08_module.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Z scores\n",
    "\n",
    "Let's sample randomly one sale price from the SalePrice column and see what can we say about the sampled value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "houses = pd.read_table('data/AmesHousing_1.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2216    220000\n",
       "Name: SalePrice, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses['SalePrice'].sample(1, random_state = 0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see we got a price of $220,000. Is a house costing $220,000 cheap, expensive, or average-priced? To answer this question, we can start with finding the mean price of a house and then figure out whether $220,000 is below or above the mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180796.0600682594"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses['SalePrice'].mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sampled house ($220,000) is clearly more expensive than the average house (roughly $180,796), but is this price slightly above the average or extremely above the average? The answer depends on the standard deviation of the distribution of sale prices."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see why this is true, consider below two normally-distributed samples of sale prices (we made this simplifying assumption of normality for teaching purposes). Both samples have the same mean ¯x=180,000, but the sample standard deviations s are different (on the left, s=40,000, while on the right s=10,000). \n",
    "\n",
    "![Standard deviations](img/s2m5_different_sds.svg)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sample with a greater standard deviation (s=40000), we can see that $220,000 is fairly close to the mean, indicating that houses at that price are common and thus not very expensive (relative to the other houses on the market).\n",
    "\n",
    "For the other sample (s=10000), we see $220,000 on the far right of the distribution, indicating that houses at that price are uncommon and thus very expensive (relative to the other houses on the market).\n",
    "\n",
    "Now let's try to figure out how far off from the mean a value of $220,000 is in the distribution of the SalePrice variable."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task \n",
    "\n",
    "+ Generate a kernel density plot for the SalePrice variable to find out how far off $220,000 is from the mean.\n",
    "    + Generate the plot using `Series.plot.kde()`.\n",
    "        + The limits of the x-axis should be the minimum and the maximum value of the SalePrice variable. To set the limits you can use the xlim parameter of `Series.plot.kde()`.\n",
    "    + Plot a vertical line to indicate visually the location of the mean using `plt.axvline()`.\n",
    "        + The color of the line should be black, and its label should be 'Mean'. You can use the color and label parameters of `plt.axvline()`.\n",
    "    + Plot a vertical line to indicate visually the standard deviation distance above the mean — you'll have to generate a vertical line for the sum of the mean and standard deviation.\n",
    "        + Assume that the data is a population and compute the standard deviation without using Bessel's correction.\n",
    "        + The color of the line should be red and its label should be 'Standard deviation'.\n",
    "    + Plot a vertical line for the $220,000 price.\n",
    "        + The color of the line should be orange and its label should be '220000'.\n",
    "    + Display all the labels using `plt.legend()`.\n",
    "\n",
    "+ Examine the graph and figure out whether a price of $220,000 is very expensive. If it's very expensive, assign True to variable named very_expensive, otherwise assign False.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "houses['SalePrice'].plot.kde(xlim = (houses['SalePrice'].min(),\n",
    "                                    houses['SalePrice'].max()))\n",
    "\n",
    "st_dev = houses['SalePrice'].std(ddof = 0)\n",
    "mean = houses['SalePrice'].mean()\n",
    "plt.axvline(mean, color = 'Black', label = 'Mean')\n",
    "plt.axvline(mean + st_dev, color = 'Red', label = 'Standard deviation')\n",
    "plt.axvline(220000, color = 'Orange', label = '220000')\n",
    "plt.legend()\n",
    "\n",
    "very_expensive = False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Standard Deviations\n",
    "\n",
    "A better to assess a point distance from the mean\n",
    "\n",
    "#### Task\n",
    "\n",
    "Find out the number of standard deviations away from the mean for a price of $220,000 in the distribution of the SalePrice variable.\n",
    "\n",
    "+ Measure the distance between $220,000 and the mean of the SalePrice column.\n",
    "+ Divide the distance by the standard deviation of the SalePrice column (assume the data we have is a population) to find the number of standard deviations away from the mean.\n",
    "    + Assign your result to a variable named st_devs_away.\n",
    "    + If you can't understand why we divide, think about this way: if we have a distance of 6 and a standard deviation of 2, then that is three standard deviations away because 6:2=3\n",
    "\n",
    "+ Does the number of standard deviations match our visual estimate from the last exercise?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = 220000 - houses['SalePrice'].mean()\n",
    "st_devs_away = distance / houses['SalePrice'].std(ddof = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4908280788718362"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_devs_away"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Z scores definition\n",
    "\n",
    "The value representing the number of standard deviations away from the mean is commonly known as the standard score, or the z-score.\n",
    "It can either be negative or positive\n",
    "\n",
    "Z score has two parts\n",
    "\n",
    "+ The sign, which indicates whether the value is above or below the mean.\n",
    "+ The value, which indicates the number of standard deviations that a value is away from the mean.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task\n",
    "\n",
    "+ Write a function that takes in a value, the array the value belongs to, and returns the z-score of that value. Inside the function's definition:\n",
    "    + Compute the mean of the array.\n",
    "    + Compute the standard deviation of the array. Make sure your function is flexible enough to compute z-scores for both samples and populations.\n",
    "        + You can use the `std()` function from numpy.\n",
    "    + Find out the distance between the value and the mean of the array.\n",
    "    + Compute the z-score by dividing the distance to the standard deviation of the array.\n",
    "    + Return the z-score.\n",
    "\n",
    "+ Compute the z-score for min_val, mean_val, max_val, which are already defined in the code editor. Assume that the values come from a population.\n",
    "    + Assign the z-score for min_val to a variable named min_z.\n",
    "    + Assign the z-score for mean_val to a variable named mean_z.\n",
    "    + Assign the z-score for max_val to a variable named max_z.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_val = houses['SalePrice'].min()\n",
    "mean_val = houses['SalePrice'].mean()\n",
    "max_val = houses['SalePrice'].max()\n",
    "\n",
    "def z_score(value, array, bessel = 0):\n",
    "    mean = sum(array) / len(array)\n",
    "    \n",
    "    from numpy import std\n",
    "    st_dev = std(array, ddof = bessel)\n",
    "    \n",
    "    distance = value - mean\n",
    "    z = distance / st_dev\n",
    "    \n",
    "    return z\n",
    "\n",
    "min_z = z_score(min_val, houses['SalePrice'])\n",
    "mean_z = z_score(mean_val, houses['SalePrice'])\n",
    "max_z = z_score(max_val, houses['SalePrice'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Locating Values in Different Distributions\n",
    "\n",
    "+ Find out the location for which $200,000 has the z-score closest to 0. Code-wise, there are several ways to complete this task, and we encourage you to think of a way yourself. Below we describe one way to complete this task:\n",
    "\n",
    "+ Isolate the data for each of the five neighborhoods. The neigborhoods are described in the Neighborhood column. These are the abbreviations for our neighborhoods of interest:\n",
    "    + 'NAmes' for North Ames.\n",
    "    + 'CollgCr' for College Creek.\n",
    "    + 'OldTown' for Old Town.\n",
    "    + 'Edwards' for Edwards.\n",
    "    + 'Somerst' for Somerset.\n",
    "\n",
    "+ For example, to isolate the data for North Ames you can do `houses[houses['Neighborhood'] == 'NAmes']` and save the data to a variable.\n",
    "+ Find the z-score of a $200,000 price for each of the five data sets you isolated. Assume that each data set is a population.\n",
    "+ Examine the z-scores to find the best location to invest in. Assign your answer as a string to the variable best_investment. Choose between the following strings: 'North Ames', 'College Creek', 'Old Town', 'Edwards', and 'Somerset'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'NAmes': 1.7239665910370237, 'CollgCr': -0.03334366282705464, 'OldTown': 1.7183080926865524, 'Edwards': 1.443576193848941, 'Somerst': -0.5186390646965722}\n"
     ]
    }
   ],
   "source": [
    "north_ames = houses[houses['Neighborhood'] == 'NAmes']\n",
    "clg_creek = houses[houses['Neighborhood'] == 'CollgCr']\n",
    "old_town = houses[houses['Neighborhood'] == 'OldTown']\n",
    "edwards = houses[houses['Neighborhood'] == 'Edwards']\n",
    "somerset = houses[houses['Neighborhood'] == 'Somerst']\n",
    "\n",
    "# Find the z-score for 200000 for every location\n",
    "z_by_location = {}\n",
    "for data, neighborhood in [(north_ames, 'NAmes'), (clg_creek, 'CollgCr'),\n",
    "                     (old_town, 'OldTown'), (edwards, 'Edwards'),\n",
    "                     (somerset, 'Somerst')]:\n",
    "    \n",
    "    z_by_location[neighborhood] = z_score(200000, data['SalePrice'],\n",
    "                                          bessel = 0)\n",
    "\n",
    "# Find the location with the z-score closest to 0\n",
    "print(z_by_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_investment = 'College Creek'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming Distributions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Z-scores are often used to transform entire distributions by converting all the values to z-scores. Consider, for instance, the distribution of the SalePrice column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "houses['SalePrice'].plot.kde(xlim = (houses['SalePrice'].min(),\n",
    "                                    houses['SalePrice'].max()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can transform the entire distribution by converting each value to a z-score. In the next code block, we will:\n",
    "\n",
    "+ Convert each value to a z-score by using the `Series.apply()` method.\n",
    "+  Store the z-scores in a separate column named z_prices.\n",
    "+ Plot side by side the kernel density plot for the original values and the kernel density plot for the z-scores.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = houses['SalePrice'].mean()\n",
    "st_dev = houses['SalePrice'].std(ddof = 0)\n",
    "\n",
    "houses['z_prices'] = houses['SalePrice'].apply(\n",
    "    lambda x: ((x - mean) / st_dev))\n",
    "\n",
    "plt.figure(figsize = (11,3.5))\n",
    "plt.subplot(1,2,1)\n",
    "houses['z_prices'].plot.kde(xlim = (houses['z_prices'].min(),\n",
    "                                houses['z_prices'].max()\n",
    "                                ))\n",
    "plt.subplot(1,2,2)\n",
    "houses['SalePrice'].plot.kde(xlim = (houses['SalePrice'].min(),\n",
    "                                    houses['SalePrice'].max()\n",
    "                                     ))\n",
    "                                     \n",
    "plt.tight_layout() # otherwise the plots will overlay partially"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the shape of the initial distribution is preserved perfectly in the new distribution of z-scores. Now lets find the mean and standard deviation when values have been converted to z scores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task \n",
    "\n",
    "+ We've already transformed the distribution of the SalePrice variable to distribution of z-scores and saved the new values to a column named z_prices.\n",
    "    + Compute the mean of the z_prices column and assign the result to a variable named z_mean_price.\n",
    "    + Compute the standard deviation of the z_prices column and assign the result to a variable named z_stdev_price. Assume that you're computing the standard deviation for a population.\n",
    "\n",
    "+ Transform the distribution of the Lot Area variable into a distribution of z-scores.\n",
    "    + Compute the mean of the new distribution of z-scores and assign the result to a variable named z_mean_area.\n",
    "    + Compute the standard deviation of the new distribution of z-scores and assign the result to a variable named z_stdev_area. Assume that you're computing the standard deviation for a population.\n",
    "\n",
    "+ Compare the values of z_mean_price and z_mean_area. What do you observe? How can you explain that?\n",
    "\n",
    "+ Compare the values of z_stdev_price and z_stdev_area. What do you observe? How can you explain that?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "houses['z_prices'] = houses['SalePrice'].apply(\n",
    "    lambda x: ((x - mean) / st_dev)\n",
    "    )\n",
    "z_mean_price = houses['z_prices'].mean()\n",
    "z_stdev_price = houses['z_prices'].std(ddof = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transforming 'Lot Area'\n",
    "mean_area = houses['Lot Area'].mean()\n",
    "stdev_area = houses['Lot Area'].std(ddof = 0)\n",
    "houses['z_area'] = houses['Lot Area'].apply(\n",
    "    lambda x: ((x - mean_area) / stdev_area)\n",
    "    )\n",
    "\n",
    "z_mean_area = houses['z_area'].mean()\n",
    "z_stdev_area = houses['z_area'].std(ddof = 0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A distribution of z-scores is often called a standard distribution"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task\n",
    "\n",
    "+ Standardize the population of values stored in the population variable and compute its mean μz and its standard deviation σz.\n",
    "\n",
    "+ Assign the value of μz to a variable named mean_z.\n",
    "+ Assign the value of σz to a variable named stdev_z.\n",
    "+ This time we should get μz=0 and σz=1 because there'll be no rounding errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import std, mean\n",
    "population = [0,8,0,8]\n",
    "mean_pop = mean(population)\n",
    "stdev_pop = std(population, ddof = 0)\n",
    "\n",
    "standardized_pop = []\n",
    "for value in population:\n",
    "    z = (value - mean_pop) / stdev_pop\n",
    "    standardized_pop.append(z)\n",
    "    \n",
    "mean_z = mean(standardized_pop)\n",
    "stdev_z = std(standardized_pop, ddof = 0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Standardization for Comparisons"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardizing distributions can prove very useful when we need to compare values coming from different systems of measurement. Let's say there are two companies in Ames which offer services in evaluating overall house quality. Inspectors from each company examine houses and rate them with an index score which describes the overall quality of the house."
   ]
  }
 ],
 "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": "3.10.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "0e1f169f5711a90ff09b1ce155e8835a9bac5ac4c242468736f19be364069406"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}