(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() method calculated the length of individual elements inside the dataframe.