Variables and Data Types: Integers, Floats, Strings, and Booleans

Hello again. In the previous lesson, you learned how to write code in a Colab cell, run it, and read the output underneath. Now you will give your code a memory: variables let you store a value under a meaningful name and use that value later.

By the end of this lesson, you will be able to assign values to variables, choose clear variable names, and recognize four foundational Python data types: integers, floating-point numbers, strings, and Boolean values. These distinctions matter immediately in coding and will become especially important once you begin working with tabular data.

---

Variables: naming a value

A variable is a name that refers to a value. In this line:

age = 21

• age is the variable name.
• = is the assignment operator.
• 21 is the value being assigned.

Read this as: “Assign the value 21 to the name age.” It is not a mathematical claim that two things are equal. Python records that, in the current notebook session, age refers to 21.

Try this in a fresh Colab code cell:

age = 21
print(age)

The output is:

21

When Python encounters age without quotation marks, it looks up the value stored under that name. Compare:

language = "Python"

print(language)
print("language")

Output:

Python
language

The first line prints the value stored in language. The second prints the literal text language, because quotation marks tell Python that the contents are text.

A variable may be assigned a new value later:

score = 10
print(score)

score = 15
print(score)

Output:

10
15

After the second assignment, score refers to 15; it does not still refer to 10. In a notebook, this also means cell run order matters. If you rerun an earlier cell that assigns score = 10, you have changed the value available to later cells.

This short video from Bro Code, Python variables for beginners, gives a visual introduction to variables and the four types you will use here.

---

Four essential types of values

Python determines a value’s type from how you write it. The same characters can represent different types depending on their syntax.

Type	Python name	What it represents	Examples
Integer	int	Whole numbers	0, 42, -8
Floating-point number	float	Numbers written with a decimal point	3.14, -0.5, 12.0
String	str	Text, enclosed in quotes	"hello", 'A12', "42"
Boolean	bool	One of two logical states	True, False

Integers

An integer is a whole number: no decimal point appears in its written form.

number_of_students = 28
year_started = 2026
temperature_change = -4

print(number_of_students)
print(year_started)
print(temperature_change)

These values can be used for counts, positions, years, or other quantities that are naturally whole.

Floating-point numbers

A float is a number with a decimal point. Even when the decimal part is zero, Python treats it as a float.

average_rating = 4.8
distance_km = 2.5
price = 12.0

print(average_rating)
print(distance_km)
print(price)

Here, 12 and 12.0 have similar numerical meanings, but they are different Python types:

whole_number = 12
decimal_number = 12.0

The first is an int; the second is a float.

Strings

A string is text enclosed in quotation marks. You can use either single quotes or double quotes, provided the opening and closing quotes match.

name = "Sam"
course = 'Python Foundations'
postal_code = "02138"

A string can contain letters, spaces, punctuation, and digits. The key question is not “Does it look like a number?” but “Should Python treat it as a number?”

quantity = 42
item_code = "042"

quantity is an integer because you might calculate with it. item_code is text because it is an identifier: preserving the leading zero matters. This distinction will matter later when data is loaded from CSV files. A column of identification codes may look numerical but should often remain text.

Boolean values

A Boolean represents one of two states:

is_logged_in = True
has_finished = False

The only Boolean literals are exactly:

True
False

They must begin with capital letters and must not be surrounded by quotation marks.

has_access = True      # Boolean
has_access_text = "True"  # String, not Boolean

Booleans will become useful in the next lesson when your programs need to choose between actions based on conditions.

---

Ask Python: type()

When you are uncertain, do not guess. Python has a built-in type() function that reports the type of a value or variable.

Run this cell:

number_of_days = 7
completion_rate = 0.75
project_name = "Data cleanup"
is_submitted = False

print(type(number_of_days))
print(type(completion_rate))
print(type(project_name))
print(type(is_submitted))

You should see:

<class 'int'>
<class 'float'>
<class 'str'>
<class 'bool'>

The <class '...'> wording reflects how Python implements types internally. For now, focus on the final labels: int, float, str, and bool.

Python does not require you to declare a variable’s type before assigning it. The current value determines the type:

value = 8
print(type(value))

value = "eight"
print(type(value))

Output:

<class 'int'>
<class 'str'>

This is valid Python, though clear programs usually keep a variable’s meaning consistent. Changing a variable called customer_id from a text ID to a mathematical quantity, for example, would make later code harder to understand.

For a concise written reference, read the relevant parts of Programiz’s Python Variables and Literals.

---

Naming variables clearly

Python variable names should communicate what their values mean. Prefer:

student_count = 28
average_score = 86.5
is_complete = False

over vague names such as:

x = 28
a = 86.5
flag = False

Short names are sometimes appropriate in a tiny calculation, but descriptive names are much more helpful as programs grow.

Use these practical rules:

• Start a variable name with a letter or an underscore.
• After that, use letters, digits, and underscores.
• Do not use spaces or hyphens.
• Python distinguishes uppercase from lowercase letters: score and Score are different names.
• Avoid reserved Python words such as True, False, and if.
• For names containing multiple words, use lowercase words separated by underscores: total_cost, user_name, is_active.

Name	Valid?	Reason
book_title	Yes	Clear and follows the usual underscore style
score2	Yes	Digits are allowed after the first character
2nd_score	No	A name cannot begin with a digit
second score	No	Spaces are not allowed
second-score	No	Python reads - as subtraction
True	No	It is a reserved Boolean literal

---

A small notebook record

Create a new cell and enter the following code. Before running it, identify the type you expect for each variable.

book_title = "Python for Data Analysis"
pages_read = 42
reading_progress = 0.35
finished = False

print(book_title)
print(pages_read)
print(reading_progress)
print(finished)

print(type(book_title))
print(type(pages_read))
print(type(reading_progress))
print(type(finished))

You have just represented four kinds of information that could plausibly appear in a dataset:

• a descriptive label,
• a whole-number count,
• a decimal measurement,
• and a yes-or-no status.

Try changing finished to True, then rerun the cell. Next, change pages_read from 42 to "42" and inspect its type again. The displayed characters look similar, but Python now interprets the value differently because quotation marks change it into text.

---

Key takeaways

A variable is a meaningful name assigned to a value with =. You can print the variable’s value by writing its name without quotation marks, and a later assignment replaces the variable’s current value.

The four types to recognize are:

• int for whole numbers, such as 42
• float for decimals, such as 42.0
• str for quoted text, such as "42"
• bool for the unquoted values True and False

Use type() whenever you need to verify what Python thinks a value is. In the next lesson, you will use these values with arithmetic, string operations, and type conversions to deliberately produce new results.