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 of many methods for its proper functioning.
iloc() are one of those methods. These are used in slicing of 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. Working of both of these methods is explained in the sample dataset of cars.
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
loc() can accept the boolean data unlike
iloc() . Many operations can be performed using the
loc() method like-
1. Selecting data according to some conditions :
2. Selecting a range of rows from the DataFrame :
3. Updating the value of any column :
iloc() is a indexed based selecting method which means that we have to pass integer index in the method to select specific row/column. This method does not include the last element of the range passed in it unlike
iloc() does not accept the boolean data unlike
loc(). Operations performed using
1. Selecting rows using integer indices:
2. Selecting a range of columns and rows simultaneously:
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