loop on a dataframe rows in python

To loop through a dataframe row by row in Python using Pandas, there are several approaches:

1. Using iterrows() method:

main.py
for index, row in dataframe.iterrows():
    # loop body
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This method returns an iterator of index and row data as tuples, where the index is the index of the row and the row is a pandas series representing the row data.

2. Using apply() method:

main.py
def func(row):
    # loop body

result = dataframe.apply(func, axis=1)
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This method takes a function as an argument and applies it to each row of the dataframe. The axis=1 argument indicates that we are applying the function to rows.

3. Using iloc[] indexer:

main.py
for i in range(len(dataframe)):
    row = dataframe.iloc[i]
    # loop body
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This method uses integer-based indexing to access the rows of the dataframe.

Each of these methods has its own advantages and disadvantages, depending on the specific use case.