Dictionaries in Python#

🐍 Data Analysis Using Python 📚 Strings & Data Structures Lesson 023

Important

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Looking values up by key#

Lists and tuples hold values in order, accessed by position. But often you want to look a value up by a meaningful key — a customer’s name, a product code, a field label — rather than a numeric position. The dictionary is Python’s key-value structure, and it is one of the most important for data work. This lesson covers dictionaries.

What a dictionary is#

A dictionary stores key-value pairs — each value is associated with a key that identifies it, written with curly braces:

customer = {
    "name": "Jane Smith",
    "region": "North",
    "sales": 1000,
}

Values are looked up by key, not position:

customer["name"]          # "Jane Smith"
customer["sales"]         # 1000

Where a list answers “what is at position 2?”, a dictionary answers “what is the value for ‘name’?” — access by meaningful key rather than numeric index. This makes dictionaries ideal for representing records with named fields.

Modifying dictionaries#

Dictionaries are mutable — pairs can be added, changed, and removed:

customer["email"] = "jane@example.com"    # add a new key-value pair
customer["sales"] = 1200                    # update an existing value
del customer["region"]                      # remove a pair

Assigning to a key either adds it (if new) or updates it (if it exists); del removes a pair. Dictionaries grow and change like lists, but keyed rather than ordered.

Why dictionaries matter#

Dictionaries are fundamental to data work for several reasons. They represent records naturally — a row of data as field-name-to-value pairs ({"name": ..., "sales": ...}), which is exactly how structured data is often held. They enable fast lookup by key (far faster than searching a list). And they are the structure behind much of Python’s data ecosystem — JSON data is dictionaries, pandas DataFrames can be built from them, and configuration and mappings are dictionaries. Understanding key-value access is understanding a core pattern of representing and retrieving structured data.

The caveat#

Dictionaries have specific rules and pitfalls. Keys must be unique — assigning to an existing key overwrites its value rather than adding a second, so duplicate keys silently lose data. Keys must be immutable — strings, numbers, and tuples can be keys, but lists cannot (their mutability would break the dictionary’s lookup), which is one reason tuples exist. And the classic error: accessing a key that does not exist (customer["phone"] when there is no phone) raises a KeyError and stops the program — a frequent bug when data may be missing a field. The next lesson covers handling this and other advanced dictionary usage safely. Keys unique and immutable, and access defensively.

Hint