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Notebook: Document aggregation_methods
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Erik Parmann committed Aug 9, 2019
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"xhat"
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Using custom or multiple aggregation methods\n",
"TimeSeriesDataset supports customization of the aggregation method used for the resampled buckets, and it can even use multiple aggregation methods. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Custom aggregation method"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
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" asgb.19ZT3950%2FY%2FPRIM \\\n",
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"# Remember to load the first cell to have the required imports\n",
"dataset = TimeSeriesDataset(from_ts=dateutil.parser.isoparse('2016-07-01T00:10:00+00:00'),\n",
" to_ts=dateutil.parser.isoparse('2017-01-01T00:00:00+00:00'),\n",
" tag_list=[\n",
" 'asgb.19ZT3950%2FY%2FPRIM',\n",
" 'asgb.19PST3925%2FDispMeasOut%2FPRIM'\n",
" ],\n",
" aggregation_methods=\"max\",\n",
" data_provider=data_provider)\n",
"X, y = dataset.get_data()\n",
"X.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multiple aggregation methods\n",
"When using multiple aggregation methods the returned dataframe will have multi-level columns, with the tag-name as top-level and aggregation method as the second level. "
]
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" <th>tag</th>\n",
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" <th colspan=\"3\" halign=\"left\">asgb.19PST3925%2FDispMeasOut%2FPRIM</th>\n",
" </tr>\n",
" <tr>\n",
" <th>aggregation_method</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>mean</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>mean</th>\n",
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" <td>99.945984</td>\n",
" <td>99.989201</td>\n",
" <td>46.330772</td>\n",
" <td>46.327229</td>\n",
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" <td>99.989201</td>\n",
" <td>46.330772</td>\n",
" <td>46.327229</td>\n",
" <td>46.329</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"tag asgb.19ZT3950%2FY%2FPRIM \\\n",
"aggregation_method max min mean \n",
"2016-07-01 07:40:00+00:00 100.032417 99.945984 99.989201 \n",
"2016-07-01 07:50:00+00:00 100.032417 99.945984 99.989201 \n",
"2016-07-01 08:00:00+00:00 100.032417 99.945984 99.989201 \n",
"2016-07-01 08:10:00+00:00 100.032417 99.945984 99.989201 \n",
"2016-07-01 08:20:00+00:00 100.032417 99.945984 99.989201 \n",
"\n",
"tag asgb.19PST3925%2FDispMeasOut%2FPRIM \\\n",
"aggregation_method max min \n",
"2016-07-01 07:40:00+00:00 46.330772 46.327229 \n",
"2016-07-01 07:50:00+00:00 46.330772 46.327229 \n",
"2016-07-01 08:00:00+00:00 46.330772 46.327229 \n",
"2016-07-01 08:10:00+00:00 46.330772 46.327229 \n",
"2016-07-01 08:20:00+00:00 46.330772 46.327229 \n",
"\n",
"tag \n",
"aggregation_method mean \n",
"2016-07-01 07:40:00+00:00 46.329 \n",
"2016-07-01 07:50:00+00:00 46.329 \n",
"2016-07-01 08:00:00+00:00 46.329 \n",
"2016-07-01 08:10:00+00:00 46.329 \n",
"2016-07-01 08:20:00+00:00 46.329 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remember to load the first cell to have the required imports\n",
"dataset = TimeSeriesDataset(from_ts=dateutil.parser.isoparse('2016-07-01T00:10:00+00:00'),\n",
" to_ts=dateutil.parser.isoparse('2017-01-01T00:00:00+00:00'),\n",
" tag_list=[\n",
" 'asgb.19ZT3950%2FY%2FPRIM',\n",
" 'asgb.19PST3925%2FDispMeasOut%2FPRIM'\n",
" ],\n",
" aggregation_methods=[\"max\",\"min\",\"mean\"],\n",
" data_provider=data_provider)\n",
"X, y = dataset.get_data()\n",
"X.head()"
]
}
],
"metadata": {
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