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In this blog post I am going to show you an easy way to clean your data (which is often fixing data issues or mis-spelt data) using the new feature Launch Data Wranger using DataFrames
I had previously blogged about using Pandas data frames but this required extra steps and details, if you are interested in that blog post you can find it here: Did you know that there is an easy way to shape your data in Fabric Notebooks using Data Wrangler?
In this blog post I am going to show you how I cleaned up the data in my location column.
The first step I did was to load the CSV into a data frame in my PySpark notebook as shown below.
df_staging = spark.read.format(“csv”).option(“header”,“true”).load(“Files/Delay_Data/*.csv”)
# View the df_staging is bringing back data.
df_staging.show(10)
Once I had run this I could then see the output of the data from my CSV files.
Next, to transform my data, I need to get my data frame into the Data Wrangler with the steps below.
- I clicked on Data.
- Then I clicked on “Transform DataFrame in Data Wrangler”
- Finally, I select the name of my data frame “df_staging”
In the steps below is where I now transformed my data for the Location column.
I expanded Operations and then selected “Find and replace”
I then completed the following steps below to complete the find and replace.
- I clicked on the drop down and selected the column called “Location”
-
For the Old value, is the value I am searching for to replace.
- In my example I am looking for “Coast”
- I wanted to replace the value with nothing, so I pressed the space bar once.
- Once I was happy with the results I clicked on Apply.
I could then see the changes in the green column before I applied them.
I then clicked Apply (Number 4 from above).
If I wanted to see the code, I could see this in the section as shown below which is writing the code for me.
Then applied by clicking on “Add code to notebook” as shown below.
I could then see the code in the notebook as shown below.
The final step was to take my transformed data which I inserted into my Lakehouse table as shown below.
I could then go into my Lakehouse and view the “Delays” table using the SQL Endpoint.
I hope that you found this useful and that you now can easily transform your data in Fabric using the Notebooks and data wranglers.
Any comments or suggestions are most welcome!
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