{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(lecture04:scatter)=\n",
    "# Scatter plots\n",
    "\n",
    "Scatter plots are a nice way to investigate if there are correlations between measured scalar variables.\n",
    "To see what they are, let's load the standard libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(rc={\"figure.dpi\":100, 'savefig.dpi':300})\n",
    "sns.set_context('notebook')\n",
    "sns.set_style(\"ticks\")\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats('retina', 'svg')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's also download a dataset we introduced in {ref}`lecture03:pandas`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import os\n",
    "def download(url, local_filename=None):\n",
    "    \"\"\"\n",
    "    Downloads the file in the ``url`` and saves it in the current working directory.\n",
    "    \"\"\"\n",
    "    data = requests.get(url)\n",
    "    if local_filename is None:\n",
    "        local_filename = os.path.basename(url)\n",
    "    with open(local_filename, 'wb') as fd:\n",
    "        fd.write(data.content)\n",
    "   \n",
    "# The url of the file we want to download\n",
    "url = 'https://raw.githubusercontent.com/PurdueMechanicalEngineering/me-297-intro-to-data-science/master/data/temp_price.csv'\n",
    "download(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>household</th>\n",
       "      <th>date</th>\n",
       "      <th>score</th>\n",
       "      <th>t_out</th>\n",
       "      <th>t_unit</th>\n",
       "      <th>hvac</th>\n",
       "      <th>price</th>\n",
       "      <th>Price per week</th>\n",
       "      <th>Price per day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a1</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>85</td>\n",
       "      <td>38.599231</td>\n",
       "      <td>71.580704</td>\n",
       "      <td>35.113758</td>\n",
       "      <td>0.17303</td>\n",
       "      <td>6.075734</td>\n",
       "      <td>0.867962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a10</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>70</td>\n",
       "      <td>38.599231</td>\n",
       "      <td>73.286260</td>\n",
       "      <td>63.949057</td>\n",
       "      <td>0.17303</td>\n",
       "      <td>11.065105</td>\n",
       "      <td>1.580729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a11</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>61</td>\n",
       "      <td>38.599231</td>\n",
       "      <td>74.252046</td>\n",
       "      <td>147.612108</td>\n",
       "      <td>0.17303</td>\n",
       "      <td>25.541323</td>\n",
       "      <td>3.648760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a12</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>65</td>\n",
       "      <td>38.599231</td>\n",
       "      <td>73.708482</td>\n",
       "      <td>74.394518</td>\n",
       "      <td>0.17303</td>\n",
       "      <td>12.872483</td>\n",
       "      <td>1.838926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a13</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>66</td>\n",
       "      <td>38.599231</td>\n",
       "      <td>73.549554</td>\n",
       "      <td>173.095836</td>\n",
       "      <td>0.17303</td>\n",
       "      <td>29.950772</td>\n",
       "      <td>4.278682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  household        date  score      t_out     t_unit        hvac    price  \\\n",
       "0        a1  2019-01-06     85  38.599231  71.580704   35.113758  0.17303   \n",
       "1       a10  2019-01-06     70  38.599231  73.286260   63.949057  0.17303   \n",
       "2       a11  2019-01-06     61  38.599231  74.252046  147.612108  0.17303   \n",
       "3       a12  2019-01-06     65  38.599231  73.708482   74.394518  0.17303   \n",
       "4       a13  2019-01-06     66  38.599231  73.549554  173.095836  0.17303   \n",
       "\n",
       "   Price per week  Price per day  \n",
       "0        6.075734       0.867962  \n",
       "1       11.065105       1.580729  \n",
       "2       25.541323       3.648760  \n",
       "3       12.872483       1.838926  \n",
       "4       29.950772       4.278682  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "temp_price = pd.read_csv('temp_price.csv')\n",
    "temp_price.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to clean them up as we did before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>household</th>\n",
       "      <th>date</th>\n",
       "      <th>score</th>\n",
       "      <th>t_out</th>\n",
       "      <th>t_unit</th>\n",
       "      <th>hvac</th>\n",
       "      <th>price</th>\n",
       "      <th>week_price</th>\n",
       "      <th>daily_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a1</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>85</td>\n",
       "      <td>38.599231</td>\n",
       "      <td>71.580704</td>\n",
       "      <td>35.113758</td>\n",
       "      <td>0.17303</td>\n",
       "      <td>6.075734</td>\n",
       "      <td>0.867962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a10</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>70</td>\n",
       "      <td>38.599231</td>\n",
       "      <td>73.286260</td>\n",
       "      <td>63.949057</td>\n",
       "      <td>0.17303</td>\n",
       "      <td>11.065105</td>\n",
       "      <td>1.580729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a11</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>61</td>\n",
       "      <td>38.599231</td>\n",
       "      <td>74.252046</td>\n",
       "      <td>147.612108</td>\n",
       "      <td>0.17303</td>\n",
       "      <td>25.541323</td>\n",
       "      <td>3.648760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a12</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>65</td>\n",
       "      <td>38.599231</td>\n",
       "      <td>73.708482</td>\n",
       "      <td>74.394518</td>\n",
       "      <td>0.17303</td>\n",
       "      <td>12.872483</td>\n",
       "      <td>1.838926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a13</td>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>66</td>\n",
       "      <td>38.599231</td>\n",
       "      <td>73.549554</td>\n",
       "      <td>173.095836</td>\n",
       "      <td>0.17303</td>\n",
       "      <td>29.950772</td>\n",
       "      <td>4.278682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  household        date  score      t_out     t_unit        hvac    price  \\\n",
       "0        a1  2019-01-06     85  38.599231  71.580704   35.113758  0.17303   \n",
       "1       a10  2019-01-06     70  38.599231  73.286260   63.949057  0.17303   \n",
       "2       a11  2019-01-06     61  38.599231  74.252046  147.612108  0.17303   \n",
       "3       a12  2019-01-06     65  38.599231  73.708482   74.394518  0.17303   \n",
       "4       a13  2019-01-06     66  38.599231  73.549554  173.095836  0.17303   \n",
       "\n",
       "   week_price  daily_price  \n",
       "0    6.075734     0.867962  \n",
       "1   11.065105     1.580729  \n",
       "2   25.541323     3.648760  \n",
       "3   12.872483     1.838926  \n",
       "4   29.950772     4.278682  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data = temp_price.dropna(axis=0).rename(columns={'Price per week': 'week_price',\n",
    "                                                       'Price per day': 'daily_price'})\n",
    "clean_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the dataset includes only one day, 01/06/2019, and that the average temperature is 38.6 degrees F.\n",
    "We have 50 different records each one corresponding to a different appartment in the same residential building.\n",
    "\n",
    "Let us see first, how energy consumption `hvac` correlates with the weekly bill `week_price`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(clean_data['hvac'], clean_data['week_price'])\n",
    "ax.set_xlabel('HVAC energy consumed (kWh)')\n",
    "ax.set_ylabel('Price per week ($)');"
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    "This kind of plot is called a \"scatter plot.\"\n",
    "Okay, this makes sense. The more energy energy a unit consumes the higher the energy bill.\n",
    "The relationship between the two is linear reflecting the fact that each household is paying for the same price per kWh.\n",
    "\n",
    "The relationship depicted here is also **causal**.\n",
    "The energy is consumption is causing the bill be higher.\n",
    "However, this causality direction does not come from the data alone.\n",
    "It comes from us applying using our knowledge about how energy bills are calculated...\n",
    "\n",
    "What other causal relationships could we have?\n",
    "Let's look at the temperature of eafch household `t_unit` vs the energy consumed `hvac`.\n",
    "Here is the scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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    "fig, ax = plt.subplots()\n",
    "ax.scatter(clean_data['t_unit'], clean_data['hvac'])\n",
    "ax.set_xlabel('Unit Temperature (F)')\n",
    "ax.set_ylabel('HVAC energy consumed (kWh)');"
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    "We observe that higher unit temperature, in general, leads to higher HVAC energy consumption.\n",
    "However, the relation is not one-to-one.\n",
    "This is because the appartments in this building have different physical characteristics.\n",
    "For example, an appartment that is at the corner of the the building has more of each external surfaces to the environment and thus it needs more energy to maintain a given temperature than an appartment that is, say, in the middle of the building.\n",
    "As a matter of fact, notice that there are some appartments that consume zero energy even though the external temperature is 38 degrees F.\n",
    "These guys are getting their heating for free from their neighbors!\n",
    "\n",
    "So, the relationship between unit temperature and HVAC energy consumption is causal to some degree.\n",
    "But there are other variables that affect it as well.\n",
    "Here, these are: the physical characteristics of the appartment and the temperature of the neighboring units.\n",
    "More often than not, this is the situation we find ourselves when dealing with real datasets."
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    "## Question\n",
    "\n",
    "In the code block below, do the scatter plot between unit temperature `t_unit` and the variable `score`.\n",
    "The variable `score` is a measure we developed in our NSF project to characterize how well occupants behave related to energy consumption.\n",
    "It is a number between zero and 100.\n",
    "The bigger the score, the better the household is at conserving energy.\n",
    "Note that we calculate this score using the detailed behavior of the household during an entire week.\n",
    "We look at things like the temperature setpoint the occupants pick when they are home during the day, when they sleep, and when they are away.\n",
    "We do not just look at the unit temperature.\n",
    "Think:\n",
    "+ Is there a correlation between `t_unit` and `score`?\n",
    "+ Based on what I told you about the calculation of the `score`, is there a causal relationship beteween the variables?"
   ]
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