| Besides lists, dictionaries are perhaps the most flexible built-in data type in Python. If you think of lists as ordered collections of objects, dictionaries are unordered collections; their chief distinction is that items are stored and fetched in dictionaries by key, instead of positional offset. Being a built-in type, dictionaries can replace many of the searching algorithms and data structures you might have to implement manually in lower-level languages indexing a dictionary is a very fast search operation. Dictionaries also sometimes do the work of records and symbol tables used in other languages, can represent sparse (mostly empty) data structures, and much more. In terms of their main properties, dictionaries are:
- Accessed by key, not offset
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Dictionaries are sometimes called associative arrays or hashes. They associate a set of values with keys, so that you can fetch an item out of a dictionary using the key that stores it. You use the same indexing operation to get components in a dictionary, but the index takes the form of a key, not a relative offset.
- Unordered collections of arbitrary objects
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Unlike lists, items stored in a dictionary aren't kept in any particular order; in fact, Python randomizes their order in order to provide quick lookup. Keys provide the symbolic (not physical) location of items in a dictionary.
- Variable length, heterogeneous, arbitrarily nestable
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Like lists, dictionaries can grow and shrink in place (without making a copy), they can contain objects of any type, and support nesting to any depth (they can contain lists, other dictionaries, and so on).
- Of the category mutable mapping
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Dictionaries can be changed in place by assigning to indexes, but don't support the sequence operations that work on strings and lists. Because dictionaries are unordered collections, operations that depend on a fixed order (e.g., concatenation, slicing) don't make sense. Instead, dictionaries are the only built-in representative of the mapping type category objects that map keys to values.
- Tables of object references (hash tables)
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If lists are arrays of object references, dictionaries are unordered tables of object references. Internally, dictionaries are implemented as hash tables (data structures that support very fast retrieval), which start small and grow on demand. Moreover, Python employs optimized hashing algorithms to find keys, so retrieval is very fast. Dictionaries store object references (not copies), just like lists.
Table 6-2 summarizes some of the most common dictionary operations (see the library manual for a complete list). Dictionaries are written as a series of key:value pairs, separated by commas, and enclosed in curly braces.[4] An empty dictionary is an empty set of braces, and dictionaries can be nested by writing one as a value inside another dictionary, or within a list or tuple. [4] The same note about the relative rarity of literals applies here: dictionaries are often built up by assigning to new keys at runtime, rather than writing literals. But see the following section on changing dictionaries; lists and dictionaries are grown in different ways. Assignment to new keys works for dictionaries, but fails for lists (lists are grown with append instead).
Table 6-2. Common dictionary literals and operations| Operation | Interpretation |
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| D1 = { } | Empty dictionary | | D2 = {'spam': 2, 'eggs': 3} | Two-item dictionary | | D3 = {'food': {'ham': 1, 'egg': 2}} | Nesting | | D2['eggs']d3['food']['ham'] | Indexing by key | | D2.has_key('eggs'), 'eggs' in D2D2.keys( )D2.values( )D2.copy( )D2.get(key, default)D2.update(D1) | Methods: membership test, keys list, values list, copies, defaults, merge, etc. | | len(d1) | Length (number stored entries) | | D2[key] = 42del d2[key] | Adding/changing, deleting | | D4 = dict(zip(keyslist, valslist)) | Construction (Chapter 10) |
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