What's the Difference Between Pandas `loc` and `iloc` for Data Selection?

How iloc and loc Differ in Pandas:

In data manipulation using Pandas, iloc and loc are two commonly used slicing methods that often cause confusion. Understanding their fundamental difference is essential for efficient data management.

Label vs. Location

The key distinction between loc and iloc lies in how they select data:

• loc: Accesses data based on labels, such as row or column names (index and column labels).
• iloc: Accesses data based on integer locations within the object's axis (row/column numbers).

Examples:

Consider a DataFrame df with a non-monotonic index containing letters:

import pandas as pd

df = pd.DataFrame({'col1': ['a', 'b', 'c', 'd', 'e', 'f']}, index=[49, 48, 47, 0, 1, 2])

loc (Label-Based Slicing):

• df.loc[0]: Retrieves the row with index label "0" ('d').
• df.loc[0:1]: Returns two rows with labels 0 and 1 ('d' and 'e').

iloc (Location-Based Slicing):

• df.iloc[0]: Gets the value at index location 0 ('a').
• df.iloc[0:1]: Selects one row at index location 0 ('a').

Key Differences:

The following table highlights the differences between loc and iloc in various scenarios:

Object	Description	loc	iloc
0	Single item	Value at index label 0 ('d')	Value at index location 0 ('a')
0:1	Slice	Two rows (labels 0 and 1)	One row (first row at location 0)
1:47	Slice with out-of-bounds end	Zero rows (empty Series)	Five rows (location 1 onwards)
1:47:-1	Slice with negative step	Three rows (labels 1 back to 47)	Zero rows (empty Series)
[2, 0]	Integer list	Two rows with given labels	Two rows with given locations
s > 'e'	Boolean series (indicating true values)	One row (containing 'f')	NotImplementedError
(s>'e').values	Boolean array	One row (containing 'f')	Same as loc
999	Integer object not in index	KeyError	IndexError (out of bounds)
-1	Integer object not in index	KeyError	Returns last value in s
lambda x: x.index[3]	Callable applied to series (here returning 3rd item in index)	s.loc[s.index[3]]	s.iloc[s.index[3]]

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