615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.741796</td>\n",
       "      <td>0.481993</td>\n",
       "      <td>1.433089</td>\n",
       "      <td>0.294335</td>\n",
       "      <td>0.384199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.764357</td>\n",
       "      <td>0.508657</td>\n",
       "      <td>1.477317</td>\n",
       "      <td>0.299854</td>\n",
       "      <td>0.416754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">United States</th>\n",
       "      <th>2013</th>\n",
       "      <td>10.687214</td>\n",
       "      <td>2.118639</td>\n",
       "      <td>15.853796</td>\n",
       "      <td>2.353381</td>\n",
       "      <td>2.600198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>11.000619</td>\n",
       "      <td>2.209555</td>\n",
       "      <td>16.242526</td>\n",
       "      <td>2.334071</td>\n",
       "      <td>2.732228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>11.409800</td>\n",
       "      <td>2.222228</td>\n",
       "      <td>16.710459</td>\n",
       "      <td>2.373130</td>\n",
       "      <td>2.881337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>11.722133</td>\n",
       "      <td>2.219937</td>\n",
       "      <td>16.972348</td>\n",
       "      <td>2.407981</td>\n",
       "      <td>2.936004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>12.019266</td>\n",
       "      <td>2.287071</td>\n",
       "      <td>17.348627</td>\n",
       "      <td>2.405743</td>\n",
       "      <td>3.069954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "variable            Consumption   Exports        GDP  GovExpend   Imports\n",
       "country       year                                                       \n",
       "Canada        2000     0.677713  0.499526   1.342805   0.270553  0.380823\n",
       "              2001     0.694230  0.484696   1.366590   0.279767  0.362023\n",
       "              2002     0.721974  0.490465   1.407725   0.286094  0.368615\n",
       "              2003     0.741796  0.481993   1.433089   0.294335  0.384199\n",
       "              2004     0.764357  0.508657   1.477317   0.299854  0.416754\n",
       "...                         ...       ...        ...        ...       ...\n",
       "United States 2013    10.687214  2.118639  15.853796   2.353381  2.600198\n",
       "              2014    11.000619  2.209555  16.242526   2.334071  2.732228\n",
       "              2015    11.409800  2.222228  16.710459   2.373130  2.881337\n",
       "              2016    11.722133  2.219937  16.972348   2.407981  2.936004\n",
       "              2017    12.019266  2.287071  17.348627   2.405743  3.069954\n",
       "\n",
       "[72 rows x 5 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi_melted.pivot(index=[\"country\", \"year\"], columns=\"variable\", values=\"value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi_melted.pivot_table?"
   ]
  }
 ],
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