Selecting Columns and Rows

Reviewed & published by Brayan K

Selecting in pandas means pulling out the specific columns or rows you care about from a DataFrame using square-bracket indexing — the everyday way you narrow a table down to just the data you need.

Part of the free Pandas course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

Master single-column vs multi-column selection, row slicing, and the attribute-access shortcut — the foundation of every analysis you'll do.

What You'll Learn in This Lesson

• Select one column with df["col"] (returns a Series)
• Select multiple columns with df[["a", "b"]] (returns a DataFrame)
• The difference between a Series and a DataFrame
• Select rows by position with slicing df[0:3]
• Use df.col attribute access — and when it breaks
• Build a small DataFrame from a dictionary to practice

1 Selecting a Single Column — df["col"]

To grab one column, put its name in square brackets after the DataFrame: df["age"]. This returns a Series — a one-dimensional labeled array, basically a single column with its index attached.

Let's build a small DataFrame from a dictionary and pull out one column:

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Carol"],
    "age":  [25, 30, 35],
    "city": ["NYC", "LA", "Chicago"],
})

ages = df["age"]   # a Series
print(type(ages))  # <class 'pandas.core.series.Series'>

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Carol"],
    "age":  [25, 30, 35],
    "city": ["NYC", "LA", "Chicago"],
})

# Select the "age" column -> returns a Series
ages = df["age"]
print(ages)

print(type(ages))   # <class 'pandas.core.series.Series'>
print(ages.mean())  # 30.0  -> you can call methods on a Series

# ✅ Expected output:
# 0    25
# 1    30
# 2    35
# Name: age, dtype: int64
# <class 'pandas.core.series.Series'>
# 30.0

2 Selecting Multiple Columns — df[["a", "b"]]

To select more than one column, pass a list of column names inside the brackets. Because you pass a list, you end up with double brackets: df[["name", "city"]]. The result is a DataFrame, not a Series.

subset = df[["name", "city"]]   # list inside brackets -> DataFrame
print(type(subset))             # <class 'pandas.core.frame.DataFrame'>

🧠 The bracket rule in one line:

• df["age"] → single brackets → Series
• df[["age"]] → double brackets → DataFrame (one column)
• df[["age", "city"]] → list → DataFrame (many columns)

You also control column order this way — the columns appear in the order you list them, which is handy for reshaping output.

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Carol"],
    "age":  [25, 30, 35],
    "city": ["NYC", "LA", "Chicago"],
})

# Pass a LIST of columns -> get a DataFrame back
subset = df[["city", "name"]]   # note: reordered on purpose
print(subset)

print(type(subset))   # <class 'pandas.core.frame.DataFrame'>

# Double brackets keep DataFrame shape even for ONE column
print(df[["age"]])    # still a DataFrame, with the "age" header

# ✅ Expected output:
#       city   name
# 0      NYC  Alice
# 1       LA    Bob
# 2  Chicago  Carol
# <class 'pandas.core.frame.DataFrame'>
#    age
# 0   25
# 1   30
# 2   35

3 Selecting Rows by Slice — df[0:3]

Square brackets behave differently when you pass a slice. df[0:3] selects rows by position (0, 1, 2) — just like slicing a Python list. The end index is exclusive.

print(df[0:2])   # first two rows (positions 0 and 1)
print(df[1:])    # everything from row 1 onward

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Carol", "Dan"],
    "age":  [25, 30, 35, 40],
})

# A SLICE selects rows by position (end is exclusive)
print(df[0:2])

print(df[2:])

# ✅ Expected output:
#     name  age
# 0  Alice   25
# 1    Bob   30
#     name  age
# 2  Carol   35
# 3    Dan   40

⚡ Shortcut: Attribute Access (df.col)

Pandas lets you read a column as if it were an attribute: df.age instead of df["age"]. It's a nice shortcut while exploring, but it has real limits.

• df.age — simple name ✓
• df.city — no spaces ✓
• df["first name"] — has a space ✗
• df.count — clashes with a method ✗
• df.2024 — not a valid identifier ✗

import pandas as pd

df = pd.DataFrame({
    "product": ["Pen", "Notebook", "Eraser"],
    "price":   [1.50, 3.00, 0.75],
    "stock":   [120, 80, 200],
})

# 🎯 YOUR TURN:
# 1) Select just the "price" column as a Series.
# 2) Select "product" and "stock" together as a DataFrame.
# Replace the ___ blanks below.

price_series = df[___]            # hint: single brackets, one name
two_cols     = df[___]            # hint: a LIST of two names

print(price_series)
print(two_cols)

# Expected output:
# 0    1.50
# 1    3.00
# 2    0.75
# Name: price, dtype: float64
#     product  stock
# 0       Pen    120
# 1  Notebook     80
# 2    Eraser    200

📋 Quick Reference

🎯 Mini Challenge: Build and Slice a Table

Build a DataFrame of three movies, then practice every selection style you learned.

• Create columns: title, year, rating
• Pull rating as a Series and print its average
• Select title and rating as a DataFrame
• Slice the first two rows

import pandas as pd

movies = pd.DataFrame({
    "title":  ["Inception", "Coco", "Parasite"],
    "year":   [2010, 2017, 2019],
    "rating": [8.8, 8.4, 8.5],
})

# 1) rating as a Series + average
print(movies["rating"].mean())     # 8.566666666666666

# 2) two columns as a DataFrame
print(movies[["title", "rating"]])

# 3) first two rows
print(movies[0:2])

# ✅ Expected output:
# 8.566666666666668
#        title  rating
# 0  Inception     8.8
# 1       Coco     8.4
# 2   Parasite     8.5
#        title  year  rating
# 0  Inception  2010     8.8
# 1       Coco  2017     8.4

❓ Frequently Asked Questions

Lesson complete — you can carve up any DataFrame!

You now know that single brackets give a Series, a list of names gives a DataFrame, and a slice selects rows. You also know when the handy df.col shortcut breaks down.

Practice quiz

Continue this course

• Previous: Inspecting Data: head, tail, info, describe
• Next: Filtering with Boolean Masks — Keep only the rows that match your conditions using boolean masks
• Quick reference: Pandas cheat sheet › Selecting & Filtering

Frequently asked questions

What is the difference between df["col"] and df[["col"]]?

df["col"] returns a one-dimensional Series, while df[["col"]] returns a DataFrame with a single column. Use double brackets when you want to keep the DataFrame shape or select multiple columns at once.

Why does df[0:3] select rows but df[0] raises an error?

When you pass a slice like 0:3 to a DataFrame, pandas treats it as row selection by position. A bare integer like df[0] is interpreted as a column label lookup, so it fails unless a column is literally named 0.

Should I use df.column attribute access?

Attribute access (df.age) is convenient for quick exploration, but it only works when the column name has no spaces, does not clash with a DataFrame method, and is a valid Python identifier. Bracket access df["age"] always works and is the safer habit.