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The date field is getting changed while reading data from source .xls file to the dataframe. In the source xl file all columns are strings but i am not sure why date column alone behaves differentlyIn Source file date is 1/24/1947.In pyspark datafram...
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how about using inferschema one single time to create a correct DF, then create a schema from the df-schema.something like this f.e.from pyspark.sql.types import StructType
# Save schema from the original DataFrame into json:
schema_json = df.s...
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The date field is getting changed while reading data from source .xls file to the dataframe. In the source xl file all columns are strings but i am not sure why date column alone behaves differentlyIn Source file date is 1/24/2022.In dataframe it is ...
- 4914 Views
- 4 replies
- 10 kudos
Latest Reply
Hi Team, @Merca Ovnerud​ I am also facing same issue , below is the code snippet which I am using df=spark.read.format("com.crealytics.spark.excel").option("header","true").load("/mnt/dataplatform/Tenant_PK/Results.xlsx")I have a couple of date colum...
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I have been trying to extract one date field from cosmos which looks like as below:"lastModifiedDate" : { "$date" : 1668443121840 }when the above field is extracted using Databricks it gets converted into a date format which looks like this...
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Consider a table that gets partitioned on a date field. But, I'm filtering a column that is not partitioned. Now, with this filter condition whether all the files are parsed to attain the required result set, or does any data skipping happens?
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