{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "102b1cf9-33a5-4310-af4d-bcd17d598d29",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## BUFR: using SYNOP data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e11bb141-5f5d-42c2-9083-3cccbf9b7799",
   "metadata": {},
   "source": [
    "We load a BUFR file with SYNOP observations taken from ECMWF MARS archive. First we ensure the example file is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41f29800-f75d-4adf-a7b9-7fb0381dadd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import earthkit.data as ekd\n",
    "ekd.download_example_file(\"synop_10.bufr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0430464-fd40-4964-b64d-4629ddd98909",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ekd.from_source(\"file\", \"synop_10.bufr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "07571864-48af-451b-be4a-6a42436d193c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ds)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a1f9fd95-d020-4941-b616-f01da9177b4e",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "We use :py:meth:`~data.readers.bufr.bufr.BUFRList.ls` to see metadata from the header section of the BUFR messages: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30e7749a-ea8b-43d2-90d2-31907f228513",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edition</th>\n",
       "      <th>type</th>\n",
       "      <th>subtype</th>\n",
       "      <th>c</th>\n",
       "      <th>mv</th>\n",
       "      <th>lv</th>\n",
       "      <th>subsets</th>\n",
       "      <th>compr</th>\n",
       "      <th>typicalDate</th>\n",
       "      <th>typicalTime</th>\n",
       "      <th>ident</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>20230602</td>\n",
       "      <td>120000</td>\n",
       "      <td>91648</td>\n",
       "      <td>-10.75</td>\n",
       "      <td>179.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20230602</td>\n",
       "      <td>120000</td>\n",
       "      <td>89514</td>\n",
       "      <td>-70.77</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20230602</td>\n",
       "      <td>120000</td>\n",
       "      <td>60545</td>\n",
       "      <td>33.77</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20230602</td>\n",
       "      <td>120000</td>\n",
       "      <td>30823</td>\n",
       "      <td>51.83</td>\n",
       "      <td>107.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20230602</td>\n",
       "      <td>120000</td>\n",
       "      <td>30846</td>\n",
       "      <td>51.35</td>\n",
       "      <td>112.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20230602</td>\n",
       "      <td>120000</td>\n",
       "      <td>48352</td>\n",
       "      <td>17.86</td>\n",
       "      <td>102.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20230602</td>\n",
       "      <td>120000</td>\n",
       "      <td>98747</td>\n",
       "      <td>8.41</td>\n",
       "      <td>124.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20230602</td>\n",
       "      <td>120000</td>\n",
       "      <td>68267</td>\n",
       "      <td>-26.50</td>\n",
       "      <td>29.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20230602</td>\n",
       "      <td>120000</td>\n",
       "      <td>68592</td>\n",
       "      <td>-29.60</td>\n",
       "      <td>31.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20230602</td>\n",
       "      <td>120000</td>\n",
       "      <td>91701</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-171.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   edition  type  subtype   c  mv  lv  subsets  compr typicalDate typicalTime  \\\n",
       "0        3     0        1  98  13   1        1      0    20230602      120000   \n",
       "1        3     0        1  98  13   1        1      0    20230602      120000   \n",
       "2        3     0        1  98  13   1        1      0    20230602      120000   \n",
       "3        3     0        1  98  13   1        1      0    20230602      120000   \n",
       "4        3     0        1  98  13   1        1      0    20230602      120000   \n",
       "5        3     0        1  98  13   1        1      0    20230602      120000   \n",
       "6        3     0        1  98  13   1        1      0    20230602      120000   \n",
       "7        3     0        1  98  13   1        1      0    20230602      120000   \n",
       "8        3     0        1  98  13   1        1      0    20230602      120000   \n",
       "9        3     0        1  98  13   1        1      0    20230602      120000   \n",
       "\n",
       "   ident    lat     lon  \n",
       "0  91648 -10.75  179.50  \n",
       "1  89514 -70.77   11.75  \n",
       "2  60545  33.77    2.93  \n",
       "3  30823  51.83  107.60  \n",
       "4  30846  51.35  112.47  \n",
       "5  48352  17.86  102.75  \n",
       "6  98747   8.41  124.61  \n",
       "7  68267 -26.50   29.98  \n",
       "8  68592 -29.60   31.12  \n",
       "9  91701  -2.77 -171.72  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442edb8e-8b80-4397-9eea-2cc9a607c28e",
   "metadata": {},
   "source": [
    "#### Extracting 2m temperature"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ab0308f1-a43d-4713-844d-49cbfe03453a",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "BUFR data can be extracted into a Pandas dataframe using :py:meth:`~data.readers.bufr.bufr.BUFRList.to_pandas`, which passes all the arguments to the `read_bufr() <https://pdbufr.readthedocs.io/en/latest/read_bufr.html>`_ method from :xref:`pdbufr`.\n",
    "\n",
    "SYNOP data can be encoded into BUFR in many different ways. For the data we have the location and the 2m temperature can be extracted in the following way into a Pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "53ac87d6-09b9-46c3-bc54-07fd2a175b05",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>heightOfStation</th>\n",
       "      <th>airTemperatureAt2M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-10.75</td>\n",
       "      <td>179.50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-70.77</td>\n",
       "      <td>11.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.77</td>\n",
       "      <td>2.93</td>\n",
       "      <td>763.0</td>\n",
       "      <td>296.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.83</td>\n",
       "      <td>107.60</td>\n",
       "      <td>515.0</td>\n",
       "      <td>291.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.35</td>\n",
       "      <td>112.47</td>\n",
       "      <td>743.0</td>\n",
       "      <td>287.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.86</td>\n",
       "      <td>102.75</td>\n",
       "      <td>176.0</td>\n",
       "      <td>307.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.41</td>\n",
       "      <td>124.61</td>\n",
       "      <td>188.0</td>\n",
       "      <td>299.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-26.50</td>\n",
       "      <td>29.98</td>\n",
       "      <td>1774.0</td>\n",
       "      <td>281.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-29.60</td>\n",
       "      <td>31.12</td>\n",
       "      <td>105.0</td>\n",
       "      <td>299.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-2.77</td>\n",
       "      <td>-171.72</td>\n",
       "      <td>2.0</td>\n",
       "      <td>302.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude  heightOfStation  airTemperatureAt2M\n",
       "0    -10.75     179.50              3.0               300.4\n",
       "1    -70.77      11.75              NaN               255.2\n",
       "2     33.77       2.93            763.0               296.3\n",
       "3     51.83     107.60            515.0               291.6\n",
       "4     51.35     112.47            743.0               287.4\n",
       "5     17.86     102.75            176.0               307.9\n",
       "6      8.41     124.61            188.0               299.4\n",
       "7    -26.50      29.98           1774.0               281.9\n",
       "8    -29.60      31.12            105.0               299.8\n",
       "9     -2.77    -171.72              2.0               302.1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ds.to_pandas(columns=[\"latitude\", \"longitude\",                          \n",
    "                           \"heightOfStation\",\"airTemperatureAt2M\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cbe46fc-bacc-4857-a482-32a6a38719cb",
   "metadata": {},
   "source": [
    "#### Using filters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20806a27-ed82-4cc6-ad55-ed4c55baa748",
   "metadata": {},
   "source": [
    "Specify station WMO IDs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "26cebaca-9b17-4e15-9564-d92804886ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>heightOfStation</th>\n",
       "      <th>airTemperatureAt2M</th>\n",
       "      <th>WMO_station_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-70.77</td>\n",
       "      <td>11.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255.2</td>\n",
       "      <td>89514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.35</td>\n",
       "      <td>112.47</td>\n",
       "      <td>743.0</td>\n",
       "      <td>287.4</td>\n",
       "      <td>30846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude  heightOfStation  airTemperatureAt2M  WMO_station_id\n",
       "0    -70.77      11.75              NaN               255.2           89514\n",
       "1     51.35     112.47            743.0               287.4           30846"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ds.to_pandas(columns=[\"latitude\", \"longitude\",                          \n",
    "                           \"heightOfStation\",\"airTemperatureAt2M\", \"WMO_station_id\"],\n",
    "                 filters={\"WMO_station_id\": [30846, 89514]})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2290edb-ed8e-47b7-a059-51e31e84051e",
   "metadata": {},
   "source": [
    "Temperature values <= 290 K:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "04ec7393-8969-4f46-82a6-5feb8f8a54c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>heightOfStation</th>\n",
       "      <th>airTemperatureAt2M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-70.77</td>\n",
       "      <td>11.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.35</td>\n",
       "      <td>112.47</td>\n",
       "      <td>743.0</td>\n",
       "      <td>287.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-26.50</td>\n",
       "      <td>29.98</td>\n",
       "      <td>1774.0</td>\n",
       "      <td>281.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude  heightOfStation  airTemperatureAt2M\n",
       "0    -70.77      11.75              NaN               255.2\n",
       "1     51.35     112.47            743.0               287.4\n",
       "2    -26.50      29.98           1774.0               281.9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ds.to_pandas(columns=[\"latitude\", \"longitude\",                          \n",
    "                           \"heightOfStation\",\"airTemperatureAt2M\"],\n",
    "                 filters={\"airTemperatureAt2M\": slice(None,290)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c35127-834f-4f1a-981d-ae9532bd6d3a",
   "metadata": {},
   "source": [
    "Temperature values >= 290 K and <= 300 K:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d75fe63b-baaa-43fa-8689-c3838e6b0fe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>heightOfStation</th>\n",
       "      <th>airTemperatureAt2M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.77</td>\n",
       "      <td>2.93</td>\n",
       "      <td>763</td>\n",
       "      <td>296.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.83</td>\n",
       "      <td>107.60</td>\n",
       "      <td>515</td>\n",
       "      <td>291.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.41</td>\n",
       "      <td>124.61</td>\n",
       "      <td>188</td>\n",
       "      <td>299.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-29.60</td>\n",
       "      <td>31.12</td>\n",
       "      <td>105</td>\n",
       "      <td>299.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude  heightOfStation  airTemperatureAt2M\n",
       "0     33.77       2.93              763               296.3\n",
       "1     51.83     107.60              515               291.6\n",
       "2      8.41     124.61              188               299.4\n",
       "3    -29.60      31.12              105               299.8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ds.to_pandas(columns=[\"latitude\", \"longitude\",                          \n",
    "                           \"heightOfStation\",\"airTemperatureAt2M\"],\n",
    "                 filters={\"airTemperatureAt2M\": slice(290,300)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee9ed59e-8b2f-4aaa-86bc-7e77805f089c",
   "metadata": {},
   "source": [
    "Temperature values >= 300 K:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c7611278-9426-46fd-8c49-402545122414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>heightOfStation</th>\n",
       "      <th>airTemperatureAt2M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-10.75</td>\n",
       "      <td>179.50</td>\n",
       "      <td>3</td>\n",
       "      <td>300.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.86</td>\n",
       "      <td>102.75</td>\n",
       "      <td>176</td>\n",
       "      <td>307.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.77</td>\n",
       "      <td>-171.72</td>\n",
       "      <td>2</td>\n",
       "      <td>302.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude  heightOfStation  airTemperatureAt2M\n",
       "0    -10.75     179.50                3               300.4\n",
       "1     17.86     102.75              176               307.9\n",
       "2     -2.77    -171.72                2               302.1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ds.to_pandas(columns=[\"latitude\", \"longitude\",                          \n",
    "                           \"heightOfStation\",\"airTemperatureAt2M\"],\n",
    "                 filters={\"airTemperatureAt2M\": slice(300, None)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb71e23d-d813-471e-8749-c283ee7e9ada",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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