{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "if not any(path.endswith('textbook') for path in sys.path):\n",
    "    sys.path.append(os.path.abspath('../../..'))\n",
    "from textbook_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "(ch:pa_eda)=\n",
    "# Exploring PurpleAir and AQS Measurements "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Let's explore the cleaned dataset of matched AQS and PurpleAir PM2.5 readings and look for insights that might help us in modeling.\n",
    "Our main interest is in the relationship between the two sources of air quality measurements. But we want to keep in mind the scope of the data, like how these data are situated in time and place. We learned from our data cleaning that we are working with daily averages of PM2.5 for a couple of years and that we have data from dozens of locations across the US."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "First we review the entire cleaned dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "csv_file = 'data/cleaned_purpleair_aqs/Full24hrdataset.csv'\n",
    "usecols = ['Date', 'ID', 'region', 'PM25FM', 'PM25cf1', 'TempC', 'RH', 'Dewpoint']\n",
    "full_df = (pd.read_csv(csv_file, usecols=usecols, parse_dates=['Date'])\n",
    "        .dropna())\n",
    "full_df.columns = ['date', 'id', 'region', 'pm25aqs', 'pm25pa', 'temp', 'rh', 'dew']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>date</th>\n",
       "      <th>id</th>\n",
       "      <th>region</th>\n",
       "      <th>pm25aqs</th>\n",
       "      <th>pm25pa</th>\n",
       "      <th>temp</th>\n",
       "      <th>rh</th>\n",
       "      <th>dew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-17</td>\n",
       "      <td>AK1</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.62</td>\n",
       "      <td>18.03</td>\n",
       "      <td>38.56</td>\n",
       "      <td>3.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>AK1</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.49</td>\n",
       "      <td>16.12</td>\n",
       "      <td>49.40</td>\n",
       "      <td>5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>AK1</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.80</td>\n",
       "      <td>19.90</td>\n",
       "      <td>29.97</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12427</th>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>WI6</td>\n",
       "      <td>North</td>\n",
       "      <td>15.6</td>\n",
       "      <td>25.30</td>\n",
       "      <td>1.71</td>\n",
       "      <td>65.78</td>\n",
       "      <td>-4.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12428</th>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>WI6</td>\n",
       "      <td>North</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>-14.38</td>\n",
       "      <td>48.21</td>\n",
       "      <td>-23.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12429</th>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>WI6</td>\n",
       "      <td>North</td>\n",
       "      <td>5.8</td>\n",
       "      <td>9.44</td>\n",
       "      <td>5.08</td>\n",
       "      <td>52.20</td>\n",
       "      <td>-4.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12246 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date   id  region  pm25aqs  pm25pa   temp     rh    dew\n",
       "0     2019-05-17  AK1  Alaska      6.7    8.62  18.03  38.56   3.63\n",
       "1     2019-05-18  AK1  Alaska      3.8    3.49  16.12  49.40   5.44\n",
       "2     2019-05-21  AK1  Alaska      4.0    3.80  19.90  29.97   1.73\n",
       "...          ...  ...     ...      ...     ...    ...    ...    ...\n",
       "12427 2019-02-20  WI6   North     15.6   25.30   1.71  65.78  -4.08\n",
       "12428 2019-03-04  WI6   North     14.0    8.21 -14.38  48.21 -23.02\n",
       "12429 2019-03-22  WI6   North      5.8    9.44   5.08  52.20  -4.02\n",
       "\n",
       "[12246 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We include an explanation for each of the columns in our dataframe in the following table:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "| Column  | Description                                                                                                                      |\n",
    "|---------|----------------------------------------------------------------------------------------------------------------------------------|\n",
    "| date    | Date of the observation                                                                                                          |\n",
    "| id      | A unique label for a site, formatted as the US state abbreviation with a number (we performed data cleaning for site ID `CA1`) |\n",
    "| region  | The name of the region, which corresponds to a group of sites (the `CA1` site is located in the `West` region)                 |\n",
    "| pm25aqs | The PM2.5 measurement from the AQS sensor                                                                                       |\n",
    "| pm25pa  | The PM2.5 measurement from the PurpleAir sensor                                                                                 |\n",
    "| temp    | Temperature, in Celcius                                                                                                         |\n",
    "| rh      | Relative humidity, ranging from 0% to 100%                                                                                       |\n",
    "| dew     | The dew point (a higher dew point means more moisture is in the air)                                                     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Let's start with making a few simple visualizations to gain insight. Since the scope involves measurements over time at particular locations, we can choose one location with many measurements and make a line plot of the weekly average air quality. To choose, let's find the sites with many records:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "IA3    830\n",
       "NC4    699\n",
       "CA2    659\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df['id'].value_counts()[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "The location labeled `NC4`, has nearly 700 observations. To smooth the line plot a bit, let's plot weekly averages:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "nc4 = full_df.loc[full_df['id'] =='NC4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_nc4 = (nc4.set_index('date')\n",
    " .resample('W')\n",
    " ['pm25aqs', 'pm25pa']\n",
    " .mean()\n",
    " .reset_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(ts_nc4, x='date', y='pm25aqs',\n",
    "              labels={'date':'', 'pm25aqs':'PM2.5 weekly avg'},\n",
    "              width=500, height=250)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=ts_nc4['date'], y=ts_nc4['pm25pa'],\n",
    "                         line=dict(color='black', dash='dot')))\n",
    "\n",
    "fig.update_yaxes(range=[0,30])\n",
    "fig.update_layout(showlegend=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We see that most PM2.5 values for the AQS sensor (solid line) range between 5.0 and 15.0 µg m⁻³.\n",
    "The PurpleAir sensor follows the up-and-down pattern of the AQS sensor, which is reassuring. \n",
    "But the measurements are consistently higher than AQS and, in some cases, quite a bit higher, which tells us that a correction might be helpful. \n",
    "\n",
    "Next, let's consider the distributions of the PM2.5 readings for the two sensors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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          "title": {
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           "xanchor": "center"
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          "width": 350,
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           "zerolinecolor": "rgb(36,36,36)"
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         "domain": [
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         "title": {
          "text": "AQS readings"
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         "domain": [
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         "title": {
          "text": "PurpleAir readings"
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         "type": "linear"
        },
        "yaxis": {
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         "autorange": true,
         "domain": [
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         "title": {
          "text": "percent"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "left= px.histogram(nc4, x='pm25aqs', histnorm='percent')\n",
    "right = px.histogram(nc4, x='pm25pa', histnorm='percent')\n",
    "\n",
    "fig = left_right(left, right, width=600, height=250)\n",
    "fig.update_xaxes(title='AQS readings', col=1, row=1)\n",
    "fig.update_xaxes(title='PurpleAir readings', col=2, row=1)\n",
    "fig.update_yaxes(title='percent', col=1, row=1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Both distributions are skewed right, which often happens when there's a lower bound on values (in this case, 0).\n",
    "A better way to compare these two distributions is with a quantile-quantile plot (see {numref}`Chapter %s <ch:eda>`).\n",
    "With a q–q plot it can be easier to compare means, spreads, and tails:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "percs = np.arange(1, 100, 1)\n",
    "aqs_qs = np.percentile(nc4['pm25aqs'], percs, interpolation='lower')\n",
    "pa_qs = np.percentile(nc4['pm25pa'], percs, interpolation='lower')\n",
    "perc_df = pd.DataFrame({'percentile': percs, 'aqs_qs':aqs_qs, 'pa_qs':pa_qs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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          "ternary": {
           "aaxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           },
           "baxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           },
           "bgcolor": "white",
           "caxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           }
          },
          "title": {
           "x": 0.5,
           "xanchor": "center"
          },
          "width": 350,
          "xaxis": {
           "automargin": true,
           "gridcolor": "rgb(232,232,232)",
           "linecolor": "rgb(36,36,36)",
           "showgrid": true,
           "showline": true,
           "ticks": "outside",
           "title": {
            "standoff": 15
           },
           "zeroline": false,
           "zerolinecolor": "rgb(36,36,36)"
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "rgb(232,232,232)",
           "linecolor": "rgb(36,36,36)",
           "showgrid": true,
           "showline": true,
           "ticks": "outside",
           "title": {
            "standoff": 15
           },
           "zeroline": false,
           "zerolinecolor": "rgb(36,36,36)"
          }
         }
        },
        "width": 350,
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          0.9833620117313919,
          18.637470988268607
         ],
         "title": {
          "text": "AQS quantiles"
         },
         "type": "linear"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -0.7915462365118486,
          35.03937849372512
         ],
         "title": {
          "text": "PurpleAir quantiles"
         },
         "type": "linear"
        }
       }
      },
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(perc_df, x='aqs_qs', y='pa_qs',\n",
    "                 labels={'aqs_qs': 'AQS quantiles',\n",
    "                         'pa_qs': 'PurpleAir quantiles'},\n",
    "                 width=350, height=250)\n",
    "\n",
    "\n",
    "fig.add_trace(go.Scatter(x=[2, 13], y=[1, 25], \n",
    "               mode='lines', line=dict(dash='dash', width=4)))\n",
    "fig.update_layout(showlegend=False)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "The quantile–quantile plot is roughly linear. We overlaid a dashed line with a slope of 2.2; it lines up the quantiles well, which indicates the spread of the PurpleAir measurements is about twice that of AQS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "What we can't see in the q–q plot or the side-by-side histograms is how the sensor readings vary together. Let's look at this next. First, we take a look at the distribution of difference between the two readings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "diffs = (nc4['pm25pa'] - nc4['pm25aqs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
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   "source": [
    "fig = px.histogram(diffs, histnorm='percent', \n",
    "                   width=350, height=250)\n",
    "\n",
    "fig.update_xaxes(range=[-10,30], title=\"Difference: PA&#8211;AQS reading\")\n",
    "fig.update_traces(xbins=dict(\n",
    "        start=-10.0, end=30.0, size=2)\n",
    "                 )\n",
    "fig.update_layout(showlegend=False)\n",
    "fig.show()"
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   "metadata": {
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   "source": [
    "If the instruments are in perfect agreement, we will see a spike at 0. If the instruments are in agreement and there is a measurement error with no bias, we expect to see a distribution centered at 0. Instead, we see that 90% of the time, the PurpleAir measurement is larger than the AQS 24-hour average, and about 25% of the time it is more than 10 µg/m<sup>3</sup> higher, which is a lot given the AQS averages tend to be between 5 µg/m<sup>3</sup> and 10 µg/m<sup>3</sup>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
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   "source": [
    "A scatter plot can give us additional insight into the relationship between the measurements from these two instruments. Since we are interested in finding a general relationship, regardless of time and location, we include all of our average readings in the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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          8.5,
          7.4,
          7.8,
          11.7,
          6.9,
          5.9,
          5.6,
          14.5,
          12.9,
          6.5,
          5.7,
          2.1,
          4.5,
          11.2,
          6.3,
          6.1,
          4.5,
          9.1,
          7.4,
          8.5,
          2.3,
          5.6,
          2.9,
          7.3,
          7.4,
          4.5,
          2.9,
          6.9,
          5.3,
          4.3,
          3.9,
          14.4,
          4.8,
          11.3,
          7.3,
          2.5,
          19.1,
          1.8,
          6.4,
          2.8,
          6.1,
          4.4,
          2.7,
          11.4,
          15.3,
          2.5,
          2,
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          8.3,
          4.5,
          7.3,
          3.4,
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          2.1,
          1.8,
          6.8,
          3.7,
          4.1,
          6.7,
          5.3,
          6.9,
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          5,
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          7.8,
          5.6,
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          5.3,
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          5,
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          5.4,
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          7.4,
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          10.9,
          10.2,
          3,
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          6.8,
          8.5,
          6.7,
          11.6,
          9.1,
          11.5,
          8,
          6.8,
          8.8,
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          7.7,
          5,
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          10.4,
          9.8,
          11.2,
          11.1,
          9.2,
          10,
          5.4,
          4.9,
          10.8,
          11.7,
          7.3,
          18.9,
          14.6,
          10.4,
          7.1,
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          6.6,
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          9.7,
          7.4,
          15.8,
          5.1,
          9,
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          6.8,
          8.3,
          6.9,
          12.5,
          7.9,
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          8.5,
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\" 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\" preserveAspectRatio=\"none\" x=\"0\" y=\"0\" width=\"350px\" height=\"250px\"/></g><defs id=\"topdefs-4aede5\"><g class=\"clips\"/></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"infolayer\"><g class=\"g-gtitle\"/><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"203\" y=\"239.20625\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">AQS PM2.5</text></g><g class=\"g-ytitle\" transform=\"translate(3.5966796875,0)\"><text class=\"ytitle\" transform=\"rotate(-90,10.403125000000003,101)\" x=\"10.403125000000003\" y=\"101\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">PurpleAir PM2.5</text></g></g></svg>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.scatter(full_df, x='pm25aqs', y='pm25pa', width=350, height=250,\n",
    "          labels={'pm25aqs':'AQS PM2.5', 'pm25pa':'PurpleAir PM2.5'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "While the relationship looks linear, all but a handful of readings are in the bottom-left corner of the plot. \n",
    "Let's remake the scatter plot and zoom in on the bulk of the data to get a better look. We also add a smooth curve to the plot to help us see the relationship better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df = full_df.loc[(full_df['pm25aqs'] < 50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "AQS PM2.5=%{x}<br>PurpleAir PM2.5=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#1F77B4",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "showlegend": false,
         "type": "scattergl",
         "x": [
          6.7,
          3.8,
          4,
          4.7,
          3.2,
          4.2,
          4,
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          8.7,
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          17.3,
          8.8,
          11,
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          8.7,
          16.9,
          14.3,
          12.3,
          12.7,
          5.5,
          9.3,
          8.7,
          15.4,
          5.7,
          39.5,
          8.3,
          4.8,
          4.5,
          5.7,
          2.2,
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          2.3,
          0.8,
          1.6,
          1.6,
          2.7,
          4.2,
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          6.3,
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          3,
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          5,
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          3,
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          7.2,
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          7,
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          10,
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           "zerolinecolor": "rgb(36,36,36)"
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "rgb(232,232,232)",
           "linecolor": "rgb(36,36,36)",
           "showgrid": true,
           "showline": true,
           "ticks": "outside",
           "title": {
            "standoff": 15
           },
           "zeroline": false,
           "zerolinecolor": "rgb(36,36,36)"
          }
         }
        },
        "width": 350,
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -5.300774465210356,
          50.397996465210355
         ],
         "title": {
          "text": "AQS PM2.5"
         },
         "type": "linear"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -7.166678257338976,
          96.74410087638661
         ],
         "title": {
          "text": "PurpleAir PM2.5"
         },
         "type": "linear"
        }
       }
      },
      "image/png": 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preserveAspectRatio=\"none\" x=\"0\" y=\"0\" width=\"350px\" height=\"250px\"/></g><defs id=\"topdefs-8b334f\"><g class=\"clips\"/></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"infolayer\"><g class=\"g-gtitle\"/><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"199\" y=\"239.20625\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">AQS PM2.5</text></g><g class=\"g-ytitle\" transform=\"translate(3.9716796875,0)\"><text class=\"ytitle\" transform=\"rotate(-90,10.028125000000003,101)\" x=\"10.028125000000003\" y=\"101\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">PurpleAir PM2.5</text></g></g></svg>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.scatter(full_df, x='pm25aqs', y='pm25pa', \n",
    "           trendline='lowess', trendline_color_override=\"orange\",\n",
    "           labels={'pm25aqs':'AQS PM2.5', 'pm25pa':'PurpleAir PM2.5'},\n",
    "           width=350, height=250)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "The relationship looks roughly linear, but there is a slight bend in the curve for small values of AQS. When the air is very clean, the PurpleAir sensor doesn't pick up as much particulate matter and so is more accurate. Also, we can see that the curve should go through the point (0, 0). Despite the slight bend in the relationship,\n",
    "the linear association (correlation) between these two measurements is high:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.  , 0.88],\n",
       "       [0.88, 1.  ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(full_df['pm25aqs'], full_df['pm25pa'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "Before starting this analysis, we expected that PurpleAir measurements\n",
    "would generally overestimate the PM2.5. \n",
    "And indeed, this is reflected in the scatter plot, but we also see that there appears to be a strong linear relationship\n",
    "between the measurements from these two instruments that will be helpful in calibrating the PurpleAir sensor. "
   ]
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "pygments_lexer": "ipython3",
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 },
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