Difference between loc() and iloc() in Pandas DataFrame

Pandas library of python is very useful for the manipulation of mathematical data and is widely used in the field of machine learning. It comprises many methods for its proper functioning. loc() and iloc() are one of those methods. These are used in slicing data from the Pandas DataFrame. They help in the convenient selection of data from the DataFrame. They are used in filtering the data according to some conditions. The working of both of these methods is explained in the sample dataset of cars.

loc() : loc() is label-based data selecting method which means that we have to pass the name of the row or column which we want to select. This method includes the last element of the range passed in it, unlike iloc(). loc() can accept the boolean data unlike iloc() .

iloc() : iloc() is an indexed-based selecting method which means that we have to pass integer index in the method to select a specific row/column. This method does not include the last element of the range passed in it unlike loc(). iloc() does not accept the boolean data unlike loc().

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