The above example iterates through every row in a DataFrame by applying transformations to the data, since I need a DataFrame back, I have converted the result of RDD to a DataFrame with new column names. Note
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Pandas is a powerful library for working with data in Python, and the DataFrame is one of its most widely used data structures. One common task when working with DataFrames is to iterate over the rows and perform some action on each row. ...
Like any other data structure, Pandas Series also has a way to iterate (loop through) over rows and access elements of each row. You can use the for loop to iterate over the pandas Series. AdvertisementsYou can also use multiple functions to iterate over a pandas Series like iteritems(),...
arcpy.da.FeatureClassToNumPyArray(fc, '*') #create a pandas DataFrame object from the NumPy array df = DataFrame(nparr, columns=['ObjectId', 'Layer', 'Row', 'Col']) #access unique values for the field uniqueValues = numpy.unique(df['Layer']) for uniqueValue in u...
iterating over aDataFrameto modify the data, vectorization would be a quicker alternative. Also, it's discouraged to modify data while iterating over rows as Pandas sometimes returns a copy of the data in the row and not its reference, which means that not all data will actually be changed...