Python Fundamentals#

🐍 Data Analysis Using Python 🐍 Python Fundamentals Lesson 002

Important

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The building blocks#

With the motivation established, this lesson covers the fundamentals of Python — the core building blocks from which every program is made. These basics (values, expressions, statements, and how code runs) are the vocabulary the rest of the section builds on, introduced here in Python’s clean syntax.

Values and expressions#

Python works with values — the data a program manipulates:

42              # an integer
3.14            # a float (decimal number)
"hello"         # a string (text)
True            # a boolean (True or False)

Values combine into expressions that Python evaluates to a result:

2 + 3           # evaluates to 5
"data" + "!"    # evaluates to "data!" (string concatenation)
10 > 5          # evaluates to True

An expression is anything Python can compute a value from — the basic unit of doing something with data.

Statements and printing#

A statement is an instruction Python executes. The print function displays a value, the standard way to see a program’s output:

print("Hello, data analysis!")
print(2 + 3)                       # displays 5

Programs are sequences of statements executed top to bottom, in order — the “sequence of instructions” from the previous lesson made concrete.

Variables: naming values#

A variable stores a value under a name, so it can be reused and changed (a dedicated lesson follows):

sales = 1000
tax_rate = 0.08
total = sales + sales * tax_rate
print(total)                       # displays 1080.0

Assigning with = binds a name to a value; the name then stands for the value wherever used. Variables are what let a program hold and manipulate data through meaningful names rather than raw values.

Python’s readability#

A defining feature of Python is indentation as structure — Python uses indentation (spaces at the start of a line) to group code into blocks, where other languages use braces. This enforces the visual clarity that makes Python readable, and it means consistent indentation is not optional style but required syntax. This readability — clean syntax, English-like keywords, meaningful indentation — is much of why Python suits analysts, and why code written in it is comparatively easy to read and maintain.

The caveat#

Python’s fundamentals are simple individually but combine into subtle behaviour, and a few basics trip up beginners. Python is case-sensitive (Sales and sales are different names); indentation errors (inconsistent spaces) are a common frustration precisely because indentation is meaningful; and the distinction between values and the variables naming them matters as programs grow. These are learned by writing and running code, not by reading about it — which is why the environment for doing so, the next lesson’s subject, matters. The fundamentals reward practice: type the examples, run them, and change them to see what happens. The next lesson covers where to do that.

Hint