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Tag: apache-spark

Get value from Spark dataframe when rows are dictionaries

I have a PySpark dataframe that looks like this: Values Column {[0.0, 54.04, 48…. Sector A {[0.0, 55.4800000… Sector A If I show the first element of the column ‘Values’ without truncating the data, it looks like this: {[0.0, 54.04, 48.19, 68.59, 61.81, 54.730000000000004, 48.51, 57.03, 59.49, 55.44, 60.56, 52.52, 51.44, 55.06, 55.27, 54.61, 55.89, 56.5, 45.4, 68.63, 63.88, 48.25,

Apache Spark unable to recognize columns in UTF-16 csv file

Question: Why I am getting following error on the last line of the code below, how the issue can be resolved? AttributeError: ‘DataFrame’ object has no attribute ‘OrderID’ CSV File encoding: UTF-16 LE BOM Number of columns: 150 Rows: 5000 Language etc.: Python, Apache Spark, Azure-Databricks MySampleDataFile.txt: Code sample: Output of display(df.limit(4)) It successfully displays the content of df in

Caching a PySpark Dataframe

Suppose we have a PySpark dataframe df with ~10M rows. Also let the columns be [col_a, col_b]. Which would be faster: or Would caching df_test make sense here? Answer It won’t make much difference. it is just one loop where you can skip cache like below Here spark is loading Data once in memory. If you want to use df_sample

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