Datasets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "# matplotlib.use('nbagg')            # For interactive plotting. Doesn't play well with pandas plotting.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on package statsmodels.datasets in statsmodels:\n",
      "\n",
      "NAME\n",
      "    statsmodels.datasets - Datasets module\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    anes96 (package)\n",
      "    cancer (package)\n",
      "    ccard (package)\n",
      "    china_smoking (package)\n",
      "    co2 (package)\n",
      "    committee (package)\n",
      "    copper (package)\n",
      "    cpunish (package)\n",
      "    elnino (package)\n",
      "    engel (package)\n",
      "    fair (package)\n",
      "    fertility (package)\n",
      "    grunfeld (package)\n",
      "    heart (package)\n",
      "    interest_inflation (package)\n",
      "    longley (package)\n",
      "    macrodata (package)\n",
      "    modechoice (package)\n",
      "    nile (package)\n",
      "    randhie (package)\n",
      "    scotland (package)\n",
      "    spector (package)\n",
      "    stackloss (package)\n",
      "    star98 (package)\n",
      "    statecrime (package)\n",
      "    strikes (package)\n",
      "    sunspots (package)\n",
      "    template_data\n",
      "    tests (package)\n",
      "    utils\n",
      "\n",
      "FUNCTIONS\n",
      "    check_internet(url=None)\n",
      "        Check if internet is available\n",
      "    \n",
      "    clear_data_home(data_home=None)\n",
      "        Delete all the content of the data home cache.\n",
      "    \n",
      "    get_data_home(data_home=None)\n",
      "        Return the path of the statsmodels data dir.\n",
      "        \n",
      "        This folder is used by some large dataset loaders to avoid\n",
      "        downloading the data several times.\n",
      "        \n",
      "        By default the data dir is set to a folder named 'statsmodels_data'\n",
      "        in the user home folder.\n",
      "        \n",
      "        Alternatively, it can be set by the 'STATSMODELS_DATA' environment\n",
      "        variable or programatically by giving an explit folder path. The\n",
      "        '~' symbol is expanded to the user home folder.\n",
      "        \n",
      "        If the folder does not already exist, it is automatically created.\n",
      "    \n",
      "    get_rdataset(dataname, package='datasets', cache=False)\n",
      "        download and return R dataset\n",
      "        \n",
      "        Parameters\n",
      "        ----------\n",
      "        dataname : str\n",
      "            The name of the dataset you want to download\n",
      "        package : str\n",
      "            The package in which the dataset is found. The default is the core\n",
      "            'datasets' package.\n",
      "        cache : bool or str\n",
      "            If True, will download this data into the STATSMODELS_DATA folder.\n",
      "            The default location is a folder called statsmodels_data in the\n",
      "            user home folder. Otherwise, you can specify a path to a folder to\n",
      "            use for caching the data. If False, the data will not be cached.\n",
      "        \n",
      "        Returns\n",
      "        -------\n",
      "        dataset : Dataset instance\n",
      "            A `statsmodels.data.utils.Dataset` instance. This objects has\n",
      "            attributes:\n",
      "        \n",
      "            * data - A pandas DataFrame containing the data\n",
      "            * title - The dataset title\n",
      "            * package - The package from which the data came\n",
      "            * from_cache - Whether not cached data was retrieved\n",
      "            * __doc__ - The verbatim R documentation.\n",
      "        \n",
      "        \n",
      "        Notes\n",
      "        -----\n",
      "        If the R dataset has an integer index. This is reset to be zero-based.\n",
      "        Otherwise the index is preserved. The caching facilities are dumb. That\n",
      "        is, no download dates, e-tags, or otherwise identifying information\n",
      "        is checked to see if the data should be downloaded again or not. If the\n",
      "        dataset is in the cache, it's used.\n",
      "    \n",
      "    webuse(data, baseurl='https://www.stata-press.com/data/r11/', as_df=True)\n",
      "        Download and return an example dataset from Stata.\n",
      "        \n",
      "        Parameters\n",
      "        ----------\n",
      "        data : str\n",
      "            Name of dataset to fetch.\n",
      "        baseurl : str\n",
      "            The base URL to the stata datasets.\n",
      "        as_df : bool\n",
      "            Deprecated. Always returns a DataFrame\n",
      "        \n",
      "        Returns\n",
      "        -------\n",
      "        dta : DataFrame\n",
      "            A DataFrame containing the Stata dataset.\n",
      "        \n",
      "        Examples\n",
      "        --------\n",
      "        >>> dta = webuse('auto')\n",
      "        \n",
      "        Notes\n",
      "        -----\n",
      "        Make sure baseurl has trailing forward slash. Doesn't do any\n",
      "        error checking in response URLs.\n",
      "\n",
      "DATA\n",
      "    __all__ = ['anes96', 'cancer', 'committee', 'ccard', 'copper', 'cpunis...\n",
      "    test = <statsmodels.tools._testing.PytestTester object>\n",
      "\n",
      "FILE\n",
      "    /usr/local/lib/python3.5/dist-packages/statsmodels/datasets/__init__.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# List available datasets\n",
    "# Also see http://www.statsmodels.org/dev/datasets/index.html\n",
    "\n",
    "help(sm.datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "statsmodels.datasets.utils.Dataset"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-29</th>\n",
       "      <td>316.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-05</th>\n",
       "      <td>317.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-12</th>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-19</th>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-26</th>\n",
       "      <td>316.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2\n",
       "1958-03-29  316.1\n",
       "1958-04-05  317.3\n",
       "1958-04-12  317.6\n",
       "1958-04-19  317.5\n",
       "1958-04-26  316.4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2284 entries, 1958-03-29 to 2001-12-29\n",
      "Freq: W-SAT\n",
      "Data columns (total 1 columns):\n",
      "co2    2225 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 35.7 KB\n"
     ]
    }
   ],
   "source": [
    "# Load a dataset\n",
    "\n",
    "ds1 = sm.datasets.co2.load_pandas()\n",
    "type(ds1)                # tells you the type of the object: statsmodels.datasets.utils.Dataset\n",
    "ds1.data.head()          # Dataset.data is a pandas DataFrame\n",
    "ds1.data.info()          # Tells you about the pandas DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe41db07a20>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds1.data.plot()          # pandas DataFrames have a plot method. \n",
    "                         # https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 203 entries, 0 to 202\n",
      "Data columns (total 14 columns):\n",
      "year        203 non-null float64\n",
      "quarter     203 non-null float64\n",
      "realgdp     203 non-null float64\n",
      "realcons    203 non-null float64\n",
      "realinv     203 non-null float64\n",
      "realgovt    203 non-null float64\n",
      "realdpi     203 non-null float64\n",
      "cpi         203 non-null float64\n",
      "m1          203 non-null float64\n",
      "tbilrate    203 non-null float64\n",
      "unemp       203 non-null float64\n",
      "pop         203 non-null float64\n",
      "infl        203 non-null float64\n",
      "realint     203 non-null float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 22.3 KB\n"
     ]
    },
    {
     "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "    }\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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13324.600</td>\n",
       "      <td>9267.7</td>\n",
       "      <td>1990.693</td>\n",
       "      <td>991.551</td>\n",
       "      <td>9838.3</td>\n",
       "      <td>216.889</td>\n",
       "      <td>1474.7</td>\n",
       "      <td>1.17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>305.270</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13141.920</td>\n",
       "      <td>9195.3</td>\n",
       "      <td>1857.661</td>\n",
       "      <td>1007.273</td>\n",
       "      <td>9920.4</td>\n",
       "      <td>212.174</td>\n",
       "      <td>1576.5</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6.9</td>\n",
       "      <td>305.952</td>\n",
       "      <td>-8.79</td>\n",
       "      <td>8.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12925.410</td>\n",
       "      <td>9209.2</td>\n",
       "      <td>1558.494</td>\n",
       "      <td>996.287</td>\n",
       "      <td>9926.4</td>\n",
       "      <td>212.671</td>\n",
       "      <td>1592.8</td>\n",
       "      <td>0.22</td>\n",
       "      <td>8.1</td>\n",
       "      <td>306.547</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12901.504</td>\n",
       "      <td>9189.0</td>\n",
       "      <td>1456.678</td>\n",
       "      <td>1023.528</td>\n",
       "      <td>10077.5</td>\n",
       "      <td>214.469</td>\n",
       "      <td>1653.6</td>\n",
       "      <td>0.18</td>\n",
       "      <td>9.2</td>\n",
       "      <td>307.226</td>\n",
       "      <td>3.37</td>\n",
       "      <td>-3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12990.341</td>\n",
       "      <td>9256.0</td>\n",
       "      <td>1486.398</td>\n",
       "      <td>1044.088</td>\n",
       "      <td>10040.6</td>\n",
       "      <td>216.385</td>\n",
       "      <td>1673.9</td>\n",
       "      <td>0.12</td>\n",
       "      <td>9.6</td>\n",
       "      <td>308.013</td>\n",
       "      <td>3.56</td>\n",
       "      <td>-3.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  quarter    realgdp  realcons   realinv  realgovt  realdpi  \\\n",
       "198  2008.0      3.0  13324.600    9267.7  1990.693   991.551   9838.3   \n",
       "199  2008.0      4.0  13141.920    9195.3  1857.661  1007.273   9920.4   \n",
       "200  2009.0      1.0  12925.410    9209.2  1558.494   996.287   9926.4   \n",
       "201  2009.0      2.0  12901.504    9189.0  1456.678  1023.528  10077.5   \n",
       "202  2009.0      3.0  12990.341    9256.0  1486.398  1044.088  10040.6   \n",
       "\n",
       "         cpi      m1  tbilrate  unemp      pop  infl  realint  \n",
       "198  216.889  1474.7      1.17    6.0  305.270 -3.16     4.33  \n",
       "199  212.174  1576.5      0.12    6.9  305.952 -8.79     8.91  \n",
       "200  212.671  1592.8      0.22    8.1  306.547  0.94    -0.71  \n",
       "201  214.469  1653.6      0.18    9.2  307.226  3.37    -3.19  \n",
       "202  216.385  1673.9      0.12    9.6  308.013  3.56    -3.44  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the US macroeconomic data \n",
    "ds2 = sm.datasets.macrodata.load_pandas()\n",
    "USMacro = ds2.data\n",
    "USMacro.info()\n",
    "USMacro.head()\n",
    "USMacro.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "      <th>yq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1959q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1959q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1959q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1959q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1960q1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959        1  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959        2  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959        3  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959        4  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960        1  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint      yq  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  1959q1  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  1959q2  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  1959q3  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  1959q4  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  1960q1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a variable for year and quarter\n",
    "\n",
    "USMacro[['year','quarter']] = USMacro[['year','quarter']].astype(int)       # Casting of columns\n",
    "\n",
    "USMacro['yq'] = USMacro['year'].astype(str) + 'q'+ USMacro['quarter'].astype(str)  # String concatenation\n",
    "\n",
    "USMacro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plots using subplots functionality\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2,ncols=2,figsize=(12,6))\n",
    "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
    "\n",
    "_ = USMacro.plot(ax=axes[0,0],x='yq',y=['realgdp','realcons','realinv','realgovt'])\n",
    "\n",
    "_ = USMacro.plot(ax=axes[0,1],x='yq',y='infl')\n",
    "\n",
    "_ = USMacro.plot(ax=axes[1,0],x='yq',y='unemp')\n",
    "\n",
    "_ = USMacro.plot(ax=axes[1,1],x='yq',y='pop')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}