loc vs iloc

Select rows and columns by label (loc) or by integer position (iloc)

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Explanation

loc — select by label (row index label, column name): ``python df.loc[0] # row with index label 0 df.loc[0, 'name'] # row 0, column 'name' df.loc[0:2, 'name':'age'] # slice by label (inclusive both ends)

iloc — select by integer position (0-based, like NumPy): ``python df.iloc[0] # first row (position 0) df.iloc[0, 1] # row 0, column at position 1 df.iloc[0:3, 0:2] # first 3 rows, first 2 columns (exclusive end)

Critical difference:

loc slices are inclusive at both ends
iloc slices are exclusive at the end (like Python/NumPy)

python

# If index is [10, 20, 30, 40]:
df.loc[10:30]    # rows with labels 10, 20, 30 (3 rows)
df.iloc[0:3]     # rows at positions 0, 1, 2 (3 rows)

When to use which:

Use loc when you know the label (column name, named index)
Use iloc when you know the position (first row, last 5 rows)

Examples

loc and iloc side by side

iloc[-2:] selects the last 2 rows regardless of total length

import pandas as pd

df = pd.DataFrame({'name':['Alice','Bob','Carol','Dave'],'age':[25,30,35,28],'salary':[50,65,80,55]})

# First row by position
print(df.iloc[0])        # Alice, 25, 50

# Rows 1-2, columns 'name' and 'age'
print(df.loc[1:2, ['name','age']])

# Last 2 rows
print(df.iloc[-2:])

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