Rows & Subsets: loc / iloc

Selecting columns was easy. To pull out rows — or a rectangle of rows and columns — you use .loc and .iloc, exactly like a Series but now with two directions.

1Grab a rectangle

The syntax is df.loc[rows, columns]. Drag the pickers to select a subset and watch the golden rule in action: .loc keeps the end, .iloc drops it.

loc / iloc subset explorer

Grab a rectangle of the DataFrame by rows AND columns. .loc uses labels and INCLUDES the end; .iloc uses positions and EXCLUDES it.

row startrow endcol startcol end

df.loc['ID 1':'ID 4', 'Name':'IP']

	Name	English	IP	Maths
ID 1	Rinku	65	89	72
ID 2	Ritu	77	83	69
ID 3	Pankaj	72	95	87
ID 4	Ajay	81	99	94
ID 5	Aditya	92	87	96

Selected 4 rows × 3 columns.

End labels are INCLUDED ✓

2.loc — by label, inclusive

.loc uses the row and column labels, and both the start and end labels are included. Use : on either side to mean "all".

You can also select rows and columns discretely (specific, non-adjacent items) by passing a list of labels instead of a slice:

df_loc.py

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

print("--- The whole df ---")
print(df)

# rows ID 1 to ID 3, columns Name to IP Marks (all inclusive)
print("\n--- df.loc['ID 1':'ID 3', 'Name':'IP Marks'] ---")
print(df.loc['ID 1':'ID 3', 'Name':'IP Marks'])

# Discrete selection: specific rows and columns using lists
print("\n--- df.loc[['ID 1', 'ID 3', 'ID 5'], ['Name', 'IP Marks']] ---")
print(df.loc[['ID 1', 'ID 3', 'ID 5'], ['Name', 'IP Marks']])

# a single row (all its columns)
print("\n--- df.loc['ID 2'] ---")
print(df.loc['ID 2'])

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

print("--- The whole df ---")
print(df)

# rows ID 1 to ID 3, columns Name to IP Marks (all inclusive)
print("\n--- df.loc['ID 1':'ID 3', 'Name':'IP Marks'] ---")
print(df.loc['ID 1':'ID 3', 'Name':'IP Marks'])

# Discrete selection: specific rows and columns using lists
print("\n--- df.loc[['ID 1', 'ID 3', 'ID 5'], ['Name', 'IP Marks']] ---")
print(df.loc[['ID 1', 'ID 3', 'ID 5'], ['Name', 'IP Marks']])

# a single row (all its columns)
print("\n--- df.loc['ID 2'] ---")
print(df.loc['ID 2'])

3.iloc — by position, exclusive

.iloc uses integer positions (starting at 0), and the end position is excluded — just like list slicing.

Just like with .loc, you can select specific rows and columnsdiscretely by passing a list of integer positions:

df_iloc.py

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

print("--- The whole df ---")
print(df)

# rows 0,1 and columns 0,1,2  (end excluded)
print("\n--- df.iloc[0:2, 0:3] ---")
print(df.iloc[0:2, 0:3])

# Discrete selection: specific row/column index positions using lists
print("\n--- df.iloc[[0, 2, 4], [0, 2]] ---")
print(df.iloc[[0, 2, 4], [0, 2]])

# every row, first two columns
print("\n--- df.iloc[:, 0:2] ---")
print(df.iloc[:, 0:2])

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

print("--- The whole df ---")
print(df)

# rows 0,1 and columns 0,1,2  (end excluded)
print("\n--- df.iloc[0:2, 0:3] ---")
print(df.iloc[0:2, 0:3])

# Discrete selection: specific row/column index positions using lists
print("\n--- df.iloc[[0, 2, 4], [0, 2]] ---")
print(df.iloc[[0, 2, 4], [0, 2]])

# every row, first two columns
print("\n--- df.iloc[:, 0:2] ---")
print(df.iloc[:, 0:2])

4.loc with conditions

A superpower of .loc (that .iloc doesn't have): you can pass a condition for the rows and even list the columns you want in the same call.

df_loc_cond.py

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

print("--- The whole df ---")
print(df)

# rows where IP Marks > 88, only Name and IP Marks columns
print("\n--- df.loc[df['IP Marks'] > 88, ['Name', 'IP Marks']] ---")
print(df.loc[df['IP Marks'] > 88, ['Name', 'IP Marks']])

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

print("--- The whole df ---")
print(df)

# rows where IP Marks > 88, only Name and IP Marks columns
print("\n--- df.loc[df['IP Marks'] > 88, ['Name', 'IP Marks']] ---")
print(df.loc[df['IP Marks'] > 88, ['Name', 'IP Marks']])

5A single value: at & iat

To read one cell, the fastest tools are .at (by labels) and .iat (by positions) — the DataFrame twins of the Series .at/.iat.

df_at_iat.py

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

print("--- The whole df ---")
print(df)
print()

print("df.at['ID 4', 'Maths Marks'] :", df.at['ID 4', 'Maths Marks'])
print("df.iat[3, 3]                :", df.iat[3, 3])

# the [] shortcut is column-first, then row
print("df['Maths Marks']['ID 4']   :", df['Maths Marks']['ID 4'])

import pandas as pd

df = pd.DataFrame({
    'Name':          ['Rinku', 'Ritu', 'Pankaj', 'Ajay', 'Aditya'],
    'English Marks': [65, 77, 72, 81, 92],
    'IP Marks':      [89, 83, 95, 99, 87],
    'Maths Marks':   [72, 69, 87, 94, 96],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4', 'ID 5'])

print("--- The whole df ---")
print(df)
print()

print("df.at['ID 4', 'Maths Marks'] :", df.at['ID 4', 'Maths Marks'])
print("df.iat[3, 3]                :", df.iat[3, 3])

# the [] shortcut is column-first, then row
print("df['Maths Marks']['ID 4']   :", df['Maths Marks']['ID 4'])

Key Takeaway

.loc = labels, end included. .iloc = positions, end excluded. Both take [rows, columns]. For a single cell use .at (labels) or .iat (positions).

Quick Check

df has rows ID 1–ID 5. What does df.loc['ID 2':'ID 4'] return?

Quick Check

What does df.iloc[1:3] return (positions)?

🎬 Watch the video

DataFrame: loc, iloc, at, iat, in 6:12

One animated video of this topic, narrated. Watch it before the lessons for the big picture, or after them to revise.

Watch on YouTube ↗All the videos, in order ↗

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