{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distributions\n",
    "> A Summary of lecture \"Exploratory Data Analysis in Python\", via datacamp\n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Chanseok Kang\n",
    "- categories: [Python, Datacamp]\n",
    "- image: images/income-cdf.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probability mass functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from empiricaldist import Pmf, Cdf\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>year</th>\n",
       "      <th>age</th>\n",
       "      <th>cohort</th>\n",
       "      <th>sex</th>\n",
       "      <th>race</th>\n",
       "      <th>educ</th>\n",
       "      <th>realinc</th>\n",
       "      <th>gunlaw</th>\n",
       "      <th>grass</th>\n",
       "      <th>wtssall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1972</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13537.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1972</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18951.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1915.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30458.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.3339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1972</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>37226.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30458.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   age  cohort  sex  race  educ  realinc  gunlaw  grass  wtssall\n",
       "0  1972  26.0  1946.0    1     1  18.0  13537.0     2.0    NaN   0.8893\n",
       "1  1972  38.0  1934.0    2     1  12.0  18951.0     1.0    NaN   0.4446\n",
       "2  1972  57.0  1915.0    1     1  12.0  30458.0     1.0    NaN   1.3339\n",
       "3  1972  61.0  1911.0    2     1  14.0  37226.0     1.0    NaN   0.8893\n",
       "4  1972  59.0  1913.0    1     1  12.0  30458.0     2.0    NaN   0.8893"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gss = pd.read_hdf('./dataset/gss.hdf5', 'gss')\n",
    "gss.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a PMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1972    1613\n",
      "1973    1504\n",
      "1974    1484\n",
      "1975    1490\n",
      "1976    1499\n",
      "1977    1530\n",
      "1978    1532\n",
      "1980    1468\n",
      "1982    1860\n",
      "1983    1599\n",
      "1984    1473\n",
      "1985    1534\n",
      "1986    1470\n",
      "1987    1819\n",
      "1988    1481\n",
      "1989    1537\n",
      "1990    1372\n",
      "1991    1517\n",
      "1993    1606\n",
      "1994    2992\n",
      "1996    2904\n",
      "1998    2832\n",
      "2000    2817\n",
      "2002    2765\n",
      "2004    2812\n",
      "2006    4510\n",
      "2008    2023\n",
      "2010    2044\n",
      "2012    1974\n",
      "2014    2538\n",
      "2016    2867\n",
      "Name: year, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "year = gss['year']\n",
    "# Compute the PMF for year\n",
    "pmf_year = Pmf.from_seq(year, normalize=False)\n",
    "\n",
    "# Print the result\n",
    "print(pmf_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a PMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'PMF')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the age column\n",
    "age = gss['age']\n",
    "\n",
    "# Make a PMF of age\n",
    "pmf_age = Pmf.from_seq(age)\n",
    "\n",
    "# Plot the PMF\n",
    "pmf_age.bar()\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('PMF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cumulative distribution functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a CDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2539137136526389\n",
      "0.7460862863473612\n"
     ]
    }
   ],
   "source": [
    "# Select the age column\n",
    "age = gss['age']\n",
    "\n",
    "# Compute the CDF of age\n",
    "cdf_age = Cdf.from_seq(age)\n",
    "\n",
    "# Calculate the CDF of 30\n",
    "print(cdf_age(30))\n",
    "print(1 - cdf_age(30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "income = gss['realinc']\n",
    "\n",
    "cdf_income = Cdf.from_seq(income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29676.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate the 75th percentile\n",
    "percentile_75th = cdf_income.inverse(0.75)\n",
    "\n",
    "# Calculate the 25th percentile\n",
    "percentile_25th = cdf_income.inverse(0.25)\n",
    "\n",
    "# Calculate the interquartile range\n",
    "iqr = percentile_75th - percentile_25th\n",
    "\n",
    "# Print the interquartile range\n",
    "print(iqr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a CDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CDF')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot it\n",
    "cdf_income.plot()\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Income (1986 USD)')\n",
    "plt.ylabel('CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract education levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5308807991547402\n"
     ]
    }
   ],
   "source": [
    "# Select educ\n",
    "educ = gss['educ']\n",
    "\n",
    "# Bachelor`s degree\n",
    "bach = (educ >= 16)\n",
    "\n",
    "# Associate degree\n",
    "assc = ((educ >= 14) & (educ < 16))\n",
    "\n",
    "# High school (12 or fewer years of education)\n",
    "high = (educ <= 12)\n",
    "print(high.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot income CDFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "income = gss['realinc']\n",
    "\n",
    "# Plot the CDFs\n",
    "Cdf.from_seq(income[high]).plot(label='High school')\n",
    "Cdf.from_seq(income[assc]).plot(label='Associate')\n",
    "Cdf.from_seq(income[bach]).plot(label='Bachelor')\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Income (1986 USD)')\n",
    "plt.ylabel('CDF')\n",
    "plt.legend()\n",
    "plt.savefig('../images/income-cdf.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.371148677933652 0.4290043733010077\n"
     ]
    }
   ],
   "source": [
    "# Extract realinc and compute its log\n",
    "log_income = np.log10(income)\n",
    "\n",
    "# Compute mean and standard deviation\n",
    "mean = np.mean(log_income)\n",
    "std = np.std(log_income)\n",
    "print(mean, std)\n",
    "\n",
    "# Make a norm object\n",
    "dist = norm(mean, std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing CDFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CDF')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the model CDF\n",
    "xs = np.linspace(2, 5.5)\n",
    "ys = dist.cdf(xs)\n",
    "\n",
    "# Plot the model CDF\n",
    "plt.plot(xs, ys, color='gray')\n",
    "\n",
    "# Create and plot the Cdf of log_income\n",
    "Cdf.from_seq(log_income).plot()\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('log10 of realinc')\n",
    "plt.ylabel('CDF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CDF')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the model CDF\n",
    "xs = np.linspace(2, 5.5)\n",
    "ys = dist.pdf(xs)\n",
    "\n",
    "# Plot the model CDF\n",
    "plt.plot(xs, ys, color='gray')\n",
    "\n",
    "# Plot the data KDE\n",
    "sns.kdeplot(log_income)\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('log10 of realinc')\n",
    "plt.ylabel('CDF')"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}