Python Dictionaries: Create, Access, Update, and Iterate dict

A Dictionary Is a Lookup Table

Lists are indexed by position. Dictionaries are indexed by key — usually a string or a number — and each key maps to a value. That one shift unlocks a huge category of programs: inventories, configs, JSON payloads, user profiles, caches.

The keys and values can be almost anything. Keys need to be hashable (so: numbers, strings, tuples; not lists or other dicts). Values have no restriction — a dict can hold anything, including other dicts or lists.

Creating Dictionaries

Several ways, each with a place:

Reading Values

Two ways, and the choice matters:

Pick by intent:

• Use [] when the key must be there. If it isn't, that's a bug and you want the loud error.
• Use .get() when "not present" is a valid case. It lets you write defaults without try/except.

Adding, Updating, Deleting

Assigning to a key either creates or updates it:

update() merges another dict or iterable of pairs:

Shared keys in overrides overwrite the ones in settings.

Checking for a Key

in checks the keys, not the values:

To check values specifically, use user.values():

How to Iterate a Dictionary

You have three views you can loop over:

.items() is what you reach for most of the time — it gives you the pair, unpacked, ready to use.

Iteration order in modern Python (3.7+) matches insertion order. A dict is not a "magic bag" — if you add a then b then c, iterating yields them in that order. That's a language guarantee you can rely on.

How to Sort a Dictionary

Dictionaries preserve insertion order, so "sorting" a dict really means building a new dict whose keys are inserted in the order you want:

sorted() returns a list of pairs; wrapping that in dict(...) reconstructs a dictionary with the new order. If you only need the sorted pairs (not a dict), skip the outer dict() call.

How to Merge Two Dictionaries

Python 3.9+ gives you the | operator:

Values from the right-hand dict win on key collisions. .update() is the in-place equivalent. On older Python versions, use {**defaults, **overrides}.

The setdefault Pattern

A common need: "if the key is there, use its value; if not, set a default and then use it."

Or, better, use collections.Counter:

Counter is a dict subclass that does counting by default. It's the right call whenever you're counting occurrences.

Dictionary Comprehensions

Parallel to list comprehensions, but for building dicts:

Same rules as list comprehensions, but with key: value between the braces.

Dict as JSON's Twin

Python dicts line up almost exactly with JSON objects. Converting between them is a single call:

This is why dicts show up everywhere in web work — your API requests and responses are basically dict manipulation with some network in the middle.

A Practical Example

A tiny config-like program that tracks a user profile:

Nested dicts, mutable values, .get() with a default — the basic toolkit for any record-shaped data.

Moving On

Lists, tuples, sets, dicts. With those four collections in hand, you can model almost any data you'll encounter early on. Next, we'll learn the list comprehension — Python's compact way to transform one collection into another.