Chapter_02.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "**Python solutions to Gary Koop's \"Analysis of Economic Data\" (4th ed., 2013)**\n",
    "\n",
    "**Chapter 2: Basic Data Handling**\n",
    "\n",
    "Jakub Janus, [jakub.janus@uek.krakow.pl](jakub.janus@uek.krakow.pl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2.1\n",
    "(a) Recreate Figure 2.1 from the data located in file EXRUK.XLS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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     "data": {
      "text/plain": [
       "            Pence per dollar\n",
       "t                           \n",
       "1947-01-01           402.934\n",
       "1947-02-01           402.740\n",
       "1947-03-01           402.735\n",
       "1947-04-01           402.743\n",
       "1947-05-01           402.740\n",
       "...                      ...\n",
       "1996-06-01           154.160\n",
       "1996-07-01           155.300\n",
       "1996-08-01           154.990\n",
       "1996-09-01           155.930\n",
       "1996-10-01           158.630\n",
       "\n",
       "[598 rows x 1 columns]"
      ],
      "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>Pence per dollar</th>\n    </tr>\n    <tr>\n      <th>t</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1947-01-01</th>\n      <td>402.934</td>\n    </tr>\n    <tr>\n      <th>1947-02-01</th>\n      <td>402.740</td>\n    </tr>\n    <tr>\n      <th>1947-03-01</th>\n      <td>402.735</td>\n    </tr>\n    <tr>\n      <th>1947-04-01</th>\n      <td>402.743</td>\n    </tr>\n    <tr>\n      <th>1947-05-01</th>\n      <td>402.740</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1996-06-01</th>\n      <td>154.160</td>\n    </tr>\n    <tr>\n      <th>1996-07-01</th>\n      <td>155.300</td>\n    </tr>\n    <tr>\n      <th>1996-08-01</th>\n      <td>154.990</td>\n    </tr>\n    <tr>\n      <th>1996-09-01</th>\n      <td>155.930</td>\n    </tr>\n    <tr>\n      <th>1996-10-01</th>\n      <td>158.630</td>\n    </tr>\n  </tbody>\n</table>\n<p>598 rows × 1 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "import pandas as pd\n",
    "colnames = ['t', 'Pence per dollar']\n",
    "exkruk = pd.read_excel(r'datasets\\EXRUK.xls', header = None, names = colnames, index_col = 0)\n",
    "exkruk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.ticker as ticker\n",
    "fig, ax = plt.subplots(figsize = (14,5))\n",
    "ax.plot('Pence per dollar', data = exkruk, color = 'k', linestyle = '-')\n",
    "ax.set_ylabel('Pence per dollar')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylim(0, 450)\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b-%Y'))\n",
    "locator = mdates.YearLocator(1)\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "for tick in ax.xaxis.get_major_ticks()[1::2]:\n",
    "    tick.set_visible(False)\n",
    "plt.xticks(rotation = 90) \n",
    "ax.margins(x = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) File INCOME.XLS contains data on the natural logarithm of personal income and consumption in the USA from 1954Q1 to 1994Q2.6 Make one time series graph that contains both of these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              Income  Consumption\n",
       "Date                             \n",
       "1954-01-01  6.968380     6.876368\n",
       "1954-04-01  6.962811     6.884384\n",
       "1954-07-01  6.978214     6.900630\n",
       "1954-10-01  6.997322     6.918102\n",
       "1955-01-01  7.004156     6.932545\n",
       "...              ...          ...\n",
       "1993-10-01  8.228924     8.162294\n",
       "1994-01-01  8.237268     8.173671\n",
       "1994-04-01  8.245778     8.176909\n",
       "1994-07-01  8.253462     8.184438\n",
       "1994-10-01  8.271548     8.196875\n",
       "\n",
       "[164 rows x 2 columns]"
      ],
      "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>Income</th>\n      <th>Consumption</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1954-01-01</th>\n      <td>6.968380</td>\n      <td>6.876368</td>\n    </tr>\n    <tr>\n      <th>1954-04-01</th>\n      <td>6.962811</td>\n      <td>6.884384</td>\n    </tr>\n    <tr>\n      <th>1954-07-01</th>\n      <td>6.978214</td>\n      <td>6.900630</td>\n    </tr>\n    <tr>\n      <th>1954-10-01</th>\n      <td>6.997322</td>\n      <td>6.918102</td>\n    </tr>\n    <tr>\n      <th>1955-01-01</th>\n      <td>7.004156</td>\n      <td>6.932545</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1993-10-01</th>\n      <td>8.228924</td>\n      <td>8.162294</td>\n    </tr>\n    <tr>\n      <th>1994-01-01</th>\n      <td>8.237268</td>\n      <td>8.173671</td>\n    </tr>\n    <tr>\n      <th>1994-04-01</th>\n      <td>8.245778</td>\n      <td>8.176909</td>\n    </tr>\n    <tr>\n      <th>1994-07-01</th>\n      <td>8.253462</td>\n      <td>8.184438</td>\n    </tr>\n    <tr>\n      <th>1994-10-01</th>\n      <td>8.271548</td>\n      <td>8.196875</td>\n    </tr>\n  </tbody>\n</table>\n<p>164 rows × 2 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "income = pd.read_excel(r'datasets\\INCOME.xls', index_col = 0, parse_dates = True)\n",
    "income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "income.plot(color = ['g', 'r']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c) Transform the logged personal income data to growth rates. Note that the\n",
    "percentage change in personal income between period t − 1 and t is approximately\n",
    "100×[ln(Y_t) − ln(Yt−1)] and the data provided in INCOME.XLS is\n",
    "already logged. Make a time series graph of the series you have created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Date\n",
       "1954-01-01        NaN\n",
       "1954-04-01   -0.55684\n",
       "1954-07-01    1.54022\n",
       "1954-10-01    1.91080\n",
       "1955-01-01    0.68341\n",
       "               ...   \n",
       "1993-10-01    1.05685\n",
       "1994-01-01    0.83433\n",
       "1994-04-01    0.85105\n",
       "1994-07-01    0.76839\n",
       "1994-10-01    1.80864\n",
       "Name: Income, Length: 164, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "d_income = 100*income['Income'].diff(1)\n",
    "d_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "d_income.plot(color = 'r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2.2\n",
    "(a) Recreate the histogram in Figure 2.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import numpy as np\n",
    "gdp_pc = pd.read_excel(r'datasets\\GDPPC.xls', index_col = 0, header = None)\n",
    "my_bins = [0, 2000, 4000, 6000, 8000, 10000, 12000, 14000, 16000, 18000]  \n",
    "plt.figure()\n",
    "hist = gdp_pc[3].hist(bins = my_bins, color = 'grey', grid = False, rwidth = 0.7)\n",
    "hist.set(xlabel = 'Bin', ylabel = 'Frequency')\n",
    "plt.xticks(np.arange(1000, 18000, step = 2000), (2000, 4000, 6000, 8000, 10000, 12000, 14000, 16000, 'More'), rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) Create histograms using different class intervals. For instance, begin by letting\n",
    "your software package choose default values and see what you get, then try\n",
    "values of your own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "gdp_pc[3].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2.3\n",
    "The file FOREST.XLS contains data on the percentage increase in cropland from\n",
    "1980 to 1990 and on the percentage increase in permanent pasture over the same\n",
    "period.\n",
    "\n",
    "(a) Construct and interpret XY-plots of these two variables (one at a time) against deforestation.\n",
    "\n",
    "(b) Does there seem to be a positive relationship between deforestation and expansion of pasture land?\n",
    "\n",
    "(c) How about between deforestation and the expansion of cropland?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    Forest loss  Pop dens  Crop ch  Pasture ch\n",
       "0           0.7     357.0     27.9         0.0\n",
       "1           0.7      48.0      1.7         0.0\n",
       "2           0.8     932.0     14.5         0.0\n",
       "3           0.7     366.0     17.9         0.0\n",
       "4           0.8      83.0      2.2         0.0\n",
       "..          ...       ...      ...         ...\n",
       "65          0.6     327.0      4.1         5.8\n",
       "66          1.7     409.0      9.4        29.2\n",
       "67          2.4     117.0     26.7        33.5\n",
       "68          0.4     179.0      6.1         0.0\n",
       "69          1.2     234.0      4.3         2.9\n",
       "\n",
       "[70 rows x 4 columns]"
      ],
      "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>Forest loss</th>\n      <th>Pop dens</th>\n      <th>Crop ch</th>\n      <th>Pasture ch</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.7</td>\n      <td>357.0</td>\n      <td>27.9</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.7</td>\n      <td>48.0</td>\n      <td>1.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.8</td>\n      <td>932.0</td>\n      <td>14.5</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.7</td>\n      <td>366.0</td>\n      <td>17.9</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.8</td>\n      <td>83.0</td>\n      <td>2.2</td>\n      <td>0.0</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>65</th>\n      <td>0.6</td>\n      <td>327.0</td>\n      <td>4.1</td>\n      <td>5.8</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>1.7</td>\n      <td>409.0</td>\n      <td>9.4</td>\n      <td>29.2</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>2.4</td>\n      <td>117.0</td>\n      <td>26.7</td>\n      <td>33.5</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>0.4</td>\n      <td>179.0</td>\n      <td>6.1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>1.2</td>\n      <td>234.0</td>\n      <td>4.3</td>\n      <td>2.9</td>\n    </tr>\n  </tbody>\n</table>\n<p>70 rows × 4 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "forest = pd.read_excel(r'datasets\\FOREST.xls')\n",
    "forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "forest.plot.scatter(x = 'Pasture ch', y = 'Forest loss', c = 'brown', s = 25, marker = 'o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "forest.plot.scatter(x = 'Forest loss', y = 'Crop ch', c = 'blue', s = 25, marker = 'o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2.4\n",
    "Construct and interpret descriptive statistics for the pasture change and cropland change variables in FOREST.XLS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "pasture_ch_mean =  3.002857142857143 \ncropland_ch_mean =  6.937142857142858 \npasture_ch_std =  8.44480753641681 \ncropland_ch_std =  8.305021554624306\n"
     ]
    }
   ],
   "source": [
    "print('pasture_ch_mean = ', forest['Pasture ch'].mean(), '\\n' \n",
    "    'cropland_ch_mean = ', forest['Crop ch'].mean(), '\\n'\n",
    "    'pasture_ch_std = ', forest['Pasture ch'].std(), '\\n'\n",
    "    'cropland_ch_std = ', forest['Crop ch'].std())"
   ]
  }
 ],
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