I want to iterate every row of a dataframe without using collect. Here is my current implementation: val df = spark.read.csv("/tmp/s0v00fc/test_dir") import scala.collection.mutable.Map var m1 = Map[Int, Int]() var m4 = Map[Int, Int]() var j = 1 def Test(m:Int, ...
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row number: 0 index: Java value: 20000 row number: 1 index: Spark value: 25000 row number: 2 index: PySpark value: 23000 row number: 3 index: Pandas value: 28000 row number: 4 index: NumPy value: 55000 row number: 5 index: Python value: 23000 row number: 6 index: Oracle value: ...
fori, rowindf.iterrows():print(f"Index:{i}")print(f"{row}\n") In the for loop,irepresents the index column (our DataFrame has indices fromid001toid006) androwcontains the data for that index in all columns. Our output would look like this: Index: id001 first_name John last_name...
This method applies a function to each row or column of the DataFrame. The function can be passed as an argument and is applied to each row, and the results are combined into a new DataFrame. Here is an example of how to use theapply()method to iterate over rows: ...
\Documents\ArcGIS\Default.gdb" fc = ws + "\\MyFeatureClass" #create a NumPy array from the input feature class nparr = arcpy.da.FeatureClassToNumPyArray(fc, '*') #create a pandas DataFrame object from the NumPy array df = DataFrame(nparr, columns=['ObjectId', 'Laye...