examples/Examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1844d66d-366d-4b3e-857c-6f1745172759",
   "metadata": {},
   "source": [
    "# Here we will go through some requested problems."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3eb2ebc-00e1-49fc-9481-ca65af8ea252",
   "metadata": {},
   "source": [
    "## Data grouping in Pandas. (e.g. how to simply calculate and plot weekly averages of time series data.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4649617b-9adb-4ef1-8564-b26203fd5a44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  # Convention suggest to import as pd\n",
    "from pathlib import Path  # Importing a sub-module of the pathlib library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec61a607-006a-4135-bf38-d289a8b8ac98",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RECORD</th>\n",
       "      <th>batt_volt</th>\n",
       "      <th>PTemp</th>\n",
       "      <th>Pressure_kPa</th>\n",
       "      <th>Temp_C_Avg</th>\n",
       "      <th>Temp_C_Std</th>\n",
       "      <th>RH_Avg</th>\n",
       "      <th>RH_Std</th>\n",
       "      <th>Vap_Pressure_kPa_Avg</th>\n",
       "      <th>Vap_Pressure_kPa_Std</th>\n",
       "      <th>...</th>\n",
       "      <th>PWDa_Code_1hour</th>\n",
       "      <th>PWDa_H2O_Int_Avg_1min</th>\n",
       "      <th>PWDa_Total_H2O_mm</th>\n",
       "      <th>PWDa_Total_Snow_mm</th>\n",
       "      <th>PCP_Rate</th>\n",
       "      <th>WS_Slope</th>\n",
       "      <th>WS_Offset</th>\n",
       "      <th>TBRG_SN</th>\n",
       "      <th>RainCoefA</th>\n",
       "      <th>RainCoefB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMESTAMP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-05 13:00:00</th>\n",
       "      <td>303333</td>\n",
       "      <td>11.73</td>\n",
       "      <td>6.048</td>\n",
       "      <td>98.78</td>\n",
       "      <td>2.240</td>\n",
       "      <td>0.029</td>\n",
       "      <td>87.6</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.002</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62.31</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>1.024</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-05 13:01:00</th>\n",
       "      <td>303334</td>\n",
       "      <td>11.72</td>\n",
       "      <td>6.048</td>\n",
       "      <td>98.78</td>\n",
       "      <td>2.299</td>\n",
       "      <td>0.017</td>\n",
       "      <td>87.5</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62.31</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>1.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05 13:02:00</th>\n",
       "      <td>303335</td>\n",
       "      <td>12.02</td>\n",
       "      <td>6.048</td>\n",
       "      <td>98.78</td>\n",
       "      <td>2.309</td>\n",
       "      <td>0.016</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>14</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05 13:03:00</th>\n",
       "      <td>303336</td>\n",
       "      <td>11.70</td>\n",
       "      <td>6.048</td>\n",
       "      <td>98.78</td>\n",
       "      <td>2.331</td>\n",
       "      <td>0.014</td>\n",
       "      <td>87.1</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62.31</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>1.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05 13:04:00</th>\n",
       "      <td>303337</td>\n",
       "      <td>11.71</td>\n",
       "      <td>6.048</td>\n",
       "      <td>98.78</td>\n",
       "      <td>2.334</td>\n",
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       "      <td>14</td>\n",
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       "      <td>0.098</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>1.024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     RECORD  batt_volt  PTemp  Pressure_kPa  Temp_C_Avg  \\\n",
       "TIMESTAMP                                                                 \n",
       "2019-11-05 13:00:00  303333      11.73  6.048         98.78       2.240   \n",
       "2019-11-05 13:01:00  303334      11.72  6.048         98.78       2.299   \n",
       "2019-11-05 13:02:00  303335      12.02  6.048         98.78       2.309   \n",
       "2019-11-05 13:03:00  303336      11.70  6.048         98.78       2.331   \n",
       "2019-11-05 13:04:00  303337      11.71  6.048         98.78       2.334   \n",
       "\n",
       "                     Temp_C_Std  RH_Avg  RH_Std  Vap_Pressure_kPa_Avg  \\\n",
       "TIMESTAMP                                                               \n",
       "2019-11-05 13:00:00       0.029    87.6   0.318                 0.629   \n",
       "2019-11-05 13:01:00       0.017    87.5   0.144                 0.631   \n",
       "2019-11-05 13:02:00       0.016    87.0   0.193                 0.628   \n",
       "2019-11-05 13:03:00       0.014    87.1   0.157                 0.629   \n",
       "2019-11-05 13:04:00       0.014    86.9   0.110                 0.628   \n",
       "\n",
       "                     Vap_Pressure_kPa_Std  ...  PWDa_Code_1hour  \\\n",
       "TIMESTAMP                                  ...                    \n",
       "2019-11-05 13:00:00                 0.002  ...                0   \n",
       "2019-11-05 13:01:00                 0.001  ...                0   \n",
       "2019-11-05 13:02:00                 0.001  ...                0   \n",
       "2019-11-05 13:03:00                 0.001  ...                0   \n",
       "2019-11-05 13:04:00                 0.001  ...                0   \n",
       "\n",
       "                     PWDa_H2O_Int_Avg_1min  PWDa_Total_H2O_mm  \\\n",
       "TIMESTAMP                                                       \n",
       "2019-11-05 13:00:00                      0              62.31   \n",
       "2019-11-05 13:01:00                      0              62.31   \n",
       "2019-11-05 13:02:00                      0              62.31   \n",
       "2019-11-05 13:03:00                      0              62.31   \n",
       "2019-11-05 13:04:00                      0              62.31   \n",
       "\n",
       "                     PWDa_Total_Snow_mm  PCP_Rate  WS_Slope  WS_Offset  \\\n",
       "TIMESTAMP                                                                \n",
       "2019-11-05 13:00:00                  14         0     0.098          0   \n",
       "2019-11-05 13:01:00                  14         0     0.098          0   \n",
       "2019-11-05 13:02:00                  14         0     0.098          0   \n",
       "2019-11-05 13:03:00                  14         0     0.098          0   \n",
       "2019-11-05 13:04:00                  14         0     0.098          0   \n",
       "\n",
       "                     TBRG_SN  RainCoefA  RainCoefB  \n",
       "TIMESTAMP                                           \n",
       "2019-11-05 13:00:00      118          0      1.024  \n",
       "2019-11-05 13:01:00      118          0      1.024  \n",
       "2019-11-05 13:02:00      118          0      1.024  \n",
       "2019-11-05 13:03:00      118          0      1.024  \n",
       "2019-11-05 13:04:00      118          0      1.024  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = Path('..', 'data', 'sgpmetE13.b1', 'sgpmetE13.00.20191105.150801.raw.dat')\n",
    "df = pd.read_csv(filename, delimiter=',', header=0, skiprows=[0, 2, 3], parse_dates=[0], index_col=[0])\n",
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ec7eae-2452-4425-a14e-791a394e9679",
   "metadata": {},
   "source": [
    "We can use the resample() method with a duration in string format and the mean() method to indicate how to perform the averaging. If this raises an error, it may be because the index of the Dataframe is just a number, not the timestamp read from the file. We indicated that the index of the Dataframe is TIMESTAMP with the index_col=[0] keyword. If you do not do this upon reading the data, set it after the data is read in with df = df.set_index('TIMESTAMP')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ad6fcf3-eafd-46ae-8a45-b2e2252e675f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIMESTAMP\n",
       "2019-11-05 13:00:00    2.240\n",
       "2019-11-05 13:01:00    2.299\n",
       "2019-11-05 13:02:00    2.309\n",
       "2019-11-05 13:03:00    2.331\n",
       "2019-11-05 13:04:00    2.334\n",
       "2019-11-05 13:05:00    2.346\n",
       "2019-11-05 13:06:00    2.325\n",
       "2019-11-05 13:07:00    2.248\n",
       "2019-11-05 13:08:00    2.196\n",
       "2019-11-05 13:09:00    2.146\n",
       "Name: Temp_C_Avg, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature = df['Temp_C_Avg']  # Extract one column as a Pandas Series\n",
    "temperature[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8aa4bd9c-2790-4579-ae34-35e6d10dba07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIMESTAMP\n",
       "2019-11-05 13:00:00    2.2774\n",
       "2019-11-05 13:10:00    2.3260\n",
       "2019-11-05 13:20:00    3.0298\n",
       "2019-11-05 13:30:00    3.3490\n",
       "2019-11-05 13:40:00    3.5721\n",
       "2019-11-05 13:50:00    3.9559\n",
       "Freq: 10T, Name: Temp_C_Avg, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature = temperature.resample('10min').mean()  # Resample to 10 minute avergae blocks\n",
    "temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d9052d5-80d0-44f2-9483-098e467dfb9a",
   "metadata": {},
   "source": [
    "We can also perform the resampling on the Dataframe with one call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "215dd748-85a6-4b5c-87ad-884ad4fe6932",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RECORD</th>\n",
       "      <th>batt_volt</th>\n",
       "      <th>PTemp</th>\n",
       "      <th>Pressure_kPa</th>\n",
       "      <th>Temp_C_Avg</th>\n",
       "      <th>Temp_C_Std</th>\n",
       "      <th>RH_Avg</th>\n",
       "      <th>RH_Std</th>\n",
       "      <th>Vap_Pressure_kPa_Avg</th>\n",
       "      <th>Vap_Pressure_kPa_Std</th>\n",
       "      <th>...</th>\n",
       "      <th>PWDa_Code_1hour</th>\n",
       "      <th>PWDa_H2O_Int_Avg_1min</th>\n",
       "      <th>PWDa_Total_H2O_mm</th>\n",
       "      <th>PWDa_Total_Snow_mm</th>\n",
       "      <th>PCP_Rate</th>\n",
       "      <th>WS_Slope</th>\n",
       "      <th>WS_Offset</th>\n",
       "      <th>TBRG_SN</th>\n",
       "      <th>RainCoefA</th>\n",
       "      <th>RainCoefB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMESTAMP</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2019-11-05 13:00:00</th>\n",
       "      <td>303337.5</td>\n",
       "      <td>11.747</td>\n",
       "      <td>6.0480</td>\n",
       "      <td>98.780</td>\n",
       "      <td>2.2774</td>\n",
       "      <td>0.0190</td>\n",
       "      <td>86.84</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.098</td>\n",
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       "      <td>118.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.024</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-05 13:10:00</th>\n",
       "      <td>303347.5</td>\n",
       "      <td>11.747</td>\n",
       "      <td>6.0801</td>\n",
       "      <td>98.780</td>\n",
       "      <td>2.3260</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>86.91</td>\n",
       "      <td>0.1578</td>\n",
       "      <td>0.6275</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05 13:20:00</th>\n",
       "      <td>303357.5</td>\n",
       "      <td>11.799</td>\n",
       "      <td>6.3276</td>\n",
       "      <td>98.792</td>\n",
       "      <td>3.0298</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>84.73</td>\n",
       "      <td>0.1608</td>\n",
       "      <td>0.6431</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05 13:30:00</th>\n",
       "      <td>303367.5</td>\n",
       "      <td>11.834</td>\n",
       "      <td>6.7329</td>\n",
       "      <td>98.805</td>\n",
       "      <td>3.3490</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>83.31</td>\n",
       "      <td>0.2114</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05 13:40:00</th>\n",
       "      <td>303377.5</td>\n",
       "      <td>11.860</td>\n",
       "      <td>7.2724</td>\n",
       "      <td>98.835</td>\n",
       "      <td>3.5721</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>83.75</td>\n",
       "      <td>0.2158</td>\n",
       "      <td>0.6606</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05 13:50:00</th>\n",
       "      <td>303387.5</td>\n",
       "      <td>11.867</td>\n",
       "      <td>7.8961</td>\n",
       "      <td>98.855</td>\n",
       "      <td>3.9559</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>83.24</td>\n",
       "      <td>0.1789</td>\n",
       "      <td>0.6746</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       RECORD  batt_volt   PTemp  Pressure_kPa  Temp_C_Avg  \\\n",
       "TIMESTAMP                                                                    \n",
       "2019-11-05 13:00:00  303337.5     11.747  6.0480        98.780      2.2774   \n",
       "2019-11-05 13:10:00  303347.5     11.747  6.0801        98.780      2.3260   \n",
       "2019-11-05 13:20:00  303357.5     11.799  6.3276        98.792      3.0298   \n",
       "2019-11-05 13:30:00  303367.5     11.834  6.7329        98.805      3.3490   \n",
       "2019-11-05 13:40:00  303377.5     11.860  7.2724        98.835      3.5721   \n",
       "2019-11-05 13:50:00  303387.5     11.867  7.8961        98.855      3.9559   \n",
       "\n",
       "                     Temp_C_Std  RH_Avg  RH_Std  Vap_Pressure_kPa_Avg  \\\n",
       "TIMESTAMP                                                               \n",
       "2019-11-05 13:00:00      0.0190   86.84  0.1880                0.6250   \n",
       "2019-11-05 13:10:00      0.0255   86.91  0.1578                0.6275   \n",
       "2019-11-05 13:20:00      0.0249   84.73  0.1608                0.6431   \n",
       "2019-11-05 13:30:00      0.0163   83.31  0.2114                0.6471   \n",
       "2019-11-05 13:40:00      0.0158   83.75  0.2158                0.6606   \n",
       "2019-11-05 13:50:00      0.0203   83.24  0.1789                0.6746   \n",
       "\n",
       "                     Vap_Pressure_kPa_Std  ...  PWDa_Code_1hour  \\\n",
       "TIMESTAMP                                  ...                    \n",
       "2019-11-05 13:00:00                0.0014  ...              0.0   \n",
       "2019-11-05 13:10:00                0.0013  ...              0.0   \n",
       "2019-11-05 13:20:00                0.0011  ...              0.0   \n",
       "2019-11-05 13:30:00                0.0020  ...              0.0   \n",
       "2019-11-05 13:40:00                0.0020  ...              0.0   \n",
       "2019-11-05 13:50:00                0.0015  ...              0.0   \n",
       "\n",
       "                     PWDa_H2O_Int_Avg_1min  PWDa_Total_H2O_mm  \\\n",
       "TIMESTAMP                                                       \n",
       "2019-11-05 13:00:00                    0.0              62.31   \n",
       "2019-11-05 13:10:00                    0.0              62.31   \n",
       "2019-11-05 13:20:00                    0.0              62.31   \n",
       "2019-11-05 13:30:00                    0.0              62.31   \n",
       "2019-11-05 13:40:00                    0.0              62.31   \n",
       "2019-11-05 13:50:00                    0.0              62.31   \n",
       "\n",
       "                     PWDa_Total_Snow_mm  PCP_Rate  WS_Slope  WS_Offset  \\\n",
       "TIMESTAMP                                                                \n",
       "2019-11-05 13:00:00                14.0       0.0     0.098        0.0   \n",
       "2019-11-05 13:10:00                14.0       0.0     0.098        0.0   \n",
       "2019-11-05 13:20:00                14.0       0.0     0.098        0.0   \n",
       "2019-11-05 13:30:00                14.0       0.0     0.098        0.0   \n",
       "2019-11-05 13:40:00                14.0       0.0     0.098        0.0   \n",
       "2019-11-05 13:50:00                14.0       0.0     0.098        0.0   \n",
       "\n",
       "                     TBRG_SN  RainCoefA  RainCoefB  \n",
       "TIMESTAMP                                           \n",
       "2019-11-05 13:00:00    118.0        0.0      1.024  \n",
       "2019-11-05 13:10:00    118.0        0.0      1.024  \n",
       "2019-11-05 13:20:00    118.0        0.0      1.024  \n",
       "2019-11-05 13:30:00    118.0        0.0      1.024  \n",
       "2019-11-05 13:40:00    118.0        0.0      1.024  \n",
       "2019-11-05 13:50:00    118.0        0.0      1.024  \n",
       "\n",
       "[6 rows x 32 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.resample('10min').mean()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f02f4ae-4d59-4b51-8268-ecbfa28a7b89",
   "metadata": {},
   "source": [
    "## Aligning time stamps from separate Dataframes\n",
    "There are many ways to accomplish this. This example will focus on using Pandas, and specifically the merge method to put both data into one Dataframe after the data is merged."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dadc25ca-3913-4b64-97f3-b7e65f7e6743",
   "metadata": {},
   "source": [
    "First we will read in data from the same day of two different datastreams. Working with netCDF data files is better with Xarray but we can easily read the data with Xarray and convert to Pandas. This means we can still use the space we are most comfortable working with. Notice we do not need to set the time as the index as we did above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d2e0885-8cbf-45e4-b553-32aa2fa1bd1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
    "filename = Path('..', 'data', 'sgpmetE13.b1', 'sgpmetE13.b1.20191101.000000.cdf')\n",
    "ds_met = xr.open_dataset(filename)\n",
    "\n",
    "# This part is just removing some variables to make viewing easier.\n",
    "keep_variables = ['atmos_pressure', 'temp_mean', 'rh_mean', 'time']\n",
    "for var in list(ds_met.data_vars):\n",
    "    if var not in keep_variables:\n",
    "        del ds_met[var]\n",
    "\n",
    "filename = Path('..', 'data', 'sgpecorsfE14.b1', 'sgpecorsfE14.b1.20191101.000000.nc')\n",
    "ds_ecor = xr.open_dataset(filename)\n",
    "\n",
    "# This part is just removing some variables to make viewing easier.\n",
    "keep_variables = ['air_temperature', 'air_pressure', 'relative_humidity', 'time']\n",
    "for var in list(ds_ecor.data_vars):\n",
    "    if var not in keep_variables:\n",
    "        del ds_ecor[var]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c72cd83c-bd6a-4163-a7d2-2e1cb86e3131",
   "metadata": {},
   "source": [
    "Notice that the time steps do not match exactly. The MET is one minute and ECOR is 30 minute time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c33ed7cd-0998-4d11-9ac9-93a25b5d034f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MET: (1440,) ['2019-11-01T00:00:00.000000000' '2019-11-01T00:01:00.000000000']\n",
      "ECOR: (48,) ['2019-11-01T00:00:00.000000000' '2019-11-01T00:30:00.000000000']\n"
     ]
    }
   ],
   "source": [
    "print('MET:', ds_met['time'].shape, ds_met['time'].values[:2])\n",
    "print('ECOR:', ds_ecor['time'].shape, ds_ecor['time'].values[:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd9154f-bf5b-41f6-98c1-750c95ae04f8",
   "metadata": {},
   "source": [
    "Since we desire to work in the Pandas Space we can convert the Xarray Dataset to Pandas Dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51c4390a-616b-43e9-a23b-4731184c7840",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_met = ds_met.to_dataframe()\n",
    "df_ecor = ds_ecor.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d78c73-cca7-436e-8f33-8de24fceed9c",
   "metadata": {},
   "source": [
    "Now we can use the merge() method to merge the two Dataframes into one. We need to indicate what column we want to use as indexing with on=\"time\" and which Dataframe is the timestep to match to using how=\"left\". Notice this indicates the left Dataframe (df_ecor) time step is our desire. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5ddf45d-19b1-4f62-a90f-da45c081f8c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['air_temperature', 'air_pressure', 'relative_humidity',\n",
      "       'atmos_pressure', 'temp_mean', 'rh_mean'],\n",
      "      dtype='object')\n",
      "(48, 6)\n"
     ]
    }
   ],
   "source": [
    "df_merged = pd.merge(df_ecor, df_met, how='left', on='time')\n",
    "print(df_merged.columns)\n",
    "print(df_merged.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f6b122-7dd5-4d08-9c33-a615dbd64735",
   "metadata": {},
   "source": [
    "And just to show that the values are actually subsampled we can perform a difference. Notice that the difference hovers around the differnce between Kelvin and Degrees Celsius."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44e29a9c-9c50-48a0-8486-acc55318b3bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "2019-11-01 00:00:00    273.815002\n",
       "2019-11-01 00:30:00    273.296997\n",
       "2019-11-01 01:00:00    273.117981\n",
       "2019-11-01 01:30:00    273.539001\n",
       "2019-11-01 02:00:00    273.498016\n",
       "dtype: float32"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference = df_merged['air_temperature'] - df_merged['temp_mean']\n",
    "difference[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccbec0af-60c7-46c9-8428-e89ac1b1a656",
   "metadata": {},
   "source": [
    "## Align along time but keep separate\n",
    "But what if we don't want to put both data into the same object? We can use the align() method to modify the two data frames and align them on the same time grid. We need to provide axis=0 to ensure it aligns on the time axis only and it does not try to align on variable names too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aa3838b9-dffd-4ee2-9abc-b2c5e467e5b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MET: (48, 3)\n",
      "ECOR: (48, 3)\n"
     ]
    }
   ],
   "source": [
    "met_align, ecor_align = df_met.align(df_ecor, join='right', axis=0)\n",
    "print('MET:', met_align.shape)\n",
    "print('ECOR:', ecor_align.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53c0594a-f28b-4045-8858-07814555a7c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "2019-11-01 00:00:00    273.815002\n",
       "2019-11-01 00:30:00    273.296997\n",
       "2019-11-01 01:00:00    273.117981\n",
       "2019-11-01 01:30:00    273.539001\n",
       "2019-11-01 02:00:00    273.498016\n",
       "dtype: float32"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference = ecor_align['air_temperature'] - met_align['temp_mean']\n",
    "difference[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5d2822-a3d0-4ced-b796-b649a604ebff",
   "metadata": {},
   "source": [
    "## We can do the same actions in the Xarray Space using the Xarray Datasets\n",
    "Notice the number of values in the time dimension is 48. That is the number of samples in the ECOR dataset. The join='left' indicates which Dataset to use for indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82044ca5-ec68-4dbd-a828-55c58a485c59",
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-var-dims {\n",
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       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (time: 48)\n",
       "Coordinates:\n",
       "  * time               (time) datetime64[ns] 2019-11-01 ... 2019-11-01T23:30:00\n",
       "Data variables:\n",
       "    air_temperature    (time) float32 277.7 275.8 275.4 ... 287.2 285.7 284.2\n",
       "    air_pressure       (time) float32 98.9 98.91 98.92 ... 98.59 98.63 98.67\n",
       "    relative_humidity  (time) float32 41.58 51.52 51.75 ... 30.69 35.61 40.09\n",
       "    atmos_pressure     (time) float32 98.76 98.77 98.77 ... 98.46 98.49 98.55\n",
       "    temp_mean          (time) float32 3.876 2.506 2.286 ... 13.92 12.77 11.27\n",
       "    rh_mean            (time) float32 52.58 57.79 58.48 ... 36.04 38.76 42.54\n",
       "Attributes: (12/13)\n",
       "    command_line:          ecorsf_ingest -s sgp -f E14 -D\n",
       "    Conventions:           ARM-1.2\n",
       "    process_version:       ingest-ecorsf-1.2-0.el7\n",
       "    dod_version:           ecorsf-b1-1.3\n",
       "    input_source:          /data/collection/sgp/sgpecorsfE14.00/2019-10-31T23...\n",
       "    site_id:               sgp\n",
       "    ...                    ...\n",
       "    facility_id:           E14\n",
       "    data_level:            b1\n",
       "    location_description:  Southern Great Plains (SGP), Lamont, Oklahoma\n",
       "    datastream:            sgpecorsfE14.b1\n",
       "    doi:                   10.5439/1494128\n",
       "    history:               created by user dsmgr on machine garnet at 2019-12...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-91207fc5-5b42-48f7-ba2b-eb58b8e076f2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-91207fc5-5b42-48f7-ba2b-eb58b8e076f2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 48</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6e142828-b25f-45e0-9955-4f7c55bb8402' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e142828-b25f-45e0-9955-4f7c55bb8402' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-11-01 ... 2019-11-01T23:30:00</div><input id='attrs-8ec9fba4-ba78-4847-983d-58b7e874e94f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ec9fba4-ba78-4847-983d-58b7e874e94f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bbf298e-176f-4dcb-ae32-53c1fe36b0f3' class='xr-var-data-in' type='checkbox'><label for='data-9bbf298e-176f-4dcb-ae32-53c1fe36b0f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T01:00:00.000000000&#x27;, &#x27;2019-11-01T01:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T02:00:00.000000000&#x27;, &#x27;2019-11-01T02:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T03:00:00.000000000&#x27;, &#x27;2019-11-01T03:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T04:00:00.000000000&#x27;, &#x27;2019-11-01T04:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T05:00:00.000000000&#x27;, &#x27;2019-11-01T05:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T06:00:00.000000000&#x27;, &#x27;2019-11-01T06:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T07:00:00.000000000&#x27;, &#x27;2019-11-01T07:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T08:00:00.000000000&#x27;, &#x27;2019-11-01T08:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T09:00:00.000000000&#x27;, &#x27;2019-11-01T09:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T10:00:00.000000000&#x27;, &#x27;2019-11-01T10:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T11:00:00.000000000&#x27;, &#x27;2019-11-01T11:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T12:00:00.000000000&#x27;, &#x27;2019-11-01T12:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T13:00:00.000000000&#x27;, &#x27;2019-11-01T13:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T14:00:00.000000000&#x27;, &#x27;2019-11-01T14:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T15:00:00.000000000&#x27;, &#x27;2019-11-01T15:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T16:00:00.000000000&#x27;, &#x27;2019-11-01T16:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T17:00:00.000000000&#x27;, &#x27;2019-11-01T17:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T18:00:00.000000000&#x27;, &#x27;2019-11-01T18:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T19:00:00.000000000&#x27;, &#x27;2019-11-01T19:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T20:00:00.000000000&#x27;, &#x27;2019-11-01T20:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T21:00:00.000000000&#x27;, &#x27;2019-11-01T21:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T22:00:00.000000000&#x27;, &#x27;2019-11-01T22:30:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T23:00:00.000000000&#x27;, &#x27;2019-11-01T23:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7fa6646-e370-45f1-9e34-b86142f54dce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7fa6646-e370-45f1-9e34-b86142f54dce' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>277.7 275.8 275.4 ... 285.7 284.2</div><input id='attrs-901a4c6b-a014-43c5-8a68-2354bedb6714' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-901a4c6b-a014-43c5-8a68-2354bedb6714' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a0b0b1e-8778-4f18-a1f3-94f94ae4a76e' class='xr-var-data-in' type='checkbox'><label for='data-4a0b0b1e-8778-4f18-a1f3-94f94ae4a76e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean temperature of ambient air, calculated from high frequency air temperature readings</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>ancillary_variables :</span></dt><dd>qc_air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd></dl></div><div class='xr-var-data'><pre>array([277.691, 275.803, 275.404, 275.182, 275.006, 274.412, 273.547, 273.202,\n",
       "       273.174, 273.366, 273.357, 273.422, 273.515, 273.289, 273.214, 272.978,\n",
       "       272.474, 272.346, 271.95 , 273.137, 273.062, 272.56 , 272.538, 273.091,\n",
       "       273.233, 272.324, 271.949, 272.731, 274.122, 276.245, 277.804, 279.974,\n",
       "       281.721, 282.941, 283.993, 285.286, 286.302, 286.845, 287.004, 287.306,\n",
       "       287.773, 287.848, 288.026, 288.074, 287.932, 287.2  , 285.748, 284.191],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>98.9 98.91 98.92 ... 98.63 98.67</div><input id='attrs-e72b34ba-d254-4881-8082-8a9b1f9f524b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e72b34ba-d254-4881-8082-8a9b1f9f524b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc14aff7-895d-40dd-b12f-e914b1d5b872' class='xr-var-data-in' type='checkbox'><label for='data-bc14aff7-895d-40dd-b12f-e914b1d5b872' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean pressure of ambient air, calculated from high frequency air pressure readings</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>ancillary_variables :</span></dt><dd>qc_air_pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([98.897705, 98.9094  , 98.9238  , 98.909294, 98.89471 , 98.8811  ,\n",
       "       98.8565  , 98.843895, 98.838   , 98.8095  , 98.788   , 98.778   ,\n",
       "       98.774605, 98.7549  , 98.7284  , 98.7019  , 98.67921 , 98.662796,\n",
       "       98.649895, 98.6259  , 98.6201  , 98.6097  , 98.6125  , 98.604706,\n",
       "       98.587   , 98.591   , 98.6104  , 98.650795, 98.6582  , 98.652794,\n",
       "       98.6612  , 98.6292  , 98.6269  , 98.601295, 98.574295, 98.5707  ,\n",
       "       98.5348  , 98.5298  , 98.51321 , 98.4697  , 98.4715  , 98.4855  ,\n",
       "       98.5023  , 98.5308  , 98.5575  , 98.5904  , 98.630295, 98.6738  ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>41.58 51.52 51.75 ... 35.61 40.09</div><input id='attrs-e983e3d3-97bb-48e4-9188-de653480adc4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e983e3d3-97bb-48e4-9188-de653480adc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88f058b1-ffa3-496d-8563-1e0267e1ff3d' class='xr-var-data-in' type='checkbox'><label for='data-88f058b1-ffa3-496d-8563-1e0267e1ff3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Ambient relative humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd></dl></div><div class='xr-var-data'><pre>array([41.5824, 51.5248, 51.7517, 53.6633, 51.7512, 56.3698, 62.2883, 64.3273,\n",
       "       64.1429, 63.1162, 63.3857, 63.7499, 63.8495, 64.5816, 65.8674, 69.1754,\n",
       "       72.8204, 74.0844, 77.0006, 67.0547, 67.7483, 70.4752, 70.189 , 64.7925,\n",
       "       63.2847, 69.8765, 72.7552, 68.0377, 65.0888, 54.6668, 49.6791, 42.7949,\n",
       "       36.5838, 33.3051, 32.6405, 29.1671, 26.9482, 26.6217, 25.9987, 25.5003,\n",
       "       25.8574, 26.2398, 25.6841, 26.074 , 27.4367, 30.6879, 35.6075, 40.0854],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atmos_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>98.76 98.77 98.77 ... 98.49 98.55</div><input id='attrs-11ad02f7-03ca-45c8-a226-762d577c0d25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11ad02f7-03ca-45c8-a226-762d577c0d25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-618656b7-8878-4fa8-9526-721e5ff82202' class='xr-var-data-in' type='checkbox'><label for='data-618656b7-8878-4fa8-9526-721e5ff82202' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Atmospheric pressure</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>valid_min :</span></dt><dd>80.0</dd><dt><span>valid_max :</span></dt><dd>110.0</dd><dt><span>valid_delta :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><pre>array([98.76, 98.77, 98.77, 98.76, 98.75, 98.74, 98.72, 98.7 , 98.7 , 98.67,\n",
       "       98.66, 98.65, 98.64, 98.62, 98.59, 98.56, 98.53, 98.53, 98.5 , 98.48,\n",
       "       98.49, 98.49, 98.48, 98.45, 98.46, 98.46, 98.5 , 98.52, 98.53, 98.52,\n",
       "       98.5 , 98.48, 98.46, 98.44, 98.44, 98.41, 98.38, 98.38, 98.34, 98.31,\n",
       "       98.31, 98.33, 98.36, 98.39, 98.41, 98.46, 98.49, 98.55], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.876 2.506 2.286 ... 12.77 11.27</div><input id='attrs-dadfc981-ffc4-4f04-a4f7-72afb69545f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dadfc981-ffc4-4f04-a4f7-72afb69545f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58a4c6e8-f8cf-487e-a58e-012aedc5409d' class='xr-var-data-in' type='checkbox'><label for='data-58a4c6e8-f8cf-487e-a58e-012aedc5409d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-40.0</dd><dt><span>valid_max :</span></dt><dd>50.0</dd><dt><span>valid_delta :</span></dt><dd>20.0</dd></dl></div><div class='xr-var-data'><pre>array([ 3.876,  2.506,  2.286,  1.643,  1.508,  0.94 ,  0.326, -0.072,  0.661,\n",
       "        0.494,  0.379,  0.386,  0.561,  0.592,  0.395, -0.267, -0.541, -0.592,\n",
       "       -0.333,  0.686, -0.266, -0.252,  0.019,  0.596,  0.078, -0.264,  0.023,\n",
       "        0.389,  2.277,  4.306,  5.995,  8.28 ,  9.95 , 11.03 , 12.35 , 13.69 ,\n",
       "       14.39 , 14.55 , 15.   , 14.89 , 15.51 , 15.47 , 15.78 , 15.86 , 15.43 ,\n",
       "       13.92 , 12.77 , 11.27 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rh_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>52.58 57.79 58.48 ... 38.76 42.54</div><input id='attrs-aac85ce5-3657-4071-be07-16c68f42ebaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aac85ce5-3657-4071-be07-16c68f42ebaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-083aaa51-b855-4ca6-ba76-07021ec334bc' class='xr-var-data-in' type='checkbox'><label for='data-083aaa51-b855-4ca6-ba76-07021ec334bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Relative humidity mean</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>-2.0</dd><dt><span>valid_max :</span></dt><dd>104.0</dd><dt><span>valid_delta :</span></dt><dd>30.0</dd></dl></div><div class='xr-var-data'><pre>array([52.58, 57.79, 58.48, 60.85, 61.6 , 63.72, 67.67, 70.56, 66.1 , 67.19,\n",
       "       68.91, 68.51, 67.44, 67.3 , 70.12, 74.18, 75.5 , 77.02, 75.5 , 67.94,\n",
       "       73.14, 72.73, 70.2 , 65.45, 68.29, 70.09, 69.25, 68.73, 64.15, 56.27,\n",
       "       49.38, 43.53, 38.04, 34.64, 32.21, 28.23, 27.9 , 26.24, 26.  , 26.58,\n",
       "       25.59, 26.31, 25.47, 26.69, 26.95, 36.04, 38.76, 42.54], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0362f74-834a-4d35-aa70-1a13f7592583' class='xr-section-summary-in' type='checkbox'  ><label for='section-a0362f74-834a-4d35-aa70-1a13f7592583' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-78e6d4e8-8e57-40eb-a458-6b5097a5fcc3' class='xr-index-data-in' type='checkbox'/><label for='index-78e6d4e8-8e57-40eb-a458-6b5097a5fcc3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2019-11-01 00:00:00&#x27;, &#x27;2019-11-01 00:30:00&#x27;,\n",
       "               &#x27;2019-11-01 01:00:00&#x27;, &#x27;2019-11-01 01:30:00&#x27;,\n",
       "               &#x27;2019-11-01 02:00:00&#x27;, &#x27;2019-11-01 02:30:00&#x27;,\n",
       "               &#x27;2019-11-01 03:00:00&#x27;, &#x27;2019-11-01 03:30:00&#x27;,\n",
       "               &#x27;2019-11-01 04:00:00&#x27;, &#x27;2019-11-01 04:30:00&#x27;,\n",
       "               &#x27;2019-11-01 05:00:00&#x27;, &#x27;2019-11-01 05:30:00&#x27;,\n",
       "               &#x27;2019-11-01 06:00:00&#x27;, &#x27;2019-11-01 06:30:00&#x27;,\n",
       "               &#x27;2019-11-01 07:00:00&#x27;, &#x27;2019-11-01 07:30:00&#x27;,\n",
       "               &#x27;2019-11-01 08:00:00&#x27;, &#x27;2019-11-01 08:30:00&#x27;,\n",
       "               &#x27;2019-11-01 09:00:00&#x27;, &#x27;2019-11-01 09:30:00&#x27;,\n",
       "               &#x27;2019-11-01 10:00:00&#x27;, &#x27;2019-11-01 10:30:00&#x27;,\n",
       "               &#x27;2019-11-01 11:00:00&#x27;, &#x27;2019-11-01 11:30:00&#x27;,\n",
       "               &#x27;2019-11-01 12:00:00&#x27;, &#x27;2019-11-01 12:30:00&#x27;,\n",
       "               &#x27;2019-11-01 13:00:00&#x27;, &#x27;2019-11-01 13:30:00&#x27;,\n",
       "               &#x27;2019-11-01 14:00:00&#x27;, &#x27;2019-11-01 14:30:00&#x27;,\n",
       "               &#x27;2019-11-01 15:00:00&#x27;, &#x27;2019-11-01 15:30:00&#x27;,\n",
       "               &#x27;2019-11-01 16:00:00&#x27;, &#x27;2019-11-01 16:30:00&#x27;,\n",
       "               &#x27;2019-11-01 17:00:00&#x27;, &#x27;2019-11-01 17:30:00&#x27;,\n",
       "               &#x27;2019-11-01 18:00:00&#x27;, &#x27;2019-11-01 18:30:00&#x27;,\n",
       "               &#x27;2019-11-01 19:00:00&#x27;, &#x27;2019-11-01 19:30:00&#x27;,\n",
       "               &#x27;2019-11-01 20:00:00&#x27;, &#x27;2019-11-01 20:30:00&#x27;,\n",
       "               &#x27;2019-11-01 21:00:00&#x27;, &#x27;2019-11-01 21:30:00&#x27;,\n",
       "               &#x27;2019-11-01 22:00:00&#x27;, &#x27;2019-11-01 22:30:00&#x27;,\n",
       "               &#x27;2019-11-01 23:00:00&#x27;, &#x27;2019-11-01 23:30:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf4de01e-321b-42a3-bdbb-489bc9bb3b44' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf4de01e-321b-42a3-bdbb-489bc9bb3b44' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>command_line :</span></dt><dd>ecorsf_ingest -s sgp -f E14 -D</dd><dt><span>Conventions :</span></dt><dd>ARM-1.2</dd><dt><span>process_version :</span></dt><dd>ingest-ecorsf-1.2-0.el7</dd><dt><span>dod_version :</span></dt><dd>ecorsf-b1-1.3</dd><dt><span>input_source :</span></dt><dd>/data/collection/sgp/sgpecorsfE14.00/2019-10-31T233000_smart3-00324.zip</dd><dt><span>site_id :</span></dt><dd>sgp</dd><dt><span>platform_id :</span></dt><dd>ecorsf</dd><dt><span>facility_id :</span></dt><dd>E14</dd><dt><span>data_level :</span></dt><dd>b1</dd><dt><span>location_description :</span></dt><dd>Southern Great Plains (SGP), Lamont, Oklahoma</dd><dt><span>datastream :</span></dt><dd>sgpecorsfE14.b1</dd><dt><span>doi :</span></dt><dd>10.5439/1494128</dd><dt><span>history :</span></dt><dd>created by user dsmgr on machine garnet at 2019-12-02 20:44:05, using ingest-ecorsf-1.2-0.el7</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (time: 48)\n",
       "Coordinates:\n",
       "  * time               (time) datetime64[ns] 2019-11-01 ... 2019-11-01T23:30:00\n",
       "Data variables:\n",
       "    air_temperature    (time) float32 277.7 275.8 275.4 ... 287.2 285.7 284.2\n",
       "    air_pressure       (time) float32 98.9 98.91 98.92 ... 98.59 98.63 98.67\n",
       "    relative_humidity  (time) float32 41.58 51.52 51.75 ... 30.69 35.61 40.09\n",
       "    atmos_pressure     (time) float32 98.76 98.77 98.77 ... 98.46 98.49 98.55\n",
       "    temp_mean          (time) float32 3.876 2.506 2.286 ... 13.92 12.77 11.27\n",
       "    rh_mean            (time) float32 52.58 57.79 58.48 ... 36.04 38.76 42.54\n",
       "Attributes: (12/13)\n",
       "    command_line:          ecorsf_ingest -s sgp -f E14 -D\n",
       "    Conventions:           ARM-1.2\n",
       "    process_version:       ingest-ecorsf-1.2-0.el7\n",
       "    dod_version:           ecorsf-b1-1.3\n",
       "    input_source:          /data/collection/sgp/sgpecorsfE14.00/2019-10-31T23...\n",
       "    site_id:               sgp\n",
       "    ...                    ...\n",
       "    facility_id:           E14\n",
       "    data_level:            b1\n",
       "    location_description:  Southern Great Plains (SGP), Lamont, Oklahoma\n",
       "    datastream:            sgpecorsfE14.b1\n",
       "    doi:                   10.5439/1494128\n",
       "    history:               created by user dsmgr on machine garnet at 2019-12..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_merged = xr.merge([ds_ecor, ds_met], join='left', combine_attrs='override')\n",
    "ds_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0dd5c85-0f2a-4473-9b55-9dcb8ca43107",
   "metadata": {},
   "source": [
    "Or we can return two Xarray Datasets with the time steps converted to match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d14a29b-e769-409e-b4fa-f64528ae7e42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECOR: (48,)\n",
      "MET: (48,)\n"
     ]
    }
   ],
   "source": [
    "ds_ecor_align, ds_met_align = xr.align(ds_ecor, ds_met, join='left')\n",
    "print('ECOR:', ds_ecor_align['time'].shape)\n",
    "print('MET:', ds_met_align['time'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe5d12a-e179-46a4-944c-e5b16d34deca",
   "metadata": {},
   "source": [
    "## Both of these examples assumes the time steps match up exactly. What if we want to match to the nearest value?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab749141-8258-4e39-b67d-2cb8e469b0da",
   "metadata": {},
   "source": [
    "To demonstrate this we will modify the MET aligned to ECOR timestamp Dataframe to add some randomness to the time steps. Notice that the time column values are no longer rounded to :00 seconds. This means matching exactly to ECOR will find few matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8e8d0419-58ed-48fb-80ac-7d466b97f33c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atmos_pressure</th>\n",
       "      <th>temp_mean</th>\n",
       "      <th>rh_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2019-10-31 23:59:56</th>\n",
       "      <td>98.760002</td>\n",
       "      <td>3.876</td>\n",
       "      <td>52.580002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 00:30:09</th>\n",
       "      <td>98.769997</td>\n",
       "      <td>2.506</td>\n",
       "      <td>57.790001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 01:00:04</th>\n",
       "      <td>98.769997</td>\n",
       "      <td>2.286</td>\n",
       "      <td>58.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 01:30:00</th>\n",
       "      <td>98.760002</td>\n",
       "      <td>1.643</td>\n",
       "      <td>60.849998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 01:59:57</th>\n",
       "      <td>98.750000</td>\n",
       "      <td>1.508</td>\n",
       "      <td>61.599998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     atmos_pressure  temp_mean    rh_mean\n",
       "time                                                     \n",
       "2019-10-31 23:59:56       98.760002      3.876  52.580002\n",
       "2019-11-01 00:30:09       98.769997      2.506  57.790001\n",
       "2019-11-01 01:00:04       98.769997      2.286  58.480000\n",
       "2019-11-01 01:30:00       98.760002      1.643  60.849998\n",
       "2019-11-01 01:59:57       98.750000      1.508  61.599998"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "\n",
    "df_met_reindex = met_align.copy()\n",
    "random_seconds = np.random.randint(-10, 10, size=df_met_reindex.index.size).astype('timedelta64[s]')\n",
    "subset_time = df_met_reindex.index + random_seconds\n",
    "df_met_reindex = df_met_reindex.set_index(subset_time)\n",
    "df_met_reindex[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c860db85-249e-49c0-b506-3c379abc379c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>air_temperature</th>\n",
       "      <th>air_pressure</th>\n",
       "      <th>relative_humidity</th>\n",
       "      <th>atmos_pressure</th>\n",
       "      <th>temp_mean</th>\n",
       "      <th>rh_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2019-11-01 00:00:00</th>\n",
       "      <td>277.691010</td>\n",
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       "      <td>41.582401</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 00:30:00</th>\n",
       "      <td>275.803009</td>\n",
       "      <td>98.909401</td>\n",
       "      <td>51.524799</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 01:00:00</th>\n",
       "      <td>275.403992</td>\n",
       "      <td>98.923798</td>\n",
       "      <td>51.751701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 01:30:00</th>\n",
       "      <td>275.182007</td>\n",
       "      <td>98.909294</td>\n",
       "      <td>53.663300</td>\n",
       "      <td>98.760002</td>\n",
       "      <td>1.643</td>\n",
       "      <td>60.849998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 02:00:00</th>\n",
       "      <td>275.006012</td>\n",
       "      <td>98.894707</td>\n",
       "      <td>51.751202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 02:30:00</th>\n",
       "      <td>274.411987</td>\n",
       "      <td>98.881104</td>\n",
       "      <td>56.369801</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 03:00:00</th>\n",
       "      <td>273.546997</td>\n",
       "      <td>98.856499</td>\n",
       "      <td>62.288300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 03:30:00</th>\n",
       "      <td>273.201996</td>\n",
       "      <td>98.843895</td>\n",
       "      <td>64.327301</td>\n",
       "      <td>98.699997</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>70.559998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 04:00:00</th>\n",
       "      <td>273.174011</td>\n",
       "      <td>98.837997</td>\n",
       "      <td>64.142899</td>\n",
       "      <td>98.699997</td>\n",
       "      <td>0.661</td>\n",
       "      <td>66.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 04:30:00</th>\n",
       "      <td>273.365997</td>\n",
       "      <td>98.809502</td>\n",
       "      <td>63.116199</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     air_temperature  air_pressure  relative_humidity  \\\n",
       "time                                                                    \n",
       "2019-11-01 00:00:00       277.691010     98.897705          41.582401   \n",
       "2019-11-01 00:30:00       275.803009     98.909401          51.524799   \n",
       "2019-11-01 01:00:00       275.403992     98.923798          51.751701   \n",
       "2019-11-01 01:30:00       275.182007     98.909294          53.663300   \n",
       "2019-11-01 02:00:00       275.006012     98.894707          51.751202   \n",
       "2019-11-01 02:30:00       274.411987     98.881104          56.369801   \n",
       "2019-11-01 03:00:00       273.546997     98.856499          62.288300   \n",
       "2019-11-01 03:30:00       273.201996     98.843895          64.327301   \n",
       "2019-11-01 04:00:00       273.174011     98.837997          64.142899   \n",
       "2019-11-01 04:30:00       273.365997     98.809502          63.116199   \n",
       "\n",
       "                     atmos_pressure  temp_mean    rh_mean  \n",
       "time                                                       \n",
       "2019-11-01 00:00:00             NaN        NaN        NaN  \n",
       "2019-11-01 00:30:00             NaN        NaN        NaN  \n",
       "2019-11-01 01:00:00             NaN        NaN        NaN  \n",
       "2019-11-01 01:30:00       98.760002      1.643  60.849998  \n",
       "2019-11-01 02:00:00             NaN        NaN        NaN  \n",
       "2019-11-01 02:30:00             NaN        NaN        NaN  \n",
       "2019-11-01 03:00:00             NaN        NaN        NaN  \n",
       "2019-11-01 03:30:00       98.699997     -0.072  70.559998  \n",
       "2019-11-01 04:00:00       98.699997      0.661  66.099998  \n",
       "2019-11-01 04:30:00             NaN        NaN        NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged = pd.merge(df_ecor, df_met_reindex, how='left', on='time')\n",
    "df_merged[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37bd3075-d61c-497c-8db5-719c570a14c3",
   "metadata": {},
   "source": [
    "This does not produce the result we desire. Most of the MET values are set to NaN since there are few exact matches. Instead of using merge() or align(), we can use the reindex() method to match the nearest value to within the tolerance. Notice that the tolerance needs to be provided in a time space, like Numpy timedelta64."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "13799878-e5b5-47c8-b01e-d081feb28de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_met_reindex = df_met_reindex.reindex(index=df_ecor.index, method='nearest',\n",
    "                                        tolerance=np.timedelta64(5, 's'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8e6edc1-27ef-4921-a425-33a6bea3a443",
   "metadata": {},
   "source": [
    "But be careful- we need to inspect the results to see what values we actually have. Where a match is found it returns a value, but where no match is found within the tolerance, the values are set to NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "102920ad-9d09-458e-9d54-7cab2f19dba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>atmos_pressure</th>\n",
       "      <th>temp_mean</th>\n",
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       "  <tbody>\n",
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       "      <th>2019-11-01 00:00:00</th>\n",
       "      <td>98.760002</td>\n",
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       "      <th>2019-11-01 00:30:00</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2019-11-01 01:00:00</th>\n",
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       "      <th>2019-11-01 01:30:00</th>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 02:00:00</th>\n",
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       "      <th>2019-11-01 03:00:00</th>\n",
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       "      <th>2019-11-01 03:30:00</th>\n",
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       "      <td>70.559998</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 04:00:00</th>\n",
       "      <td>98.699997</td>\n",
       "      <td>0.661</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 04:30:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     atmos_pressure  temp_mean    rh_mean\n",
       "time                                                     \n",
       "2019-11-01 00:00:00       98.760002      3.876  52.580002\n",
       "2019-11-01 00:30:00             NaN        NaN        NaN\n",
       "2019-11-01 01:00:00       98.769997      2.286  58.480000\n",
       "2019-11-01 01:30:00       98.760002      1.643  60.849998\n",
       "2019-11-01 02:00:00       98.750000      1.508  61.599998\n",
       "2019-11-01 02:30:00       98.739998      0.940  63.720001\n",
       "2019-11-01 03:00:00             NaN        NaN        NaN\n",
       "2019-11-01 03:30:00       98.699997     -0.072  70.559998\n",
       "2019-11-01 04:00:00       98.699997      0.661  66.099998\n",
       "2019-11-01 04:30:00             NaN        NaN        NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_met_reindex[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "611db681-7f7c-4d4e-8f96-55d55cd4b960",
   "metadata": {},
   "source": [
    "To clean up the Dataframe to only have valid data we can drop where values are set to NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "efb60a94-5bd7-4f49-84b9-5a316eab6cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_met_reindex = df_met_reindex.dropna(how='all')\n",
    "df_met_reindex.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5c565219-2b5c-44d6-9729-1e22e573d73e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2019-11-01 17:30:00</th>\n",
       "      <td>98.410004</td>\n",
       "      <td>13.690</td>\n",
       "      <td>28.230000</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 18:30:00</th>\n",
       "      <td>98.379997</td>\n",
       "      <td>14.550</td>\n",
       "      <td>26.240000</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 20:00:00</th>\n",
       "      <td>98.309998</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 20:30:00</th>\n",
       "      <td>98.330002</td>\n",
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       "      <td>26.309999</td>\n",
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       "    <tr>\n",
       "      <th>2019-11-01 21:00:00</th>\n",
       "      <td>98.360001</td>\n",
       "      <td>15.780</td>\n",
       "      <td>25.469999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 21:30:00</th>\n",
       "      <td>98.389999</td>\n",
       "      <td>15.860</td>\n",
       "      <td>26.690001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 22:00:00</th>\n",
       "      <td>98.410004</td>\n",
       "      <td>15.430</td>\n",
       "      <td>26.950001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 23:00:00</th>\n",
       "      <td>98.489998</td>\n",
       "      <td>12.770</td>\n",
       "      <td>38.759998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     atmos_pressure  temp_mean    rh_mean\n",
       "time                                                     \n",
       "2019-11-01 00:00:00       98.760002      3.876  52.580002\n",
       "2019-11-01 01:00:00       98.769997      2.286  58.480000\n",
       "2019-11-01 01:30:00       98.760002      1.643  60.849998\n",
       "2019-11-01 02:00:00       98.750000      1.508  61.599998\n",
       "2019-11-01 02:30:00       98.739998      0.940  63.720001\n",
       "2019-11-01 03:30:00       98.699997     -0.072  70.559998\n",
       "2019-11-01 04:00:00       98.699997      0.661  66.099998\n",
       "2019-11-01 05:00:00       98.660004      0.379  68.910004\n",
       "2019-11-01 06:30:00       98.620003      0.592  67.300003\n",
       "2019-11-01 07:00:00       98.589996      0.395  70.120003\n",
       "2019-11-01 08:30:00       98.529999     -0.592  77.019997\n",
       "2019-11-01 09:00:00       98.500000     -0.333  75.500000\n",
       "2019-11-01 10:00:00       98.489998     -0.266  73.139999\n",
       "2019-11-01 10:30:00       98.489998     -0.252  72.730003\n",
       "2019-11-01 11:00:00       98.480003      0.019  70.199997\n",
       "2019-11-01 11:30:00       98.449997      0.596  65.449997\n",
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       "2019-11-01 20:30:00       98.330002     15.470  26.309999\n",
       "2019-11-01 21:00:00       98.360001     15.780  25.469999\n",
       "2019-11-01 21:30:00       98.389999     15.860  26.690001\n",
       "2019-11-01 22:00:00       98.410004     15.430  26.950001\n",
       "2019-11-01 23:00:00       98.489998     12.770  38.759998"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_met_reindex"
   ]
  }
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