# Get dataset from a tag query in PowerTable **URL:** \ **Category:** Ignition\ **Tags:** perspective\ **Created:** [August 11, 2023, 12:58pm UTC](https://forum.inductiveautomation.com/t/get-dataset-from-a-tag-query-in-powertable/77755 "2023-08-11T12:58:05Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![italogustavoaraujo](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/italogustavoaraujo/32/55235_2.png) [@italogustavoaraujo](https://forum.inductiveautomation.com/u/italogustavoaraujo)\ **Post date:** [August 11, 2023, 12:58pm UTC](https://forum.inductiveautomation.com/t/get-dataset-from-a-tag-query-in-powertable/77755/1 "2023-08-11T12:58:05Z")
Hello everyone, I have a PowerTable and I would like to get its dataset so I can format and export it in excel, because the native option only exports in CSV, and I didn't find any more options in the menu. ![image](https://us1.discourse-cdn.com/inductiveautomation/original/3X/a/7/a757929e13af03e86394dbb5c1960f1f6f3d111e.png) ---
**Author:** ![pturmel](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/pturmel/32/3295_2.png) [@pturmel](https://forum.inductiveautomation.com/u/pturmel)\ **Post date:** [August 11, 2023, 1:02pm UTC](https://forum.inductiveautomation.com/t/get-dataset-from-a-tag-query-in-powertable/77755/2 "2023-08-11T13:02:13Z")
You would run your own historian query with the corresponding settings and convert that to your target format. ---
**Author:** ![paul-griffith](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/paul-griffith/32/37249_2.png) [@paul-griffith](https://forum.inductiveautomation.com/u/paul-griffith)\ **Post date:** [August 11, 2023, 1:39pm UTC](https://forum.inductiveautomation.com/t/get-dataset-from-a-tag-query-in-powertable/77755/3 "2023-08-11T13:39:55Z")
You should be doing this anyways, in my opinion, because a chart should always be on a more efficient sampling rate for performance, and an export can be made to use a higher sample rate or natural return. I think it's an antipattern the charts make it so tempting to directly export their data, but what do I know... ---
**Author:** ![pturmel](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/pturmel/32/3295_2.png) [@pturmel](https://forum.inductiveautomation.com/u/pturmel)\ **Post date:** [August 11, 2023, 1:42pm UTC](https://forum.inductiveautomation.com/t/get-dataset-from-a-tag-query-in-powertable/77755/4 "2023-08-11T13:42:16Z")
> [@paul-griffith](#): > > make it so tempting Trying to be feature-comparable to Vision. Vision can handle high-resolution datasets and therefore exporting from the chart itself is perfectly reasonable. Perspective, not so much. ---
**Author:** ![italogustavoaraujo](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/italogustavoaraujo/32/55235_2.png) [@italogustavoaraujo](https://forum.inductiveautomation.com/u/italogustavoaraujo)\ **Post date:** [August 11, 2023, 5:22pm UTC](https://forum.inductiveautomation.com/t/get-dataset-from-a-tag-query-in-powertable/77755/5 "2023-08-11T17:22:37Z")
To solve it, I created the following code: ```auto pens = value['pens'] startDate = value['startDate'] endDate = value['endDate'] # Número de resultados que você deseja trazer num_results = 50 # Lista para armazenar os dados das tags dados_das_tags = {} # Loop através dos objetos na lista e buscar os dados for objeto in pens: if "data" in objeto and "source" in objeto["data"]: caminho_da_tag = objeto["data"]["source"] # Verifica se o caminho da tag está presente if caminho_da_tag: tag_path_parts = caminho_da_tag.split(":") if len(tag_path_parts) > 2: tag_name = tag_path_parts[-1] # Pega o nome da tag após "tag:" historico_da_tag = system.tag.queryTagHistory( paths=[caminho_da_tag], startDate=startDate, endDate=endDate, returnSize=num_results, # Especifica o número de resultados desejado aggregationMode="LastValue", # Modo de agregação LastValue returnFormat='Wide' # Formato de retorno amplo ) # Lista para armazenar os valores desta tag valores_da_tag = [] # Loop para obter os valores for i in range(historico_da_tag.getRowCount()): timestamp = historico_da_tag.getValueAt(i, "t_stamp") valor = historico_da_tag.getValueAt(i, tag_name) # Obtém o valor da coluna correspondente ao nome da tag timestamp_str = system.date.format(timestamp, "dd/MM/yyyy HH:mm:ss") valor_formatado = "{:.2f}".format(round(valor, 2)) # Formata o valor com duas casas decimais valores_da_tag.append([timestamp_str, valor_formatado]) dados_das_tags[tag_name] = valores_da_tag else: dados_das_tags[tag_name] = [] else: dados_das_tags[tag_name] = [] else: dados_das_tags[objeto["name"]] = [] # Construir um novo dataset vazio com as colunas desejadas headers = ["time"] for tag_name in dados_das_tags.keys(): headers.append(tag_name) combined_dataset = system.dataset.toDataSet(headers, []) # Preencher o dataset combinado com os dados dos datasets individuais for i in range(num_results): row = [] row.append(dados_das_tags[list(dados_das_tags.keys())[0]][i][0]) # Adicione a coluna de tempo for tag_name in list(dados_das_tags.keys()): if len(dados_das_tags[tag_name]) > i: row.append(dados_das_tags[tag_name][i][1]) else: row.append(None) combined_dataset = system.dataset.addRow(combined_dataset, row) return combined_dataset ``` Now just export to excel. Thanks!