So far we have looked at what programming is, computer programs, programming languages, translators, the Python interactive shell, IDEs, and we have even written our first Python program. Now let's begin writing meaningful programs.
Every application we interact with manages data. Take Instagram - we see usernames, number of followers, number of likes, bio text, verified status, number of posts. All of this is data.
Move to M-PESA. We have account balance, phone number, PIN, ID number, transaction amount, recipient number. Again - data.
You can pick any other software - your bank app, Gmail, TikTok, Netflix. They all have data. The computer needs a way to store all this information. That means we must learn how Python represents data.
In Python, and in programming generally, we call these data types.
A data type is a category that tells Python what kind of information we're working with and what operations we can perform on it.
Later, we'll discover that technically these are classes, but let us not jump there yet. For now let's think of data types simply as different categories of data.
Let's begin exploring the main types.
This concept comes straight from mathematics. Remember learning about whole numbers in school? Numbers like 1, 2, 3, 100, -5? These are integers. In mathematics, integers are the set {..., -3, -2, -1, 0, 1, 2, 3, ...} - they go on forever in both directions but never have decimal points.
Just like in mathematics, Python's integers can be positive, negative, or zero.
0, 6, 4, -9, 27746, -567, 849300Let's see integers in action:
print(0)
print(6)
print(4)
print(-9)
print(27746)
print(-567)
print(849300)Real-life examples that are integers:
- Your age → 19
- Number of countries in Africa → 54
- Instagram likes → 1200
- Students in a classroom → 45
- Unread WhatsApp messages → 8
Let's try some simple calculations with integers:
4 + 7 # 11
89 - 78 # 11
3 * 4 # 12
8 / 2 # 4.0 (Observe, division always gives a float)DISCOVERY MOMENT
Even though we're dividing two integers and getting a whole number result, Python gives us 4.0 instead of 4. And notice the type changed to float!
Python does this because division can produce non-integer results:
print(10 / 3) # 3.3333333333333335Since division might not always give a whole number, Python prepares for that possibility by always returning a float.
Let's try a few more operations:
# Integer division - gives just the whole part
print(10 // 3) # 3
# Modulo - gives the remainder
print(10 % 3) # 1
# Exponentiation - raising to a power
print(2 ** 3) # 8Integers behave exactly as you expect from arithmetic class - they follow the same mathematical rules you learned in school.
If we want to confirm the type of a number, we use the type() function:
type(56)
# <class 'int'>In mathematics, these are called real numbers or decimal numbers. You encountered them when learning about fractions and decimals: 3.14, 0.5, 100.25.
Examples of floats:
34.5, 0.0005, 89.455601, -9.7, 100000.0000005print(34.5)
print(0.0005)
print(89.455601)
print(-9.7)
print(100000.0000005)Why the name floating-point? Because the decimal point "floats." In other words, the decimal can move depending on how large or small the number is. This becomes important later in scientific computing, but just note the idea for now.
Real-life examples of floats:
- Height → 1.78 meters
- Weight → 63.4 kilograms
- Account balance → 4578.50 shillings
- Temperature → 29.75 °C
- Speed → 88.9 km/h
Let's perform some operations with floats:
print(45.7 + 10.2) # 55.9
print(5.0 * 2) # 10.0
print(12.6 / 4) # 3.15We can also mix integers and floats:
print(10 + 5.5) # 15.5
print(type(10 + 5.5)) # <class 'float'>Discovery Moment
When you mix integers and floats in a calculation, Python converts everything to floats for the result.
Here's something interesting about floats:
print(0.1 + 0.2)
# 0.30000000000000004This tiny imprecision happens because computers store decimal numbers in binary (0s and 1s), which can't perfectly represent all decimal fractions. This is normal and happens in most programming languages.
Floats can also represent very large or very small numbers using scientific notation:
print(3e8) # 300000000.0 (3 × 10^8)
print(1.6e-19) # 1.6e-19 (very small number)In programming, we need a way to represent text - letters, words, sentences, symbols. Unlike mathematics, which primarily deals with numbers, programming works extensively with text data. The term "string" comes from the idea of characters strung together like beads on a string.
Characters can be letters, digits, symbols, spaces, or even special characters like emojis.
We can create strings using:
- Single quotes:
'Hello' - Double quotes:
"Hello" - Triple quotes:
'''Hello'''or"""Hello"""(for longer text)
Examples:
"Michael"
'James'
"Mt. Kilimanjaro"
"A"
"B"
"1"
"Hello, I am learning Python"print("Michael")
print('James')
print("Mt. Kilimanjaro")
print("A")
print("B")
print("1")
print("Hello, I am learning Python")Important Rule
Opening quotation marks must match closing quotation marks.
Otherwise Python complains:
print("Michael") # Correct
print('Michael') # Correct
# print("Michael') # Error!Strings represent a lot of real-life data:
- Your name
- Your email address
- A password (yes, a password is a string)
- Country names
- Feedback messages
- Text in a tweet or WhatsApp message
String Operations — Concatenation
Concatenation means adding strings together.
print("Michael" + "James") # "MichaelJames"
print("Hello" + " " + "World") # "Hello World"We can repeat strings:
"Ha" * 3
# "HaHaHa"
"=" * 10
# "=========="Tip
This is useful for creating simple text separators or repeating patterns.
Strings also have a length, which we can find using the len() function:
len("Python")
# 6
len("Hello, World!")
# 13Discovery Moment
Even though "123" looks like a number, because it's in quotes, Python treats it as text:
print(123) # Number
print("123") # String
print(type(123)) # <class 'int'>
print(type("123")) # <class 'str'>Sometimes we need to convert one data type to another. This is called type casting or type conversion
Let us explore conversions one by one.
Converting to Strings
Any data type can become a string using the str() function:
print(str(45)) # "45"
print(str(23.89)) # "23.89"
print(str(True)) # "True"Converting to Integers
Strings that look like whole numbers can become integers using the int() function:
Floats can become integers (decimal part gets truncated):
print(int(23.89)) # 23 (not 24!)
print(int(99.999)) # 99 (not 100!)
print(int(-4.7)) # -4Important
The decimal part is removed, not rounded.
What happens with invalid conversions?
# These will cause errors:
# print(int("Michael")) # ValueError
# print(int("45.67")) # ValueError - can't convert decimal string directlyTo convert a decimal string to integer, go through float first:
print(int(float("45.67"))) # 45Converting to Floats
Strings that look like numbers can become floats using the float() function:
print(float("45.98")) # 45.98
print(float("25")) # 25.0Integers easily become floats:
print(float(45)) # 45.0Let's see these concepts in action with realistic scenarios:
print("=== INSTAGRAM PROFILE ===")
print("Username: " + "python_learner_2024")
print("Followers: " + str(1247))
print("Following: " + str(356))
print("Posts: " + str(89))
print("Bio: " + "Learning Python one day at a time!")
print("Average likes per post: " + str(45.7))print("=== M-PESA TRANSACTION ===")
print("Account Balance: KSH " + str(2847.50))
print("Transaction Amount: KSH " + str(500))
print("Transaction Code: " + "QK7X2M9P")
print("Recipient: " + "Jane Doe")
print("New Balance: KSH " + str(2847.50 - 500))print("=== NOW WATCHING ===")
print("Show: " + "Stranger Things")
print("Season: " + str(4))
print("Episode: " + str(7))
print("Duration: " + str(52.5) + " minutes")
print("Rating: " + str(4.8) + "/5.0")
print("Released: " + str(2024))We've explored three fundamental data types that Python uses to organize information:
Integers (int) are whole numbers without decimal points:
- Examples:
42,-17,0,1000 - Used for counting, ages, quantities, years
- Come from mathematical concept of whole numbers
- Division always produces a float, even for whole results
Floats (float) are numbers with decimal points:
- Examples:
3.14,-0.5,100.0,2.5e8 - Used for measurements, money, temperatures, ratings
- Based on mathematical concept of real/decimal numbers
- Sometimes show tiny precision differences due to binary storage
Strings (str) are sequences of characters in quotes:
- Examples:
"Hello",'Python',"123","user@email.com" - Used for text, names, messages, usernames, codes
- Can be concatenated (+) and repeated (*)
- Have measurable length with
len()
Type conversion allows changing between types:
- Any type can become a string with
str() - Number-like strings can become
int()orfloat() - Floats become integers by removing decimal part
- Invalid conversions raise
ValueError
We can always check a data's type using the type() function.
These data types form the foundation for all the information our programs will handle - from simple calculations to complex applications like the Instagram and M-PESA examples we see every day.
In our next chapter, we'll learn about variables - a way to store and reuse these different types of data throughout our programs.
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