I have files in a folder which have same structure. The only difference between them is that for each day a new file is created which is named as date of the day.
So if file is created on 11th November 2019, its name would be 11092019.xlsx.
I have created a slicer which fetches names of all files present in this folder.
Keeping in mind that file format is same and only difference is in their file name and data values in them. Is there any way so that when I select a value from slicer the respective files data will be displayed in table visual ?
It is not possible to load a file interactively based on the slicer value.
You can achieve this by,
Load all files in the folder.
Combine them into a single table, where you need to add a "File Name" column.
Use slicer to show records coming from the specific file.
Related
A new year is coming and the path where we save our data is changing.
The problem is: the data from each asset is saved in folders with the current year as it's name, so data from 2022 is saved in a folder called 2022, and so on.
I want to make a query that will autamatically indentify what years (folders) we have data from and combine them.
The data is saved in this path:
C:\Users\Projects\3. Assets\Type A\Asset Name\Control\YEAR\Data\Dataset\excel.xlsx
This asset for instance has 3 years of data 2020, 2021 and 2022.
By next week we will already have a 2023 folder with new data, usually a manually add a table.combine to the query, but we have a large number of assets and it can be tricky.
Someone knows a efficient way to automatically identify all the folders named with a year and combine the excel data inside them ?
This is the way i usually do:
Table.Combine ({Sharepoint("...2020/Data/Dataset"),Sharepoint("...2021/Data/Dataset"),Sharepoint("...2022/Data/Dataset")})
Sharepoint is a function that returns folder content from sharepoint.
Best Regards
Can you pull all the directory names at a higher level
similar to
https://exceltown.com/en/tutorials/power-bi/powerbi-com-and-power-bi-desktop/power-bi-data-sources/connect-power-query-whole-sharepoint-folder/
then filter them ?
I have a Power BI report where I load data from a CSV into a table (Original Table). I want to change the source of the table to a folder that contains multiple CSVs. The issue i'm having is that when I go to change the data source settings of my Original Table I cannot change it to folder. Original Table has many measures and I would like to avoid rewriting them all. Any ideas?
Create a new query that reads multiple csv from a folder and combines them.
Make sure the column names are the same as in your original table.
Open both queries in the advanced editor and copy the whole code from the new query to the old query
Delete the new query
new to Power BI and just built a dashboard with some finance data with the following columns,
Date|Transaction ID|Transaction Amount|Item Description|Item Key
Every month I receive a new CSV file with data for the previous month. Rather than manually adding the new data to a master file each month, is there a way to simply drop the new CSV file into a folder each month and then refresh the dashboard so it automatically includes the new data (minus the headers)? If possible, I'd also like to add a column which holds the date the new file was loaded, so each new month's file is date stamped each time it's added.
Many thanks
What you can do is use a folder as a source instead of a csv.
That folder should contain all csv files.
When all your files are load you only have to select the following option:
After that you will have all the data from the all csv files in one gigant table.
Unfortunately its not possible to add a date column with the load date.
The only way to do that is that the csv files have the date column.
Hope it helps you.
We are utilizing an excel monthly report in our power bi project that has added measure columns, and we keep the sheets the fields pull the data from in one folder. When we get each month's updated excel sheet, would we be able delete the old one, add the new report to the folder with the exact same name as the old, and refresh the power bi query to use the new updated data? All the column headers would remain the same, the only thing that would be changing is maybe the amount of rows and the data within them. If we were to keep all the names the same, the only thing changing is the data sheet itself (not the column headers just the data) would the added measure columns remain and work? The measure columns act as column data multipliers and filters, and it would be a pain to make new ones each month.
Thanks
Yes. If the file path and filename and sheet/table name all remain the same, Power BI won't know the difference and you shouldn't have trouble if the columns and headers stay consistent.
Additionally, if you don't want to rename the file or delete/move older files from the folder, you could do a Load from Folder query and sort by date created/modified and grab the top row instead of specifying the filename.
I am trying to create a parameterized dataset that imports files from GCS and puts them under each other. This all works fine (Import Data > Parameterize).
To give a bit of context, I store each day a .csv file with a different name referring to that date.
Now it happens that my provider added a new column since last month into the files. This means that files before this date have 8 columns, whereas from this date 9 columns.
However, when I parameterize, Dataprep only takes into account the columns that are matching (thus 8 columns only). Ideally I would want empty observations for the rows coming from files that did not have this new column.
How can this be achieved?
The parameterized datasets only work on a fixed schema as mentioned in the documentation:
Avoid creating datasets with parameters where individual files or tables have differing schemas.
This fixed schema is generated using one of the file found during the creation of the dataset with parameters.
If the schema has changed, then you can "refresh" it by editing the dataset with parameters and clicking save. If all the matching files contain 9 columns, you should now see 9 columns in the transformer.