Variables & Data Types

Every dataset you will ever analyze is built from a handful of basic Python types: numbers, text, true/false values, and containers that group them together. In this lesson you'll learn each type, how to inspect and convert between them, and how to reach into nested structures — the exact skill you need before pandas ever enters the picture.

Variables and naming rules

A variable is a name bound to a value with =. Python has a few hard rules and a few strong conventions:

• Names can contain letters, digits, and underscores — but can't start with a digit and can't contain spaces or symbols like - or @.
• Names are case-sensitive: number and numbeR are two different variables (an easy source of bugs — pick one casing and stick to it).
• Multi-word names use snake_case in Python: flight_schedules, not flightSchedules (camelCase is common in other languages, but underscores are the Python convention).
• Choose descriptive English names: total_price beats tp.

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flight_schedules = 10 # snake_case: the Python way number1 = 12 number2 = 3 total = number1 * number2 print(total) # Case-sensitive: these are DIFFERENT variables number = 10 numbeR = 5 print(number, numbeR)

Numbers: int and float

Python has two everyday number types: int for whole numbers and float for decimals. The built-in type() function tells you what you're holding — use it whenever you're unsure:

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integer_number = 10 float_number = 12.3 print(type(integer_number)) print(type(float_number)) print(integer_number + 1) print(integer_number * 10) print(integer_number / 10) # division always produces a float print(integer_number ** 0.5) # fractional power = square root print(type(integer_number / 10))

Note that / returns a float even when the result is a whole number — 10 / 10 is 1.0, not 1. That distinction matters when a library expects an integer (like an index or a count).

Booleans: True and False

A bool is either True or False (capitalized!). Booleans usually come from comparison operators, and they're the fuel for every if statement you'll write in the next lessons:

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age = 32 print(age > 30) # greater than print(age < 32) # less than print(age <= 32) # less than or equal print(age == 32) # equal (two = signs!) print(age != 32) # not equal is_larger_than_30 = age > 30 print(is_larger_than_30, type(is_larger_than_30))

Strings and f-strings

A str is text between quotes — single or double both work. The killer feature for data work is the f-string: put an f before the opening quote and any expression inside curly braces gets evaluated and inserted:

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name = "Budi" city = "Bandung" greeting = f"Hello, my name is {name} and I live in {city}" print(greeting) # Strings come with useful methods print(greeting.upper()) print(greeting.lower()) print(greeting.replace("Budi", "Rama")) message = "hello hello my name is Rama" print(message.count("hello")) # how many times a substring appears print(len(message)) # length in characters

Methods like .upper() return a new string — the original is unchanged. Strings in Python are immutable: you never edit one in place, you build a modified copy.

Lists: ordered, changeable collections

A list holds multiple values in order, written with square brackets. Positions are counted from zero, and you can grab ranges with slicing — list[start:stop] takes items from start up to (but not including) stop:

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animals = ["cat", "dog", "fish"] print(animals[0]) # first item - indexing starts at 0! print(animals[2]) # third item print(animals[-1]) # negative index counts from the end print(len(animals)) # how many items animals.append("cow") # add to the end (modifies the list in place) print(animals) products = ["laptop", "mouse", "keyboard", "headset", "headphone", "flash disk"] print(products[2:5]) # index 2, 3, 4 - stop index NOT included print(products[1:]) # from index 1 to the end print(products[:-1]) # everything except the last item

That "stop not included" rule trips everyone up at first, but it has a nice property: products[2:5] contains exactly 5 - 2 = 3 items, and products[:k] plus products[k:] reassembles the whole list.

Tuples: lists that can't change

A tuple looks like a list with parentheses — but it's immutable. Once created, you can't replace, add, or remove items. Use tuples for fixed records where accidental modification would be a bug (coordinates, RGB colors, database rows):

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animals = ["cat", "dog", "fish"] animals[0] = "cow" # lists allow this print(animals) point = ("dog", "cat", "cow") print(point.index("dog")) print(point.count("cow")) try: point[0] = "fish" # tuples do NOT allow this except TypeError as e: print("Error:", e)

Dictionaries: labeled data

Lists find values by position; a dict finds them by key. This is the single most important container for data science — a JSON API response, a pandas row, a model's configuration: all dictionaries at heart. Values can be anything, including other dicts and lists, which is how real-world data nests:

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person = { "name": "Rama", "age": 25, } print(person["name"]) # access by key instead of index person["hobbies"] = ["swimming", "reading"] # add a key person.update({ "address": "Jakarta", "occupation": {"company": "Jakarta Labs", "title": "developer"}, }) print(person["occupation"]["title"]) # chain keys to go deeper print(person["hobbies"][0]) # mix dict and list access print(person.get("salary", "unknown")) # .get avoids errors for missing keys print(list(person.keys()))

The chaining pattern is worth practicing: users[1]["address"]["city"] reads as "take item 1 of the list, then its address dict, then the city inside that". Work through it one bracket at a time.

Sets: unique values only

A set (curly braces, no keys) keeps only unique values and has no order. Its main use in data work is deduplication and membership tests:

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animals = {"dog", "cat", "cow", "dog"} # duplicate "dog" collapses print(animals) animals.add("fish") animals.remove("cow") print(animals) visitors = ["ana", "budi", "ana", "citra", "budi", "ana"] print(f"{len(visitors)} visits from {len(set(visitors))} unique visitors")

Type conversion and None

You can convert between types with int(), float(), str(), and bool() — essential when data arrives as text (which it very often does). Python also has None, a special value meaning "nothing here yet", which is how missing data is often represented before it becomes NaN in pandas:

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raw = "42" # a string, not a number! print(raw + "1") # string "concatenation": gives 421, not 43 print(int(raw) + 1) # convert first: 43 print(float("3.14") * 2) print(str(99) + " problems") middle_name = None # explicitly "no value" print(middle_name is None) # test for None with "is" print(type(None))

Check your understanding

5 questions · free

1. Q1.Which variable name is invalid in Python?

   Aflight_schedulesB2nd_placeCtotalPriceD_hidden

2. Q2.What does products[1:3] return for products = ['a', 'b', 'c', 'd']?

   A['b', 'c', 'd']B['a', 'b', 'c']C['b', 'c']D['a', 'b']

3. Q3.What's the key difference between a list and a tuple?

   ATuples can hold more itemsBLists are mutable (can be changed); tuples are immutableCTuples can only hold numbersDLists preserve order but tuples don't

4. Q4.Given user = {'name': 'Ana', 'address': {'city': 'Jakarta'}}, how do you get 'Jakarta'?

   Auser['city']Buser['address']['city']Cuser[1][0]Duser.address.city

5. Q5.What does '7' + '3' evaluate to?

   A10B'73'C'10'DA TypeError

Exercise: Navigate a nested user database

Create a list called users containing three dictionaries. Each has a name (string), an age (int), and an address key holding another dict with a city. Use the names Rama (25, Jakarta), Budi (30, Bandung), and Teguh (28, Jakarta). Then: (1) print Budi's city using chained access, (2) print an f-string like "Rama is 25 years old and lives in Jakarta" for the first user, and (3) collect all three cities into a list and use set() to print the unique cities.

Next up: functions — how to package the logic you just wrote so you can reuse it with a single call.