df = spark.sql('SELECT * FROM EmployeeTerritories LIMIT 100') dataframe = df.toPandas() dataframe_json = dataframe.to_json(orient='records', force_ascii=False) However, the second line throws me the error Casting from timestamp[us, tz=Etc/UTC] to timestamp[ns] wou...
In polars precision and scale are reversed from most other SQL-like definitions. In your Spark code, the DecimalType is expecting a precision then scale. col(col_name).cast(DecimalType(data_length, data_scale)) However, because the polars Decimal has a default of None for precision, and ...
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And then reference columns in other dataframes based on the index. Setup: from pyspark.sql import functions as F from functools import reduce df1 = spark.createDataFrame([('2020-02-02 08:08:08', 1, '2020-02-02 07:07:07')], ['A', 'x', 'B']) df1.printSchema()...