Chapter_03.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 3: Correlation**\n",
    "\n",
    "Jakub Janus, [jakub.janus@uek.krakow.pl](jakub.janus@uek.krakow.pl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 3.1\n",
    "(a) Using the data in FOREST.XLS, calculate and interpret the mean, standard deviation, minimum and maximum of deforestation and population density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "deforestation_mean =  1.1385714285714286 \ndeforestation_std =  0.9281886454221916 \ndeforestation_min =  0.0 \ndeforestation_max =  5.3 \npopulation_density_mean =  639.427 \npopulation_density_std =  726.3399771803076 \npopulation_density_min =  0.89 \npopulation_density_max =  2769.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "forest = pd.read_excel(r'datasets\\FOREST.xls')\n",
    "\n",
    "print('deforestation_mean = ', forest['Forest loss'].mean(), '\\n'\n",
    "      'deforestation_std = ', forest['Forest loss'].std(), '\\n'\n",
    "      'deforestation_min = ', forest['Forest loss'].min(), '\\n'\n",
    "      'deforestation_max = ', forest['Forest loss'].max(), '\\n'\n",
    "      'population_density_mean = ', forest['Pop dens'].mean(), '\\n'\n",
    "      'population_density_std = ', forest['Pop dens'].std(), '\\n'\n",
    "      'population_density_min = ', forest['Pop dens'].min(), '\\n'\n",
    "      'population_density_max = ', forest['Pop dens'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) Verify that the correlation between these two variables is 0.66."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "correlation =  0.66\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
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       "True"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "correlation = round(forest['Forest loss'].corr(forest['Pop dens']), 2)\n",
    "print('correlation = ', round(correlation, 2))\n",
    "correlation == 0.66\n",
    "\n",
    "#import numpy as np\n",
    "#correlation = np.cov(forest['Forest loss'], forest['Pop dens']) / (np.std(forest['Forest loss']) * np.std(forest['Pop dens']))\n",
    "#print('correlation = ', round(correlation[0, 1], 2))\n",
    "#correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 3.2\n",
    "(a) Using the data in HPRICE.XLS, calculate and interpret the mean, standard deviation, minimum and maximum of Y (house price, labeled “sale price” in the file), X (lot size) and Z (number of bedrooms, labeled “#bedroom”)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          sale price      lot size    #bedroom\n",
       "count     546.000000    546.000000  546.000000\n",
       "mean    68121.597070   5150.265568    2.965201\n",
       "std     26702.670926   2168.158725    0.737388\n",
       "min     25000.000000   1650.000000    1.000000\n",
       "25%     49125.000000   3600.000000    2.000000\n",
       "50%     62000.000000   4600.000000    3.000000\n",
       "75%     82000.000000   6360.000000    3.000000\n",
       "max    190000.000000  16200.000000    6.000000"
      ],
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     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "hprice = pd.read_excel(r'datasets\\HPRICE.xls')\n",
    "#using Pandas descriptive statistics\n",
    "hprice[['sale price', 'lot size', '#bedroom']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) Verify that the correlation between X and Y is as given in the example. Repeat for X and Z and then for Y and Z."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cor_X_Y = 0.54 \ncor_X_Z = 0.37 \ncor_Y_Z = 0.15\n"
     ]
    }
   ],
   "source": [
    "X = hprice['sale price']\n",
    "Y = hprice['lot size']\n",
    "Z = hprice['#bedroom']\n",
    "cor_X_Y = X.corr(Y)\n",
    "cor_X_Z = X.corr(Z)\n",
    "cor_Y_Z = Y.corr(Z)\n",
    "print('cor_X_Y =', round(cor_X_Y, 2) , '\\n'\n",
    "      'cor_X_Z =', round(cor_X_Z, 2) , '\\n'\n",
    "      'cor_Y_Z =', round(cor_Y_Z, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(c) Now add a new variable, W, the number of bathrooms (labeled “#bath”). Calculate the mean of W."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "W_mean =  1.2857142857142858\n"
     ]
    }
   ],
   "source": [
    "W = hprice['#bath']\n",
    "print('W_mean = ', W.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(d) Calculate and interpret the correlation between W and Y. Discuss to what extent it can be said that W causes Y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cor_W_Y = 0.19\n"
     ]
    }
   ],
   "source": [
    "cor_W_Y = W.corr(Y)\n",
    "print('cor_W_Y =', round(cor_W_Y, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(e) Repeat part (d) for W and X and then for W and Z."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cor_W_X = 0.52\ncor_W_Z = 0.37\n"
     ]
    }
   ],
   "source": [
    "cor_W_X = W.corr(X)\n",
    "print('cor_W_X =', round(cor_W_X, 2))\n",
    "cor_W_Z = W.corr(Z)\n",
    "print('cor_W_Z =', round(cor_W_Z, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 3.4\n",
    "The file EX34.XLS contains four variables: Y, X1, X2 and X3.\n",
    "\n",
    "(a) Calculate the correlation between Y and X1. Repeat for Y and X2, and for Y\n",
    "and X3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cor_Y_X1 = -0.01 \ncor_Y_X2 = -0.72 \ncor_Y_X3 = -1.0\n"
     ]
    }
   ],
   "source": [
    "ex34 = pd.read_excel(r'datasets\\EX34.xls')\n",
    "cor_Y_X1 = ex34['Y'].corr(ex34['X1'])\n",
    "cor_Y_X2 = ex34['Y'].corr(ex34['X2'])\n",
    "cor_Y_X3 = ex34['Y'].corr(ex34['X3'])\n",
    "print('cor_Y_X1 =', round(cor_Y_X1, 2) , '\\n'\n",
    "     'cor_Y_X2 =', round(cor_Y_X2, 2) , '\\n'\n",
    "     'cor_Y_X3 =', round(cor_Y_X3, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) Create an XY-plot involving Y and X1. Repeat for Y and X2, and for Y\n",
    "and X3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "fig, axs = plt.subplots(1, 3, figsize = (15, 5))\n",
    "axs[0].scatter(ex34['Y'], ex34['X1'])\n",
    "axs[0].set_title('Y-X1')\n",
    "axs[1].scatter(ex34['Y'], ex34['X2'])\n",
    "axs[1].set_title('Y-X2')\n",
    "axs[2].scatter(ex34['Y'], ex34['X3'])\n",
    "axs[2].set_title('Y-X3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 648x216 with 3 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# using a melted table\n",
    "ex34melt = pd.melt(ex34, id_vars = 'Y')\n",
    "import seaborn as sns\n",
    "g = sns.FacetGrid(ex34melt, col = 'variable')\n",
    "g.map(sns.scatterplot, 'value', 'Y');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 3.5\n",
    "(a) Using the data in FOREST.XLS, calculate and interpret a correlation matrix\n",
    "involving deforestation, population density, change in pasture and change in\n",
    "cropland."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "             Forest loss  Pop dens   Crop ch  Pasture ch\n",
       "Forest loss     1.000000  0.659150 -0.052436    0.301605\n",
       "Pop dens        0.659150  1.000000 -0.126898    0.088625\n",
       "Crop ch        -0.052436 -0.126898  1.000000    0.248937\n",
       "Pasture ch      0.301605  0.088625  0.248937    1.000000"
      ],
      "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>Forest loss</th>\n      <td>1.000000</td>\n      <td>0.659150</td>\n      <td>-0.052436</td>\n      <td>0.301605</td>\n    </tr>\n    <tr>\n      <th>Pop dens</th>\n      <td>0.659150</td>\n      <td>1.000000</td>\n      <td>-0.126898</td>\n      <td>0.088625</td>\n    </tr>\n    <tr>\n      <th>Crop ch</th>\n      <td>-0.052436</td>\n      <td>-0.126898</td>\n      <td>1.000000</td>\n      <td>0.248937</td>\n    </tr>\n    <tr>\n      <th>Pasture ch</th>\n      <td>0.301605</td>\n      <td>0.088625</td>\n      <td>0.248937</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "#matrix\n",
    "forest_cor_matrix = forest.corr()\n",
    "forest_cor_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#heatmap\n",
    "sns.heatmap(forest_cor_matrix, vmin=-1, vmax=1, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(b) Calculate and interpret a correlation matrix using the following variables in\n",
    "the data set HPRICE.XLS: house price, lot size, number of bedrooms, number\n",
    "of bathrooms and number of stories. How many individual correlations have\n",
    "you calculated?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            sale price  lot size  #bedroom     #bath  #stories\n",
       "sale price    1.000000  0.535796  0.366447  0.516719  0.421190\n",
       "lot size      0.535796  1.000000  0.151851  0.193833  0.083675\n",
       "#bedroom      0.366447  0.151851  1.000000  0.373769  0.407974\n",
       "#bath         0.516719  0.193833  0.373769  1.000000  0.324066\n",
       "#stories      0.421190  0.083675  0.407974  0.324066  1.000000"
      ],
      "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>sale price</th>\n      <th>lot size</th>\n      <th>#bedroom</th>\n      <th>#bath</th>\n      <th>#stories</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>sale price</th>\n      <td>1.000000</td>\n      <td>0.535796</td>\n      <td>0.366447</td>\n      <td>0.516719</td>\n      <td>0.421190</td>\n    </tr>\n    <tr>\n      <th>lot size</th>\n      <td>0.535796</td>\n      <td>1.000000</td>\n      <td>0.151851</td>\n      <td>0.193833</td>\n      <td>0.083675</td>\n    </tr>\n    <tr>\n      <th>#bedroom</th>\n      <td>0.366447</td>\n      <td>0.151851</td>\n      <td>1.000000</td>\n      <td>0.373769</td>\n      <td>0.407974</td>\n    </tr>\n    <tr>\n      <th>#bath</th>\n      <td>0.516719</td>\n      <td>0.193833</td>\n      <td>0.373769</td>\n      <td>1.000000</td>\n      <td>0.324066</td>\n    </tr>\n    <tr>\n      <th>#stories</th>\n      <td>0.421190</td>\n      <td>0.083675</td>\n      <td>0.407974</td>\n      <td>0.324066</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "hprice_selected = hprice[['sale price', 'lot size', '#bedroom', '#bath', '#stories']]\n",
    "hprice_selected_cor_matrix = hprice_selected.corr()\n",
    "hprice_selected_cor_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.heatmap(hprice_selected_cor_matrix, vmin=-1, vmax=1, annot=True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python374jvsc74a57bd0b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f",
   "display_name": "Python 3.7.4 64-bit ('base': conda)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}