Chapter09/ch09.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 9: Time Series Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Technical requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import polars as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lf = pl.scan_csv('../data/toronto_weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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: (5, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>temperature</th><th>wind_speed</th><th>pressure</th><th>humidity</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;2012-10-01 12:…</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>&quot;2012-10-01 13:…</td><td>286.26</td><td>3.0</td><td>1012.0</td><td>82.0</td></tr><tr><td>&quot;2012-10-01 14:…</td><td>286.262541</td><td>3.0</td><td>1011.0</td><td>81.0</td></tr><tr><td>&quot;2012-10-01 15:…</td><td>286.269518</td><td>3.0</td><td>1011.0</td><td>79.0</td></tr><tr><td>&quot;2012-10-01 16:…</td><td>286.276496</td><td>3.0</td><td>1010.0</td><td>77.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 5)\n",
       "┌─────────────────────┬─────────────┬────────────┬──────────┬──────────┐\n",
       "│ datetime            ┆ temperature ┆ wind_speed ┆ pressure ┆ humidity │\n",
       "│ ---                 ┆ ---         ┆ ---        ┆ ---      ┆ ---      │\n",
       "│ str                 ┆ f64         ┆ f64        ┆ f64      ┆ f64      │\n",
       "╞═════════════════════╪═════════════╪════════════╪══════════╪══════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ null        ┆ null       ┆ null     ┆ null     │\n",
       "│ 2012-10-01 13:00:00 ┆ 286.26      ┆ 3.0        ┆ 1012.0   ┆ 82.0     │\n",
       "│ 2012-10-01 14:00:00 ┆ 286.262541  ┆ 3.0        ┆ 1011.0   ┆ 81.0     │\n",
       "│ 2012-10-01 15:00:00 ┆ 286.269518  ┆ 3.0        ┆ 1011.0   ┆ 79.0     │\n",
       "│ 2012-10-01 16:00:00 ┆ 286.276496  ┆ 3.0        ┆ 1010.0   ┆ 77.0     │\n",
       "└─────────────────────┴─────────────┴────────────┴──────────┴──────────┘"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf.head().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "lf = lf.with_columns(pl.col('temperature')-273.15)"
   ]
  },
  {
   "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: (5, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>temperature</th><th>wind_speed</th><th>pressure</th><th>humidity</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;2012-10-01 12:…</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>&quot;2012-10-01 13:…</td><td>13.11</td><td>3.0</td><td>1012.0</td><td>82.0</td></tr><tr><td>&quot;2012-10-01 14:…</td><td>13.112541</td><td>3.0</td><td>1011.0</td><td>81.0</td></tr><tr><td>&quot;2012-10-01 15:…</td><td>13.119518</td><td>3.0</td><td>1011.0</td><td>79.0</td></tr><tr><td>&quot;2012-10-01 16:…</td><td>13.126496</td><td>3.0</td><td>1010.0</td><td>77.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 5)\n",
       "┌─────────────────────┬─────────────┬────────────┬──────────┬──────────┐\n",
       "│ datetime            ┆ temperature ┆ wind_speed ┆ pressure ┆ humidity │\n",
       "│ ---                 ┆ ---         ┆ ---        ┆ ---      ┆ ---      │\n",
       "│ str                 ┆ f64         ┆ f64        ┆ f64      ┆ f64      │\n",
       "╞═════════════════════╪═════════════╪════════════╪══════════╪══════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ null        ┆ null       ┆ null     ┆ null     │\n",
       "│ 2012-10-01 13:00:00 ┆ 13.11       ┆ 3.0        ┆ 1012.0   ┆ 82.0     │\n",
       "│ 2012-10-01 14:00:00 ┆ 13.112541   ┆ 3.0        ┆ 1011.0   ┆ 81.0     │\n",
       "│ 2012-10-01 15:00:00 ┆ 13.119518   ┆ 3.0        ┆ 1011.0   ┆ 79.0     │\n",
       "│ 2012-10-01 16:00:00 ┆ 13.126496   ┆ 3.0        ┆ 1010.0   ┆ 77.0     │\n",
       "└─────────────────────┴─────────────┴────────────┴──────────┴──────────┘"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf.head().collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with date and time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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: (5, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>temperature</th><th>wind_speed</th><th>pressure</th><th>humidity</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2012-10-01 13:00:00</td><td>286.26</td><td>3.0</td><td>1012.0</td><td>82.0</td></tr><tr><td>2012-10-01 14:00:00</td><td>286.262541</td><td>3.0</td><td>1011.0</td><td>81.0</td></tr><tr><td>2012-10-01 15:00:00</td><td>286.269518</td><td>3.0</td><td>1011.0</td><td>79.0</td></tr><tr><td>2012-10-01 16:00:00</td><td>286.276496</td><td>3.0</td><td>1010.0</td><td>77.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 5)\n",
       "┌─────────────────────┬─────────────┬────────────┬──────────┬──────────┐\n",
       "│ datetime            ┆ temperature ┆ wind_speed ┆ pressure ┆ humidity │\n",
       "│ ---                 ┆ ---         ┆ ---        ┆ ---      ┆ ---      │\n",
       "│ datetime[μs]        ┆ f64         ┆ f64        ┆ f64      ┆ f64      │\n",
       "╞═════════════════════╪═════════════╪════════════╪══════════╪══════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ null        ┆ null       ┆ null     ┆ null     │\n",
       "│ 2012-10-01 13:00:00 ┆ 286.26      ┆ 3.0        ┆ 1012.0   ┆ 82.0     │\n",
       "│ 2012-10-01 14:00:00 ┆ 286.262541  ┆ 3.0        ┆ 1011.0   ┆ 81.0     │\n",
       "│ 2012-10-01 15:00:00 ┆ 286.269518  ┆ 3.0        ┆ 1011.0   ┆ 79.0     │\n",
       "│ 2012-10-01 16:00:00 ┆ 286.276496  ┆ 3.0        ┆ 1010.0   ┆ 77.0     │\n",
       "└─────────────────────┴─────────────┴────────────┴──────────┴──────────┘"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf_date_parsed = pl.scan_csv('../data/toronto_weather.csv', try_parse_dates=True)\n",
    "lf_date_parsed.head().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Schema([('datetime', Datetime(time_unit='us', time_zone=None)),\n",
       "         ('temperature', Float64),\n",
       "         ('wind_speed', Float64),\n",
       "         ('pressure', Float64),\n",
       "         ('humidity', Float64)]),\n",
       " [Datetime(time_unit='us', time_zone=None),\n",
       "  Float64,\n",
       "  Float64,\n",
       "  Float64,\n",
       "  Float64])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf_date_parsed.collect_schema(), lf_date_parsed.collect_schema().dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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: (5, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>temperature</th><th>wind_speed</th><th>pressure</th><th>humidity</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2012-10-01 13:00:00</td><td>13.11</td><td>3.0</td><td>1012.0</td><td>82.0</td></tr><tr><td>2012-10-01 14:00:00</td><td>13.112541</td><td>3.0</td><td>1011.0</td><td>81.0</td></tr><tr><td>2012-10-01 15:00:00</td><td>13.119518</td><td>3.0</td><td>1011.0</td><td>79.0</td></tr><tr><td>2012-10-01 16:00:00</td><td>13.126496</td><td>3.0</td><td>1010.0</td><td>77.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 5)\n",
       "┌─────────────────────┬─────────────┬────────────┬──────────┬──────────┐\n",
       "│ datetime            ┆ temperature ┆ wind_speed ┆ pressure ┆ humidity │\n",
       "│ ---                 ┆ ---         ┆ ---        ┆ ---      ┆ ---      │\n",
       "│ datetime[μs]        ┆ f64         ┆ f64        ┆ f64      ┆ f64      │\n",
       "╞═════════════════════╪═════════════╪════════════╪══════════╪══════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ null        ┆ null       ┆ null     ┆ null     │\n",
       "│ 2012-10-01 13:00:00 ┆ 13.11       ┆ 3.0        ┆ 1012.0   ┆ 82.0     │\n",
       "│ 2012-10-01 14:00:00 ┆ 13.112541   ┆ 3.0        ┆ 1011.0   ┆ 81.0     │\n",
       "│ 2012-10-01 15:00:00 ┆ 13.119518   ┆ 3.0        ┆ 1011.0   ┆ 79.0     │\n",
       "│ 2012-10-01 16:00:00 ┆ 13.126496   ┆ 3.0        ┆ 1010.0   ┆ 77.0     │\n",
       "└─────────────────────┴─────────────┴────────────┴──────────┴──────────┘"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf = lf.with_columns(\n",
    "    pl.col('datetime').str.to_datetime()\n",
    ")\n",
    "lf.head().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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: (5, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>year</th><th>month</th><th>day</th><th>time</th></tr><tr><td>datetime[μs]</td><td>i32</td><td>i8</td><td>i8</td><td>time</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>2012</td><td>10</td><td>1</td><td>12:00:00</td></tr><tr><td>2012-10-01 13:00:00</td><td>2012</td><td>10</td><td>1</td><td>13:00:00</td></tr><tr><td>2012-10-01 14:00:00</td><td>2012</td><td>10</td><td>1</td><td>14:00:00</td></tr><tr><td>2012-10-01 15:00:00</td><td>2012</td><td>10</td><td>1</td><td>15:00:00</td></tr><tr><td>2012-10-01 16:00:00</td><td>2012</td><td>10</td><td>1</td><td>16:00:00</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 5)\n",
       "┌─────────────────────┬──────┬───────┬─────┬──────────┐\n",
       "│ datetime            ┆ year ┆ month ┆ day ┆ time     │\n",
       "│ ---                 ┆ ---  ┆ ---   ┆ --- ┆ ---      │\n",
       "│ datetime[μs]        ┆ i32  ┆ i8    ┆ i8  ┆ time     │\n",
       "╞═════════════════════╪══════╪═══════╪═════╪══════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ 2012 ┆ 10    ┆ 1   ┆ 12:00:00 │\n",
       "│ 2012-10-01 13:00:00 ┆ 2012 ┆ 10    ┆ 1   ┆ 13:00:00 │\n",
       "│ 2012-10-01 14:00:00 ┆ 2012 ┆ 10    ┆ 1   ┆ 14:00:00 │\n",
       "│ 2012-10-01 15:00:00 ┆ 2012 ┆ 10    ┆ 1   ┆ 15:00:00 │\n",
       "│ 2012-10-01 16:00:00 ┆ 2012 ┆ 10    ┆ 1   ┆ 16:00:00 │\n",
       "└─────────────────────┴──────┴───────┴─────┴──────────┘"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    lf\n",
    "    .select(\n",
    "        'datetime',\n",
    "        pl.col('datetime').dt.year().alias('year'),\n",
    "        pl.col('datetime').dt.month().alias('month'),\n",
    "        pl.col('datetime').dt.day().alias('day'),\n",
    "        pl.col('datetime').dt.time().alias('time')\n",
    "    )\n",
    "    .head().collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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: (5, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>temperature</th><th>wind_speed</th><th>pressure</th><th>humidity</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2017-01-01 00:00:00</td><td>2.44</td><td>3.0</td><td>1001.0</td><td>92.0</td></tr><tr><td>2017-01-01 01:00:00</td><td>2.19</td><td>9.0</td><td>1003.0</td><td>86.0</td></tr><tr><td>2017-01-01 02:00:00</td><td>2.41</td><td>7.0</td><td>1003.0</td><td>86.0</td></tr><tr><td>2017-01-01 03:00:00</td><td>2.42</td><td>5.0</td><td>1003.0</td><td>74.0</td></tr><tr><td>2017-01-01 04:00:00</td><td>1.77</td><td>6.0</td><td>1006.0</td><td>69.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 5)\n",
       "┌─────────────────────┬─────────────┬────────────┬──────────┬──────────┐\n",
       "│ datetime            ┆ temperature ┆ wind_speed ┆ pressure ┆ humidity │\n",
       "│ ---                 ┆ ---         ┆ ---        ┆ ---      ┆ ---      │\n",
       "│ datetime[μs]        ┆ f64         ┆ f64        ┆ f64      ┆ f64      │\n",
       "╞═════════════════════╪═════════════╪════════════╪══════════╪══════════╡\n",
       "│ 2017-01-01 00:00:00 ┆ 2.44        ┆ 3.0        ┆ 1001.0   ┆ 92.0     │\n",
       "│ 2017-01-01 01:00:00 ┆ 2.19        ┆ 9.0        ┆ 1003.0   ┆ 86.0     │\n",
       "│ 2017-01-01 02:00:00 ┆ 2.41        ┆ 7.0        ┆ 1003.0   ┆ 86.0     │\n",
       "│ 2017-01-01 03:00:00 ┆ 2.42        ┆ 5.0        ┆ 1003.0   ┆ 74.0     │\n",
       "│ 2017-01-01 04:00:00 ┆ 1.77        ┆ 6.0        ┆ 1006.0   ┆ 69.0     │\n",
       "└─────────────────────┴─────────────┴────────────┴──────────┴──────────┘"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "filtered_lf = (\n",
    "    lf\n",
    "    .filter(\n",
    "        pl.col('datetime').dt.date().is_between(\n",
    "            datetime(2017,1,1), datetime(2017,12,31)\n",
    "        ),\n",
    "        pl.col('datetime').dt.hour() < 12\n",
    "    )\n",
    ")\n",
    "filtered_lf.head().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>year_cnt</th><th>hour_cnt</th></tr><tr><td>u32</td><td>u32</td></tr></thead><tbody><tr><td>1</td><td>12</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 2)\n",
       "┌──────────┬──────────┐\n",
       "│ year_cnt ┆ hour_cnt │\n",
       "│ ---      ┆ ---      │\n",
       "│ u32      ┆ u32      │\n",
       "╞══════════╪══════════╡\n",
       "│ 1        ┆ 12       │\n",
       "└──────────┴──────────┘"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    filtered_lf\n",
    "    .select(\n",
    "        pl.col('datetime').dt.year().unique()\n",
    "        .implode()\n",
    "        .list.len()\n",
    "        .alias('year_cnt'),\n",
    "        pl.col('datetime').dt.hour().unique()\n",
    "        .implode()\n",
    "        .list.len()\n",
    "        .alias('hour_cnt')\n",
    "    )\n",
    "    .head()\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>replaced_time_zone_toronto</th><th>converted_time_zone_toronto</th></tr><tr><td>datetime[μs]</td><td>datetime[μs, America/Toronto]</td><td>datetime[μs, America/Toronto]</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>2012-10-01 12:00:00 EDT</td><td>2012-10-01 08:00:00 EDT</td></tr><tr><td>2012-10-01 13:00:00</td><td>2012-10-01 13:00:00 EDT</td><td>2012-10-01 09:00:00 EDT</td></tr><tr><td>2012-10-01 14:00:00</td><td>2012-10-01 14:00:00 EDT</td><td>2012-10-01 10:00:00 EDT</td></tr><tr><td>2012-10-01 15:00:00</td><td>2012-10-01 15:00:00 EDT</td><td>2012-10-01 11:00:00 EDT</td></tr><tr><td>2012-10-01 16:00:00</td><td>2012-10-01 16:00:00 EDT</td><td>2012-10-01 12:00:00 EDT</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "┌─────────────────────┬───────────────────────────────┬───────────────────────────────┐\n",
       "│ datetime            ┆ replaced_time_zone_toronto    ┆ converted_time_zone_toronto   │\n",
       "│ ---                 ┆ ---                           ┆ ---                           │\n",
       "│ datetime[μs]        ┆ datetime[μs, America/Toronto] ┆ datetime[μs, America/Toronto] │\n",
       "╞═════════════════════╪═══════════════════════════════╪═══════════════════════════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ 2012-10-01 12:00:00 EDT       ┆ 2012-10-01 08:00:00 EDT       │\n",
       "│ 2012-10-01 13:00:00 ┆ 2012-10-01 13:00:00 EDT       ┆ 2012-10-01 09:00:00 EDT       │\n",
       "│ 2012-10-01 14:00:00 ┆ 2012-10-01 14:00:00 EDT       ┆ 2012-10-01 10:00:00 EDT       │\n",
       "│ 2012-10-01 15:00:00 ┆ 2012-10-01 15:00:00 EDT       ┆ 2012-10-01 11:00:00 EDT       │\n",
       "│ 2012-10-01 16:00:00 ┆ 2012-10-01 16:00:00 EDT       ┆ 2012-10-01 12:00:00 EDT       │\n",
       "└─────────────────────┴───────────────────────────────┴───────────────────────────────┘"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_zones_lf = (\n",
    "    lf\n",
    "    .select(\n",
    "        'datetime',\n",
    "        pl.col('datetime').dt.replace_time_zone('America/Toronto')\n",
    "        .alias('replaced_time_zone_toronto'),\n",
    "        pl.col('datetime').dt.convert_time_zone('America/Toronto')\n",
    "        .alias('converted_time_zone_toronto')\n",
    "    )\n",
    ")\n",
    "time_zones_lf.head().collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There is more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "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: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>minus_5weeks</th><th>plus_5ms</th></tr><tr><td>datetime[μs]</td><td>datetime[μs]</td><td>datetime[μs]</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>2012-08-27 12:00:00</td><td>2012-10-01 12:00:00.005</td></tr><tr><td>2012-10-01 13:00:00</td><td>2012-08-27 13:00:00</td><td>2012-10-01 13:00:00.005</td></tr><tr><td>2012-10-01 14:00:00</td><td>2012-08-27 14:00:00</td><td>2012-10-01 14:00:00.005</td></tr><tr><td>2012-10-01 15:00:00</td><td>2012-08-27 15:00:00</td><td>2012-10-01 15:00:00.005</td></tr><tr><td>2012-10-01 16:00:00</td><td>2012-08-27 16:00:00</td><td>2012-10-01 16:00:00.005</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "┌─────────────────────┬─────────────────────┬─────────────────────────┐\n",
       "│ datetime            ┆ minus_5weeks        ┆ plus_5ms                │\n",
       "│ ---                 ┆ ---                 ┆ ---                     │\n",
       "│ datetime[μs]        ┆ datetime[μs]        ┆ datetime[μs]            │\n",
       "╞═════════════════════╪═════════════════════╪═════════════════════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ 2012-08-27 12:00:00 ┆ 2012-10-01 12:00:00.005 │\n",
       "│ 2012-10-01 13:00:00 ┆ 2012-08-27 13:00:00 ┆ 2012-10-01 13:00:00.005 │\n",
       "│ 2012-10-01 14:00:00 ┆ 2012-08-27 14:00:00 ┆ 2012-10-01 14:00:00.005 │\n",
       "│ 2012-10-01 15:00:00 ┆ 2012-08-27 15:00:00 ┆ 2012-10-01 15:00:00.005 │\n",
       "│ 2012-10-01 16:00:00 ┆ 2012-08-27 16:00:00 ┆ 2012-10-01 16:00:00.005 │\n",
       "└─────────────────────┴─────────────────────┴─────────────────────────┘"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    lf\n",
    "    .select(\n",
    "        'datetime',\n",
    "        (pl.col('datetime')-pl.duration(weeks=5)).alias('minus_5weeks'),\n",
    "        (pl.col('datetime')+pl.duration(milliseconds=5)).alias('plus_5ms'),   \n",
    "    )\n",
    "    .head()\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying rolling window calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "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: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>temperature</th><th>3hr_rollign_avg</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>null</td><td>null</td></tr><tr><td>2012-10-01 13:00:00</td><td>13.11</td><td>null</td></tr><tr><td>2012-10-01 14:00:00</td><td>13.112541</td><td>null</td></tr><tr><td>2012-10-01 15:00:00</td><td>13.119518</td><td>13.11402</td></tr><tr><td>2012-10-01 16:00:00</td><td>13.126496</td><td>13.119518</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "┌─────────────────────┬─────────────┬─────────────────┐\n",
       "│ datetime            ┆ temperature ┆ 3hr_rollign_avg │\n",
       "│ ---                 ┆ ---         ┆ ---             │\n",
       "│ datetime[μs]        ┆ f64         ┆ f64             │\n",
       "╞═════════════════════╪═════════════╪═════════════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ null        ┆ null            │\n",
       "│ 2012-10-01 13:00:00 ┆ 13.11       ┆ null            │\n",
       "│ 2012-10-01 14:00:00 ┆ 13.112541   ┆ null            │\n",
       "│ 2012-10-01 15:00:00 ┆ 13.119518   ┆ 13.11402        │\n",
       "│ 2012-10-01 16:00:00 ┆ 13.126496   ┆ 13.119518       │\n",
       "└─────────────────────┴─────────────┴─────────────────┘"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    lf\n",
    "    .select(\n",
    "        'datetime',\n",
    "        'temperature',\n",
    "        pl.col('temperature').rolling_mean(3).alias('3hr_rollign_avg')\n",
    "    )\n",
    "    .head()\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "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: (5, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>daily_avg_temp</th></tr><tr><td>date</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01</td><td>13.140854</td></tr><tr><td>2012-10-02</td><td>14.24739</td></tr><tr><td>2012-10-03</td><td>14.176875</td></tr><tr><td>2012-10-04</td><td>15.067917</td></tr><tr><td>2012-10-05</td><td>16.216458</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 2)\n",
       "┌────────────┬────────────────┐\n",
       "│ date       ┆ daily_avg_temp │\n",
       "│ ---        ┆ ---            │\n",
       "│ date       ┆ f64            │\n",
       "╞════════════╪════════════════╡\n",
       "│ 2012-10-01 ┆ 13.140854      │\n",
       "│ 2012-10-02 ┆ 14.24739       │\n",
       "│ 2012-10-03 ┆ 14.176875      │\n",
       "│ 2012-10-04 ┆ 15.067917      │\n",
       "│ 2012-10-05 ┆ 16.216458      │\n",
       "└────────────┴────────────────┘"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_avg_temperature_lf = (\n",
    "    lf\n",
    "    .select(\n",
    "        pl.col('datetime').dt.date().alias('date'),\n",
    "        'temperature'\n",
    "    )\n",
    "    .group_by('date', maintain_order=True)\n",
    "    .agg(\n",
    "        pl.col('temperature').mean().alias('daily_avg_temp')\n",
    "    )\n",
    ")\n",
    "daily_avg_temperature_lf.head().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "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: (5, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>daily_avg_temp</th><th>3day_rolling_avg</th><th>3day_rolling_min</th><th>3day_rolling_max</th></tr><tr><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01</td><td>13.140854</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2012-10-02</td><td>14.24739</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2012-10-03</td><td>14.176875</td><td>13.85504</td><td>13.140854</td><td>14.24739</td></tr><tr><td>2012-10-04</td><td>15.067917</td><td>14.497394</td><td>14.176875</td><td>15.067917</td></tr><tr><td>2012-10-05</td><td>16.216458</td><td>15.15375</td><td>14.176875</td><td>16.216458</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 5)\n",
       "┌────────────┬────────────────┬──────────────────┬──────────────────┬──────────────────┐\n",
       "│ date       ┆ daily_avg_temp ┆ 3day_rolling_avg ┆ 3day_rolling_min ┆ 3day_rolling_max │\n",
       "│ ---        ┆ ---            ┆ ---              ┆ ---              ┆ ---              │\n",
       "│ date       ┆ f64            ┆ f64              ┆ f64              ┆ f64              │\n",
       "╞════════════╪════════════════╪══════════════════╪══════════════════╪══════════════════╡\n",
       "│ 2012-10-01 ┆ 13.140854      ┆ null             ┆ null             ┆ null             │\n",
       "│ 2012-10-02 ┆ 14.24739       ┆ null             ┆ null             ┆ null             │\n",
       "│ 2012-10-03 ┆ 14.176875      ┆ 13.85504         ┆ 13.140854        ┆ 14.24739         │\n",
       "│ 2012-10-04 ┆ 15.067917      ┆ 14.497394        ┆ 14.176875        ┆ 15.067917        │\n",
       "│ 2012-10-05 ┆ 16.216458      ┆ 15.15375         ┆ 14.176875        ┆ 16.216458        │\n",
       "└────────────┴────────────────┴──────────────────┴──────────────────┴──────────────────┘"
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    daily_avg_temperature_lf\n",
    "    .select(\n",
    "        'date',\n",
    "        'daily_avg_temp',\n",
    "        pl.col('daily_avg_temp').rolling_mean(3).alias('3day_rolling_avg'),\n",
    "        pl.col('daily_avg_temp').rolling_min(3).alias('3day_rolling_min'),\n",
    "        pl.col('daily_avg_temp').rolling_max(3).alias('3day_rolling_max')\n",
    "    )\n",
    "    .head()\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "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: (10, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>daily_avg_temp</th><th>3day_rolling_avg</th><th>3day_rolling_avg2</th><th>3day_rolling_avg3</th></tr><tr><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01</td><td>13.140854</td><td>null</td><td>13.140854</td><td>13.140854</td></tr><tr><td>2012-10-02</td><td>14.24739</td><td>null</td><td>13.694122</td><td>13.694122</td></tr><tr><td>2012-10-03</td><td>14.176875</td><td>13.85504</td><td>13.85504</td><td>13.85504</td></tr><tr><td>2012-10-04</td><td>15.067917</td><td>14.497394</td><td>14.497394</td><td>14.497394</td></tr><tr><td>2012-10-05</td><td>16.216458</td><td>15.15375</td><td>15.15375</td><td>15.15375</td></tr><tr><td>2012-10-06</td><td>15.725417</td><td>15.669931</td><td>15.669931</td><td>15.669931</td></tr><tr><td>2012-10-07</td><td>10.197083</td><td>14.046319</td><td>14.046319</td><td>14.046319</td></tr><tr><td>2012-10-08</td><td>6.79625</td><td>10.90625</td><td>10.90625</td><td>10.90625</td></tr><tr><td>2012-10-09</td><td>6.735</td><td>7.909444</td><td>7.909444</td><td>7.909444</td></tr><tr><td>2012-10-10</td><td>9.305417</td><td>7.612222</td><td>7.612222</td><td>7.612222</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 5)\n",
       "┌────────────┬────────────────┬──────────────────┬───────────────────┬───────────────────┐\n",
       "│ date       ┆ daily_avg_temp ┆ 3day_rolling_avg ┆ 3day_rolling_avg2 ┆ 3day_rolling_avg3 │\n",
       "│ ---        ┆ ---            ┆ ---              ┆ ---               ┆ ---               │\n",
       "│ date       ┆ f64            ┆ f64              ┆ f64               ┆ f64               │\n",
       "╞════════════╪════════════════╪══════════════════╪═══════════════════╪═══════════════════╡\n",
       "│ 2012-10-01 ┆ 13.140854      ┆ null             ┆ 13.140854         ┆ 13.140854         │\n",
       "│ 2012-10-02 ┆ 14.24739       ┆ null             ┆ 13.694122         ┆ 13.694122         │\n",
       "│ 2012-10-03 ┆ 14.176875      ┆ 13.85504         ┆ 13.85504          ┆ 13.85504          │\n",
       "│ 2012-10-04 ┆ 15.067917      ┆ 14.497394        ┆ 14.497394         ┆ 14.497394         │\n",
       "│ 2012-10-05 ┆ 16.216458      ┆ 15.15375         ┆ 15.15375          ┆ 15.15375          │\n",
       "│ 2012-10-06 ┆ 15.725417      ┆ 15.669931        ┆ 15.669931         ┆ 15.669931         │\n",
       "│ 2012-10-07 ┆ 10.197083      ┆ 14.046319        ┆ 14.046319         ┆ 14.046319         │\n",
       "│ 2012-10-08 ┆ 6.79625        ┆ 10.90625         ┆ 10.90625          ┆ 10.90625          │\n",
       "│ 2012-10-09 ┆ 6.735          ┆ 7.909444         ┆ 7.909444          ┆ 7.909444          │\n",
       "│ 2012-10-10 ┆ 9.305417       ┆ 7.612222         ┆ 7.612222          ┆ 7.612222          │\n",
       "└────────────┴────────────────┴──────────────────┴───────────────────┴───────────────────┘"
      ]
     },
     "execution_count": 570,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    daily_avg_temperature_lf\n",
    "    .set_sorted('date')\n",
    "    .select(\n",
    "        'date',\n",
    "        'daily_avg_temp',\n",
    "        pl.col('daily_avg_temp').rolling_mean(3).alias('3day_rolling_avg'),\n",
    "        pl.col('daily_avg_temp').rolling_mean(\n",
    "            window_size=3,\n",
    "            min_periods=1\n",
    "        )\n",
    "        .alias('3day_rolling_avg2'),\n",
    "        pl.col('daily_avg_temp').mean().rolling(\n",
    "            index_column='date', \n",
    "            period='3d',\n",
    "            closed='right'\n",
    "        )\n",
    "        .alias('3day_rolling_avg3')\n",
    "    )\n",
    "    .head(10)\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "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: (10, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>daily_avg_temp</th><th>3day_rolling_avg</th><th>3day_rolling_min</th><th>3day_rolling_max</th></tr><tr><td>date</td><td>list[f64]</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01</td><td>[13.140854]</td><td>13.140854</td><td>13.140854</td><td>13.140854</td></tr><tr><td>2012-10-02</td><td>[13.140854, 14.24739]</td><td>13.694122</td><td>13.140854</td><td>14.24739</td></tr><tr><td>2012-10-03</td><td>[13.140854, 14.24739, 14.176875]</td><td>13.85504</td><td>13.140854</td><td>14.24739</td></tr><tr><td>2012-10-04</td><td>[14.24739, 14.176875, 15.067917]</td><td>14.497394</td><td>14.176875</td><td>15.067917</td></tr><tr><td>2012-10-05</td><td>[14.176875, 15.067917, 16.216458]</td><td>15.15375</td><td>14.176875</td><td>16.216458</td></tr><tr><td>2012-10-06</td><td>[15.067917, 16.216458, 15.725417]</td><td>15.669931</td><td>15.067917</td><td>16.216458</td></tr><tr><td>2012-10-07</td><td>[16.216458, 15.725417, 10.197083]</td><td>14.046319</td><td>10.197083</td><td>16.216458</td></tr><tr><td>2012-10-08</td><td>[15.725417, 10.197083, 6.79625]</td><td>10.90625</td><td>6.79625</td><td>15.725417</td></tr><tr><td>2012-10-09</td><td>[10.197083, 6.79625, 6.735]</td><td>7.909444</td><td>6.735</td><td>10.197083</td></tr><tr><td>2012-10-10</td><td>[6.79625, 6.735, 9.305417]</td><td>7.612222</td><td>6.735</td><td>9.305417</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 5)\n",
       "┌────────────┬────────────────────────────┬──────────────────┬──────────────────┬──────────────────┐\n",
       "│ date       ┆ daily_avg_temp             ┆ 3day_rolling_avg ┆ 3day_rolling_min ┆ 3day_rolling_max │\n",
       "│ ---        ┆ ---                        ┆ ---              ┆ ---              ┆ ---              │\n",
       "│ date       ┆ list[f64]                  ┆ f64              ┆ f64              ┆ f64              │\n",
       "╞════════════╪════════════════════════════╪══════════════════╪══════════════════╪══════════════════╡\n",
       "│ 2012-10-01 ┆ [13.140854]                ┆ 13.140854        ┆ 13.140854        ┆ 13.140854        │\n",
       "│ 2012-10-02 ┆ [13.140854, 14.24739]      ┆ 13.694122        ┆ 13.140854        ┆ 14.24739         │\n",
       "│ 2012-10-03 ┆ [13.140854, 14.24739,      ┆ 13.85504         ┆ 13.140854        ┆ 14.24739         │\n",
       "│            ┆ 14.176875]                 ┆                  ┆                  ┆                  │\n",
       "│ 2012-10-04 ┆ [14.24739, 14.176875,      ┆ 14.497394        ┆ 14.176875        ┆ 15.067917        │\n",
       "│            ┆ 15.067917]                 ┆                  ┆                  ┆                  │\n",
       "│ 2012-10-05 ┆ [14.176875, 15.067917,     ┆ 15.15375         ┆ 14.176875        ┆ 16.216458        │\n",
       "│            ┆ 16.216458…                 ┆                  ┆                  ┆                  │\n",
       "│ 2012-10-06 ┆ [15.067917, 16.216458,     ┆ 15.669931        ┆ 15.067917        ┆ 16.216458        │\n",
       "│            ┆ 15.725417…                 ┆                  ┆                  ┆                  │\n",
       "│ 2012-10-07 ┆ [16.216458, 15.725417,     ┆ 14.046319        ┆ 10.197083        ┆ 16.216458        │\n",
       "│            ┆ 10.197083…                 ┆                  ┆                  ┆                  │\n",
       "│ 2012-10-08 ┆ [15.725417, 10.197083,     ┆ 10.90625         ┆ 6.79625          ┆ 15.725417        │\n",
       "│            ┆ 6.79625]                   ┆                  ┆                  ┆                  │\n",
       "│ 2012-10-09 ┆ [10.197083, 6.79625,       ┆ 7.909444         ┆ 6.735            ┆ 10.197083        │\n",
       "│            ┆ 6.735]                     ┆                  ┆                  ┆                  │\n",
       "│ 2012-10-10 ┆ [6.79625, 6.735, 9.305417] ┆ 7.612222         ┆ 6.735            ┆ 9.305417         │\n",
       "└────────────┴────────────────────────────┴──────────────────┴──────────────────┴──────────────────┘"
      ]
     },
     "execution_count": 580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    daily_avg_temperature_lf\n",
    "    .set_sorted('date')\n",
    "    .rolling(\n",
    "        'date',\n",
    "        period='3d'\n",
    "    )\n",
    "    .agg(\n",
    "        pl.col('daily_avg_temp'),\n",
    "        pl.col('daily_avg_temp').mean().alias('3day_rolling_avg'),\n",
    "        pl.col('daily_avg_temp').min().alias('3day_rolling_min'),\n",
    "        pl.col('daily_avg_temp').max().alias('3day_rolling_max'),\n",
    "    )\n",
    "    .head(10)\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "application/vnd.holoviews_exec.v0+json": "",
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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"2d01d942-429f-4712-8ec7-8ff5bca346e9\",\"roots\":{\"p7588\":\"e115b3c3-3fbd-401c-aef8-b7d9b78797e3\"},\"root_ids\":[\"p7588\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [Variable]\n",
       "   :Curve   [date]   (value)"
      ]
     },
     "execution_count": 581,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p7588"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    daily_avg_temperature_lf\n",
    "    .select(\n",
    "        'date',\n",
    "        'daily_avg_temp',\n",
    "        pl.col('daily_avg_temp').rolling_mean(60).alias('60day_rolling_avg')\n",
    "    )\n",
    "    .collect()\n",
    "    .plot.line(\n",
    "        x='date', \n",
    "        y=['daily_avg_temp', '60day_rolling_avg'],\n",
    "        color=['skyblue', 'gray'],\n",
    "        width=800,\n",
    "        height=400\n",
    "    )\n",
    "    .opts(legend_position='bottom_right')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There is more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "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: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>daily_avg_temp</th><th>3day_rolling_range</th></tr><tr><td>date</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01</td><td>13.140854</td><td>null</td></tr><tr><td>2012-10-02</td><td>14.24739</td><td>null</td></tr><tr><td>2012-10-03</td><td>14.176875</td><td>1.106536</td></tr><tr><td>2012-10-04</td><td>15.067917</td><td>0.891042</td></tr><tr><td>2012-10-05</td><td>16.216458</td><td>2.039583</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "┌────────────┬────────────────┬────────────────────┐\n",
       "│ date       ┆ daily_avg_temp ┆ 3day_rolling_range │\n",
       "│ ---        ┆ ---            ┆ ---                │\n",
       "│ date       ┆ f64            ┆ f64                │\n",
       "╞════════════╪════════════════╪════════════════════╡\n",
       "│ 2012-10-01 ┆ 13.140854      ┆ null               │\n",
       "│ 2012-10-02 ┆ 14.24739       ┆ null               │\n",
       "│ 2012-10-03 ┆ 14.176875      ┆ 1.106536           │\n",
       "│ 2012-10-04 ┆ 15.067917      ┆ 0.891042           │\n",
       "│ 2012-10-05 ┆ 16.216458      ┆ 2.039583           │\n",
       "└────────────┴────────────────┴────────────────────┘"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_range(nums):\n",
    "    range = max(nums) - min(nums)\n",
    "    return range\n",
    "\n",
    "(\n",
    "    daily_avg_temperature_lf\n",
    "    .with_columns(\n",
    "        pl.col('daily_avg_temp').rolling_map(get_range, window_size=3).alias('3day_rolling_range')\n",
    "    )\n",
    "    .head()\n",
    "    .collect()\n",
    ")    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='p7684'>\n",
       "  <div id=\"e8500b1c-4cbc-44c8-9a58-9d72ea8b8333\" data-root-id=\"p7684\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"6079e4d1-02fc-4456-bb28-330293a0bbff\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Row\",\"id\":\"p7684\",\"attributes\":{\"name\":\"Row13531\",\"tags\":[\"embedded\"],\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: 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\"},\"shape\":[1887],\"dtype\":\"float64\"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       "  var render_items = [{\"docid\":\"6079e4d1-02fc-4456-bb28-330293a0bbff\",\"roots\":{\"p7684\":\"e8500b1c-4cbc-44c8-9a58-9d72ea8b8333\"},\"root_ids\":[\"p7684\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [Variable]\n",
       "   :Curve   [date]   (value)"
      ]
     },
     "execution_count": 582,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p7684"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    daily_avg_temperature_lf\n",
    "    .with_columns(\n",
    "        pl.col('daily_avg_temp').rolling_map(get_range, window_size=3).alias('3day_rolling_range')\n",
    "    )\n",
    "    .collect()\n",
    "    .plot.line(\n",
    "        x='date', \n",
    "        y=['daily_avg_temp', '3day_rolling_range'],\n",
    "        color=['skyblue', 'gray'],\n",
    "        width=800,\n",
    "        height=400\n",
    "    )\n",
    "    .opts(legend_position='bottom_right')\n",
    ")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "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: (10, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>humidity</th></tr><tr><td>datetime[μs]</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01 00:00:00</td><td>63.1</td></tr><tr><td>2012-10-08 00:00:00</td><td>62.1</td></tr><tr><td>2012-10-15 00:00:00</td><td>76.1</td></tr><tr><td>2012-10-22 00:00:00</td><td>70.0</td></tr><tr><td>2012-10-29 00:00:00</td><td>80.0</td></tr><tr><td>2012-11-05 00:00:00</td><td>68.7</td></tr><tr><td>2012-11-12 00:00:00</td><td>68.5</td></tr><tr><td>2012-11-19 00:00:00</td><td>81.0</td></tr><tr><td>2012-11-26 00:00:00</td><td>69.0</td></tr><tr><td>2012-12-03 00:00:00</td><td>82.4</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 2)\n",
       "┌─────────────────────┬──────────┐\n",
       "│ datetime            ┆ humidity │\n",
       "│ ---                 ┆ ---      │\n",
       "│ datetime[μs]        ┆ f64      │\n",
       "╞═════════════════════╪══════════╡\n",
       "│ 2012-10-01 00:00:00 ┆ 63.1     │\n",
       "│ 2012-10-08 00:00:00 ┆ 62.1     │\n",
       "│ 2012-10-15 00:00:00 ┆ 76.1     │\n",
       "│ 2012-10-22 00:00:00 ┆ 70.0     │\n",
       "│ 2012-10-29 00:00:00 ┆ 80.0     │\n",
       "│ 2012-11-05 00:00:00 ┆ 68.7     │\n",
       "│ 2012-11-12 00:00:00 ┆ 68.5     │\n",
       "│ 2012-11-19 00:00:00 ┆ 81.0     │\n",
       "│ 2012-11-26 00:00:00 ┆ 69.0     │\n",
       "│ 2012-12-03 00:00:00 ┆ 82.4     │\n",
       "└─────────────────────┴──────────┘"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    lf\n",
    "    .set_sorted('datetime')\n",
    "    .group_by_dynamic(\n",
    "        'datetime', every='1w'\n",
    "    )\n",
    "    .agg(pl.col('humidity').mean().round(1))\n",
    "    .head(10)\n",
    "    .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
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       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
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       "  var render_items = [{\"docid\":\"ddfe74aa-83f7-4804-a414-9458cfecd363\",\"roots\":{\"p13295\":\"fb4ef3d5-99f8-4d18-bd6e-3126f1d90bbb\"},\"root_ids\":[\"p13295\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Curve   [datetime]   (humidity)"
      ]
     },
     "execution_count": 673,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p13295"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "(\n",
    "    lf\n",
    "    .set_sorted('datetime')\n",
    "    .group_by_dynamic(\n",
    "        'datetime', every='1w'\n",
    "    )\n",
    "    .agg(pl.col('humidity').mean().round(1))\n",
    "    .filter(\n",
    "        pl.col('datetime').dt.date().is_between(\n",
    "            datetime(2012,10,1), \n",
    "            datetime(2012,10,31)\n",
    "        )\n",
    "    )\n",
    "    .collect()\n",
    "    .plot.line(\n",
    "        x='datetime', \n",
    "        y='humidity',\n",
    "        color=['skyblue'],\n",
    "        width=1000,\n",
    "        height=400\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "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: (10, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>humidity</th></tr><tr><td>datetime[μs]</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>null</td></tr><tr><td>2012-10-01 12:30:00</td><td>null</td></tr><tr><td>2012-10-01 13:00:00</td><td>82.0</td></tr><tr><td>2012-10-01 13:30:00</td><td>null</td></tr><tr><td>2012-10-01 14:00:00</td><td>81.0</td></tr><tr><td>2012-10-01 14:30:00</td><td>null</td></tr><tr><td>2012-10-01 15:00:00</td><td>79.0</td></tr><tr><td>2012-10-01 15:30:00</td><td>null</td></tr><tr><td>2012-10-01 16:00:00</td><td>77.0</td></tr><tr><td>2012-10-01 16:30:00</td><td>null</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 2)\n",
       "┌─────────────────────┬──────────┐\n",
       "│ datetime            ┆ humidity │\n",
       "│ ---                 ┆ ---      │\n",
       "│ datetime[μs]        ┆ f64      │\n",
       "╞═════════════════════╪══════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ null     │\n",
       "│ 2012-10-01 12:30:00 ┆ null     │\n",
       "│ 2012-10-01 13:00:00 ┆ 82.0     │\n",
       "│ 2012-10-01 13:30:00 ┆ null     │\n",
       "│ 2012-10-01 14:00:00 ┆ 81.0     │\n",
       "│ 2012-10-01 14:30:00 ┆ null     │\n",
       "│ 2012-10-01 15:00:00 ┆ 79.0     │\n",
       "│ 2012-10-01 15:30:00 ┆ null     │\n",
       "│ 2012-10-01 16:00:00 ┆ 77.0     │\n",
       "│ 2012-10-01 16:30:00 ┆ null     │\n",
       "└─────────────────────┴──────────┘"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upsampled_df = (\n",
    "    lf\n",
    "    .set_sorted('datetime')\n",
    "    .collect()\n",
    "    .upsample(\n",
    "        time_column='datetime', \n",
    "        every='30m', \n",
    "        maintain_order=True\n",
    "    )\n",
    "    .select(\n",
    "        'datetime', \n",
    "        pl.col('humidity')\n",
    "    )\n",
    ")\n",
    "upsampled_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
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       "</style>\n",
       "<small>shape: (10, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>humidity</th></tr><tr><td>datetime[μs]</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>null</td></tr><tr><td>2012-10-01 12:30:00</td><td>null</td></tr><tr><td>2012-10-01 13:00:00</td><td>82.0</td></tr><tr><td>2012-10-01 13:30:00</td><td>81.5</td></tr><tr><td>2012-10-01 14:00:00</td><td>81.0</td></tr><tr><td>2012-10-01 14:30:00</td><td>80.0</td></tr><tr><td>2012-10-01 15:00:00</td><td>79.0</td></tr><tr><td>2012-10-01 15:30:00</td><td>78.0</td></tr><tr><td>2012-10-01 16:00:00</td><td>77.0</td></tr><tr><td>2012-10-01 16:30:00</td><td>76.5</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 2)\n",
       "┌─────────────────────┬──────────┐\n",
       "│ datetime            ┆ humidity │\n",
       "│ ---                 ┆ ---      │\n",
       "│ datetime[μs]        ┆ f64      │\n",
       "╞═════════════════════╪══════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ null     │\n",
       "│ 2012-10-01 12:30:00 ┆ null     │\n",
       "│ 2012-10-01 13:00:00 ┆ 82.0     │\n",
       "│ 2012-10-01 13:30:00 ┆ 81.5     │\n",
       "│ 2012-10-01 14:00:00 ┆ 81.0     │\n",
       "│ 2012-10-01 14:30:00 ┆ 80.0     │\n",
       "│ 2012-10-01 15:00:00 ┆ 79.0     │\n",
       "│ 2012-10-01 15:30:00 ┆ 78.0     │\n",
       "│ 2012-10-01 16:00:00 ┆ 77.0     │\n",
       "│ 2012-10-01 16:30:00 ┆ 76.5     │\n",
       "└─────────────────────┴──────────┘"
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    upsampled_df\n",
    "    .with_columns(\n",
    "        pl.col('humidity').interpolate()\n",
    "    )\n",
    "    .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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Ep3NCAAAUsganc0IAAIhpCKdzQgAA/CAKp3NCAABw2Aunc0IAAOSPDadzQgAAWEcPp3NCAADM/hCnc0IAAEC2EqdzQgAAtG0Up3NCAAAoJRanc0IAAJzcF6dzQgAAEJQZp3NCAACESxunc0IAAPgCHadzQgAAbLoep3NCAADgcSCnc0IAAFQpIqdzQgAAyOAjp3NCAAA8mCWnc0IAALBPJ6dzQgAAJAcpp3NCAACYviqnc0IAAAx2LKdzQgAAgC0up3NCAAD05C+nc0IAAGicMadzQgAA3FMzp3NCAABQCzWnc0IAAMTCNqdzQgAAOHo4p3NCAACsMTqnc0IAACDpO6dzQgAAlKA9p3NCAAAIWD+nc0IAAHwPQadzQgAA8MZCp3NCAABkfkSnc0IAANg1RqdzQgAATO1Hp3NCAADApEmnc0IAADRcS6dzQgAAqBNNp3NCAAAcy06nc0IAAJCCUKdzQgAABDpSp3NCAAB48VOnc0IAAOyoVadzQgAAYGBXp3NCAADUF1mnc0IAAEjPWqdzQgAAvIZcp3NCAAAwPl6nc0IAAKT1X6dzQgAAGK1hp3NCAACMZGOnc0IAAAAcZadzQgAAdNNmp3NCAADoiminc0IAAFxCaqdzQgAA0Plrp3NCAABEsW2nc0IAALhob6dzQgAALCBxp3NCAACg13Knc0IAABSPdKdzQgAAiEZ2p3NCAAD8/Xenc0IAAHC1eadzQgAA5Gx7p3NCAABYJH2nc0IAAMzbfqdzQgAAQJOAp3NCAAC0SoKnc0IAACgChKdzQgAAnLmFp3NCAAAQcYenc0IAAIQoiadzQgAA+N+Kp3NCAABsl4ync0IAAOBOjqdzQgAAVAaQp3NCAADIvZGnc0IAADx1k6dzQgAAsCyVp3NCAAAk5Janc0IAAJibmKdzQgAADFOap3NCAACACpync0IAAPTBnadzQgAAaHmfp3NCAADcMKGnc0IAAFDooqdzQgAAxJ+kp3NCAAA4V6anc0IAAKwOqKdzQgAAIMapp3NCAACUfaunc0IAAAg1radzQgAAfOyup3NCAADwo7Cnc0IAAGRbsqdzQgAA2BK0p3NCAABMyrWnc0IAAMCBt6dzQgAANDm5p3NCAACo8Lqnc0IAAByovKdzQgAAkF++p3NCAAAEF8Cnc0IAAHjOwadzQgAA7IXDp3NCAABgPcWnc0IAANT0xqdzQgAASKzIp3NCAAC8Y8qnc0IAADAbzKdzQgAApNLNp3NCAAAYis+nc0IAAIxB0adzQgAAAPnSp3NCAAB0sNSnc0IAAOhn1qdzQgAAXB/Yp3NCAADQ1tmnc0IAAESO26dzQgAAuEXdp3NCAAAs/d6nc0IAAKC04KdzQgAAFGzip3NCAACII+Snc0IAAPza5adzQgAAcJLnp3NCAADkSemnc0IAAFgB66dzQgAAzLjsp3NCAABAcO6nc0IAALQn8KdzQgAAKN/xp3NCAACclvOnc0IAABBO9adzQgAAhAX3p3NCAAD4vPinc0IAAGx0+qdzQgAA4Cv8p3NCAABU4/2nc0IAAMia/6dzQgAAPFIBqHNCAACwCQOoc0IAACTBBKhzQgAAmHgGqHNCAAAMMAioc0IAAIDnCahzQgAA9J4LqHNCAABoVg2oc0IAANwND6hzQgAAUMUQqHNCAADEfBKoc0IAADg0FKhzQgAArOsVqHNCAAAgoxeoc0IAAJRaGahzQgAACBIbqHNCAAB8yRyoc0IAAPCAHqhzQgAAZDggqHNCAADY7yGoc0IAAEynI6hzQgAAwF4lqHNCAAA0Fieoc0IAAKjNKKhzQgAAHIUqqHNCAACQPCyoc0IAAAT0LahzQgAAeKsvqHNCAADsYjGoc0IAAGAaM6hzQgAA1NE0qHNCAABIiTaoc0IAALxAOKhzQgAAMPg5qHNCAACkrzuoc0IAABhnPahzQgAAjB4/qHNCAAAA1kCoc0IAAHSNQqhzQgAA6EREqHNCAABc/EWoc0IAANCzR6hzQgAARGtJqHNCAAC4Ikuoc0IAACzaTKhzQgAAoJFOqHNCAAAUSVCoc0IAAIgAUqhzQgAA/LdTqHNCAABwb1Woc0IAAOQmV6hzQgAAWN5YqHNCAADMlVqoc0IAAEBNXKhzQgAAtAReqHNCAAAovF+oc0IAAJxzYahzQgAAECtjqHNCAACE4mSoc0IAAPiZZqhzQgAAbFFoqHNCAADgCGqoc0IAAFTAa6hzQgAAyHdtqHNCAAA8L2+oc0IAALDmcKhzQgAAJJ5yqHNCAACYVXSoc0IAAAwNdqhzQgAAgMR3qHNCAAD0e3moc0IAAGgze6hzQgAA3Op8qHNCAABQon6oc0IAAMRZgKhzQgAAOBGCqHNCAACsyIOoc0IAACCAhahzQgAAlDeHqHNCAAAI74ioc0IAAHymiqhzQgAA8F2MqHNCAABkFY6oc0IAANjMj6hzQgAATISRqHNCAADAO5Ooc0IAADTzlKhzQgAAqKqWqHNCAAAcYpioc0IAAJAZmqhzQgAABNGbqHNCAAB4iJ2oc0IAAOw/n6hzQgAAYPegqHNCAADUrqKoc0IAAEhmpKhzQgAAvB2mqHNCAAAw1aeoc0IAAKSMqahzQgAAGESrqHNCAACM+6yoc0IAAACzrqhzQgAAdGqwqHNCAADoIbKoc0IAAFzZs6hzQgAA0JC1qHNCAABESLeoc0IAALj/uKhzQgAALLe6qHNCAACgbryoc0IAABQmvqhzQgAAiN2/qHNCAAD8lMGoc0IAAHBMw6hzQgAA5APFqHNCAABYu8aoc0IAAMxyyKhzQgAAQCrKqHNCAAC04cuoc0IAACiZzahzQgAAnFDPqHNCAAAQCNGoc0IAAIS/0qhzQgAA+HbUqHNCAABsLtaoc0IAAODl16hzQgAAVJ3ZqHNCAADIVNuoc0IAADwM3ahzQgAAsMPeqHNCAAAke+Coc0IAAJgy4qhzQgAADOrjqHNCAACAoeWoc0IAAPRY56hzQgAAaBDpqHNCAADcx+qoc0IAAFB/7KhzQgAAxDbuqHNCAAA47u+oc0IAAKyl8ahzQgAAIF3zqHNCAACUFPWoc0IAAAjM9qhzQgAAfIP4qHNCAADwOvqoc0IAAGTy+6hzQgAA2Kn9qHNCAABMYf+oc0IAAMAYAalzQgAANNACqXNCAACohwSpc0IAABw/BqlzQgAAkPYHqXNCAAAErgmpc0IAAHhlC6lzQgAA7BwNqXNCAABg1A6pc0IAANSLEKlzQgAASEMSqXNCAAC8+hOpc0IAADCyFalzQgAApGkXqXNCAAAYIRmpc0IAAIzYGqlzQgAAAJAcqXNCAAB0Rx6pc0IAAOj+H6lzQgAAXLYhqXNCAADQbSOpc0IAAEQlJalzQgAAuNwmqXNCAAAslCipc0IAAKBLKqlzQgAAFAMsqXNCAACIui2pc0IAAPxxL6lzQgAAcCkxqXNCAADk4DKpc0IAAFiYNKlzQgAAzE82qXNCAABABzipc0IAALS+OalzQgAAKHY7qXNCAACcLT2pc0IAABDlPqlzQgAAhJxAqXNCAAD4U0Kpc0IAAGwLRKlzQgAA4MJFqXNCAABUekepc0IAAMgxSalzQgAAPOlKqXNCAACwoEypc0IAACRYTqlzQgAAmA9QqXNCAAAMx1Gpc0IAAIB+U6lzQgAA9DVVqXNCAABo7Vapc0IAANykWKlzQgAAUFxaqXNCAADEE1ypc0IAADjLXalzQgAArIJfqXNCAAAgOmGpc0IAAJTxYqlzQgAACKlkqXNCAAB8YGapc0IAAPAXaKlzQgAAZM9pqXNCAADYhmupc0IAAEw+balzQgAAwPVuqXNCAAA0rXCpc0IAAKhkcqlzQgAAHBx0qXNCAACQ03Wpc0IAAASLd6lzQgAAeEJ5qXNCAADs+Xqpc0IAAGCxfKlzQgAA1Gh+qXNCAABIIICpc0IAALzXgalzQgAAMI+DqXNCAACkRoWpc0IAABj+hqlzQgAAjLWIqXNCAAAAbYqpc0IAAHQkjKlzQgAA6NuNqXNCAABck4+pc0IAANBKkalzQgAARAKTqXNCAAC4uZSpc0IAACxxlqlzQgAAoCiYqXNCAAAU4Jmpc0IAAIiXm6lzQgAA/E6dqXNCAABwBp+pc0IAAOS9oKlzQgAAWHWiqXNCAADMLKSpc0IAAEDkpalzQgAAtJunqXNCAAAoU6mpc0IAAJwKq6lzQgAAEMKsqXNCAACEea6pc0IAAPgwsKlzQgAAbOixqXNCAADgn7Opc0IAAFRXtalzQgAAyA63qXNCAAA8xripc0IAALB9uqlzQgAAJDW8qXNCAACY7L2pc0IAAAykv6lzQgAAgFvBqXNCAAD0EsOpc0IAAGjKxKlzQgAA3IHGqXNCAABQOcipc0IAAMTwyalzQgAAOKjLqXNCAACsX82pc0IAACAXz6lzQgAAlM7QqXNCAAAIhtKpc0IAAHw91KlzQgAA8PTVqXNCAABkrNepc0IAANhj2alzQgAATBvbqXNCAADA0typc0IAADSK3qlzQgAAqEHgqXNCAAAc+eGpc0IAAJCw46lzQgAABGjlqXNCAAB4H+epc0IAAOzW6KlzQgAAYI7qqXNCAADUReypc0IAAEj97alzQgAAvLTvqXNCAAAwbPGpc0IAAKQj86lzQgAAGNv0qXNCAACMkvapc0IAAABK+KlzQgAAdAH6qXNCAADouPupc0IAAFxw/alzQgAA0Cf/qXNCAABE3wCqc0IAALiWAqpzQgAALE4EqnNCAACgBQaqc0IAABS9B6pzQgAAiHQJqnNCAAD8Kwuqc0IAAHDjDKpzQgAA5JoOqnNCAABYUhCqc0IAAMwJEqpzQgAAQMETqnNCAAC0eBWqc0IAACgwF6pzQgAAnOcYqnNCAAAQnxqqc0IAAIRWHKpzQgAA+A0eqnNCAABsxR+qc0IAAOB8IapzQgAAVDQjqnNCAADI6ySqc0IAADyjJqpzQgAAsFooqnNCAAAkEiqqc0IAAJjJK6pzQgAADIEtqnNCAACAOC+qc0IAAPTvMKpzQgAAaKcyqnNCAADcXjSqc0IAAFAWNqpzQgAAxM03qnNCAAA4hTmqc0IAAKw8O6pzQgAAIPQ8qnNCAACUqz6qc0IAAAhjQKpzQgAAfBpCqnNCAADw0UOqc0IAAGSJRapzQgAA2EBHqnNCAABM+Eiqc0IAAMCvSqpzQgAANGdMqnNCAACoHk6qc0IAABzWT6pzQgAAkI1RqnNCAAAERVOqc0IAAHj8VKpzQgAA7LNWqnNCAABga1iqc0IAANQiWqpzQgAASNpbqnNCAAC8kV2qc0IAADBJX6pzQgAApABhqnNCAAAYuGKqc0IAAIxvZKpzQgAAACdmqnNCAAB03meqc0IAAOiVaapzQgAAXE1rqnNCAADQBG2qc0IAAES8bqpzQgAAuHNwqnNCAAAsK3Kqc0IAAKDic6pzQgAAFJp1qnNCAACIUXeqc0IAAPwIeapzQgAAcMB6qnNCAADkd3yqc0IAAFgvfqpzQgAAzOZ/qnNCAABAnoGqc0IAALRVg6pzQgAAKA2FqnNCAACcxIaqc0IAABB8iKpzQgAAhDOKqnNCAAD46ouqc0IAAGyijapzQgAA4FmPqnNCAABUEZGqc0IAAMjIkqpzQgAAPICUqnNCAACwN5aqc0IAACTvl6pzQgAAmKaZqnNCAAAMXpuqc0IAAIAVnapzQgAA9MyeqnNCAABohKCqc0IAANw7oqpzQgAAUPOjqnNCAADEqqWqc0IAADhip6pzQgAArBmpqnNCAAAg0aqqc0IAAJSIrKpzQgAACECuqnNCAAB896+qc0IAAPCusapzQgAAZGazqnNCAADYHbWqc0IAAEzVtqpzQgAAwIy4qnNCAAA0RLqqc0IAAKj7u6pzQgAAHLO9qnNCAACQar+qc0IAAAQiwapzQgAAeNnCqnNCAADskMSqc0IAAGBIxqpzQgAA1P/HqnNCAABIt8mqc0IAALxuy6pzQgAAMCbNqnNCAACk3c6qc0IAABiV0KpzQgAAjEzSqnNCAAAABNSqc0IAAHS71apzQgAA6HLXqnNCAABcKtmqc0IAANDh2qpzQgAARJncqnNCAAC4UN6qc0IAACwI4KpzQgAAoL/hqnNCAAAUd+Oqc0IAAIgu5apzQgAA/OXmqnNCAABwneiqc0IAAORU6qpzQgAAWAzsqnNCAADMw+2qc0IAAEB776pzQgAAtDLxqnNCAAAo6vKqc0IAAJyh9KpzQgAAEFn2qnNCAACEEPiqc0IAAPjH+apzQgAAbH/7qnNCAADgNv2qc0IAAFTu/qpzQgAAyKUAq3NCAAA8XQKrc0IAALAUBKtzQgAAJMwFq3NCAACYgwerc0IAAAw7CatzQgAAgPIKq3NCAAD0qQyrc0IAAGhhDqtzQgAA3BgQq3NCAABQ0BGrc0IAAMSHE6tzQgAAOD8Vq3NCAACs9harc0IAACCuGKtzQgAAlGUaq3NCAAAIHRyrc0IAAHzUHatzQgAA8Isfq3NCAABkQyGrc0IAANj6IqtzQgAATLIkq3NCAADAaSarc0IAADQhKKtzQgAAqNgpq3NCAAAckCurc0IAAJBHLatzQgAABP8uq3NCAAB4tjCrc0IAAOxtMqtzQgAAYCU0q3NCAADU3DWrc0IAAEiUN6tzQgAAvEs5q3NCAAAwAzurc0IAAKS6PKtzQgAAGHI+q3NCAACMKUCrc0IAAADhQatzQgAAdJhDq3NCAADoT0Wrc0IAAFwHR6tzQgAA0L5Iq3NCAABEdkqrc0IAALgtTKtzQgAALOVNq3NCAACgnE+rc0IAABRUUatzQgAAiAtTq3NCAAD8wlSrc0IAAHB6VqtzQgAA5DFYq3NCAABY6Vmrc0IAAMygW6tzQgAAQFhdq3NCAAC0D1+rc0IAACjHYKtzQgAAnH5iq3NCAAAQNmSrc0IAAITtZatzQgAA+KRnq3NCAABsXGmrc0IAAOATa6tzQgAAVMtsq3NCAADIgm6rc0IAADw6cKtzQgAAsPFxq3NCAAAkqXOrc0IAAJhgdatzQgAADBh3q3NCAACAz3irc0IAAPSGeqtzQgAAaD58q3NCAADc9X2rc0IAAFCtf6tzQgAAxGSBq3NCAAA4HIOrc0IAAKzThKtzQgAAIIuGq3NCAACUQoirc0IAAAj6iatzQgAAfLGLq3NCAADwaI2rc0IAAGQgj6tzQgAA2NeQq3NCAABMj5Krc0IAAEyPkqtzQgAA2NeQq3NCAABkII+rc0IAAPBojatzQgAAfLGLq3NCAAAI+omrc0IAAJRCiKtzQgAAIIuGq3NCAACs04Src0IAADgcg6tzQgAAxGSBq3NCAABQrX+rc0IAANz1fatzQgAAaD58q3NCAAD0hnqrc0IAAIDPeKtzQgAADBh3q3NCAACYYHWrc0IAACSpc6tzQgAAsPFxq3NCAAA8OnCrc0IAAMiCbqtzQgAAVMtsq3NCAADgE2urc0IAAGxcaatzQgAA+KRnq3NCAACE7WWrc0IAABA2ZKtzQgAAnH5iq3NCAAAox2Crc0IAALQPX6tzQgAAQFhdq3NCAADMoFurc0IAAFjpWatzQgAA5DFYq3NCAABwelarc0IAAPzCVKtzQgAAiAtTq3NCAAAUVFGrc0IAAKCcT6tzQgAALOVNq3NCAAC4LUyrc0IAAER2SqtzQgAA0L5Iq3NCAABcB0erc0IAAOhPRatzQgAAdJhDq3NCAAAA4UGrc0IAAIwpQKtzQgAAGHI+q3NCAACkujyrc0IAADADO6tzQgAAvEs5q3NCAABIlDerc0IAANTcNatzQgAAYCU0q3NCAADsbTKrc0IAAHi2MKtzQgAABP8uq3NCAACQRy2rc0IAAByQK6tzQgAAqNgpq3NCAAA0ISirc0IAAMBpJqtzQgAATLIkq3NCAADY+iKrc0IAAGRDIatzQgAA8Isfq3NCAAB81B2rc0IAAAgdHKtzQgAAlGUaq3NCAAAgrhirc0IAAKz2FqtzQgAAOD8Vq3NCAADEhxOrc0IAAFDQEatzQgAA3BgQq3NCAABoYQ6rc0IAAPSpDKtzQgAAgPIKq3NCAAAMOwmrc0IAAJiDB6tzQgAAJMwFq3NCAACwFASrc0IAADxdAqtzQgAAyKUAq3NCAABU7v6qc0IAAOA2/apzQgAAbH/7qnNCAAD4x/mqc0IAAIQQ+KpzQgAAEFn2qnNCAACcofSqc0IAACjq8qpzQgAAtDLxqnNCAABAe++qc0IAAMzD7apzQgAAWAzsqnNCAADkVOqqc0IAAHCd6KpzQgAA/OXmqnNCAACILuWqc0IAABR346pzQgAAoL/hqnNCAAAsCOCqc0IAALhQ3qpzQgAARJncqnNCAADQ4dqqc0IAAFwq2apzQgAA6HLXqnNCAAB0u9Wqc0IAAAAE1KpzQgAAjEzSqnNCAAAYldCqc0IAAKTdzqpzQgAAMCbNqnNCAAC8bsuqc0IAAEi3yapzQgAA1P/HqnNCAABgSMaqc0IAAOyQxKpzQgAAeNnCqnNCAAAEIsGqc0IAAJBqv6pzQgAAHLO9qnNCAACo+7uqc0IAADREuqpzQgAAwIy4qnNCAABM1baqc0IAANgdtapzQgAAZGazqnNCAADwrrGqc0IAAHz3r6pzQgAACECuqnNCAACUiKyqc0IAACDRqqpzQgAArBmpqnNCAAA4Yqeqc0IAAMSqpapzQgAAUPOjqnNCAADcO6Kqc0IAAGiEoKpzQgAA9MyeqnNCAACAFZ2qc0IAAAxem6pzQgAAmKaZqnNCAAAk75eqc0IAALA3lqpzQgAAPICUqnNCAADIyJKqc0IAAFQRkapzQgAA4FmPqnNCAABsoo2qc0IAAPjqi6pzQgAAhDOKqnNCAAAQfIiqc0IAAJzEhqpzQgAAKA2FqnNCAAC0VYOqc0IAAECegapzQgAAzOZ/qnNCAABYL36qc0IAAOR3fKpzQgAAcMB6qnNCAAD8CHmqc0IAAIhRd6pzQgAAFJp1qnNCAACg4nOqc0IAACwrcqpzQgAAuHNwqnNCAABEvG6qc0IAANAEbapzQgAAXE1rqnNCAADolWmqc0IAAHTeZ6pzQgAAACdmqnNCAACMb2Sqc0IAABi4YqpzQgAApABhqnNCAAAwSV+qc0IAALyRXapzQgAASNpbqnNCAADUIlqqc0IAAGBrWKpzQgAA7LNWqnNCAAB4/FSqc0IAAARFU6pzQgAAkI1RqnNCAAAc1k+qc0IAAKgeTqpzQgAANGdMqnNCAADAr0qqc0IAAEz4SKpzQgAA2EBHqnNCAABkiUWqc0IAAPDRQ6pzQgAAfBpCqnNCAAAIY0Cqc0IAAJSrPqpzQgAAIPQ8qnNCAACsPDuqc0IAADiFOapzQgAAxM03qnNCAABQFjaqc0IAANxeNKpzQgAAaKcyqnNCAAD07zCqc0IAAIA4L6pzQgAADIEtqnNCAACYySuqc0IAACQSKqpzQgAAsFooqnNCAAA8oyaqc0IAAMjrJKpzQgAAVDQjqnNCAADgfCGqc0IAAGzFH6pzQgAA+A0eqnNCAACEVhyqc0IAABCfGqpzQgAAnOcYqnNCAAAoMBeqc0IAALR4FapzQgAAQMETqnNCAADMCRKqc0IAAFhSEKpzQgAA5JoOqnNCAABw4wyqc0IAAPwrC6pzQgAAiHQJqnNCAAAUvQeqc0IAAKAFBqpzQgAALE4EqnNCAAC4lgKqc0IAAETfAKpzQgAA0Cf/qXNCAABccP2pc0IAAOi4+6lzQgAAdAH6qXNCAAAASvipc0IAAIyS9qlzQgAAGNv0qXNCAACkI/Opc0IAADBs8alzQgAAvLTvqXNCAABI/e2pc0IAANRF7KlzQgAAYI7qqXNCAADs1uipc0IAAHgf56lzQgAABGjlqXNCAACQsOOpc0IAABz54alzQgAAqEHgqXNCAAA0it6pc0IAAMDS3KlzQgAATBvbqXNCAADYY9mpc0IAAGSs16lzQgAA8PTVqXNCAAB8PdSpc0IAAAiG0qlzQgAAlM7QqXNCAAAgF8+pc0IAAKxfzalzQgAAOKjLqXNCAADE8Mmpc0IAAFA5yKlzQgAA3IHGqXNCAABoysSpc0IAAPQSw6lzQgAAgFvBqXNCAAAMpL+pc0IAAJjsvalzQgAAJDW8qXNCAACwfbqpc0IAADzGuKlzQgAAyA63qXNCAABUV7Wpc0IAAOCfs6lzQgAAbOixqXNCAAD4MLCpc0IAAIR5rqlzQgAAEMKsqXNCAACcCqupc0IAAChTqalzQgAAtJunqXNCAABA5KWpc0IAAMwspKlzQgAAWHWiqXNCAADkvaCpc0IAAHAGn6lzQgAA/E6dqXNCAACIl5upc0IAABTgmalzQgAAoCiYqXNCAAAscZapc0IAALi5lKlzQgAARAKTqXNCAADQSpGpc0IAAFyTj6lzQgAA6NuNqXNCAAB0JIypc0IAAABtiqlzQgAAjLWIqXNCAAAY/oapc0IAAKRGhalzQgAAMI+DqXNCAAC814Gpc0IAAEgggKlzQgAA1Gh+qXNCAABgsXypc0IAAOz5eqlzQgAAeEJ5qXNCAAAEi3epc0IAAJDTdalzQgAAHBx0qXNCAACoZHKpc0IAADStcKlzQgAAwPVuqXNCAABMPm2pc0IAANiGa6lzQgAAZM9pqXNCAADwF2ipc0IAAHxgZqlzQgAACKlkqXNCAACU8WKpc0IAACA6YalzQgAArIJfqXNCAAA4y12pc0IAAMQTXKlzQgAAUFxaqXNCAADcpFipc0IAAGjtVqlzQgAA9DVVqXNCAACAflOpc0IAAAzHUalzQgAAmA9QqXNCAAAkWE6pc0IAALCgTKlzQgAAPOlKqXNCAADIMUmpc0IAAFR6R6lzQgAA4MJFqXNCAABsC0Spc0IAAPhTQqlzQgAAhJxAqXNCAAAQ5T6pc0IAAJwtPalzQgAAKHY7qXNCAAC0vjmpc0IAAEAHOKlzQgAAzE82qXNCAABYmDSpc0IAAOTgMqlzQgAAcCkxqXNCAAD8cS+pc0IAAIi6LalzQgAAFAMsqXNCAACgSyqpc0IAACyUKKlzQgAAuNwmqXNCAABEJSWpc0IAANBtI6lzQgAAXLYhqXNCAADo/h+pc0IAAHRHHqlzQgAAAJAcqXNCAACM2Bqpc0IAABghGalzQgAApGkXqXNCAAAwshWpc0IAALz6E6lzQgAASEMSqXNCAADUixCpc0IAAGDUDqlzQgAA7BwNqXNCAAB4ZQupc0IAAASuCalzQgAAkPYHqXNCAAAcPwapc0IAAKiHBKlzQgAANNACqXNCAADAGAGpc0IAAExh/6hzQgAA2Kn9qHNCAABk8vuoc0IAAPA6+qhzQgAAfIP4qHNCAAAIzPaoc0IAAJQU9ahzQgAAIF3zqHNCAACspfGoc0IAADju76hzQgAAxDbuqHNCAABQf+yoc0IAANzH6qhzQgAAaBDpqHNCAAD0WOeoc0IAAICh5ahzQgAADOrjqHNCAACYMuKoc0IAACR74KhzQgAAsMPeqHNCAAA8DN2oc0IAAMhU26hzQgAAVJ3ZqHNCAADg5deoc0IAAGwu1qhzQgAA+HbUqHNCAACEv9Koc0IAABAI0ahzQgAAnFDPqHNCAAAomc2oc0IAALThy6hzQgAAQCrKqHNCAADMcsioc0IAAFi7xqhzQgAA5APFqHNCAABwTMOoc0IAAPyUwahzQgAAiN2/qHNCAAAUJr6oc0IAAKBuvKhzQgAALLe6qHNCAAC4/7ioc0IAAERIt6hzQgAA0JC1qHNCAABc2bOoc0IAAOghsqhzQgAAdGqwqHNCAAAAs66oc0IAAIz7rKhzQgAAGESrqHNCAACkjKmoc0IAADDVp6hzQgAAvB2mqHNCAABIZqSoc0IAANSuoqhzQgAAYPegqHNCAADsP5+oc0IAAHiInahzQgAABNGbqHNCAACQGZqoc0IAABximKhzQgAAqKqWqHNCAAA085Soc0IAAMA7k6hzQgAATISRqHNCAADYzI+oc0IAAGQVjqhzQgAA8F2MqHNCAAB8poqoc0IAAAjviKhzQgAAlDeHqHNCAAAggIWoc0IAAKzIg6hzQgAAOBGCqHNCAADEWYCoc0IAAFCifqhzQgAA3Op8qHNCAABoM3uoc0IAAPR7eahzQgAAgMR3qHNCAAAMDXaoc0IAAJhVdKhzQgAAJJ5yqHNCAACw5nCoc0IAADwvb6hzQgAAyHdtqHNCAABUwGuoc0IAAOAIaqhzQgAAbFFoqHNCAAD4mWaoc0IAAITiZKhzQgAAECtjqHNCAACcc2Goc0IAACi8X6hzQgAAtAReqHNCAABATVyoc0IAAMyVWqhzQgAAWN5YqHNCAADkJleoc0IAAHBvVahzQgAA/LdTqHNCAACIAFKoc0IAABRJUKhzQgAAoJFOqHNCAAAs2kyoc0IAALgiS6hzQgAARGtJqHNCAADQs0eoc0IAAFz8RahzQgAA6EREqHNCAAB0jUKoc0IAAADWQKhzQgAAjB4/qHNCAAAYZz2oc0IAAKSvO6hzQgAAMPg5qHNCAAC8QDioc0IAAEiJNqhzQgAA1NE0qHNCAABgGjOoc0IAAOxiMahzQgAAeKsvqHNCAAAE9C2oc0IAAJA8LKhzQgAAHIUqqHNCAACozSioc0IAADQWJ6hzQgAAwF4lqHNCAABMpyOoc0IAANjvIahzQgAAZDggqHNCAADwgB6oc0IAAHzJHKhzQgAACBIbqHNCAACUWhmoc0IAACCjF6hzQgAArOsVqHNCAAA4NBSoc0IAAMR8EqhzQgAAUMUQqHNCAADcDQ+oc0IAAGhWDahzQgAA9J4LqHNCAACA5wmoc0IAAAwwCKhzQgAAmHgGqHNCAAAkwQSoc0IAALAJA6hzQgAAPFIBqHNCAADImv+nc0IAAFTj/adzQgAA4Cv8p3NCAABsdPqnc0IAAPi8+KdzQgAAhAX3p3NCAAAQTvWnc0IAAJyW86dzQgAAKN/xp3NCAAC0J/Cnc0IAAEBw7qdzQgAAzLjsp3NCAABYAeunc0IAAORJ6adzQgAAcJLnp3NCAAD82uWnc0IAAIgj5KdzQgAAFGzip3NCAACgtOCnc0IAACz93qdzQgAAuEXdp3NCAABEjtunc0IAANDW2adzQgAAXB/Yp3NCAADoZ9anc0IAAHSw1KdzQgAAAPnSp3NCAACMQdGnc0IAABiKz6dzQgAApNLNp3NCAAAwG8ync0IAALxjyqdzQgAASKzIp3NCAADU9Manc0IAAGA9xadzQgAA7IXDp3NCAAB4zsGnc0IAAAQXwKdzQgAAkF++p3NCAAAcqLync0IAAKjwuqdzQgAANDm5p3NCAADAgbenc0IAAEzKtadzQgAA2BK0p3NCAABkW7Knc0IAAPCjsKdzQgAAfOyup3NCAAAINa2nc0IAAJR9q6dzQgAAIMapp3NCAACsDqinc0IAADhXpqdzQgAAxJ+kp3NCAABQ6KKnc0IAANwwoadzQgAAaHmfp3NCAAD0wZ2nc0IAAIAKnKdzQgAADFOap3NCAACYm5inc0IAACTklqdzQgAAsCyVp3NCAAA8dZOnc0IAAMi9kadzQgAAVAaQp3NCAADgTo6nc0IAAGyXjKdzQgAA+N+Kp3NCAACEKImnc0IAABBxh6dzQgAAnLmFp3NCAAAoAoSnc0IAALRKgqdzQgAAQJOAp3NCAADM236nc0IAAFgkfadzQgAA5Gx7p3NCAABwtXmnc0IAAPz9d6dzQgAAiEZ2p3NCAAAUj3Snc0IAAKDXcqdzQgAALCBxp3NCAAC4aG+nc0IAAESxbadzQgAA0Plrp3NCAABcQmqnc0IAAOiKaKdzQgAAdNNmp3NCAAAAHGWnc0IAAIxkY6dzQgAAGK1hp3NCAACk9V+nc0IAADA+XqdzQgAAvIZcp3NCAABIz1qnc0IAANQXWadzQgAAYGBXp3NCAADsqFWnc0IAAHjxU6dzQgAABDpSp3NCAACQglCnc0IAABzLTqdzQgAAqBNNp3NCAAA0XEunc0IAAMCkSadzQgAATO1Hp3NCAADYNUanc0IAAGR+RKdzQgAA8MZCp3NCAAB8D0Gnc0IAAAhYP6dzQgAAlKA9p3NCAAAg6Tunc0IAAKwxOqdzQgAAOHo4p3NCAADEwjanc0IAAFALNadzQgAA3FMzp3NCAABonDGnc0IAAPTkL6dzQgAAgC0up3NCAAAMdiync0IAAJi+KqdzQgAAJAcpp3NCAACwTyenc0IAADyYJadzQgAAyOAjp3NCAABUKSKnc0IAAOBxIKdzQgAAbLoep3NCAAD4Ah2nc0IAAIRLG6dzQgAAEJQZp3NCAACc3Benc0IAACglFqdzQgAAtG0Up3NCAABAthKnc0IAAMz+EKdzQgAAWEcPp3NCAADkjw2nc0IAAHDYC6dzQgAA/CAKp3NCAACIaQinc0IAABSyBqdzQgAAoPoEp3NCAAAsQwOnc0IAALiLAadzQgAARNT/pnNCAADQHP6mc0IAAFxl/KZzQgAA6K36pnNCAAB09vimc0IAAAA/96ZzQgAAjIf1pnNCAAAY0POmc0IAAKQY8qZzQgAAMGHwpnNCAAC8qe6mc0IAAEjy7KZzQgAA1DrrpnNCAABgg+mmc0IAAOzL56ZzQgAAeBTmpnNCAAAEXeSmc0IAAJCl4qZzQgAAHO7gpnNCAACoNt+mc0IAADR/3aZzQgAAwMfbpnNCAABMENqmc0IAANhY2KZzQgAAZKHWpnNCAADw6dSmc0IAAHwy06ZzQgAACHvRpnNCAACUw8+mc0IAACAMzqZzQgAArFTMpnNCAAA4ncqmc0IAAMTlyKZzQgAAUC7HpnNCAADcdsWmc0IAAGi/w6ZzQgAA9AfCpnNCAACAUMCmc0IAAAyZvqZzQgAAmOG8pnNCAAAkKrumc0IAALByuaZzQgAAPLu3pnNCAADIA7amc0IAAFRMtKZ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       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
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       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
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       "        embed_document(root);\n",
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       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Area   [datetime]   (humidity)"
      ]
     },
     "execution_count": 671,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p13145"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    upsampled_df\n",
    "    .with_columns(\n",
    "        pl.col('humidity').interpolate()\n",
    "    )\n",
    "    .filter(\n",
    "        pl.col('datetime').dt.date().is_between(\n",
    "            datetime(2012,10,1),\n",
    "            datetime(2012,10,31)\n",
    "        )\n",
    "    )\n",
    "    .plot.area(\n",
    "        x='datetime', \n",
    "        y='humidity',\n",
    "        color=['skyblue'],\n",
    "        width=1000,\n",
    "        height=400,\n",
    "        alpha=0.5\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "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: (10, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>humidity</th></tr><tr><td>datetime[μs]</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>null</td></tr><tr><td>2012-10-01 13:00:00</td><td>null</td></tr><tr><td>2012-10-01 14:00:00</td><td>81.0</td></tr><tr><td>2012-10-01 15:00:00</td><td>null</td></tr><tr><td>2012-10-01 16:00:00</td><td>null</td></tr><tr><td>2012-10-01 17:00:00</td><td>76.0</td></tr><tr><td>2012-10-01 18:00:00</td><td>74.0</td></tr><tr><td>2012-10-01 19:00:00</td><td>null</td></tr><tr><td>2012-10-01 20:00:00</td><td>70.0</td></tr><tr><td>2012-10-01 21:00:00</td><td>69.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 2)\n",
       "┌─────────────────────┬──────────┐\n",
       "│ datetime            ┆ humidity │\n",
       "│ ---                 ┆ ---      │\n",
       "│ datetime[μs]        ┆ f64      │\n",
       "╞═════════════════════╪══════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ null     │\n",
       "│ 2012-10-01 13:00:00 ┆ null     │\n",
       "│ 2012-10-01 14:00:00 ┆ 81.0     │\n",
       "│ 2012-10-01 15:00:00 ┆ null     │\n",
       "│ 2012-10-01 16:00:00 ┆ null     │\n",
       "│ 2012-10-01 17:00:00 ┆ 76.0     │\n",
       "│ 2012-10-01 18:00:00 ┆ 74.0     │\n",
       "│ 2012-10-01 19:00:00 ┆ null     │\n",
       "│ 2012-10-01 20:00:00 ┆ 70.0     │\n",
       "│ 2012-10-01 21:00:00 ┆ 69.0     │\n",
       "└─────────────────────┴──────────┘"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_with_gaps_lf = (\n",
    "    lf.filter(\n",
    "        ~pl.col('datetime').dt.hour().is_in([13,15,16,19])\n",
    "    )\n",
    ")\n",
    "\n",
    "(\n",
    "    datetime_with_gaps_lf\n",
    "    .set_sorted('datetime')\n",
    "    .collect()\n",
    "    .upsample(\n",
    "        time_column='datetime', \n",
    "        every='1h', \n",
    "        maintain_order=True\n",
    "    )\n",
    "    .select(\n",
    "        'datetime', \n",
    "        pl.col('humidity')\n",
    "    )\n",
    "    .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "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: (10, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>humidity</th></tr><tr><td>datetime[μs]</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>null</td></tr><tr><td>2012-10-01 12:30:00</td><td>null</td></tr><tr><td>2012-10-01 13:00:00</td><td>82.0</td></tr><tr><td>2012-10-01 13:30:00</td><td>null</td></tr><tr><td>2012-10-01 14:00:00</td><td>81.0</td></tr><tr><td>2012-10-01 14:30:00</td><td>null</td></tr><tr><td>2012-10-01 15:00:00</td><td>79.0</td></tr><tr><td>2012-10-01 15:30:00</td><td>null</td></tr><tr><td>2012-10-01 16:00:00</td><td>77.0</td></tr><tr><td>2012-10-01 16:30:00</td><td>null</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 2)\n",
       "┌─────────────────────┬──────────┐\n",
       "│ datetime            ┆ humidity │\n",
       "│ ---                 ┆ ---      │\n",
       "│ datetime[μs]        ┆ f64      │\n",
       "╞═════════════════════╪══════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ null     │\n",
       "│ 2012-10-01 12:30:00 ┆ null     │\n",
       "│ 2012-10-01 13:00:00 ┆ 82.0     │\n",
       "│ 2012-10-01 13:30:00 ┆ null     │\n",
       "│ 2012-10-01 14:00:00 ┆ 81.0     │\n",
       "│ 2012-10-01 14:30:00 ┆ null     │\n",
       "│ 2012-10-01 15:00:00 ┆ 79.0     │\n",
       "│ 2012-10-01 15:30:00 ┆ null     │\n",
       "│ 2012-10-01 16:00:00 ┆ 77.0     │\n",
       "│ 2012-10-01 16:30:00 ┆ null     │\n",
       "└─────────────────────┴──────────┘"
      ]
     },
     "execution_count": 646,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_range_lf = pl.LazyFrame({\n",
    "    'datetime': pl.datetime_range(\n",
    "        start=lf.select(pl.col('datetime').min()).collect()[0,0], \n",
    "        end=lf.select(pl.col('datetime').max()).collect()[0,0], \n",
    "        interval='30m', \n",
    "        eager=True\n",
    "    )\n",
    "})\n",
    "\n",
    "(\n",
    "    datetime_range_lf\n",
    "    .join(lf, on='datetime', how='left')\n",
    "    .select(\n",
    "        'datetime', \n",
    "        pl.col('humidity')\n",
    "    )\n",
    "    .collect()\n",
    "    .head(10)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time series forecasting with the functime library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting ready"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import polars as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lf = pl.scan_csv('../data/historical_temperatures.csv', try_parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>city</th><th>temperature</th></tr><tr><td>datetime[μs]</td><td>str</td><td>f64</td></tr></thead><tbody><tr><td>2012-10-01 12:00:00</td><td>&quot;Toronto&quot;</td><td>null</td></tr><tr><td>2012-10-01 13:00:00</td><td>&quot;Toronto&quot;</td><td>286.26</td></tr><tr><td>2012-10-01 14:00:00</td><td>&quot;Toronto&quot;</td><td>286.262541</td></tr><tr><td>2012-10-01 15:00:00</td><td>&quot;Toronto&quot;</td><td>286.269518</td></tr><tr><td>2012-10-01 16:00:00</td><td>&quot;Toronto&quot;</td><td>286.276496</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "┌─────────────────────┬─────────┬─────────────┐\n",
       "│ datetime            ┆ city    ┆ temperature │\n",
       "│ ---                 ┆ ---     ┆ ---         │\n",
       "│ datetime[μs]        ┆ str     ┆ f64         │\n",
       "╞═════════════════════╪═════════╪═════════════╡\n",
       "│ 2012-10-01 12:00:00 ┆ Toronto ┆ null        │\n",
       "│ 2012-10-01 13:00:00 ┆ Toronto ┆ 286.26      │\n",
       "│ 2012-10-01 14:00:00 ┆ Toronto ┆ 286.262541  │\n",
       "│ 2012-10-01 15:00:00 ┆ Toronto ┆ 286.269518  │\n",
       "│ 2012-10-01 16:00:00 ┆ Toronto ┆ 286.276496  │\n",
       "└─────────────────────┴─────────┴─────────────┘"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf.head().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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: (6, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>city</th></tr><tr><td>str</td></tr></thead><tbody><tr><td>&quot;Las Vegas&quot;</td></tr><tr><td>&quot;New York&quot;</td></tr><tr><td>&quot;San Francisco&quot;</td></tr><tr><td>&quot;Seattle&quot;</td></tr><tr><td>&quot;Toronto&quot;</td></tr><tr><td>&quot;Vancouver&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (6, 1)\n",
       "┌───────────────┐\n",
       "│ city          │\n",
       "│ ---           │\n",
       "│ str           │\n",
       "╞═══════════════╡\n",
       "│ Las Vegas     │\n",
       "│ New York      │\n",
       "│ San Francisco │\n",
       "│ Seattle       │\n",
       "│ Toronto       │\n",
       "│ Vancouver     │\n",
       "└───────────────┘"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf.select('city').unique().sort('city').collect()"
   ]
  },
  {
   "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: (18, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>city</th><th>datetime</th><th>temperature</th></tr><tr><td>str</td><td>datetime[μs]</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Las Vegas&quot;</td><td>2012-10-01 12:00:00</td><td>null</td></tr><tr><td>&quot;Las Vegas&quot;</td><td>2012-10-01 13:00:00</td><td>293.41</td></tr><tr><td>&quot;Las Vegas&quot;</td><td>2012-10-01 14:00:00</td><td>293.403141</td></tr><tr><td>&quot;Seattle&quot;</td><td>2012-10-01 12:00:00</td><td>null</td></tr><tr><td>&quot;Seattle&quot;</td><td>2012-10-01 13:00:00</td><td>281.8</td></tr><tr><td>&quot;Seattle&quot;</td><td>2012-10-01 14:00:00</td><td>281.797217</td></tr><tr><td>&quot;New York&quot;</td><td>2012-10-01 12:00:00</td><td>null</td></tr><tr><td>&quot;New York&quot;</td><td>2012-10-01 13:00:00</td><td>288.22</td></tr><tr><td>&quot;New York&quot;</td><td>2012-10-01 14:00:00</td><td>288.247676</td></tr><tr><td>&quot;Vancouver&quot;</td><td>2012-10-01 12:00:00</td><td>null</td></tr><tr><td>&quot;Vancouver&quot;</td><td>2012-10-01 13:00:00</td><td>284.63</td></tr><tr><td>&quot;Vancouver&quot;</td><td>2012-10-01 14:00:00</td><td>284.629041</td></tr><tr><td>&quot;San Francisco&quot;</td><td>2012-10-01 12:00:00</td><td>null</td></tr><tr><td>&quot;San Francisco&quot;</td><td>2012-10-01 13:00:00</td><td>289.48</td></tr><tr><td>&quot;San Francisco&quot;</td><td>2012-10-01 14:00:00</td><td>289.474993</td></tr><tr><td>&quot;Toronto&quot;</td><td>2012-10-01 12:00:00</td><td>null</td></tr><tr><td>&quot;Toronto&quot;</td><td>2012-10-01 13:00:00</td><td>286.26</td></tr><tr><td>&quot;Toronto&quot;</td><td>2012-10-01 14:00:00</td><td>286.262541</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (18, 3)\n",
       "┌───────────────┬─────────────────────┬─────────────┐\n",
       "│ city          ┆ datetime            ┆ temperature │\n",
       "│ ---           ┆ ---                 ┆ ---         │\n",
       "│ str           ┆ datetime[μs]        ┆ f64         │\n",
       "╞═══════════════╪═════════════════════╪═════════════╡\n",
       "│ Las Vegas     ┆ 2012-10-01 12:00:00 ┆ null        │\n",
       "│ Las Vegas     ┆ 2012-10-01 13:00:00 ┆ 293.41      │\n",
       "│ Las Vegas     ┆ 2012-10-01 14:00:00 ┆ 293.403141  │\n",
       "│ Seattle       ┆ 2012-10-01 12:00:00 ┆ null        │\n",
       "│ Seattle       ┆ 2012-10-01 13:00:00 ┆ 281.8       │\n",
       "│ …             ┆ …                   ┆ …           │\n",
       "│ San Francisco ┆ 2012-10-01 13:00:00 ┆ 289.48      │\n",
       "│ San Francisco ┆ 2012-10-01 14:00:00 ┆ 289.474993  │\n",
       "│ Toronto       ┆ 2012-10-01 12:00:00 ┆ null        │\n",
       "│ Toronto       ┆ 2012-10-01 13:00:00 ┆ 286.26      │\n",
       "│ Toronto       ┆ 2012-10-01 14:00:00 ┆ 286.262541  │\n",
       "└───────────────┴─────────────────────┴─────────────┘"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf.group_by('city').head(3).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_col, entity_col, value_col = lf.collect_schema().names()\n",
    "\n",
    "y = (\n",
    "    lf\n",
    "    .group_by_dynamic(\n",
    "        time_col,\n",
    "        every='1mo',\n",
    "        group_by=entity_col,\n",
    "    )\n",
    "    .agg(\n",
    "        (pl.col('temperature').mean()-273.15).round(1),\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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: (18, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>city</th><th>datetime</th><th>temperature</th></tr><tr><td>str</td><td>datetime[μs]</td><td>f64</td></tr></thead><tbody><tr><td>&quot;San Francisco&quot;</td><td>2012-10-01 00:00:00</td><td>16.5</td></tr><tr><td>&quot;San Francisco&quot;</td><td>2012-11-01 00:00:00</td><td>13.4</td></tr><tr><td>&quot;San Francisco&quot;</td><td>2012-12-01 00:00:00</td><td>10.3</td></tr><tr><td>&quot;Las Vegas&quot;</td><td>2012-10-01 00:00:00</td><td>20.9</td></tr><tr><td>&quot;Las Vegas&quot;</td><td>2012-11-01 00:00:00</td><td>14.4</td></tr><tr><td>&quot;Las Vegas&quot;</td><td>2012-12-01 00:00:00</td><td>9.0</td></tr><tr><td>&quot;Vancouver&quot;</td><td>2012-10-01 00:00:00</td><td>10.1</td></tr><tr><td>&quot;Vancouver&quot;</td><td>2012-11-01 00:00:00</td><td>7.2</td></tr><tr><td>&quot;Vancouver&quot;</td><td>2012-12-01 00:00:00</td><td>4.4</td></tr><tr><td>&quot;Seattle&quot;</td><td>2012-10-01 00:00:00</td><td>11.2</td></tr><tr><td>&quot;Seattle&quot;</td><td>2012-11-01 00:00:00</td><td>7.9</td></tr><tr><td>&quot;Seattle&quot;</td><td>2012-12-01 00:00:00</td><td>5.2</td></tr><tr><td>&quot;New York&quot;</td><td>2012-10-01 00:00:00</td><td>14.4</td></tr><tr><td>&quot;New York&quot;</td><td>2012-11-01 00:00:00</td><td>5.9</td></tr><tr><td>&quot;New York&quot;</td><td>2012-12-01 00:00:00</td><td>4.7</td></tr><tr><td>&quot;Toronto&quot;</td><td>2012-10-01 00:00:00</td><td>10.3</td></tr><tr><td>&quot;Toronto&quot;</td><td>2012-11-01 00:00:00</td><td>4.3</td></tr><tr><td>&quot;Toronto&quot;</td><td>2012-12-01 00:00:00</td><td>1.1</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (18, 3)\n",
       "┌───────────────┬─────────────────────┬─────────────┐\n",
       "│ city          ┆ datetime            ┆ temperature │\n",
       "│ ---           ┆ ---                 ┆ ---         │\n",
       "│ str           ┆ datetime[μs]        ┆ f64         │\n",
       "╞═══════════════╪═════════════════════╪═════════════╡\n",
       "│ San Francisco ┆ 2012-10-01 00:00:00 ┆ 16.5        │\n",
       "│ San Francisco ┆ 2012-11-01 00:00:00 ┆ 13.4        │\n",
       "│ San Francisco ┆ 2012-12-01 00:00:00 ┆ 10.3        │\n",
       "│ Las Vegas     ┆ 2012-10-01 00:00:00 ┆ 20.9        │\n",
       "│ Las Vegas     ┆ 2012-11-01 00:00:00 ┆ 14.4        │\n",
       "│ …             ┆ …                   ┆ …           │\n",
       "│ New York      ┆ 2012-11-01 00:00:00 ┆ 5.9         │\n",
       "│ New York      ┆ 2012-12-01 00:00:00 ┆ 4.7         │\n",
       "│ Toronto       ┆ 2012-10-01 00:00:00 ┆ 10.3        │\n",
       "│ Toronto       ┆ 2012-11-01 00:00:00 ┆ 4.3         │\n",
       "│ Toronto       ┆ 2012-12-01 00:00:00 ┆ 1.1         │\n",
       "└───────────────┴─────────────────────┴─────────────┘"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.group_by('city').head(3).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_train_test_sets(\n",
    "    y, \n",
    "    entity_col, \n",
    "    time_col, \n",
    "    test_size\n",
    "):\n",
    "    from functime.cross_validation import train_test_split\n",
    "\n",
    "    X = y.select(entity_col, time_col)\n",
    "    y_train, y_test = (\n",
    "        y\n",
    "        .select(entity_col, time_col, value_col)\n",
    "        .pipe(train_test_split(test_size))\n",
    "    )\n",
    "    X_train, X_test = X.pipe(train_test_split(test_size))\n",
    "\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_size = 3\n",
    "X_train, X_test, y_train, y_test = create_train_test_sets(y, entity_col, time_col, test_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_with_linear_model(\n",
    "    lags, \n",
    "    freq, \n",
    "    y_train,\n",
    "    fh\n",
    "):\n",
    "    from functime.forecasting import linear_model\n",
    "\n",
    "    forecaster = linear_model(lags=lags, freq=freq)\n",
    "    forecaster.fit(y=y_train)\n",
    "    y_pred = forecaster.predict(fh=fh)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = predict_with_linear_model(24, '1mo', y_train, test_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functime.metrics import mase\n",
    "scores = mase(y_true=y_test, y_pred=y_pred, y_train=y_train)"
   ]
  },
  {
   "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: (18, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>city</th><th>datetime</th><th>temperature</th></tr><tr><td>str</td><td>datetime[μs]</td><td>f64</td></tr></thead><tbody><tr><td>&quot;San Francisco&quot;</td><td>2017-09-01 00:00:00</td><td>19.970327</td></tr><tr><td>&quot;San Francisco&quot;</td><td>2017-10-01 00:00:00</td><td>18.315983</td></tr><tr><td>&quot;San Francisco&quot;</td><td>2017-11-01 00:00:00</td><td>16.368908</td></tr><tr><td>&quot;Vancouver&quot;</td><td>2017-09-01 00:00:00</td><td>16.471989</td></tr><tr><td>&quot;Vancouver&quot;</td><td>2017-10-01 00:00:00</td><td>14.267763</td></tr><tr><td>&quot;Vancouver&quot;</td><td>2017-11-01 00:00:00</td><td>11.530046</td></tr><tr><td>&quot;Las Vegas&quot;</td><td>2017-09-01 00:00:00</td><td>33.356968</td></tr><tr><td>&quot;Las Vegas&quot;</td><td>2017-10-01 00:00:00</td><td>26.222851</td></tr><tr><td>&quot;Las Vegas&quot;</td><td>2017-11-01 00:00:00</td><td>19.330387</td></tr><tr><td>&quot;Seattle&quot;</td><td>2017-09-01 00:00:00</td><td>16.692818</td></tr><tr><td>&quot;Seattle&quot;</td><td>2017-10-01 00:00:00</td><td>14.990093</td></tr><tr><td>&quot;Seattle&quot;</td><td>2017-11-01 00:00:00</td><td>12.816633</td></tr><tr><td>&quot;New York&quot;</td><td>2017-09-01 00:00:00</td><td>24.757906</td></tr><tr><td>&quot;New York&quot;</td><td>2017-10-01 00:00:00</td><td>22.249527</td></tr><tr><td>&quot;New York&quot;</td><td>2017-11-01 00:00:00</td><td>14.41916</td></tr><tr><td>&quot;Toronto&quot;</td><td>2017-09-01 00:00:00</td><td>21.900368</td></tr><tr><td>&quot;Toronto&quot;</td><td>2017-10-01 00:00:00</td><td>19.13233</td></tr><tr><td>&quot;Toronto&quot;</td><td>2017-11-01 00:00:00</td><td>12.422984</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (18, 3)\n",
       "┌───────────────┬─────────────────────┬─────────────┐\n",
       "│ city          ┆ datetime            ┆ temperature │\n",
       "│ ---           ┆ ---                 ┆ ---         │\n",
       "│ str           ┆ datetime[μs]        ┆ f64         │\n",
       "╞═══════════════╪═════════════════════╪═════════════╡\n",
       "│ San Francisco ┆ 2017-09-01 00:00:00 ┆ 19.970327   │\n",
       "│ San Francisco ┆ 2017-10-01 00:00:00 ┆ 18.315983   │\n",
       "│ San Francisco ┆ 2017-11-01 00:00:00 ┆ 16.368908   │\n",
       "│ Vancouver     ┆ 2017-09-01 00:00:00 ┆ 16.471989   │\n",
       "│ Vancouver     ┆ 2017-10-01 00:00:00 ┆ 14.267763   │\n",
       "│ …             ┆ …                   ┆ …           │\n",
       "│ New York      ┆ 2017-10-01 00:00:00 ┆ 22.249527   │\n",
       "│ New York      ┆ 2017-11-01 00:00:00 ┆ 14.41916    │\n",
       "│ Toronto       ┆ 2017-09-01 00:00:00 ┆ 21.900368   │\n",
       "│ Toronto       ┆ 2017-10-01 00:00:00 ┆ 19.13233    │\n",
       "│ Toronto       ┆ 2017-11-01 00:00:00 ┆ 12.422984   │\n",
       "└───────────────┴─────────────────────┴─────────────┘"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
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       "}\n",
       "</style>\n",
       "<small>shape: (6, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>city</th><th>mase</th></tr><tr><td>str</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Toronto&quot;</td><td>1.341055</td></tr><tr><td>&quot;Seattle&quot;</td><td>1.286418</td></tr><tr><td>&quot;San Francisco&quot;</td><td>0.542017</td></tr><tr><td>&quot;Las Vegas&quot;</td><td>1.151459</td></tr><tr><td>&quot;New York&quot;</td><td>1.082172</td></tr><tr><td>&quot;Vancouver&quot;</td><td>0.737569</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (6, 2)\n",
       "┌───────────────┬──────────┐\n",
       "│ city          ┆ mase     │\n",
       "│ ---           ┆ ---      │\n",
       "│ str           ┆ f64      │\n",
       "╞═══════════════╪══════════╡\n",
       "│ Toronto       ┆ 1.341055 │\n",
       "│ Seattle       ┆ 1.286418 │\n",
       "│ San Francisco ┆ 0.542017 │\n",
       "│ Las Vegas     ┆ 1.151459 │\n",
       "│ New York      ┆ 1.082172 │\n",
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       "└───────────────┴──────────┘"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(y_pred, scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  var render_items = [{\"docid\":\"b73c6f16-0b58-4543-9f9e-1c8fd3516ea7\",\"roots\":{\"p6783\":\"d17ff28a-f347-4ef0-8aba-d2b5ee66a414\"},\"root_ids\":[\"p6783\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdLayout   [city]\n",
       "   :Overlay\n",
       "      .Curve.I  :Curve   [datetime]   (temperature)\n",
       "      .Curve.II :Curve   [datetime]   (temperature)"
      ]
     },
     "execution_count": 279,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p6783"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_viz = (\n",
    "    y\n",
    "    .collect()\n",
    "    .plot.line(\n",
    "        x='datetime', y='temperature', by='city', subplots=True\n",
    "\n",
    "    )\n",
    "    .cols(2)\n",
    ") \n",
    "\n",
    "pred_viz = (\n",
    "    y_pred\n",
    "    .plot.line(\n",
    "        x='datetime', y='temperature', by='city', subplots=True\n",
    "    )\n",
    "    .cols(2)\n",
    ")\n",
    "\n",
    "actual_viz * pred_viz\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There is more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "}\n",
       "</style>\n",
       "<small>shape: (5, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>city</th><th>datetime</th><th>temperature</th><th>binned_entropy</th><th>lempel_ziv_complexity</th><th>longest_streak_above_mean</th><th>month</th></tr><tr><td>str</td><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>u64</td><td>cat</td></tr></thead><tbody><tr><td>&quot;Toronto&quot;</td><td>2012-10-01 00:00:00</td><td>10.3</td><td>2.02934</td><td>0.051913</td><td>94</td><td>&quot;10&quot;</td></tr><tr><td>&quot;Toronto&quot;</td><td>2012-11-01 00:00:00</td><td>4.3</td><td>1.969688</td><td>0.051389</td><td>87</td><td>&quot;11&quot;</td></tr><tr><td>&quot;Toronto&quot;</td><td>2012-12-01 00:00:00</td><td>1.1</td><td>2.10535</td><td>0.051075</td><td>87</td><td>&quot;12&quot;</td></tr><tr><td>&quot;Toronto&quot;</td><td>2013-01-01 00:00:00</td><td>-2.1</td><td>2.082969</td><td>0.051075</td><td>171</td><td>&quot;1&quot;</td></tr><tr><td>&quot;Toronto&quot;</td><td>2013-02-01 00:00:00</td><td>-3.4</td><td>1.946257</td><td>0.053571</td><td>130</td><td>&quot;2&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 7)\n",
       "┌─────────┬────────────────┬─────────────┬────────────────┬────────────────┬───────────────┬───────┐\n",
       "│ city    ┆ datetime       ┆ temperature ┆ binned_entropy ┆ lempel_ziv_com ┆ longest_strea ┆ month │\n",
       "│ ---     ┆ ---            ┆ ---         ┆ ---            ┆ plexity        ┆ k_above_mean  ┆ ---   │\n",
       "│ str     ┆ datetime[μs]   ┆ f64         ┆ f64            ┆ ---            ┆ ---           ┆ cat   │\n",
       "│         ┆                ┆             ┆                ┆ f64            ┆ u64           ┆       │\n",
       "╞═════════╪════════════════╪═════════════╪════════════════╪════════════════╪═══════════════╪═══════╡\n",
       "│ Toronto ┆ 2012-10-01     ┆ 10.3        ┆ 2.02934        ┆ 0.051913       ┆ 94            ┆ 10    │\n",
       "│         ┆ 00:00:00       ┆             ┆                ┆                ┆               ┆       │\n",
       "│ Toronto ┆ 2012-11-01     ┆ 4.3         ┆ 1.969688       ┆ 0.051389       ┆ 87            ┆ 11    │\n",
       "│         ┆ 00:00:00       ┆             ┆                ┆                ┆               ┆       │\n",
       "│ Toronto ┆ 2012-12-01     ┆ 1.1         ┆ 2.10535        ┆ 0.051075       ┆ 87            ┆ 12    │\n",
       "│         ┆ 00:00:00       ┆             ┆                ┆                ┆               ┆       │\n",
       "│ Toronto ┆ 2013-01-01     ┆ -2.1        ┆ 2.082969       ┆ 0.051075       ┆ 171           ┆ 1     │\n",
       "│         ┆ 00:00:00       ┆             ┆                ┆                ┆               ┆       │\n",
       "│ Toronto ┆ 2013-02-01     ┆ -3.4        ┆ 1.946257       ┆ 0.053571       ┆ 130           ┆ 2     │\n",
       "│         ┆ 00:00:00       ┆             ┆                ┆                ┆               ┆       │\n",
       "└─────────┴────────────────┴─────────────┴────────────────┴────────────────┴───────────────┴───────┘"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from functime.seasonality import add_calendar_effects\n",
    "\n",
    "y_features = (\n",
    "    lf\n",
    "    .group_by_dynamic(\n",
    "        time_col,\n",
    "        every='1mo',\n",
    "        group_by=entity_col,\n",
    "    )\n",
    "    .agg(\n",
    "        (pl.col('temperature').mean()-273.15).round(1),\n",
    "        pl.col(value_col).ts.binned_entropy(bin_count=10)\n",
    "        .alias('binned_entropy'),\n",
    "        pl.col(value_col).ts.lempel_ziv_complexity(threshold=3)\n",
    "        .alias('lempel_ziv_complexity'),\n",
    "        pl.col(value_col).ts.longest_streak_above_mean()\n",
    "        .alias('longest_streak_above_mean')\n",
    "    )\n",
    "    .pipe(add_calendar_effects(['month']))\n",
    ")\n",
    "y_features.head().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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