(1) Difference Between “apply()” and “applymap()” Method In Pandas. Main difference between “apply()” and  “applymap()” is that, “apply()” method applies the function entirely by taking a row or column as an argument. “applymap()” method applies the function elementwise to each row or each column. Example import pandas as pd df1 = pd.DataFrame({‘names’: [‘Subrat’, ‘Arpita’, ‘Abhispa’, ‘Subhada’, ‘Sonali’], ‘marks’: [67, 75, 84, 90, 99]}) df1 df1.apply(lambda x:len(x)) Output: Note: Here ‘5’ represents the total elements inside the ‘name’ and ‘marks’ columns. Here apply() considered ‘name’ and ‘column’ as a series and calculated its length. df1.applymap(lambda x: len(str(x))) Output: Note: Here applymap()

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