The DataFrame: Rows and Columns

Reviewed & published by Brayan K

A DataFrame is a two-dimensional, table-like structure of labelled rows and columns — Pandas' core object, where every column is itself a Series and all columns share one common row index.

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.

If the Series is a single column, the DataFrame is the whole spreadsheet. This is the object you'll spend 95% of your Pandas time working with.

What You'll Learn in This Lesson

• What a DataFrame is (2-D, columns are Series)
• Build a DataFrame from a dictionary of lists
• Read the size with df.shape
• List names and labels with df.columns and df.index
• See each column's type with df.dtypes
• Select one column and get a Series back

1 What Is a DataFrame?

A DataFrame is a 2-dimensional table of data: it has labelled rows (the index) and labelled columns.

• Each column is a Series (from the previous lesson)
• All columns share the same row index
• Different columns can hold different data types

Build one from a dictionary of lists: keys are column names, each list is a column's values.

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

import pandas as pd

data = {
    "name": ["Alice", "Bob", "Carol"],
    "age": [25, 30, 35],
    "city": ["London", "Paris", "Berlin"],
}

df = pd.DataFrame(data)
print(df)

# Output:
#     name  age    city
# 0  Alice   25  London
# 1    Bob   30   Paris
# 2  Carol   35  Berlin

# ✅ Expected output:
#     name  age    city
# 0  Alice   25  London
# 1    Bob   30   Paris
# 2  Carol   35  Berlin

2 Inspecting the Structure

Four attributes tell you everything about a DataFrame's shape and structure:

• df.shape — a (rows, columns) tuple
• df.columns — the column names
• df.index — the row labels
• df.dtypes — the data type of each column

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Carol"],
    "age": [25, 30, 35],
    "city": ["London", "Paris", "Berlin"],
})

print(df.shape)      # (rows, columns)
print(df.columns)    # column names
print(df.index)      # row labels
print(df.dtypes)     # type of each column

# Output:
# (3, 3)
# Index(['name', 'age', 'city'], dtype='object')
# RangeIndex(start=0, stop=3, step=1)
# name    object
# age      int64
# city    object
# dtype: object

# ✅ Expected output:
# (3, 3)
# Index(['name', 'age', 'city'], dtype='object')
# RangeIndex(start=0, stop=3, step=1)
# name    object
# age      int64
# city    object
# dtype: object

3 Selecting a Column Returns a Series

Grab a single column with square brackets and the column name. The result is a Series — exactly the object from the Series lesson:

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

Because it's a Series, every Series trick still works: df["age"].mean(), df["age"] * 2, boolean filtering, and more.

import pandas as pd

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

ages = df["age"]      # selecting one column returns a Series
print(ages)
print(type(ages))
print("Average age:", ages.mean())

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

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

import pandas as pd

df = pd.DataFrame({
    "product": ["Pen", "Notebook", "Eraser"],
    "price": [2, 5, 1],
    "stock": [100, 40, 250],
})

# 🎯 YOUR TURN: print how many rows and columns this DataFrame has
# Replace ___ with the correct attribute
print(df.___)

# Expected output:
# (3, 3)

📋 Quick Reference

🎯 Mini Challenge: Class Roster

Build a roster and explore its structure:

• Create a DataFrame with columns name, grade, and passed
• Print its shape and dtypes
• Select the grade column and print its average

import pandas as pd

roster = pd.DataFrame({
    "name": ["Ana", "Ben", "Cara", "Dan"],
    "grade": [88, 72, 95, 60],
    "passed": [True, True, True, False],
})

print("Shape:", roster.shape)
print(roster.dtypes)
print("Average grade:", roster["grade"].mean())

# Output:
# Shape: (4, 3)
# name      object
# grade      int64
# passed      bool
# dtype: object
# Average grade: 78.75

# ✅ Expected output:
# Shape: (4, 3)
# name      object
# grade      int64
# passed      bool
# dtype: object
# Average grade: 78.75

❓ Frequently Asked Questions

Lesson complete — you understand the DataFrame!

You know a DataFrame is a 2-D table whose columns are Series, you can build one from a dict of lists, inspect its shape, columns, index, and dtypes, and pull out a single column as a Series.

Practice quiz

Continue this course

• Previous: The Series: Pandas' 1-D Building Block
• Next: Creating DataFrames — Build DataFrames from dicts, lists, and NumPy arrays
• Quick reference: Pandas cheat sheet

Frequently asked questions

What is a Pandas DataFrame?

A DataFrame is a two-dimensional, table-like data structure with labelled rows and columns — like a spreadsheet or SQL table. Each column is a Pandas Series, and all columns share the same row index.

Is each column of a DataFrame a Series?

Yes. Selecting a single column with df['col'] returns a Series — a one-dimensional labelled array. A DataFrame is essentially a collection of Series that all share one common index.

How do I check the size of a DataFrame?

Use df.shape, which returns a (rows, columns) tuple. df.columns lists the column names, df.index lists the row labels, and df.dtypes shows the data type of each column.

Can columns in a DataFrame have different data types?

Yes. Unlike a NumPy array, each DataFrame column can hold a different dtype — one column can be integers, another floats, another strings — which is what makes it ideal for real-world tabular data.