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ingmars/RES_timeseries.ipynb

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iPython Notebook for extracting solar and wind from German TSOs
Raw
RES_timeseries.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RES Timeseries Data Extraction\n",
"\n",
"\n",
"This Jupyter Notebook downloads and cleans up solar and wind timeseries from the German TSOs TransnetBW and Tennet.\n",
"\n",
"## Loading some python libraries needed later"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import urllib\n",
"import os\n",
"import pandas\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configuring URLs\n",
"\n",
"Note: The placeholder syntax for the month (and year) variables in the URL look quite ugly at the moment. I use a trick there to avoid running into the problem that the default placeholder marker in python is the percentage sign, but the URL also contains percentage signs itself which must be escaped by two percentage signs %%."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conf = {\n",
" 'transnetbw': {\n",
" 'pv': \"https://www.transnetbw.de/de/kennzahlen/erneuerbare-energien/fotovoltaik?app=solar&activeTab=csv&selectMonatDownload=__ESCAPE__d&view=1&download=true\".replace(\"%\", \"%%\").replace(\"__ESCAPE__\",\"%\"),\n",
" 'wind': 'https://www.transnetbw.de/de/kennzahlen/erneuerbare-energien/windenergie?app=wind&activeTab=csv&selectMonatDownload=__ESCAPE__d&view=1&download=true'.replace(\"%\", \"%%\").replace(\"__ESCAPE__\",\"%\"),\n",
" }\n",
" ,'tennet': {\n",
" 'pv': \"http://www.tennettso.de/site/de/phpbridge?commandpath=Tatsaechliche_und_prognostizierte_Solarenergieeinspeisung%2FmonthDataSheetCsv.php&sub=total&querystring=monat%3D__ESCAPE__(year)d-__ESCAPE__(month)02d&contenttype=text%2Fx-csv\".replace(\"%\", \"%%\").replace(\"__ESCAPE__\",\"%\"),\n",
" 'wind': \"http://www.tennettso.de/site/de/phpbridge?commandpath=Tatsaechliche_und_prognostizierte_Windenergieeinspeisung%2FmonthDataSheetCsv.php&querystring=monat%3D__ESCAPE__(year)-__ESCAPE__(month)02d&contenttype=text%2Fx-csv\".replace(\"%\", \"%%\").replace(\"__ESCAPE__\",\"%\"),\n",
" },\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Downloading the data\n",
"\n",
"Here we loop through the configuration defined above by TSO (transnetbw, tennet) and Technology (pv, wind).\n",
"\n",
"We then do slightly different things depending on which TSO we're in, because Tennet has month and year as variables in the URL whereas TransnetBW only has the month in there."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"\n",
"# Download data\n",
"\n",
"for tsoName, tsoConf in conf.iteritems():\n",
" for techName, url in tsoConf.iteritems():\n",
" \n",
" if tsoName == 'tennet':\n",
" for year in range (2014, 2015): # This means a range from 2014 to 2014 (including)\n",
" path = 'csv/'+tsoName+'/'+techName+'/'+str(year)+'/'\n",
" if not os.path.exists(path): os.makedirs(path)\n",
"\n",
" for month in range(1, 13): # This means a range from 1 to 12 (including)\n",
" full_url = url % {'month': month, 'year': year}\n",
" urllib.urlretrieve(full_url, path+\"%d.csv\" % month)\n",
"\n",
" if tsoName == 'transnetbw':\n",
" path = 'csv/'+tsoName+'/'+techName+'/'\n",
" if not os.path.exists(path): os.makedirs(path)\n",
" \n",
" for month in range(1, 25): # This means the last 24 months\n",
" full_url = url % month\n",
" urllib.urlretrieve(full_url, path+\"%d.csv\" % month)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define individual read functions\n",
"\n",
"The TSOs have different columns and date formats they use in their CSV files. Here we define individual functions for the two TSOs to read-in a single monthly file in their specific format.\n",
"\n",
"We're going to use these functions in the loop for all months later on."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Define specific readData functions for the TSOs\n",
"\n",
"def readData_tennet(filePath, tsoName, techName):\n",
" data = pandas.read_csv(\n",
" filePath,\n",
" sep=\";\",\n",
" skiprows=4,\n",
" names=['datum','position','forecast_'+tsoName+'_'+techName,'actual_'+tsoName+'_'+techName,'unknown']\n",
" )\n",
"\n",
" data.fillna(method='ffill',inplace=True)\n",
"\n",
" data['hour'] = (np.trunc((data['position']-1) /4)).astype(int).astype(str)\n",
" data['minute'] = (((data['position']-1) % 4)*15).astype(int).astype(str)\n",
" data['time'] = data['datum']+' '+data['hour']+':'+data['minute']\n",
"\n",
" data['Timestamp'] = pandas.to_datetime(data['time'])\n",
"\n",
" data.set_index('Timestamp',inplace=True)\n",
"\n",
" del data['position']\n",
" del data['hour'] \n",
" del data['minute'] \n",
" del data['datum'] \n",
" del data['time'] \n",
" del data['unknown']\n",
" return data\n",
"\n",
" \n",
"def readData_transnet(filePath, tsoName, techName):\n",
" data = pandas.read_csv(\n",
" filePath,\n",
" decimal=',',\n",
" sep=\";\",\n",
" parse_dates = {'Timestamp' : ['Datum von', 'Uhrzeit von']},\n",
" index_col = \"Timestamp\",\n",
" dayfirst=True,\n",
" )\n",
"\n",
" data = data.drop('Unnamed: 6', 1)\n",
" data = data.drop('Datum bis', 1)\n",
" data = data.drop('Uhrzeit bis', 1)\n",
" data.fillna(0,inplace=True)\n",
" \n",
" forecastCol = 'forecast_'+tsoName+'_'+techName\n",
" actualCol = 'actual_'+tsoName+'_'+techName\n",
"\n",
" data.rename(\n",
" columns={'Prognose (MW)': forecastCol, 'Ist-Wert (MW)': actualCol},\n",
" inplace=True\n",
" )\n",
" \n",
"\n",
" if 'Datum' in data.columns: \n",
" del data['Datum']\n",
" if 'Uhrzeit' in data.columns: \n",
" del data['Uhrzeit'] \n",
" \n",
" return data\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing the read functions\n",
"\n",
"If you want to test the individual read functions just for checking if everything works, you can uncomment the line below."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# transnetTestDataWind = readData_transnet(\"csv/transnetbw/wind/7.csv\", 'transnetbw', 'wind')\n",
"\n",
"# And now output the TestData:\n",
"# transnetTestDataWind"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading the data into memory and cleaning it up\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"\n",
"# Read and merge data\n",
"\n",
"resultDataSet = pandas.DataFrame();\n",
"\n",
"for tsoName, tsoConf in conf.iteritems():\n",
" for techName, url in tsoConf.iteritems():\n",
"\n",
" if tsoName == 'tennet':\n",
" for year in range (2014, 2015):\n",
" for month in range(1, 12):\n",
" filePath = 'csv/'+tsoName+'/'+techName+'/'+str(year)+'/'+str(month)+'.csv'\n",
" try:\n",
" df = readData_tennet(filePath, tsoName, techName)\n",
" try:\n",
" df = readData_tennet(filePath, tsoName, techName)\n",
" resultDataSet = resultDataSet.combine_first(df)\n",
" except TypeError:\n",
" atLeastOneError = 'yes'\n",
" except ValueError:\n",
" atLeastOneError = 'yes'\n",
"\n",
" if tsoName == 'transnetbw':\n",
" for month in range(1, 20):\n",
" filePath = 'csv/'+tsoName+'/'+techName+'/'+str(month)+'.csv'\n",
" df = readData_transnet(filePath, tsoName, techName)\n",
" try:\n",
" df = readData_transnet(filePath, tsoName, techName)\n",
" resultDataSet = resultDataSet.combine_first(df)\n",
" except ValueError:\n",
" atLeastOneError = 'yes'\n",
" print 'error'\n",
"\n",
"\n",
"if 'Datum' in resultDataSet.columns: \n",
" del resultDataSet['Datum']\n",
"if 'Uhrzeit' in resultDataSet.columns: \n",
" del resultDataSet['Uhrzeit'] \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Validation and Plausibility Checks\n",
"\n",
"Ideally you would do plausibility checks on your data here. (Check for missing data, check that output is never negative, etc.)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Here you would do plausibility checks..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save to one big CSV file"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"resultDataSet.to_csv(\"csv/allData.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Display the data\n",
"\n",
"Something seems to be broken with the Tennet wind data... Other than that, the time spans of the different TSO datasets read-in above are different."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>actual_tennet_pv</th>\n",
" <th>actual_tennet_wind</th>\n",
" <th>actual_transnetbw_pv</th>\n",
" <th>actual_transnetbw_wind</th>\n",
" <th>forecast_tennet_pv</th>\n",
" <th>forecast_tennet_wind</th>\n",
" <th>forecast_transnetbw_pv</th>\n",
" <th>forecast_transnetbw_wind</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",
" <th></th>\n",
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" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 16:30:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2636</td>\n",
" <td> 32</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2759</td>\n",
" <td> 43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 16:45:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2438</td>\n",
" <td> 29</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2579</td>\n",
" <td> 45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 17:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2208</td>\n",
" <td> 33</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2383</td>\n",
" <td> 47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 17:15:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1998</td>\n",
" <td> 36</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2171</td>\n",
" <td> 49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 17:30:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1791</td>\n",
" <td> 30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1948</td>\n",
" <td> 52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 17:45:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1535</td>\n",
" <td> 32</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1718</td>\n",
" <td> 54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 18:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1292</td>\n",
" <td> 37</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1490</td>\n",
" <td> 56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 18:15:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1096</td>\n",
" <td> 36</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1267</td>\n",
" <td> 58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 18:30:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 911</td>\n",
" <td> 41</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1050</td>\n",
" <td> 60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 18:45:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 754</td>\n",
" <td> 53</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 844</td>\n",
" <td> 63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 19:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 601</td>\n",
" <td> 65</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 660</td>\n",
" <td> 65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 19:15:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 448</td>\n",
" <td> 68</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 502</td>\n",
" <td> 68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 19:30:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 359</td>\n",
" <td> 70</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 373</td>\n",
" <td> 71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 19:45:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 274</td>\n",
" <td> 79</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 279</td>\n",
" <td> 75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 20:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 199</td>\n",
" <td> 83</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 200</td>\n",
" <td> 79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 20:15:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 123</td>\n",
" <td> 95</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 123</td>\n",
" <td> 84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 20:30:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 45</td>\n",
" <td> 109</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 63</td>\n",
" <td> 89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 20:45:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 14</td>\n",
" <td> 122</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 22</td>\n",
" <td> 94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 21:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 137</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" <td> 98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 21:15:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 153</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" <td> 103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 21:30:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 174</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> 107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 21:45:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 195</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 22:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 220</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 22:15:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 224</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 22:30:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 230</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 22:45:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 243</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 23:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 231</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 23:15:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 224</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 23:30:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 235</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 23:45:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 235</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 135</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>55417 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" actual_tennet_pv actual_tennet_wind \\\n",
"Timestamp \n",
"2014-01-01 00:00:00 0 NaN \n",
"2014-01-01 00:15:00 0 NaN \n",
"2014-01-01 00:30:00 0 NaN \n",
"2014-01-01 00:45:00 0 NaN \n",
"2014-01-01 01:00:00 0 NaN \n",
"2014-01-01 01:15:00 0 NaN \n",
"2014-01-01 01:30:00 0 NaN \n",
"2014-01-01 01:45:00 0 NaN \n",
"2014-01-01 02:00:00 0 NaN \n",
"2014-01-01 02:15:00 0 NaN \n",
"2014-01-01 02:30:00 0 NaN \n",
"2014-01-01 02:45:00 0 NaN \n",
"2014-01-01 03:00:00 0 NaN \n",
"2014-01-01 03:15:00 0 NaN \n",
"2014-01-01 03:30:00 0 NaN \n",
"2014-01-01 03:45:00 0 NaN \n",
"2014-01-01 04:00:00 0 NaN \n",
"2014-01-01 04:15:00 0 NaN \n",
"2014-01-01 04:30:00 0 NaN \n",
"2014-01-01 04:45:00 0 NaN \n",
"2014-01-01 05:00:00 0 NaN \n",
"2014-01-01 05:15:00 0 NaN \n",
"2014-01-01 05:30:00 0 NaN \n",
"2014-01-01 05:45:00 0 NaN \n",
"2014-01-01 06:00:00 0 NaN \n",
"2014-01-01 06:15:00 0 NaN \n",
"2014-01-01 06:30:00 0 NaN \n",
"2014-01-01 06:45:00 0 NaN \n",
"2014-01-01 07:00:00 0 NaN \n",
"2014-01-01 07:15:00 0 NaN \n",
"... ... ... \n",
"2015-07-31 16:30:00 NaN NaN \n",
"2015-07-31 16:45:00 NaN NaN \n",
"2015-07-31 17:00:00 NaN NaN \n",
"2015-07-31 17:15:00 NaN NaN \n",
"2015-07-31 17:30:00 NaN NaN \n",
"2015-07-31 17:45:00 NaN NaN \n",
"2015-07-31 18:00:00 NaN NaN \n",
"2015-07-31 18:15:00 NaN NaN \n",
"2015-07-31 18:30:00 NaN NaN \n",
"2015-07-31 18:45:00 NaN NaN \n",
"2015-07-31 19:00:00 NaN NaN \n",
"2015-07-31 19:15:00 NaN NaN \n",
"2015-07-31 19:30:00 NaN NaN \n",
"2015-07-31 19:45:00 NaN NaN \n",
"2015-07-31 20:00:00 NaN NaN \n",
"2015-07-31 20:15:00 NaN NaN \n",
"2015-07-31 20:30:00 NaN NaN \n",
"2015-07-31 20:45:00 NaN NaN \n",
"2015-07-31 21:00:00 NaN NaN \n",
"2015-07-31 21:15:00 NaN NaN \n",
"2015-07-31 21:30:00 NaN NaN \n",
"2015-07-31 21:45:00 NaN NaN \n",
"2015-07-31 22:00:00 NaN NaN \n",
"2015-07-31 22:15:00 NaN NaN \n",
"2015-07-31 22:30:00 NaN NaN \n",
"2015-07-31 22:45:00 NaN NaN \n",
"2015-07-31 23:00:00 NaN NaN \n",
"2015-07-31 23:15:00 NaN NaN \n",
"2015-07-31 23:30:00 NaN NaN \n",
"2015-07-31 23:45:00 NaN NaN \n",
"\n",
" actual_transnetbw_pv actual_transnetbw_wind \\\n",
"Timestamp \n",
"2014-01-01 00:00:00 0 134 \n",
"2014-01-01 00:15:00 0 124 \n",
"2014-01-01 00:30:00 0 115 \n",
"2014-01-01 00:45:00 0 118 \n",
"2014-01-01 01:00:00 0 128 \n",
"2014-01-01 01:15:00 0 119 \n",
"2014-01-01 01:30:00 0 106 \n",
"2014-01-01 01:45:00 0 79 \n",
"2014-01-01 02:00:00 0 88 \n",
"2014-01-01 02:15:00 0 95 \n",
"2014-01-01 02:30:00 0 85 \n",
"2014-01-01 02:45:00 0 89 \n",
"2014-01-01 03:00:00 0 104 \n",
"2014-01-01 03:15:00 0 118 \n",
"2014-01-01 03:30:00 0 133 \n",
"2014-01-01 03:45:00 0 171 \n",
"2014-01-01 04:00:00 0 113 \n",
"2014-01-01 04:15:00 0 104 \n",
"2014-01-01 04:30:00 0 100 \n",
"2014-01-01 04:45:00 0 92 \n",
"2014-01-01 05:00:00 0 69 \n",
"2014-01-01 05:15:00 0 96 \n",
"2014-01-01 05:30:00 0 97 \n",
"2014-01-01 05:45:00 0 100 \n",
"2014-01-01 06:00:00 0 87 \n",
"2014-01-01 06:15:00 0 106 \n",
"2014-01-01 06:30:00 0 116 \n",
"2014-01-01 06:45:00 0 105 \n",
"2014-01-01 07:00:00 0 99 \n",
"2014-01-01 07:15:00 0 90 \n",
"... ... ... \n",
"2015-07-31 16:30:00 2636 32 \n",
"2015-07-31 16:45:00 2438 29 \n",
"2015-07-31 17:00:00 2208 33 \n",
"2015-07-31 17:15:00 1998 36 \n",
"2015-07-31 17:30:00 1791 30 \n",
"2015-07-31 17:45:00 1535 32 \n",
"2015-07-31 18:00:00 1292 37 \n",
"2015-07-31 18:15:00 1096 36 \n",
"2015-07-31 18:30:00 911 41 \n",
"2015-07-31 18:45:00 754 53 \n",
"2015-07-31 19:00:00 601 65 \n",
"2015-07-31 19:15:00 448 68 \n",
"2015-07-31 19:30:00 359 70 \n",
"2015-07-31 19:45:00 274 79 \n",
"2015-07-31 20:00:00 199 83 \n",
"2015-07-31 20:15:00 123 95 \n",
"2015-07-31 20:30:00 45 109 \n",
"2015-07-31 20:45:00 14 122 \n",
"2015-07-31 21:00:00 0 137 \n",
"2015-07-31 21:15:00 0 153 \n",
"2015-07-31 21:30:00 0 174 \n",
"2015-07-31 21:45:00 0 195 \n",
"2015-07-31 22:00:00 0 220 \n",
"2015-07-31 22:15:00 0 224 \n",
"2015-07-31 22:30:00 0 230 \n",
"2015-07-31 22:45:00 0 243 \n",
"2015-07-31 23:00:00 0 231 \n",
"2015-07-31 23:15:00 0 224 \n",
"2015-07-31 23:30:00 0 235 \n",
"2015-07-31 23:45:00 0 235 \n",
"\n",
" forecast_tennet_pv forecast_tennet_wind \\\n",
"Timestamp \n",
"2014-01-01 00:00:00 0 NaN \n",
"2014-01-01 00:15:00 0 NaN \n",
"2014-01-01 00:30:00 0 NaN \n",
"2014-01-01 00:45:00 0 NaN \n",
"2014-01-01 01:00:00 0 NaN \n",
"2014-01-01 01:15:00 0 NaN \n",
"2014-01-01 01:30:00 0 NaN \n",
"2014-01-01 01:45:00 0 NaN \n",
"2014-01-01 02:00:00 0 NaN \n",
"2014-01-01 02:15:00 0 NaN \n",
"2014-01-01 02:30:00 0 NaN \n",
"2014-01-01 02:45:00 0 NaN \n",
"2014-01-01 03:00:00 0 NaN \n",
"2014-01-01 03:15:00 0 NaN \n",
"2014-01-01 03:30:00 0 NaN \n",
"2014-01-01 03:45:00 0 NaN \n",
"2014-01-01 04:00:00 0 NaN \n",
"2014-01-01 04:15:00 0 NaN \n",
"2014-01-01 04:30:00 0 NaN \n",
"2014-01-01 04:45:00 0 NaN \n",
"2014-01-01 05:00:00 0 NaN \n",
"2014-01-01 05:15:00 0 NaN \n",
"2014-01-01 05:30:00 0 NaN \n",
"2014-01-01 05:45:00 0 NaN \n",
"2014-01-01 06:00:00 0 NaN \n",
"2014-01-01 06:15:00 0 NaN \n",
"2014-01-01 06:30:00 0 NaN \n",
"2014-01-01 06:45:00 0 NaN \n",
"2014-01-01 07:00:00 0 NaN \n",
"2014-01-01 07:15:00 0 NaN \n",
"... ... ... \n",
"2015-07-31 16:30:00 NaN NaN \n",
"2015-07-31 16:45:00 NaN NaN \n",
"2015-07-31 17:00:00 NaN NaN \n",
"2015-07-31 17:15:00 NaN NaN \n",
"2015-07-31 17:30:00 NaN NaN \n",
"2015-07-31 17:45:00 NaN NaN \n",
"2015-07-31 18:00:00 NaN NaN \n",
"2015-07-31 18:15:00 NaN NaN \n",
"2015-07-31 18:30:00 NaN NaN \n",
"2015-07-31 18:45:00 NaN NaN \n",
"2015-07-31 19:00:00 NaN NaN \n",
"2015-07-31 19:15:00 NaN NaN \n",
"2015-07-31 19:30:00 NaN NaN \n",
"2015-07-31 19:45:00 NaN NaN \n",
"2015-07-31 20:00:00 NaN NaN \n",
"2015-07-31 20:15:00 NaN NaN \n",
"2015-07-31 20:30:00 NaN NaN \n",
"2015-07-31 20:45:00 NaN NaN \n",
"2015-07-31 21:00:00 NaN NaN \n",
"2015-07-31 21:15:00 NaN NaN \n",
"2015-07-31 21:30:00 NaN NaN \n",
"2015-07-31 21:45:00 NaN NaN \n",
"2015-07-31 22:00:00 NaN NaN \n",
"2015-07-31 22:15:00 NaN NaN \n",
"2015-07-31 22:30:00 NaN NaN \n",
"2015-07-31 22:45:00 NaN NaN \n",
"2015-07-31 23:00:00 NaN NaN \n",
"2015-07-31 23:15:00 NaN NaN \n",
"2015-07-31 23:30:00 NaN NaN \n",
"2015-07-31 23:45:00 NaN NaN \n",
"\n",
" forecast_transnetbw_pv forecast_transnetbw_wind \n",
"Timestamp \n",
"2014-01-01 00:00:00 0 79 \n",
"2014-01-01 00:15:00 0 79 \n",
"2014-01-01 00:30:00 0 80 \n",
"2014-01-01 00:45:00 0 80 \n",
"2014-01-01 01:00:00 0 80 \n",
"2014-01-01 01:15:00 0 83 \n",
"2014-01-01 01:30:00 0 86 \n",
"2014-01-01 01:45:00 0 89 \n",
"2014-01-01 02:00:00 0 91 \n",
"2014-01-01 02:15:00 0 96 \n",
"2014-01-01 02:30:00 0 101 \n",
"2014-01-01 02:45:00 0 106 \n",
"2014-01-01 03:00:00 0 111 \n",
"2014-01-01 03:15:00 0 117 \n",
"2014-01-01 03:30:00 0 124 \n",
"2014-01-01 03:45:00 0 130 \n",
"2014-01-01 04:00:00 0 136 \n",
"2014-01-01 04:15:00 0 142 \n",
"2014-01-01 04:30:00 0 148 \n",
"2014-01-01 04:45:00 0 155 \n",
"2014-01-01 05:00:00 0 161 \n",
"2014-01-01 05:15:00 0 166 \n",
"2014-01-01 05:30:00 0 171 \n",
"2014-01-01 05:45:00 0 176 \n",
"2014-01-01 06:00:00 0 181 \n",
"2014-01-01 06:15:00 0 183 \n",
"2014-01-01 06:30:00 0 186 \n",
"2014-01-01 06:45:00 0 188 \n",
"2014-01-01 07:00:00 0 190 \n",
"2014-01-01 07:15:00 0 188 \n",
"... ... ... \n",
"2015-07-31 16:30:00 2759 43 \n",
"2015-07-31 16:45:00 2579 45 \n",
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"2015-07-31 23:45:00 0 135 \n",
"\n",
"[55417 rows x 8 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resultDataSet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example: Resample the timeseries data to 60minute\n",
"\n",
"Here we use Pandas' powerful timeseries functionality to resample the data to 60minute intervals "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 13560</td>\n",
" <td> 152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 16:00:00</th>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 10802</td>\n",
" <td> 131</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 11333</td>\n",
" <td> 170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 17:00:00</th>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 7532</td>\n",
" <td> 131</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 8220</td>\n",
" <td> 202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 18:00:00</th>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 4053</td>\n",
" <td> 167</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 4651</td>\n",
" <td> 237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 19:00:00</th>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1682</td>\n",
" <td> 282</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1814</td>\n",
" <td> 279</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 20:00:00</th>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 381</td>\n",
" <td> 409</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 408</td>\n",
" <td> 346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 21:00:00</th>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 659</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 5</td>\n",
" <td> 420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 22:00:00</th>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 917</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31 23:00:00</th>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 925</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 540</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>13848 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" actual_tennet_pv actual_tennet_wind \\\n",
"Timestamp \n",
"2014-01-01 00:00:00 0 NaN \n",
"2014-01-01 01:00:00 0 NaN \n",
"2014-01-01 02:00:00 0 NaN \n",
"2014-01-01 03:00:00 0 NaN \n",
"2014-01-01 04:00:00 0 NaN \n",
"2014-01-01 05:00:00 0 NaN \n",
"2014-01-01 06:00:00 0 NaN \n",
"2014-01-01 07:00:00 0 NaN \n",
"2014-01-01 08:00:00 240 NaN \n",
"2014-01-01 09:00:00 1740 NaN \n",
"2014-01-01 10:00:00 4451 NaN \n",
"2014-01-01 11:00:00 6976 NaN \n",
"2014-01-01 12:00:00 8847 NaN \n",
"2014-01-01 13:00:00 9347 NaN \n",
"2014-01-01 14:00:00 7358 NaN \n",
"2014-01-01 15:00:00 3353 NaN \n",
"2014-01-01 16:00:00 513 NaN \n",
"2014-01-01 17:00:00 0 NaN \n",
"2014-01-01 18:00:00 0 NaN \n",
"2014-01-01 19:00:00 0 NaN \n",
"2014-01-01 20:00:00 0 NaN \n",
"2014-01-01 21:00:00 0 NaN \n",
"2014-01-01 22:00:00 0 NaN \n",
"2014-01-01 23:00:00 0 NaN \n",
"2014-01-02 00:00:00 0 NaN \n",
"2014-01-02 01:00:00 0 NaN \n",
"2014-01-02 02:00:00 0 NaN \n",
"2014-01-02 03:00:00 0 NaN \n",
"2014-01-02 04:00:00 0 NaN \n",
"2014-01-02 05:00:00 0 NaN \n",
"... ... ... \n",
"2015-07-30 18:00:00 NaN NaN \n",
"2015-07-30 19:00:00 NaN NaN \n",
"2015-07-30 20:00:00 NaN NaN \n",
"2015-07-30 21:00:00 NaN NaN \n",
"2015-07-30 22:00:00 NaN NaN \n",
"2015-07-30 23:00:00 NaN NaN \n",
"2015-07-31 00:00:00 NaN NaN \n",
"2015-07-31 01:00:00 NaN NaN \n",
"2015-07-31 02:00:00 NaN NaN \n",
"2015-07-31 03:00:00 NaN NaN \n",
"2015-07-31 04:00:00 NaN NaN \n",
"2015-07-31 05:00:00 NaN NaN \n",
"2015-07-31 06:00:00 NaN NaN \n",
"2015-07-31 07:00:00 NaN NaN \n",
"2015-07-31 08:00:00 NaN NaN \n",
"2015-07-31 09:00:00 NaN NaN \n",
"2015-07-31 10:00:00 NaN NaN \n",
"2015-07-31 11:00:00 NaN NaN \n",
"2015-07-31 12:00:00 NaN NaN \n",
"2015-07-31 13:00:00 NaN NaN \n",
"2015-07-31 14:00:00 NaN NaN \n",
"2015-07-31 15:00:00 NaN NaN \n",
"2015-07-31 16:00:00 NaN NaN \n",
"2015-07-31 17:00:00 NaN NaN \n",
"2015-07-31 18:00:00 NaN NaN \n",
"2015-07-31 19:00:00 NaN NaN \n",
"2015-07-31 20:00:00 NaN NaN \n",
"2015-07-31 21:00:00 NaN NaN \n",
"2015-07-31 22:00:00 NaN NaN \n",
"2015-07-31 23:00:00 NaN NaN \n",
"\n",
" actual_transnetbw_pv actual_transnetbw_wind \\\n",
"Timestamp \n",
"2014-01-01 00:00:00 0 491 \n",
"2014-01-01 01:00:00 0 432 \n",
"2014-01-01 02:00:00 0 357 \n",
"2014-01-01 03:00:00 0 526 \n",
"2014-01-01 04:00:00 0 409 \n",
"2014-01-01 05:00:00 0 362 \n",
"2014-01-01 06:00:00 0 414 \n",
"2014-01-01 07:00:00 3 364 \n",
"2014-01-01 08:00:00 124 347 \n",
"2014-01-01 09:00:00 811 288 \n",
"2014-01-01 10:00:00 2253 289 \n",
"2014-01-01 11:00:00 4309 242 \n",
"2014-01-01 12:00:00 6028 144 \n",
"2014-01-01 13:00:00 5952 84 \n",
"2014-01-01 14:00:00 4639 129 \n",
"2014-01-01 15:00:00 2310 162 \n",
"2014-01-01 16:00:00 394 254 \n",
"2014-01-01 17:00:00 1 411 \n",
"2014-01-01 18:00:00 0 451 \n",
"2014-01-01 19:00:00 0 693 \n",
"2014-01-01 20:00:00 0 803 \n",
"2014-01-01 21:00:00 0 821 \n",
"2014-01-01 22:00:00 0 681 \n",
"2014-01-01 23:00:00 0 553 \n",
"2014-01-02 00:00:00 0 423 \n",
"2014-01-02 01:00:00 0 397 \n",
"2014-01-02 02:00:00 0 390 \n",
"2014-01-02 03:00:00 0 401 \n",
"2014-01-02 04:00:00 0 429 \n",
"2014-01-02 05:00:00 0 460 \n",
"... ... ... \n",
"2015-07-30 18:00:00 3792 152 \n",
"2015-07-30 19:00:00 1507 90 \n",
"2015-07-30 20:00:00 333 101 \n",
"2015-07-30 21:00:00 1 251 \n",
"2015-07-30 22:00:00 0 239 \n",
"2015-07-30 23:00:00 0 159 \n",
"2015-07-31 00:00:00 0 131 \n",
"2015-07-31 01:00:00 0 117 \n",
"2015-07-31 02:00:00 0 126 \n",
"2015-07-31 03:00:00 0 111 \n",
"2015-07-31 04:00:00 0 113 \n",
"2015-07-31 05:00:00 1 115 \n",
"2015-07-31 06:00:00 413 150 \n",
"2015-07-31 07:00:00 2342 161 \n",
"2015-07-31 08:00:00 5853 80 \n",
"2015-07-31 09:00:00 9459 78 \n",
"2015-07-31 10:00:00 12361 53 \n",
"2015-07-31 11:00:00 14304 60 \n",
"2015-07-31 12:00:00 15264 79 \n",
"2015-07-31 13:00:00 15221 76 \n",
"2015-07-31 14:00:00 14488 112 \n",
"2015-07-31 15:00:00 13221 110 \n",
"2015-07-31 16:00:00 10802 131 \n",
"2015-07-31 17:00:00 7532 131 \n",
"2015-07-31 18:00:00 4053 167 \n",
"2015-07-31 19:00:00 1682 282 \n",
"2015-07-31 20:00:00 381 409 \n",
"2015-07-31 21:00:00 0 659 \n",
"2015-07-31 22:00:00 0 917 \n",
"2015-07-31 23:00:00 0 925 \n",
"\n",
" forecast_tennet_pv forecast_tennet_wind \\\n",
"Timestamp \n",
"2014-01-01 00:00:00 0 NaN \n",
"2014-01-01 01:00:00 0 NaN \n",
"2014-01-01 02:00:00 0 NaN \n",
"2014-01-01 03:00:00 0 NaN \n",
"2014-01-01 04:00:00 0 NaN \n",
"2014-01-01 05:00:00 0 NaN \n",
"2014-01-01 06:00:00 0 NaN \n",
"2014-01-01 07:00:00 12 NaN \n",
"2014-01-01 08:00:00 568 NaN \n",
"2014-01-01 09:00:00 2910 NaN \n",
"2014-01-01 10:00:00 4778 NaN \n",
"2014-01-01 11:00:00 5759 NaN \n",
"2014-01-01 12:00:00 5590 NaN \n",
"2014-01-01 13:00:00 4614 NaN \n",
"2014-01-01 14:00:00 3127 NaN \n",
"2014-01-01 15:00:00 1504 NaN \n",
"2014-01-01 16:00:00 171 NaN \n",
"2014-01-01 17:00:00 0 NaN \n",
"2014-01-01 18:00:00 0 NaN \n",
"2014-01-01 19:00:00 0 NaN \n",
"2014-01-01 20:00:00 0 NaN \n",
"2014-01-01 21:00:00 0 NaN \n",
"2014-01-01 22:00:00 0 NaN \n",
"2014-01-01 23:00:00 0 NaN \n",
"2014-01-02 00:00:00 0 NaN \n",
"2014-01-02 01:00:00 0 NaN \n",
"2014-01-02 02:00:00 0 NaN \n",
"2014-01-02 03:00:00 0 NaN \n",
"2014-01-02 04:00:00 0 NaN \n",
"2014-01-02 05:00:00 0 NaN \n",
"... ... ... \n",
"2015-07-30 18:00:00 NaN NaN \n",
"2015-07-30 19:00:00 NaN NaN \n",
"2015-07-30 20:00:00 NaN NaN \n",
"2015-07-30 21:00:00 NaN NaN \n",
"2015-07-30 22:00:00 NaN NaN \n",
"2015-07-30 23:00:00 NaN NaN \n",
"2015-07-31 00:00:00 NaN NaN \n",
"2015-07-31 01:00:00 NaN NaN \n",
"2015-07-31 02:00:00 NaN NaN \n",
"2015-07-31 03:00:00 NaN NaN \n",
"2015-07-31 04:00:00 NaN NaN \n",
"2015-07-31 05:00:00 NaN NaN \n",
"2015-07-31 06:00:00 NaN NaN \n",
"2015-07-31 07:00:00 NaN NaN \n",
"2015-07-31 08:00:00 NaN NaN \n",
"2015-07-31 09:00:00 NaN NaN \n",
"2015-07-31 10:00:00 NaN NaN \n",
"2015-07-31 11:00:00 NaN NaN \n",
"2015-07-31 12:00:00 NaN NaN \n",
"2015-07-31 13:00:00 NaN NaN \n",
"2015-07-31 14:00:00 NaN NaN \n",
"2015-07-31 15:00:00 NaN NaN \n",
"2015-07-31 16:00:00 NaN NaN \n",
"2015-07-31 17:00:00 NaN NaN \n",
"2015-07-31 18:00:00 NaN NaN \n",
"2015-07-31 19:00:00 NaN NaN \n",
"2015-07-31 20:00:00 NaN NaN \n",
"2015-07-31 21:00:00 NaN NaN \n",
"2015-07-31 22:00:00 NaN NaN \n",
"2015-07-31 23:00:00 NaN NaN \n",
"\n",
" forecast_transnetbw_pv forecast_transnetbw_wind \n",
"Timestamp \n",
"2014-01-01 00:00:00 0 318 \n",
"2014-01-01 01:00:00 0 338 \n",
"2014-01-01 02:00:00 0 394 \n",
"2014-01-01 03:00:00 0 482 \n",
"2014-01-01 04:00:00 0 581 \n",
"2014-01-01 05:00:00 0 674 \n",
"2014-01-01 06:00:00 0 738 \n",
"2014-01-01 07:00:00 0 746 \n",
"2014-01-01 08:00:00 123 686 \n",
"2014-01-01 09:00:00 740 580 \n",
"2014-01-01 10:00:00 1976 465 \n",
"2014-01-01 11:00:00 3116 368 \n",
"2014-01-01 12:00:00 3849 274 \n",
"2014-01-01 13:00:00 4053 184 \n",
"2014-01-01 14:00:00 3226 126 \n",
"2014-01-01 15:00:00 1633 100 \n",
"2014-01-01 16:00:00 371 137 \n",
"2014-01-01 17:00:00 0 245 \n",
"2014-01-01 18:00:00 0 405 \n",
"2014-01-01 19:00:00 0 547 \n",
"2014-01-01 20:00:00 0 647 \n",
"2014-01-01 21:00:00 0 700 \n",
"2014-01-01 22:00:00 0 710 \n",
"2014-01-01 23:00:00 0 684 \n",
"2014-01-02 00:00:00 0 544 \n",
"2014-01-02 01:00:00 0 464 \n",
"2014-01-02 02:00:00 0 444 \n",
"2014-01-02 03:00:00 0 434 \n",
"2014-01-02 04:00:00 0 427 \n",
"2014-01-02 05:00:00 0 455 \n",
"... ... ... \n",
"2015-07-30 18:00:00 4059 239 \n",
"2015-07-30 19:00:00 1692 200 \n",
"2015-07-30 20:00:00 366 170 \n",
"2015-07-30 21:00:00 6 160 \n",
"2015-07-30 22:00:00 0 160 \n",
"2015-07-30 23:00:00 0 153 \n",
"2015-07-31 00:00:00 0 138 \n",
"2015-07-31 01:00:00 0 125 \n",
"2015-07-31 02:00:00 0 122 \n",
"2015-07-31 03:00:00 0 99 \n",
"2015-07-31 04:00:00 0 76 \n",
"2015-07-31 05:00:00 0 58 \n",
"2015-07-31 06:00:00 439 47 \n",
"2015-07-31 07:00:00 2249 40 \n",
"2015-07-31 08:00:00 5611 40 \n",
"2015-07-31 09:00:00 9165 40 \n",
"2015-07-31 10:00:00 12029 49 \n",
"2015-07-31 11:00:00 14020 67 \n",
"2015-07-31 12:00:00 15137 83 \n",
"2015-07-31 13:00:00 15405 100 \n",
"2015-07-31 14:00:00 14897 129 \n",
"2015-07-31 15:00:00 13560 152 \n",
"2015-07-31 16:00:00 11333 170 \n",
"2015-07-31 17:00:00 8220 202 \n",
"2015-07-31 18:00:00 4651 237 \n",
"2015-07-31 19:00:00 1814 279 \n",
"2015-07-31 20:00:00 408 346 \n",
"2015-07-31 21:00:00 5 420 \n",
"2015-07-31 22:00:00 0 499 \n",
"2015-07-31 23:00:00 0 540 \n",
"\n",
"[13848 rows x 8 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resultDataSet.resample('60Min',how='sum')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
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