I am reading the csv data with Defined Schema. Whereas in that valid data from header is getting overwrited with schema.
I want header to get in 1st row of dataframe.
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in one of my projects, I need to write each row of a dataframe into a separate S3 file in json format. In the actual implementation, map/foreach's input is a Row, though I don't seem to find any member function on Row that could transform a row into json format.
I'm using spark df and don't want to convert it to pandas (as it involves sending everything to the driver?), hence cannot use the to_json function. Is there any other way to do it? I can definitely write my own json converter based on my specific df schema, but just wondering if there is a readily available module.
I am reading a parquet file into a dataframe. My goal is to verify that my time data (column type in parquet : timestamp) are ISO 8601.
The dates in time column look like this : 2021-03-13T05:34:27.100Z or 2021-03-13T05:34:27.100+0000
But when I read my dataframe, pyspark transform 2021-03-13T05:34:27.100Z into 2021-03-13 05:34:27.100
I want to keep the original format, but I can't figure out how to stop pyspark from doing this. I tried to use a custom schema with string for dates but I get this error: Parquet column cannot be converted in file file.snappy.parquet. Column: [time], Expected: string, Found: INT96
Also I tried using conf parameters but it didn't work for me.
I want to convert the DataFrame to nested json. Sourse Data:-
DataFrame have data value like :-
Expected Output:-
I have to convert DataFrame value to Nested Json like : -
Appreciate your help !
If you want to persist the data then save dataframe with format json
df.write.json("path")
You can use toJSON function, which will convert dataframe to Dataset[String]
df.toJSON
If there's only one element then you can further manipulate to get string
df.toJSON.take(1).head
Thanks.
I am trying to export a pandas dataframe into a parquet format using the following:-
df.to_parquet("codeset.parquet", index=False)
I don't want to have index column in the parquet file so is this automatically done by to_parquet command or how can I get around this so that there is no index column included in the exported parquet.
I'm trying to remove header from the Dataset<Row> which is created with the data from csv file. There are bunch of ways to do it.
So, I'm wondering whether the first row in Dataset<Row> is always equals to the first row in the file (from which the Dataset<Row> is created)?
When you read the files, the records in the RDD/Dataframe/Dataset are in the order as they were in the files. But if you perform any operation that requires shuffling the order changes.
So you can remove the first row as soon as reading the file and before any operation that requires shuffling.
The best option would be using csv data source as
spark.read.option("header", true).csv(path)
This will take the first row as a header and use it as column name.