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certainly! the iterrows() method in the pandas library is a convenient way to iterate over rows in a dataframe. however, it's important to note that using iterrows() can be less efficient compared to other methods like vectorized operations. it returns an iterator that yields pairs of index and pandas series objects, representing each row in the dataframe. here's a tutorial with a code example to demonstrate how to use iterrows():
in this example, we first create a simple dataframe with columns 'name', 'age', and 'city'. we then use the iterrows() method to iterate over each row in the dataframe. inside the loop, we access the index and the row data as a series object. in the modification example, we update the 'age' column by adding 5 to each value using the at method.
it's worth mentioning that while iterrows() is straightforward, it might not be the most efficient option for large dataframes. for performance improvements, consider using vectorized operations whenever possible.
keep in mind that modifying a dataframe within a loop using iterrows() can be slow for large datasets, and alternatives like apply() or vectorized operations should be considered for better performance.
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