What are the key differences between Pandas' `loc` and `iloc` methods for DataFrame slicing?

How are iloc and loc different?

iloc and loc are two methods for slicing a DataFrame in Pandas. Both methods can be used to select rows and columns, but they differ in how they interpret the input.

loc gets rows (and/or columns) with particular labels.

iloc gets rows (and/or columns) at integer locations.

To demonstrate, consider a series s of characters with a non-monotonic integer index:

>>> s = pd.Series(list("abcdef"), index=[49, 48, 47, 0, 1, 2])
49    a
48    b
47    c
0     d
1     e
2     f

s.loc[0]    # value at index label 0
'd'

s.iloc[0]   # value at index location 0
'a'

s.loc[0:1]  # rows at index labels between 0 and 1 (inclusive)
0    d
1    e

s.iloc[0:1] # rows at index location between 0 and 1 (exclusive)
49    a

Here are some of the differences/similarities between s.loc and s.iloc when passed various objects:

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