For getting 3rd, 4th & 6th row of a datafile “df”in Python programming, we can write: df.loc[[2,3,5]]  df.loc[[3,4,5]]  df.iloc[3,4,6]  None of the above

The correct answer is: df.iloc[[2,3,5]]

Here's why:

In Python, pandas library is used for data manipulation and analysis. It is used to extract data and store it in a desirable format. The .loc and .iloc indexers are used in pandas to retrieve data.

• .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 python and numpy indexing.

• .iloc is a more strict data 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 .loc indexer.

In Python, indexing syntax can be used as a substitute for the .iloc method, The basic format is df.iloc[rows, columns].

For getting 3rd, 4th & 6th row of a datafile “df” in Python programming, we can write: df.iloc[[2,3,5]]. Here