![]() ![]() Power Apps Community Power Automate Community Power Virtual Agents Community Power Pages Community On this special episode of Power Platform Connections, David Warner and Hugo Bernier interview Microsoft Business Applications MVPs Geetha Sivasailam & Chris Piasecki live in Redmond, alongside the latest news, videos, product updates, and community blogs. No need to remove the first two lines, the flow handles it just the way it is. Here is the flow (you can also view it here, )īecause this flow may be difficult for you to copy, I have exported it so you can import it into your environment: The split function takes a regular expression as pattern so you can write something which works for you.įinal note the limit parameter is supported since spark >= 3.The flow below produces the following output: Note that your real data might be more complex or structured differently and this solution might not work. Splitted = F.split(F.col('value'), ',', limit=cols_to_split)ĭf.select(.alias(f'col') for i in range(cols_to_split)]).show() From the resulting array we select the columns we want. Here we read the csv as a text file and split it up to the 4th comma. For your example this can can be done as below. ![]() If that is not an option can split the file yourself after reading it. The best option would be to ask your source to deliver a better formatted csv file (or use a different separator) The problem here is that your csv separator is also used in the json column without it being escaped or the column being quoted.
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