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Read CSV File using Column Header Names instead of column Index in Power Automate

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Posted on by TarunEY

I have a comma separated csv file like below

EMPID	EmpName	EMPCountry	EmpSalary
1	Tarun	INDIA	100
2	Alex	US	300
3	John	UK	200

currently we are reading the same values in power automate using the compose action after skipping the first row and splitting the data rows from the second row using comma(",") based on column Index 0,1,2 etc..

{
"EMPID": "@{outputs('splitByComma')?[0]}",
"EMPName": "@{outputs('splitByComma')?[1]}",
"EMPCountry": "@{outputs('splitByComma')?[2]}",
"EmpSalary ": "@{outputs('splitByComma')?[3]}",
}

Some the csv file may have the same columns names but in the different order like below

EmpName	EmpSalary	EMPID	EMPCountry
Tarun	100	1	INDIA
Alex	300	2	US
John	200	3	UK

so I need a way to get the cell values using Column Headers instead of column Index in Power Automate

Any suggestion or approaches please.

Thanks in Advance,

Tarun.

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Building Power Apps

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v-bofeng-msft on at

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Hi @TarunEY ,

The point is that convert the csv file into an array. I've made a test for your reference:

1\skip(split(variables('TheCSVFile'),decodeUriComponent('%0A')),1)

2\split(split(variables('TheCSVFile'),decodeUriComponent('%0A'))[0],',')[0]
3\split(split(variables('TheCSVFile'),decodeUriComponent('%0A'))[0],',')[1]
4\split(split(variables('TheCSVFile'),decodeUriComponent('%0A'))[0],',')[2]
5\split(split(variables('TheCSVFile'),decodeUriComponent('%0A'))[0],',')[3]

6\split(items('Apply_to_each'),',')[0]
7\split(items('Apply_to_each'),',')[1]
8\split(items('Apply_to_each'),',')[2]
9\split(items('Apply_to_each'),',')[3]

The Result:

Best Regards,

Bof

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Vannieljevla 53 on at

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This solution worked for me, thanks! For anyone wanting to shave off run time: you can put the code into a Select action, instead of an Apply to Each - Append to array variable. Since both are working with json, you shouldn't have to change your code.

For my case, the runtime for this step reduced from 7 seconds to 2. I only have ~700 rows of data, but for larger data sets this can make a difference.

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