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
I want t o iterate every row of a dataframe without using collect. Here is my current implementation: val df = - 87093
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(),...
import pandas as pd # Create a sample DataFrame df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}) # Iterate over rows in the DataFrame for index, row in df.iterrows(): # Access data for each column by column name print(index, row['A'], row...
\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', 'Lay...
Now, to iterate over thisDataFrame, we'll use theitems()function: df.items() This returns agenerator: <generator object DataFrame.items at 0x7f3c064c1900> We can use this to generate pairs ofcol_nameanddata. These pairs will contain a column name and every row of data for that column....