{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🦾 Pivot from long to wide format\n",
    "\n",
    "Data is returned as a long format, dataframe. This means there is one unique observation (air temperature, humidity, wind speed, etc.) per row. It's often useful to pivot variables into columns. This can be done with Polars. I encourage users of SynopticPy to become familiar with Polars for DataFrame manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import polars as pl\n",
    "import seaborn as sns\n",
    "\n",
    "import synoptic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚚💨 Speedy delivery from Synoptic latest service.\n",
      "📦 Received data from 2 stations.\n"
     ]
    },
    {
     "data": {
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       "<small>shape: (4, 20)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date_time</th><th>variable</th><th>sensor_index</th><th>is_derived</th><th>value</th><th>units</th><th>id</th><th>stid</th><th>name</th><th>elevation</th><th>latitude</th><th>longitude</th><th>mnet_id</th><th>state</th><th>timezone</th><th>elev_dem</th><th>period_of_record_start</th><th>period_of_record_end</th><th>is_restricted</th><th>is_active</th></tr><tr><td>datetime[μs, UTC]</td><td>str</td><td>u32</td><td>bool</td><td>f64</td><td>str</td><td>u32</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>u32</td><td>str</td><td>str</td><td>f64</td><td>datetime[μs, UTC]</td><td>datetime[μs, UTC]</td><td>bool</td><td>bool</td></tr></thead><tbody><tr><td>2024-10-18 05:55:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>4.983</td><td>&quot;Celsius&quot;</td><td>1</td><td>&quot;WBB&quot;</td><td>&quot;U of U William Browning Buildi…</td><td>4806.0</td><td>40.76623</td><td>-111.84755</td><td>153</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>4727.7</td><td>1997-01-01 00:00:00 UTC</td><td>2024-10-18 05:10:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-10-18 05:55:00 UTC</td><td>&quot;dew_point_temperature&quot;</td><td>1</td><td>true</td><td>3.28</td><td>&quot;Celsius&quot;</td><td>1</td><td>&quot;WBB&quot;</td><td>&quot;U of U William Browning Buildi…</td><td>4806.0</td><td>40.76623</td><td>-111.84755</td><td>153</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>4727.7</td><td>1997-01-01 00:00:00 UTC</td><td>2024-10-18 05:10:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-10-18 05:45:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>7.222</td><td>&quot;Celsius&quot;</td><td>37032</td><td>&quot;UKBKB&quot;</td><td>&quot;EW2355 Spanish Fork&quot;</td><td>4734.0</td><td>40.09867</td><td>-111.62767</td><td>65</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>4740.8</td><td>2013-03-13 00:00:00 UTC</td><td>2024-10-18 05:00:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-10-18 05:45:00 UTC</td><td>&quot;dew_point_temperature&quot;</td><td>1</td><td>true</td><td>5.18</td><td>&quot;Celsius&quot;</td><td>37032</td><td>&quot;UKBKB&quot;</td><td>&quot;EW2355 Spanish Fork&quot;</td><td>4734.0</td><td>40.09867</td><td>-111.62767</td><td>65</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>4740.8</td><td>2013-03-13 00:00:00 UTC</td><td>2024-10-18 05:00:00 UTC</td><td>false</td><td>true</td></tr></tbody></table></div>"
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       "shape: (4, 20)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
       "│ date_time ┆ variable  ┆ sensor_in ┆ is_derive ┆ … ┆ period_of ┆ period_of ┆ is_restri ┆ is_activ │\n",
       "│ ---       ┆ ---       ┆ dex       ┆ d         ┆   ┆ _record_s ┆ _record_e ┆ cted      ┆ e        │\n",
       "│ datetime[ ┆ str       ┆ ---       ┆ ---       ┆   ┆ tart      ┆ nd        ┆ ---       ┆ ---      │\n",
       "│ μs, UTC]  ┆           ┆ u32       ┆ bool      ┆   ┆ ---       ┆ ---       ┆ bool      ┆ bool     │\n",
       "│           ┆           ┆           ┆           ┆   ┆ datetime[ ┆ datetime[ ┆           ┆          │\n",
       "│           ┆           ┆           ┆           ┆   ┆ μs, UTC]  ┆ μs, UTC]  ┆           ┆          │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
       "│ 2024-10-1 ┆ air_temp  ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆           ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 05:55:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:10:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ dew_point ┆ 1         ┆ true      ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆ _temperat ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 05:55:00  ┆ ure       ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:10:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ air_temp  ┆ 1         ┆ false     ┆ … ┆ 2013-03-1 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆           ┆           ┆           ┆   ┆ 3         ┆ 8         ┆           ┆          │\n",
       "│ 05:45:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:00:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ dew_point ┆ 1         ┆ true      ┆ … ┆ 2013-03-1 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆ _temperat ┆           ┆           ┆   ┆ 3         ┆ 8         ┆           ┆          │\n",
       "│ 05:45:00  ┆ ure       ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:00:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = synoptic.Latest(stid=\"ukbkb,wbb\", vars=\"air_temp,dew_point_temperature\")\n",
    "df = s.df()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SynopticPy provides a basic pivot method that likely gets the job done in most cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div><style>\n",
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       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (2, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date_time</th><th>stid</th><th>latitude</th><th>longitude</th><th>elevation</th><th>air_temp</th><th>dew_point_temperature</th></tr><tr><td>datetime[μs, UTC]</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2024-10-18 05:45:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>4.989</td><td>3.14</td></tr><tr><td>2024-10-18 05:45:00 UTC</td><td>&quot;UKBKB&quot;</td><td>40.09867</td><td>-111.62767</td><td>4734.0</td><td>7.222</td><td>5.18</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (2, 7)\n",
       "┌─────────────────────┬───────┬──────────┬────────────┬───────────┬──────────┬─────────────────────┐\n",
       "│ date_time           ┆ stid  ┆ latitude ┆ longitude  ┆ elevation ┆ air_temp ┆ dew_point_temperatu │\n",
       "│ ---                 ┆ ---   ┆ ---      ┆ ---        ┆ ---       ┆ ---      ┆ re                  │\n",
       "│ datetime[μs, UTC]   ┆ str   ┆ f64      ┆ f64        ┆ f64       ┆ f64      ┆ ---                 │\n",
       "│                     ┆       ┆          ┆            ┆           ┆          ┆ f64                 │\n",
       "╞═════════════════════╪═══════╪══════════╪════════════╪═══════════╪══════════╪═════════════════════╡\n",
       "│ 2024-10-18 05:45:00 ┆ WBB   ┆ 40.76623 ┆ -111.84755 ┆ 4806.0    ┆ 4.989    ┆ 3.14                │\n",
       "│ UTC                 ┆       ┆          ┆            ┆           ┆          ┆                     │\n",
       "│ 2024-10-18 05:45:00 ┆ UKBKB ┆ 40.09867 ┆ -111.62767 ┆ 4734.0    ┆ 7.222    ┆ 5.18                │\n",
       "│ UTC                 ┆       ┆          ┆            ┆           ┆          ┆                     │\n",
       "└─────────────────────┴───────┴──────────┴────────────┴───────────┴──────────┴─────────────────────┘"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.synoptic.pivot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a pivotted DataFrame, you can use the `with_wind_uv` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚚💨 Speedy delivery from Synoptic timeseries service.\n",
      "📦 Received data from 1 stations.\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<small>shape: (716, 20)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date_time</th><th>variable</th><th>sensor_index</th><th>is_derived</th><th>value</th><th>units</th><th>id</th><th>stid</th><th>name</th><th>elevation</th><th>latitude</th><th>longitude</th><th>mnet_id</th><th>state</th><th>timezone</th><th>elev_dem</th><th>period_of_record_start</th><th>period_of_record_end</th><th>is_restricted</th><th>is_active</th></tr><tr><td>datetime[μs, UTC]</td><td>str</td><td>u32</td><td>bool</td><td>f64</td><td>str</td><td>u32</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>u32</td><td>str</td><td>str</td><td>f64</td><td>datetime[μs, UTC]</td><td>datetime[μs, UTC]</td><td>bool</td><td>bool</td></tr></thead><tbody><tr><td>2024-10-17 17:56:00 UTC</td><td>&quot;wind_speed&quot;</td><td>1</td><td>false</td><td>1.451</td><td>&quot;m/s&quot;</td><td>1</td><td>&quot;WBB&quot;</td><td>&quot;U of U William Browning 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UTC</td><td>false</td><td>true</td></tr><tr><td>2024-10-18 05:48:00 UTC</td><td>&quot;wind_speed&quot;</td><td>1</td><td>false</td><td>2.274</td><td>&quot;m/s&quot;</td><td>1</td><td>&quot;WBB&quot;</td><td>&quot;U of U William Browning Buildi…</td><td>4806.0</td><td>40.76623</td><td>-111.84755</td><td>153</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>4727.7</td><td>1997-01-01 00:00:00 UTC</td><td>2024-10-18 05:40:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-10-18 05:49:00 UTC</td><td>&quot;wind_speed&quot;</td><td>1</td><td>false</td><td>1.857</td><td>&quot;m/s&quot;</td><td>1</td><td>&quot;WBB&quot;</td><td>&quot;U of U William Browning Buildi…</td><td>4806.0</td><td>40.76623</td><td>-111.84755</td><td>153</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>4727.7</td><td>1997-01-01 00:00:00 UTC</td><td>2024-10-18 05:40:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-10-18 05:50:00 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      ],
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       "shape: (716, 20)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
       "│ date_time ┆ variable  ┆ sensor_in ┆ is_derive ┆ … ┆ period_of ┆ period_of ┆ is_restri ┆ is_activ │\n",
       "│ ---       ┆ ---       ┆ dex       ┆ d         ┆   ┆ _record_s ┆ _record_e ┆ cted      ┆ e        │\n",
       "│ datetime[ ┆ str       ┆ ---       ┆ ---       ┆   ┆ tart      ┆ nd        ┆ ---       ┆ ---      │\n",
       "│ μs, UTC]  ┆           ┆ u32       ┆ bool      ┆   ┆ ---       ┆ ---       ┆ bool      ┆ bool     │\n",
       "│           ┆           ┆           ┆           ┆   ┆ datetime[ ┆ datetime[ ┆           ┆          │\n",
       "│           ┆           ┆           ┆           ┆   ┆ μs, UTC]  ┆ μs, UTC]  ┆           ┆          │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 7         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 17:56:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 7         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 17:57:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 7         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 17:58:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 7         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 17:59:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 7         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 18:00:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ …         ┆ …         ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …        │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 05:47:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 05:48:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 05:49:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 05:50:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 1997-01-0 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 8         ┆           ┆          │\n",
       "│ 05:55:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synoptic.TimeSeries(stid=\"WBB\", vars=\"wind_speed,wind_speed\", recent=\"12h\").df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚚💨 Speedy delivery from Synoptic timeseries service.\n",
      "📦 Received data from 1 stations.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
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       "}\n",
       "</style>\n",
       "<small>shape: (713, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date_time</th><th>stid</th><th>latitude</th><th>longitude</th><th>elevation</th><th>wind_speed</th><th>wind_direction</th><th>wind_u</th><th>wind_v</th></tr><tr><td>datetime[μs, UTC]</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2024-10-17 17:54:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>1.255</td><td>183.9</td><td>0.085359</td><td>1.252094</td></tr><tr><td>2024-10-17 17:55:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>0.324</td><td>183.3</td><td>0.018651</td><td>0.323463</td></tr><tr><td>2024-10-17 17:56:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>1.451</td><td>206.4</td><td>0.645166</td><td>1.299678</td></tr><tr><td>2024-10-17 17:57:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>1.029</td><td>202.4</td><td>0.392121</td><td>0.951358</td></tr><tr><td>2024-10-17 17:58:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>1.554</td><td>190.8</td><td>0.291191</td><td>1.526474</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2024-10-18 05:42:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>2.042</td><td>271.1</td><td>2.041624</td><td>-0.039201</td></tr><tr><td>2024-10-18 05:43:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>1.734</td><td>285.7</td><td>1.669307</td><td>-0.469221</td></tr><tr><td>2024-10-18 05:44:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>2.809</td><td>281.9</td><td>2.748632</td><td>-0.579228</td></tr><tr><td>2024-10-18 05:45:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>2.999</td><td>285.6</td><td>2.888525</td><td>-0.806491</td></tr><tr><td>2024-10-18 05:50:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>null</td><td>null</td><td>null</td><td>null</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (713, 9)\n",
       "┌─────────────┬──────┬──────────┬────────────┬───┬────────────┬─────────────┬──────────┬───────────┐\n",
       "│ date_time   ┆ stid ┆ latitude ┆ longitude  ┆ … ┆ wind_speed ┆ wind_direct ┆ wind_u   ┆ wind_v    │\n",
       "│ ---         ┆ ---  ┆ ---      ┆ ---        ┆   ┆ ---        ┆ ion         ┆ ---      ┆ ---       │\n",
       "│ datetime[μs ┆ str  ┆ f64      ┆ f64        ┆   ┆ f64        ┆ ---         ┆ f64      ┆ f64       │\n",
       "│ , UTC]      ┆      ┆          ┆            ┆   ┆            ┆ f64         ┆          ┆           │\n",
       "╞═════════════╪══════╪══════════╪════════════╪═══╪════════════╪═════════════╪══════════╪═══════════╡\n",
       "│ 2024-10-17  ┆ WBB  ┆ 40.76623 ┆ -111.84755 ┆ … ┆ 1.255      ┆ 183.9       ┆ 0.085359 ┆ 1.252094  │\n",
       "│ 17:54:00    ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ UTC         ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ 2024-10-17  ┆ WBB  ┆ 40.76623 ┆ -111.84755 ┆ … ┆ 0.324      ┆ 183.3       ┆ 0.018651 ┆ 0.323463  │\n",
       "│ 17:55:00    ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ UTC         ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ 2024-10-17  ┆ WBB  ┆ 40.76623 ┆ -111.84755 ┆ … ┆ 1.451      ┆ 206.4       ┆ 0.645166 ┆ 1.299678  │\n",
       "│ 17:56:00    ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ UTC         ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ 2024-10-17  ┆ WBB  ┆ 40.76623 ┆ -111.84755 ┆ … ┆ 1.029      ┆ 202.4       ┆ 0.392121 ┆ 0.951358  │\n",
       "│ 17:57:00    ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ UTC         ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ 2024-10-17  ┆ WBB  ┆ 40.76623 ┆ -111.84755 ┆ … ┆ 1.554      ┆ 190.8       ┆ 0.291191 ┆ 1.526474  │\n",
       "│ 17:58:00    ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ UTC         ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ …           ┆ …    ┆ …        ┆ …          ┆ … ┆ …          ┆ …           ┆ …        ┆ …         │\n",
       "│ 2024-10-18  ┆ WBB  ┆ 40.76623 ┆ -111.84755 ┆ … ┆ 2.042      ┆ 271.1       ┆ 2.041624 ┆ -0.039201 │\n",
       "│ 05:42:00    ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ UTC         ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ 2024-10-18  ┆ WBB  ┆ 40.76623 ┆ -111.84755 ┆ … ┆ 1.734      ┆ 285.7       ┆ 1.669307 ┆ -0.469221 │\n",
       "│ 05:43:00    ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ UTC         ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ 2024-10-18  ┆ WBB  ┆ 40.76623 ┆ -111.84755 ┆ … ┆ 2.809      ┆ 281.9       ┆ 2.748632 ┆ -0.579228 │\n",
       "│ 05:44:00    ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ UTC         ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ 2024-10-18  ┆ WBB  ┆ 40.76623 ┆ -111.84755 ┆ … ┆ 2.999      ┆ 285.6       ┆ 2.888525 ┆ -0.806491 │\n",
       "│ 05:45:00    ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ UTC         ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ 2024-10-18  ┆ WBB  ┆ 40.76623 ┆ -111.84755 ┆ … ┆ null       ┆ null        ┆ null     ┆ null      │\n",
       "│ 05:50:00    ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "│ UTC         ┆      ┆          ┆            ┆   ┆            ┆             ┆          ┆           │\n",
       "└─────────────┴──────┴──────────┴────────────┴───┴────────────┴─────────────┴──────────┴───────────┘"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = (\n",
    "    synoptic.TimeSeries(stid=\"WBB\", vars=\"wind_speed,wind_direction\", recent=\"12h\")\n",
    "    .df()\n",
    "    .synoptic.pivot()\n",
    "    .synoptic.with_wind_uv()\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customized pivots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (2, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date_time</th><th>stid</th><th>latitude</th><th>longitude</th><th>elevation</th><th>air_temp</th><th>dew_point_temperature</th></tr><tr><td>datetime[μs, UTC]</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2024-10-18 05:40:00 UTC</td><td>&quot;WBB&quot;</td><td>40.76623</td><td>-111.84755</td><td>4806.0</td><td>5.089</td><td>3.19</td></tr><tr><td>2024-10-18 05:30:00 UTC</td><td>&quot;UKBKB&quot;</td><td>40.09867</td><td>-111.62767</td><td>4734.0</td><td>7.222</td><td>5.18</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (2, 7)\n",
       "┌─────────────────────┬───────┬──────────┬────────────┬───────────┬──────────┬─────────────────────┐\n",
       "│ date_time           ┆ stid  ┆ latitude ┆ longitude  ┆ elevation ┆ air_temp ┆ dew_point_temperatu │\n",
       "│ ---                 ┆ ---   ┆ ---      ┆ ---        ┆ ---       ┆ ---      ┆ re                  │\n",
       "│ datetime[μs, UTC]   ┆ str   ┆ f64      ┆ f64        ┆ f64       ┆ f64      ┆ ---                 │\n",
       "│                     ┆       ┆          ┆            ┆           ┆          ┆ f64                 │\n",
       "╞═════════════════════╪═══════╪══════════╪════════════╪═══════════╪══════════╪═════════════════════╡\n",
       "│ 2024-10-18 05:40:00 ┆ WBB   ┆ 40.76623 ┆ -111.84755 ┆ 4806.0    ┆ 5.089    ┆ 3.19                │\n",
       "│ UTC                 ┆       ┆          ┆            ┆           ┆          ┆                     │\n",
       "│ 2024-10-18 05:30:00 ┆ UKBKB ┆ 40.09867 ┆ -111.62767 ┆ 4734.0    ┆ 7.222    ┆ 5.18                │\n",
       "│ UTC                 ┆       ┆          ┆            ┆           ┆          ┆                     │\n",
       "└─────────────────────┴───────┴──────────┴────────────┴───────────┴──────────┴─────────────────────┘"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(\n",
    "    on=\"variable\",\n",
    "    index=[\"date_time\", \"stid\", \"latitude\", \"longitude\", \"elevation\"],\n",
    "    values=\"value\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Special Case: Multiple Sensors\n",
    "\n",
    "If your set of stations has multiple sensors the measure the same variable (e.g., NAA measures outdoor air temperature and greenhouse air temperature) you will need to take care with how you perform your pivot. Either filter the rows for a single sensor ID, or pivot on both the variable _and_ sensor columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚚💨 Speedy delivery from Synoptic latest service.\n",
      "📦 Received data from 1 stations.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3, 20)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date_time</th><th>variable</th><th>sensor_index</th><th>is_derived</th><th>value</th><th>units</th><th>id</th><th>stid</th><th>name</th><th>elevation</th><th>latitude</th><th>longitude</th><th>mnet_id</th><th>state</th><th>timezone</th><th>elev_dem</th><th>period_of_record_start</th><th>period_of_record_end</th><th>is_restricted</th><th>is_active</th></tr><tr><td>datetime[μs, UTC]</td><td>str</td><td>u32</td><td>bool</td><td>f64</td><td>str</td><td>u32</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>u32</td><td>str</td><td>str</td><td>f64</td><td>datetime[μs, UTC]</td><td>datetime[μs, UTC]</td><td>bool</td><td>bool</td></tr></thead><tbody><tr><td>2024-10-18 05:25:00 UTC</td><td>&quot;air_temp&quot;</td><td>1</td><td>false</td><td>5.306</td><td>&quot;Celsius&quot;</td><td>43136</td><td>&quot;NAA&quot;</td><td>&quot;Neil Armstrong Academy&quot;</td><td>4260.0</td><td>40.71152</td><td>-112.01448</td><td>153</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>4252.0</td><td>2014-05-27 00:00:00 UTC</td><td>2024-10-18 05:40:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-10-18 05:25:00 UTC</td><td>&quot;air_temp&quot;</td><td>2</td><td>false</td><td>11.306</td><td>&quot;Celsius&quot;</td><td>43136</td><td>&quot;NAA&quot;</td><td>&quot;Neil Armstrong Academy&quot;</td><td>4260.0</td><td>40.71152</td><td>-112.01448</td><td>153</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>4252.0</td><td>2014-05-27 00:00:00 UTC</td><td>2024-10-18 05:40:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-10-18 05:25:00 UTC</td><td>&quot;wind_speed&quot;</td><td>1</td><td>false</td><td>2.639</td><td>&quot;m/s&quot;</td><td>43136</td><td>&quot;NAA&quot;</td><td>&quot;Neil Armstrong Academy&quot;</td><td>4260.0</td><td>40.71152</td><td>-112.01448</td><td>153</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>4252.0</td><td>2014-05-27 00:00:00 UTC</td><td>2024-10-18 05:40:00 UTC</td><td>false</td><td>true</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3, 20)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
       "│ date_time ┆ variable  ┆ sensor_in ┆ is_derive ┆ … ┆ period_of ┆ period_of ┆ is_restri ┆ is_activ │\n",
       "│ ---       ┆ ---       ┆ dex       ┆ d         ┆   ┆ _record_s ┆ _record_e ┆ cted      ┆ e        │\n",
       "│ datetime[ ┆ str       ┆ ---       ┆ ---       ┆   ┆ tart      ┆ nd        ┆ ---       ┆ ---      │\n",
       "│ μs, UTC]  ┆           ┆ u32       ┆ bool      ┆   ┆ ---       ┆ ---       ┆ bool      ┆ bool     │\n",
       "│           ┆           ┆           ┆           ┆   ┆ datetime[ ┆ datetime[ ┆           ┆          │\n",
       "│           ┆           ┆           ┆           ┆   ┆ μs, UTC]  ┆ μs, UTC]  ┆           ┆          │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
       "│ 2024-10-1 ┆ air_temp  ┆ 1         ┆ false     ┆ … ┆ 2014-05-2 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆           ┆           ┆           ┆   ┆ 7         ┆ 8         ┆           ┆          │\n",
       "│ 05:25:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ air_temp  ┆ 2         ┆ false     ┆ … ┆ 2014-05-2 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆           ┆           ┆           ┆   ┆ 7         ┆ 8         ┆           ┆          │\n",
       "│ 05:25:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 2014-05-2 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 8         ┆ d         ┆           ┆           ┆   ┆ 7         ┆ 8         ┆           ┆          │\n",
       "│ 05:25:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:40:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice that NAA has two different reports for air temperature\n",
    "df = synoptic.Latest(stid=\"naa\", vars=\"air_temp,wind_speed\").df()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>stid</th><th>date_time</th><th>latitude</th><th>longitude</th><th>air_temp</th><th>wind_speed</th></tr><tr><td>str</td><td>datetime[μs, UTC]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;NAA&quot;</td><td>2024-10-18 05:25:00 UTC</td><td>40.71152</td><td>-112.01448</td><td>5.306</td><td>2.639</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 6)\n",
       "┌──────┬─────────────────────────┬──────────┬────────────┬──────────┬────────────┐\n",
       "│ stid ┆ date_time               ┆ latitude ┆ longitude  ┆ air_temp ┆ wind_speed │\n",
       "│ ---  ┆ ---                     ┆ ---      ┆ ---        ┆ ---      ┆ ---        │\n",
       "│ str  ┆ datetime[μs, UTC]       ┆ f64      ┆ f64        ┆ f64      ┆ f64        │\n",
       "╞══════╪═════════════════════════╪══════════╪════════════╪══════════╪════════════╡\n",
       "│ NAA  ┆ 2024-10-18 05:25:00 UTC ┆ 40.71152 ┆ -112.01448 ┆ 5.306    ┆ 2.639      │\n",
       "└──────┴─────────────────────────┴──────────┴────────────┴──────────┴────────────┘"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter sensor ID, then pivot\n",
    "df.filter(sensor_index=1).pivot(\n",
    "    \"variable\", index=[\"stid\", \"date_time\", \"latitude\", \"longitude\"], values=\"value\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>stid</th><th>date_time</th><th>latitude</th><th>longitude</th><th>{&quot;air_temp&quot;,1}</th><th>{&quot;air_temp&quot;,2}</th><th>{&quot;wind_speed&quot;,1}</th></tr><tr><td>str</td><td>datetime[μs, UTC]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;NAA&quot;</td><td>2024-10-18 05:25:00 UTC</td><td>40.71152</td><td>-112.01448</td><td>5.306</td><td>11.306</td><td>2.639</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 7)\n",
       "┌──────┬────────────────┬──────────┬────────────┬────────────────┬────────────────┬────────────────┐\n",
       "│ stid ┆ date_time      ┆ latitude ┆ longitude  ┆ {\"air_temp\",1} ┆ {\"air_temp\",2} ┆ {\"wind_speed\", │\n",
       "│ ---  ┆ ---            ┆ ---      ┆ ---        ┆ ---            ┆ ---            ┆ 1}             │\n",
       "│ str  ┆ datetime[μs,   ┆ f64      ┆ f64        ┆ f64            ┆ f64            ┆ ---            │\n",
       "│      ┆ UTC]           ┆          ┆            ┆                ┆                ┆ f64            │\n",
       "╞══════╪════════════════╪══════════╪════════════╪════════════════╪════════════════╪════════════════╡\n",
       "│ NAA  ┆ 2024-10-18     ┆ 40.71152 ┆ -112.01448 ┆ 5.306          ┆ 11.306         ┆ 2.639          │\n",
       "│      ┆ 05:25:00 UTC   ┆          ┆            ┆                ┆                ┆                │\n",
       "└──────┴────────────────┴──────────┴────────────┴────────────────┴────────────────┴────────────────┘"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pivot on both variable and sensor column\n",
    "df.pivot(\n",
    "    [\"variable\", \"sensor_index\"],\n",
    "    index=[\"stid\", \"date_time\", \"latitude\", \"longitude\"],\n",
    "    values=\"value\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['stid',\n",
       " 'date_time',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " '{\"air_temp\",1}',\n",
       " '{\"air_temp\",2}',\n",
       " '{\"wind_speed\",1}']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(\n",
    "    [\"variable\", \"sensor_index\"],\n",
    "    index=[\"stid\", \"date_time\", \"latitude\", \"longitude\"],\n",
    "    values=\"value\",\n",
    ").columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Special Case: Measured and derived values\n",
    "\n",
    "Some stations have both measured and derived variables, like dew point temperature. Your pivot options are \n",
    "1. Filter to use only the measured value\n",
    "1. Pivot on both `variable` _and_ `derived` columns.\n",
    "1. Use an aggregattion function to take **mean** or **first** of the two values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚚💨 Speedy delivery from Synoptic latest service.\n",
      "📦 Received data from 1 stations.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3, 20)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date_time</th><th>variable</th><th>sensor_index</th><th>is_derived</th><th>value</th><th>units</th><th>id</th><th>stid</th><th>name</th><th>elevation</th><th>latitude</th><th>longitude</th><th>mnet_id</th><th>state</th><th>timezone</th><th>elev_dem</th><th>period_of_record_start</th><th>period_of_record_end</th><th>is_restricted</th><th>is_active</th></tr><tr><td>datetime[μs, UTC]</td><td>str</td><td>u32</td><td>bool</td><td>f64</td><td>str</td><td>u32</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>u32</td><td>str</td><td>str</td><td>f64</td><td>datetime[μs, UTC]</td><td>datetime[μs, UTC]</td><td>bool</td><td>bool</td></tr></thead><tbody><tr><td>2024-10-17 05:35:00 UTC</td><td>&quot;dew_point_temperature&quot;</td><td>1</td><td>false</td><td>4.0</td><td>&quot;Celsius&quot;</td><td>6470</td><td>&quot;KU69&quot;</td><td>&quot;DUCHESNE&quot;</td><td>5826.0</td><td>40.1919</td><td>-110.38099</td><td>1</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>5810.4</td><td>2007-10-11 00:00:00 UTC</td><td>2024-10-17 05:15:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-10-17 05:35:00 UTC</td><td>&quot;wind_speed&quot;</td><td>1</td><td>false</td><td>1.543</td><td>&quot;m/s&quot;</td><td>6470</td><td>&quot;KU69&quot;</td><td>&quot;DUCHESNE&quot;</td><td>5826.0</td><td>40.1919</td><td>-110.38099</td><td>1</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>5810.4</td><td>2007-10-11 00:00:00 UTC</td><td>2024-10-17 05:15:00 UTC</td><td>false</td><td>true</td></tr><tr><td>2024-10-17 05:35:00 UTC</td><td>&quot;dew_point_temperature&quot;</td><td>1</td><td>true</td><td>3.93</td><td>&quot;Celsius&quot;</td><td>6470</td><td>&quot;KU69&quot;</td><td>&quot;DUCHESNE&quot;</td><td>5826.0</td><td>40.1919</td><td>-110.38099</td><td>1</td><td>&quot;UT&quot;</td><td>&quot;America/Denver&quot;</td><td>5810.4</td><td>2007-10-11 00:00:00 UTC</td><td>2024-10-17 05:15:00 UTC</td><td>false</td><td>true</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3, 20)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
       "│ date_time ┆ variable  ┆ sensor_in ┆ is_derive ┆ … ┆ period_of ┆ period_of ┆ is_restri ┆ is_activ │\n",
       "│ ---       ┆ ---       ┆ dex       ┆ d         ┆   ┆ _record_s ┆ _record_e ┆ cted      ┆ e        │\n",
       "│ datetime[ ┆ str       ┆ ---       ┆ ---       ┆   ┆ tart      ┆ nd        ┆ ---       ┆ ---      │\n",
       "│ μs, UTC]  ┆           ┆ u32       ┆ bool      ┆   ┆ ---       ┆ ---       ┆ bool      ┆ bool     │\n",
       "│           ┆           ┆           ┆           ┆   ┆ datetime[ ┆ datetime[ ┆           ┆          │\n",
       "│           ┆           ┆           ┆           ┆   ┆ μs, UTC]  ┆ μs, UTC]  ┆           ┆          │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
       "│ 2024-10-1 ┆ dew_point ┆ 1         ┆ false     ┆ … ┆ 2007-10-1 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 7         ┆ _temperat ┆           ┆           ┆   ┆ 1         ┆ 7         ┆           ┆          │\n",
       "│ 05:35:00  ┆ ure       ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:15:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ wind_spee ┆ 1         ┆ false     ┆ … ┆ 2007-10-1 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 7         ┆ d         ┆           ┆           ┆   ┆ 1         ┆ 7         ┆           ┆          │\n",
       "│ 05:35:00  ┆           ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:15:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "│ 2024-10-1 ┆ dew_point ┆ 1         ┆ true      ┆ … ┆ 2007-10-1 ┆ 2024-10-1 ┆ false     ┆ true     │\n",
       "│ 7         ┆ _temperat ┆           ┆           ┆   ┆ 1         ┆ 7         ┆           ┆          │\n",
       "│ 05:35:00  ┆ ure       ┆           ┆           ┆   ┆ 00:00:00  ┆ 05:15:00  ┆           ┆          │\n",
       "│ UTC       ┆           ┆           ┆           ┆   ┆ UTC       ┆ UTC       ┆           ┆          │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = synoptic.Latest(stid=\"KU69\", vars=\"wind_speed,dew_point_temperature\").df()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>stid</th><th>dew_point_temperature</th><th>wind_speed</th></tr><tr><td>str</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;KU69&quot;</td><td>3.965</td><td>1.543</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 3)\n",
       "┌──────┬───────────────────────┬────────────┐\n",
       "│ stid ┆ dew_point_temperature ┆ wind_speed │\n",
       "│ ---  ┆ ---                   ┆ ---        │\n",
       "│ str  ┆ f64                   ┆ f64        │\n",
       "╞══════╪═══════════════════════╪════════════╡\n",
       "│ KU69 ┆ 3.965                 ┆ 1.543      │\n",
       "└──────┴───────────────────────┴────────────┘"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pivot, calculate the mean if more than one value per variable\n",
    "df.pivot(\"variable\", index=\"stid\", values=\"value\", aggregate_function=\"mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>stid</th><th>dew_point_temperature</th><th>wind_speed</th></tr><tr><td>str</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;KU69&quot;</td><td>4.0</td><td>1.543</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 3)\n",
       "┌──────┬───────────────────────┬────────────┐\n",
       "│ stid ┆ dew_point_temperature ┆ wind_speed │\n",
       "│ ---  ┆ ---                   ┆ ---        │\n",
       "│ str  ┆ f64                   ┆ f64        │\n",
       "╞══════╪═══════════════════════╪════════════╡\n",
       "│ KU69 ┆ 4.0                   ┆ 1.543      │\n",
       "└──────┴───────────────────────┴────────────┘"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pivot, use the first value if more than one value per variable\n",
    "df.pivot(\"variable\", index=\"stid\", values=\"value\", aggregate_function=\"first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>stid</th><th>latitude</th><th>longitude</th><th>{&quot;dew_point_temperature&quot;,false}</th><th>{&quot;dew_point_temperature&quot;,true}</th><th>{&quot;wind_speed&quot;,false}</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;KU69&quot;</td><td>40.1919</td><td>-110.38099</td><td>4.0</td><td>3.93</td><td>1.543</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 6)\n",
       "┌──────┬──────────┬────────────┬──────────────────────┬──────────────────────┬─────────────────────┐\n",
       "│ stid ┆ latitude ┆ longitude  ┆ {\"dew_point_temperat ┆ {\"dew_point_temperat ┆ {\"wind_speed\",false │\n",
       "│ ---  ┆ ---      ┆ ---        ┆ ure\",false…          ┆ ure\",true}           ┆ }                   │\n",
       "│ str  ┆ f64      ┆ f64        ┆ ---                  ┆ ---                  ┆ ---                 │\n",
       "│      ┆          ┆            ┆ f64                  ┆ f64                  ┆ f64                 │\n",
       "╞══════╪══════════╪════════════╪══════════════════════╪══════════════════════╪═════════════════════╡\n",
       "│ KU69 ┆ 40.1919  ┆ -110.38099 ┆ 4.0                  ┆ 3.93                 ┆ 1.543               │\n",
       "└──────┴──────────┴────────────┴──────────────────────┴──────────────────────┴─────────────────────┘"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pivot on both variable and derived columns\n",
    "df.sort(\"variable\").pivot(\n",
    "    [\"variable\", \"is_derived\"],\n",
    "    index=[\"stid\", \"latitude\", \"longitude\"],\n",
    "    values=\"value\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot many station variables\n",
    "Let's pivot requests with more station data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚚💨 Speedy delivery from Synoptic latest service.\n",
      "📦 Received data from 131 stations.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (131, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>stid</th><th>latitude</th><th>longitude</th><th>shortname</th><th>air_temp</th><th>dew_point_temperature</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>str</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;KSLC&quot;</td><td>40.77069</td><td>-111.96503</td><td>&quot;ASOS/AWOS&quot;</td><td>71.6</td><td>35.55</td></tr><tr><td>&quot;KU42&quot;</td><td>40.6196</td><td>-111.99016</td><td>&quot;ASOS/AWOS&quot;</td><td>68.0</td><td>35.06</td></tr><tr><td>&quot;KHIF&quot;</td><td>41.11112</td><td>-111.96229</td><td>&quot;ASOS/AWOS&quot;</td><td>68.18</td><td>35.6</td></tr><tr><td>&quot;KOGD&quot;</td><td>41.19406</td><td>-112.01681</td><td>&quot;ASOS/AWOS&quot;</td><td>71.6</td><td>37.35</td></tr><tr><td>&quot;KBMC&quot;</td><td>41.5464</td><td>-112.0601</td><td>&quot;ASOS/AWOS&quot;</td><td>71.42</td><td>30.02</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;TT509&quot;</td><td>37.32322</td><td>-112.18486</td><td>&quot;RAWS&quot;</td><td>49.0</td><td>24.3</td></tr><tr><td>&quot;KSPK&quot;</td><td>40.145</td><td>-111.6677</td><td>&quot;ASOS/AWOS&quot;</td><td>64.4</td><td>37.4</td></tr><tr><td>&quot;TT773&quot;</td><td>38.62606</td><td>-111.9419</td><td>&quot;RAWS&quot;</td><td>43.0</td><td>23.95</td></tr><tr><td>&quot;KU64&quot;</td><td>37.93243</td><td>-109.34122</td><td>&quot;ASOS/AWOS&quot;</td><td>45.32</td><td>35.06</td></tr><tr><td>&quot;K40U&quot;</td><td>40.9833</td><td>-109.6833</td><td>&quot;ASOS/AWOS&quot;</td><td>57.56</td><td>34.7</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (131, 6)\n",
       "┌───────┬──────────┬────────────┬───────────┬──────────┬───────────────────────┐\n",
       "│ stid  ┆ latitude ┆ longitude  ┆ shortname ┆ air_temp ┆ dew_point_temperature │\n",
       "│ ---   ┆ ---      ┆ ---        ┆ ---       ┆ ---      ┆ ---                   │\n",
       "│ str   ┆ f64      ┆ f64        ┆ str       ┆ f64      ┆ f64                   │\n",
       "╞═══════╪══════════╪════════════╪═══════════╪══════════╪═══════════════════════╡\n",
       "│ KSLC  ┆ 40.77069 ┆ -111.96503 ┆ ASOS/AWOS ┆ 71.6     ┆ 35.55                 │\n",
       "│ KU42  ┆ 40.6196  ┆ -111.99016 ┆ ASOS/AWOS ┆ 68.0     ┆ 35.06                 │\n",
       "│ KHIF  ┆ 41.11112 ┆ -111.96229 ┆ ASOS/AWOS ┆ 68.18    ┆ 35.6                  │\n",
       "│ KOGD  ┆ 41.19406 ┆ -112.01681 ┆ ASOS/AWOS ┆ 71.6     ┆ 37.35                 │\n",
       "│ KBMC  ┆ 41.5464  ┆ -112.0601  ┆ ASOS/AWOS ┆ 71.42    ┆ 30.02                 │\n",
       "│ …     ┆ …        ┆ …          ┆ …         ┆ …        ┆ …                     │\n",
       "│ TT509 ┆ 37.32322 ┆ -112.18486 ┆ RAWS      ┆ 49.0     ┆ 24.3                  │\n",
       "│ KSPK  ┆ 40.145   ┆ -111.6677  ┆ ASOS/AWOS ┆ 64.4     ┆ 37.4                  │\n",
       "│ TT773 ┆ 38.62606 ┆ -111.9419  ┆ RAWS      ┆ 43.0     ┆ 23.95                 │\n",
       "│ KU64  ┆ 37.93243 ┆ -109.34122 ┆ ASOS/AWOS ┆ 45.32    ┆ 35.06                 │\n",
       "│ K40U  ┆ 40.9833  ┆ -109.6833  ┆ ASOS/AWOS ┆ 57.56    ┆ 34.7                  │\n",
       "└───────┴──────────┴────────────┴───────────┴──────────┴───────────────────────┘"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = synoptic.Latest(\n",
    "    state=\"UT\",\n",
    "    network=[1, 2],\n",
    "    vars=\"air_temp,dew_point_temperature\",\n",
    "    units=\"english\",\n",
    "    complete=1,  # to get mesonet shortname\n",
    ").df()\n",
    "\n",
    "# Pivot\n",
    "df = df.pivot(\n",
    "    \"variable\",\n",
    "    index=[\"stid\", \"latitude\", \"longitude\", \"shortname\"],\n",
    "    values=\"value\",\n",
    "    aggregate_function=\"first\",\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/blaylock/miniconda3/envs/synoptic2/lib/python3.12/site-packages/pyproj/__init__.py:89: UserWarning: pyproj unable to set database path.\n",
      "  _pyproj_global_context_initialize()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot on a map\n",
    "\n",
    "from herbie.toolbox import EasyMap, ccrs, pc\n",
    "\n",
    "ax = EasyMap(\"10m\", figsize=(8, 8)).STATES().LAKES().ROADS().ax\n",
    "art = sns.scatterplot(\n",
    "    df.filter(pl.col(\"longitude\") < -60),  # One station has bad longitude metadata\n",
    "    ax=ax,\n",
    "    x=\"longitude\",\n",
    "    y=\"latitude\",\n",
    "    hue=\"air_temp\",\n",
    "    style=\"shortname\",\n",
    "    edgecolor=\"k\",\n",
    "    palette=\"Spectral_r\",\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "synoptic2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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