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A Tutorial Introduction
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Tuples
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To create simple data structures, you can pack a collection of values together into a single object using a tupleYou create a tuple by enclosing a group of values in parentheses like this:
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stock = ('GOOG', 100, 49010) address = ('wwwpythonorg', 80) person = (first_name, last_name, phone)
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Python often recognizes that a tuple is intended even if the parentheses are missing:
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stock = 'GOOG', 100, 49010 address = 'wwwpythonorg',80 person = first_name, last_name, phone
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For completeness, 0- and 1-element tuples can be defined, but have special syntax:
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a = () b = (item,) c = item, # 0-tuple (empty tuple) # 1-tuple (note the trailing comma) # 1-tuple (note the trailing comma)
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The values in a tuple can be extracted by numerical index just like a list However, it is more common to unpack tuples into a set of variables like this:
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Although tuples support most of the same operations as lists (such as indexing, slicing, and concatenation), the contents of a tuple cannot be modified after creation (that is, you cannot replace, delete, or append new elements to an existing tuple)This reflects the fact that a tuple is best viewed as a single object consisting of several parts, not as a collection of distinct objects to which you might insert or remove items Because there is so much overlap between tuples and lists, some programmers are inclined to ignore tuples altogether and simply use lists because they seem to be more flexible Although this works, it wastes memory if your program is going to create a large number of small lists (that is, each containing fewer than a dozen items)This is because lists slightly overallocate memory to optimize the performance of operations that add new items Because tuples are immutable, they use a more compact representation where there is no extra space Tuples and lists are often used together to represent data For example, this program shows how you might read a file consisting of different columns of data separated by commas:
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# File containing lines of the form "name,shares,price" filename = "portfoliocsv" portfolio = [] for line in open(filename): fields = linesplit(",") # Split each line into a list name = fields[0] # Extract and convert individual fields shares = int(fields[1]) price = float(fields[2]) stock = (name,shares,price) # Create a tuple (name, shares, price) portfolioappend(stock) # Append to list of records
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The split() method of strings splits a string into a list of fields separated by the given delimiter characterThe resulting portfolio data structure created by this program
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Sets
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looks like a two-dimension array of rows and columns Each row is represented by a tuple and can be accessed as follows:
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>>> portfolio[0] ('GOOG', 100, 49010) >>> portfolio[1] ('MSFT', 50, 5423) >>>
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Individual items of data can be accessed like this:
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>>> portfolio[1][1] 50 >>> portfolio[1][2] 5423 >>>
Here s an easy way to loop over all of the records and expand fields into a set of variables:
total = 00 for name, shares, price in portfolio: total += shares * price
Sets
A set is used to contain an unordered collection of objectsTo create a set, use the set() function and supply a sequence of items such as follows:
s = set([3,5,9,10]) t = set("Hello") # Create a set of numbers # Create a set of unique characters
Unlike lists and tuples, sets are unordered and cannot be indexed by numbers Moreover, the elements of a set are never duplicated For example, if you inspect the value of t from the preceding code, you get the following:
>>> t set(['H', 'e', 'l', 'o'])
Notice that only one 'l' appears Sets support a standard collection of operations, including union, intersection, difference, and symmetric difference Here s an example:
a b c d = = = = t t t t | & ^ s s s s # # # # Union of t and s Intersection of t and s Set difference (items in t, but not in s) Symmetric difference (items in t or s, but not both)
New items can be added to a set using add() or update():