The 80/20

Everything in Python is an object with a type, and variables are just names that refer to those objects. The type determines the legal operations, so a string of digits is not a number until you convert it, and dividing two integers with / always gives you a float. When something behaves strangely, ask two questions: what does this name currently refer to, and what type is it. print(x) and type(x) answer both in two seconds.

Assignment rebinds names; it does not copy values. Two names can refer to the same mutable list, so a change through one shows up through the other. Immutable values like numbers, strings, and tuples cannot be changed in place, which is why methods like upper return a new string instead of modifying the original.

An if statement chooses between blocks based on a boolean condition; a for loop repeats a block once per item in a sequence; a while loop repeats until a condition stops being true. Indentation, not braces, defines what belongs to each block, so consistent four-space indentation is part of the language and not a style preference.

Most loop code fits one of a few patterns worth memorizing: accumulate a total, count matches, build a new list, find the best item, or stop early with break when you have what you need. Recognizing which pattern you need turns a blank page into filling in a template.

A function packages steps under a name, takes inputs as parameters, and sends a result back with return. The discipline that pays off is one job per function and a name that describes that job, so reading the calling code tells you the story without opening the definitions. Parameters are local, so a well-written function depends only on what you pass in and affects only what it returns, which makes it easy to test in isolation.

The distinction beginners must nail is print versus return. Printing writes text for a human and gives back None; returning hands a real value to your program so you can store it, compare it, or feed it to the next function.

Use a list when order matters and items are of the same kind: a queue of filenames, a sequence of daily temperatures. Use a dictionary when each value needs a name or you need instant lookup by key: settings, a word count, a record with fields. A list of dictionaries is the standard way to hold a table of records, and it is the shape almost all real data arrives in from CSV files and web APIs.

Dictionary lookup by key stays fast no matter how many entries there are, while searching a list means checking items one by one. When you find yourself scanning a list repeatedly to find a matching item, that is the signal to build a dictionary keyed on the thing you keep searching for.

Strings come with methods that solve the boring problems: strip removes surrounding whitespace, lower normalizes case for comparison, split breaks text into a list on a separator, join glues a list back together, replace substitutes text, and startswith or in test for content. All of them return new strings, because strings are immutable.

For output, f-strings are the tool: f"{name}: {amount:.2f}" inserts values and controls formatting in one readable line. Doing comparisons on user-entered text without stripping and lowercasing it first is one of the most common sources of bugs that only appear with real data.

When Python fails it tells you the file, the line, the exception type, and a message. Read from the bottom up: the last line names the problem, and the lines above show the chain of calls that led there. The type alone often solves it, since TypeError means wrong kind of value, NameError means a typo or a variable used before assignment, IndexError and KeyError mean you asked for something not there, and ValueError means the right type carrying an impossible value.

Handle expected failures with try and except for that specific exception, and let unexpected ones crash loudly so you find them. To locate a bug, print the variables just before the failing line and compare them to what you assumed, then shrink the input until you have the smallest case that still fails.

Python ships with modules covering most everyday needs: csv and json for data files, pathlib for paths, datetime for dates, re for pattern matching, random for sampling, collections for specialized containers, and math and statistics for calculations. Learning to check whether a module already exists before writing forty lines of your own is a professional habit, not a shortcut.

For everything else, pip installs third-party packages such as requests for web calls or pandas for tabular data. Create a virtual environment per project with python3 -m venv .venv and activate it, so projects do not fight over versions, and record what you installed so the project still runs next year on another machine.

Names hold values of specific types; if, for, and while decide which lines run and how often; functions package those lines under a name so you can reuse and test them; lists and dictionaries hold your data in the shape that makes the work easy; string methods and f-strings clean input and produce output; tracebacks tell you exactly where your assumptions were wrong; and the standard library plus pip means you write the part that is actually yours. Every Python program you will ever read is these seven ideas composed at larger and larger scale.