An Introduction to Python Lists  Lists: Creating, Accessing, Modifying, Searching, Sorting and Printing Lists   Per 11/8/2013  

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Table of Contents An Introduction to Python Lists .................................................................................................................................................................................. 2 

Overview ......................................................................................................................................................................................................................... 2 

Creating Lists ................................................................................................................................................................................................................. 2 

Accessing Lists .............................................................................................................................................................................................................. 3 

Looping Over Lists ....................................................................................................................................................................................................... 3 

Modifying Lists .............................................................................................................................................................................................................. 4 

Searching Lists .............................................................................................................................................................................................................. 6 

Sorting Lists ................................................................................................................................................................................................................... 8 

Printing Lists ................................................................................................................................................................................................................. 9 

Performance Notes ...................................................................................................................................................................................................... 9 

The Python Tutorial ....................................................................................................................................................................................... 10 

5. Data Structures .......................................................................................................................................................................................... 10 

5.1. More on Lists ....................................................................................................................................................................................... 10 

5.1.1. Using Lists as Stacks .................................................................................................................................................................... 12 

5.1.2. Using Lists as Queues .................................................................................................................................................................. 12 

5.1.3. Functional Programming Tools .................................................................................................................................................. 13 

5.1.4. List Comprehensions ................................................................................................................................................................... 15 

5.2. The del statement ............................................................................................................................................................................. 18 

5.3. Tuples and Sequences ........................................................................................................................................................................ 19 

5.4. Sets ....................................................................................................................................................................................................... 21 

5.5. Dictionaries .......................................................................................................................................................................................... 22 

5.6. Looping Techniques ........................................................................................................................................................................... 23 

5.7. More on Conditions ............................................................................................................................................................................. 26 

5.8. Comparing Sequences and Other Types .......................................................................................................................................... 26 

Table Of Contents .................................................................................................................................................................................... 28  

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An Introduction to Python Lists Fredrik Lundh | August 2006

Overview

The list type is a container that holds a number of other objects, in a given order. The list type implements the sequence protocol, and also allows you to add and remove objects from the sequence.

Creating Lists

To create a list, put a number of expressions in square brackets:

L = [] L = [expression, ...] This construct is known as a “list display”. Python also supports computed lists, called “list comprehensions”. In its simplest form, a list

comprehension has the following syntax:

L = [expression for variable in sequence] where the expression is evaluated once, for every item in the sequence.

The expressions can be anything; you can put all kinds of objects in lists, including other lists, and multiple references to a single object.

You can also use the built-in list type object to create lists:

L = list() # empty list L = list(sequence) L = list(expression for variable in sequence) The sequence can be any kind of sequence object or iterable, including tuples and generators. If you pass in another list, the list function

makes a copy.

Note that Python creates a single new list every time you execute the [] expression. No more, no less. And Python never creates a new list if you assign a list to a variable.

A = B = [] # both names will point to the same list

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A = [] B = A # both names will point to the same list A = []; B = [] # independent lists For information on how to add items to a list once you’ve created it, see Modifying Lists below.

Accessing Lists

Lists implement the standard sequence interface; len(L) returns the number of items in the list, L[i] returns the item at index i (the first item has index 0), and L[i:j] returns a new list, containing the objects between iand j.

n = len(L) item = L[index] seq = L[start:stop] If you pass in a negative index, Python adds the length of the list to the index. L[-1] can be used to access the last item in a list.

For normal indexing, if the resulting index is outside the list, Python raises an IndexError exception. Slices are treated as boundaries instead, and the result will simply contain all items between the boundaries.

Lists also support slice steps:

seq = L[start:stop:step] seq = L[::2] # get every other item, starting with the first seq = L[1::2] # get every other item, starting with the second

Looping Over Lists

The for-in statement makes it easy to loop over the items in a list:

for item in L: print item If you need both the index and the item, use the enumerate function:

for index, item in enumerate(L): print index, item If you need only the index, use range and len:

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for index in range(len(L)): print index The list object supports the iterator protocol. To explicitly create an iterator, use the built-in iter function:

i = iter(L) item = i.next() # fetch first value item = i.next() # fetch second value Python provides various shortcuts for common list operations. For example, if a list contains numbers, the built-in sum function gives

you the sum:

v = sum(L) total = sum(L, subtotal) average = float(sum(L)) / len(L) If a list contains strings, you can combine the string into a single long string using the join string method:

s = ''.join(L) Python also provides built-in operations to search for items, and to sort the list. These operations are described below.

Modifying Lists

The list type also allows you to assign to individual items or slices, and to delete them.

L[i] = obj L[i:j] = sequence Note that operations that modify the list will modify it in place. This means that if you have multiple variables that point to the same list,

all variables will be updated at the same time.

L = [] M = L # modify both lists L.append(obj) To create a separate list, you can use slicing or the list function to quickly create a copy:

L = [] M = L[:] # create a copy # modify L only L.append(obj)

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You can also add items to an existing sequence. The append method adds a single item to the end of the list, theextend method adds items from another list (or any sequence) to the end, and insert inserts an item at a given index, and move the remaining items to the right.

L.append(item) L.extend(sequence) L.insert(index, item) To insert items from another list or sequence at some other location, use slicing syntax:

L[index:index] = sequence You can also remove items. The del statement can be used to remove an individual item, or to remove all items identified by a slice.

The pop method removes an individual item and returns it, while remove searches for an item, and removes the first matching item from the list.

del L[i] del L[i:j] item = L.pop() # last item item = L.pop(0) # first item item = L.pop(index) L.remove(item) The del statement and the pop method does pretty much the same thing, except that pop returns the removed item.

Finally, the list type allows you to quickly reverse the order of the list.

L.reverse() Reversing is fast, so temporarily reversing the list can often speed things up if you need to remove and insert a bunch of items at the

beginning of the list:

L.reverse() # append/insert/pop/delete at far end L.reverse() Note that the for-in statement maintains an internal index, which is incremented for each loop iteration. This means that if you modify

the list you’re looping over, the indexes will get out of sync, and you may end up skipping over items, or process the same item multiple times. To work around this, you can loop over a copy of the list:

for object in L[:]: if not condition: del L[index] Alternatively, you can use create a new list, and append to it:

out = [] for object in L: if condition:

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out.append(object) A common pattern is to apply a function to every item in a list, and replace the item with the return value from the function:

for index, object in enumerate(L): L[index] = function(object) out = [] for object in L: out.append(function(object)) The above can be better written using either the built-in map function, or as a list comprehension:

out = map(function, L) out = [function(object) for object in L] For straightforward function calls, the map solution is more efficient, since the function object only needs to be fetched once. For other

constructs (e.g. expressions or calls to object methods), you have to use a callback or alambda to wrap the operation; in such cases, the list comprehension is more efficient, and usually also easier to read.

Again, if you need both the item and the index, use enumerate:

out = [function(index, object) for index, object in enumerate(L)] You can use the list type to implement simple data structures, such as stacks and queues.

stack = [] stack.append(object) # push object = stack.pop() # pop from end queue = [] queue.append(object) # push object = queue.pop(0) # pop from beginning The list type isn’t optimized for this, so this works best when the structures are small (typically a few hundred items or smaller). For larger

structures, you may need a specialized data structure, such as collections.deque.

Another data structure for which a list works well in practice, as long as the structure is reasonably small, is an LRU (least-recently-used) container. The following statements moves an object to the end of the list:

lru.remove(item) lru.append(item) If you do the above every time you access an item in the LRU list, the least recently used items will move towards the beginning of the list.

(for a simple cache implementation using this approach, see Caching.)

Searching Lists

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The in operator can be used to check if an item is present in the list:

if value in L: print "list contains", value To get the index of the first matching item, use index:

i = L.index(value) The index method does a linear search, and stops at the first matching item. If no matching item is found, it raises

a ValueError exception.

try: i = L.index(value) except ValueError: i = -1 # no match To get the index for all matching items, you can use a loop, and pass in a start index:

i = -1 try: while 1: i = L.index(value, i+1) print "match at", i except ValueError: pass Moving the loop into a helper function makes it easier to use:

def findall(L, value, start=0): # generator version i = start - 1 try: i = L.index(value, i+1) yield i except ValueError: pass for index in findall(L, value): print "match at", i To count matching items, use the count method:

n = L.count(value) Note that count loops over the entire list, so if you just want to check if a value is present in the list, you should use in or, where

applicable, index.

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To get the smallest or largest item in a list, use the built-in min and max functions:

lo = min(L) hi = max(L) As with sort (see below), you can pass in a key function that is used to map the list items before they are compared:

lo = min(L, key=int) hi = max(L, key=int)

Sorting Lists

The sort method sorts a list in place.

L.sort() To get a sorted copy, use the built-in sorted function:

out = sorted(L) An in-place sort is slightly more efficient, since Python does not have to allocate a new list to hold the result.

By default, Python’s sort algorithm determines the order by comparing the objects in the list against each other. You can override this by passing in a callable object that takes two items, and returns -1 for “less than”, 0 for “equal”, and 1 for “greater than”. The built-in cmp function is often useful for this:

def compare(a, b): return cmp(int(a), int(b)) # compare as integers L.sort(compare) def compare_columns(a, b): # sort on ascending index 0, descending index 2 return cmp(a[0], b[0]) or cmp(b[2], a[2]) out = sorted(L, compare_columns) Alternatively, you can specify a mapping between list items and search keys. If you do this, the sort algorithm will make one pass over the

data to build a key array, and then sort both the key array and the list based on the keys.

L.sort(key=int) out = sorted(L, key=int) If the transform is complex, or the list is large, this can be a lot faster than using a compare function, since the items only have to be

transformed once.

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Python’s sort is stable; the order of items that compare equal will be preserved.

Printing Lists

By default, the list type does a repr on all items, and adds brackets and commas as necessary. In other words, for built-in types, the printed list looks like the corresponding list display:

print [1, 2, 3] # prints [1, 2, 3] To control formatting, use the string join method, combined with either map or a list comprehension or generator expression.

print "".join(L) # if all items are strings print ", ".join(map(str, L)) print "|".join(str(v) for v in L if v > 0) To print a list of string fragments to a file, you can use writelines instead of write:

sys.stdout.writelines(L) # if all items are strings

Performance Notes

The list object consists of two internal parts; one object header, and one separately allocated array of object references. The latter is reallocated as necessary.

The list has the following performance characteristics:

• The list object stores pointers to objects, not the actual objects themselves. The size of a list in memory depends on the number of objects in the list, not the size of the objects. • The time needed to get or set an individual item is constant, no matter what the size of the list is (also known as “O(1)” behaviour). • The time needed to append an item to the list is “amortized constant”; whenever the list needs to allocate more memory, it allocates

room for a few items more than it actually needs, to avoid having to reallocate on each call (this assumes that the memory allocator is fast; for huge lists, the allocation overhead may push the behaviour towards O(n*n)). • The time needed to insert an item depends on the size of the list, or more exactly, how many items that are to the right of the inserted

item (O(n)). In other words, inserting items at the end is fast, but inserting items at the beginning can be relatively slow, if the list is large. • The time needed to remove an item is about the same as the time needed to insert an item at the same location; removing items at the

end is fast, removing items at the beginning is slow. • The time needed to reverse a list is proportional to the list size (O(n)). • The time needed to sort a list varies; the worst case is O(n log n), but typical cases are often a lot better than that. Last Updated: November 2006

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The Python Tutorial

5. Data Structures This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.

5.1. More on Lists

The list data type has some more methods. Here are all of the methods of list objects:

list.append(x) Add an item to the end of the list; equivalent to a[len(a):] = [x].

list.extend(L) Extend the list by appending all the items in the given list; equivalent to a[len(a):] = L.

list.insert(i, x) Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x)inserts at

the front of the list, and a.insert(len(a), x) is equivalent to a.append(x).

list.remove(x) Remove the first item from the list whose value is x. It is an error if there is no such item.

list.pop([i]) Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in

the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)

list.index(x) Return the index in the list of the first item whose value is x. It is an error if there is no such item.

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list.count(x) Return the number of times x appears in the list.

list.sort() Sort the items of the list, in place.

list.reverse() Reverse the elements of the list, in place.

An example that uses most of the list methods:

>>>

>>> a = [66.25, 333, 333, 1, 1234.5] >>> print a.count(333), a.count(66.25), a.count('x') 2 1 0 >>> a.insert(2, -1) >>> a.append(333) >>> a [66.25, 333, -1, 333, 1, 1234.5, 333] >>> a.index(333) 1 >>> a.remove(333) >>> a [66.25, -1, 333, 1, 1234.5, 333] >>> a.reverse() >>> a [333, 1234.5, 1, 333, -1, 66.25] >>> a.sort() >>> a [-1, 1, 66.25, 333, 333, 1234.5]

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5.1.1. Using Lists as Stacks

The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append(). To retrieve an item from the top of the stack, use pop() without an explicit index. For example:

>>>

>>> stack = [3, 4, 5] >>> stack.append(6) >>> stack.append(7) >>> stack [3, 4, 5, 6, 7] >>> stack.pop() 7 >>> stack [3, 4, 5, 6] >>> stack.pop() 6 >>> stack.pop() 5 >>> stack [3, 4]

5.1.2. Using Lists as Queues

It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).

To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:

>>>

>>> from collections import deque >>> queue = deque(["Eric", "John", "Michael"]) >>> queue.append("Terry") # Terry arrives >>> queue.append("Graham") # Graham arrives >>> queue.popleft() # The first to arrive now leaves

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'Eric' >>> queue.popleft() # The second to arrive now leaves 'John' >>> queue # Remaining queue in order of arrival deque(['Michael', 'Terry', 'Graham'])

5.1.3. Functional Programming Tools

There are three built-in functions that are very useful when used with lists: filter(), map(), and reduce().

filter(function, sequence) returns a sequence consisting of those items from the sequence for which function(item) is true. Ifsequence is a string or tuple, the result will be of the same type; otherwise, it is always a list. For example, to compute a sequence of numbers not divisible by 2 or 3:

>>>

>>> def f(x): return x % 2 != 0 and x % 3 != 0 ... >>> filter(f, range(2, 25)) [5, 7, 11, 13, 17, 19, 23]

map(function, sequence) calls function(item) for each of the sequence’s items and returns a list of the return values. For example, to compute some cubes:

>>>

>>> def cube(x): return x*x*x ... >>> map(cube, range(1, 11)) [1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]

More than one sequence may be passed; the function must then have as many arguments as there are sequences and is called with the corresponding item from each sequence (or None if some sequence is shorter than another). For example:

>>>

>>> seq = range(8) >>> def add(x, y): return x+y

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... >>> map(add, seq, seq) [0, 2, 4, 6, 8, 10, 12, 14]

reduce(function, sequence) returns a single value constructed by calling the binary function function on the first two items of the sequence, then on the result and the next item, and so on. For example, to compute the sum of the numbers 1 through 10:

>>>

>>> def add(x,y): return x+y ... >>> reduce(add, range(1, 11)) 55

If there’s only one item in the sequence, its value is returned; if the sequence is empty, an exception is raised.

A third argument can be passed to indicate the starting value. In this case the starting value is returned for an empty sequence, and the function is first applied to the starting value and the first sequence item, then to the result and the next item, and so on. For example,

>>>

>>> def sum(seq): ... def add(x,y): return x+y ... return reduce(add, seq, 0) ... >>> sum(range(1, 11)) 55 >>> sum([]) 0

Don’t use this example’s definition of sum(): since summing numbers is such a common need, a built-in function sum(sequence) is already provided, and works exactly like this.

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5.1.4. List Comprehensions

List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.

For example, assume we want to create a list of squares, like:

>>>

>>> squares = [] >>> for x in range(10): ... squares.append(x**2) ... >>> squares [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

We can obtain the same result with:

squares = [x**2 for x in range(10)]

This is also equivalent to squares = map(lambda x: x**2, range(10)), but it’s more concise and readable.

A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:

>>>

>>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y] [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

and it’s equivalent to:

>>>

>>> combs = []

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>>> for x in [1,2,3]: ... for y in [3,1,4]: ... if x != y: ... combs.append((x, y)) ... >>> combs [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

Note how the order of the for and if statements is the same in both these snippets.

If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.

>>>

>>> vec = [-4, -2, 0, 2, 4] >>> # create a new list with the values doubled >>> [x*2 for x in vec] [-8, -4, 0, 4, 8] >>> # filter the list to exclude negative numbers >>> [x for x in vec if x >= 0] [0, 2, 4] >>> # apply a function to all the elements >>> [abs(x) for x in vec] [4, 2, 0, 2, 4] >>> # call a method on each element >>> freshfruit = [' banana', ' loganberry ', 'passion fruit '] >>> [weapon.strip() for weapon in freshfruit] ['banana', 'loganberry', 'passion fruit'] >>> # create a list of 2-tuples like (number, square) >>> [(x, x**2) for x in range(6)] [(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)] >>> # the tuple must be parenthesized, otherwise an error is raised >>> [x, x**2 for x in range(6)] File "<stdin>", line 1 [x, x**2 for x in range(6)] ^ SyntaxError: invalid syntax >>> # flatten a list using a listcomp with two 'for' >>> vec = [[1,2,3], [4,5,6], [7,8,9]] >>> [num for elem in vec for num in elem] [1, 2, 3, 4, 5, 6, 7, 8, 9]

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List comprehensions can contain complex expressions and nested functions:

>>>

>>> from math import pi >>> [str(round(pi, i)) for i in range(1, 6)] ['3.1', '3.14', '3.142', '3.1416', '3.14159']

5.1.4.1. Nested List Comprehensions

The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.

Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:

>>>

>>> matrix = [ ... [1, 2, 3, 4], ... [5, 6, 7, 8], ... [9, 10, 11, 12], ... ]

The following list comprehension will transpose rows and columns:

>>>

>>> [[row[i] for row in matrix] for i in range(4)] [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

As we saw in the previous section, the nested listcomp is evaluated in the context of the for that follows it, so this example is equivalent to:

>>>

>>> transposed = [] >>> for i in range(4): ... transposed.append([row[i] for row in matrix]) ... >>> transposed

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[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

which, in turn, is the same as:

>>>

>>> transposed = [] >>> for i in range(4): ... # the following 3 lines implement the nested listcomp ... transposed_row = [] ... for row in matrix: ... transposed_row.append(row[i]) ... transposed.append(transposed_row) ... >>> transposed [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

In the real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case:

>>>

>>> zip(*matrix) [(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]

See Unpacking Argument Lists for details on the asterisk in this line.

5.2. The del statement

There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop()method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:

>>>

>>> a = [-1, 1, 66.25, 333, 333, 1234.5] >>> del a[0] >>> a

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[1, 66.25, 333, 333, 1234.5] >>> del a[2:4] >>> a [1, 66.25, 1234.5] >>> del a[:] >>> a []

del can also be used to delete entire variables:

>>>

>>> del a

Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.

5.3. Tuples and Sequences

We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples ofsequence data types (see Sequence Types — str, unicode, list, tuple, bytearray, buffer, xrange). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.

A tuple consists of a number of values separated by commas, for instance:

>>>

>>> t = 12345, 54321, 'hello!' >>> t[0] 12345 >>> t (12345, 54321, 'hello!') >>> # Tuples may be nested: ... u = t, (1, 2, 3, 4, 5) >>> u ((12345, 54321, 'hello!'), (1, 2, 3, 4, 5)) >>> # Tuples are immutable: ... t[0] = 88888 Traceback (most recent call last): File "<stdin>", line 1, in <module>

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TypeError: 'tuple' object does not support item assignment >>> # but they can contain mutable objects: ... v = ([1, 2, 3], [3, 2, 1]) >>> v ([1, 2, 3], [3, 2, 1])

As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.

Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples areimmutable, and usually contain an heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples). Lists are mutable, and their elements are usually homogeneous and are accessed by iterating over the list.

A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:

>>>

>>> empty = () >>> singleton = 'hello', # <-- note trailing comma >>> len(empty) 0 >>> len(singleton) 1 >>> singleton ('hello',)

The statement t = 12345, 54321, 'hello!' is an example of tuple packing: the values 12345, 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:

>>>

>>> x, y, z = t

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This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires the list of variables on the left to have the same number of elements as the length of the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.

5.4. Sets

Python also includes a data type for sets. A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.

Curly braces or the set() function can be used to create sets. Note: to create an empty set you have to use set(), not {}; the latter creates an empty dictionary, a data structure that we discuss in the next section.

Here is a brief demonstration:

>>>

>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana'] >>> fruit = set(basket) # create a set without duplicates >>> fruit set(['orange', 'pear', 'apple', 'banana']) >>> 'orange' in fruit # fast membership testing True >>> 'crabgrass' in fruit False >>> # Demonstrate set operations on unique letters from two words ... >>> a = set('abracadabra') >>> b = set('alacazam') >>> a # unique letters in a set(['a', 'r', 'b', 'c', 'd']) >>> a - b # letters in a but not in b set(['r', 'd', 'b']) >>> a | b # letters in either a or b set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l']) >>> a & b # letters in both a and b set(['a', 'c']) >>> a ^ b # letters in a or b but not both

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set(['r', 'd', 'b', 'm', 'z', 'l'])

Similarly to list comprehensions, set comprehensions are also supported:

>>>

>>> a = {x for x in 'abracadabra' if x not in 'abc'} >>> a set(['r', 'd'])

5.5. Dictionaries

Another useful data type built into Python is the dictionary (see Mapping Types — dict). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend().

It is best to think of a dictionary as an unordered set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {}. Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.

The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del. If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.

The keys() method of a dictionary object returns a list of all the keys used in the dictionary, in arbitrary order (if you want it sorted, just apply the sorted() function to it). To check whether a single key is in the dictionary, use the in keyword.

Here is a small example using a dictionary:

>>>

>>> tel = {'jack': 4098, 'sape': 4139}

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>>> tel['guido'] = 4127 >>> tel {'sape': 4139, 'guido': 4127, 'jack': 4098} >>> tel['jack'] 4098 >>> del tel['sape'] >>> tel['irv'] = 4127 >>> tel {'guido': 4127, 'irv': 4127, 'jack': 4098} >>> tel.keys() ['guido', 'irv', 'jack'] >>> 'guido' in tel True

The dict() constructor builds dictionaries directly from sequences of key-value pairs:

>>>

>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)]) {'sape': 4139, 'jack': 4098, 'guido': 4127}

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

>>>

>>> {x: x**2 for x in (2, 4, 6)} {2: 4, 4: 16, 6: 36}

When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

>>>

>>> dict(sape=4139, guido=4127, jack=4098) {'sape': 4139, 'jack': 4098, 'guido': 4127}

5.6. Looping Techniques

When looping through a sequence, the position index and corresponding value can be retrieved at the same time using theenumerate() function.

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>>>

>>> for i, v in enumerate(['tic', 'tac', 'toe']): ... print i, v ... 0 tic 1 tac 2 toe

To loop over two or more sequences at the same time, the entries can be paired with the zip() function.

>>>

>>> questions = ['name', 'quest', 'favorite color'] >>> answers = ['lancelot', 'the holy grail', 'blue'] >>> for q, a in zip(questions, answers): ... print 'What is your {0}? It is {1}.'.format(q, a) ... What is your name? It is lancelot. What is your quest? It is the holy grail. What is your favorite color? It is blue.

To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.

>>>

>>> for i in reversed(xrange(1,10,2)): ... print i ... 9 7 5 3 1

To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.

>>>

>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana'] >>> for f in sorted(set(basket)):

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... print f

... apple banana orange pear

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the iteritems()method.

>>>

>>> knights = {'gallahad': 'the pure', 'robin': 'the brave'} >>> for k, v in knights.iteritems(): ... print k, v ... gallahad the pure robin the brave

To change a sequence you are iterating over while inside the loop (for example to duplicate certain items), it is recommended that you first make a copy. Looping over a sequence does not implicitly make a copy. The slice notation makes this especially convenient:

>>>

>>> words = ['cat', 'window', 'defenestrate'] >>> for w in words[:]: # Loop over a slice copy of the entire list. ... if len(w) > 6: ... words.insert(0, w) ... >>> words ['defenestrate', 'cat', 'window', 'defenestrate']

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5.7. More on Conditions

The conditions used in while and if statements can contain any operators, not just comparisons.

The comparison operators in and not in check whether a value occurs (does not occur) in a sequence. The operators is and isnot compare whether two objects are really the same object; this only matters for mutable objects like lists. All comparison operators have the same priority, which is lower than that of all numerical operators.

Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c.

Comparisons may be combined using the Boolean operators and and or, and the outcome of a comparison (or of any other Boolean expression) may be negated with not. These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C. As always, parentheses can be used to express the desired composition.

The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C. When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.

It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,

>>>

>>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance' >>> non_null = string1 or string2 or string3 >>> non_null 'Trondheim'

Note that in Python, unlike C, assignment cannot occur inside expressions. C programmers may grumble about this, but it avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.

5.8. Comparing Sequences and Other Types

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Sequence objects may be compared to other objects with the same sequence type. The comparison uses lexicographicalordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the ASCII ordering for individual characters. Some examples of comparisons between sequences of the same type:

(1, 2, 3) < (1, 2, 4) [1, 2, 3] < [1, 2, 4] 'ABC' < 'C' < 'Pascal' < 'Python' (1, 2, 3, 4) < (1, 2, 4) (1, 2) < (1, 2, -1) (1, 2, 3) == (1.0, 2.0, 3.0) (1, 2, ('aa', 'ab')) < (1, 2, ('abc', 'a'), 4)

Note that comparing objects of different types is legal. The outcome is deterministic but arbitrary: the types are ordered by their name. Thus, a list is always smaller than a string, a string is always smaller than a tuple, etc. [1] Mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc.

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Footnotes

[1]  The rules for comparing objects of different types should not be relied upon; they may change in a future version of the language. 

Table Of Contents

• 5. Data Structures 5.1. More on Lists 5.1.1. Using Lists as Stacks 5.1.2. Using Lists as Queues 5.1.3. Functional Programming Tools 5.1.4. List Comprehensions 5.1.4.1. Nested List Comprehensions 5.2. The del statement 5.3. Tuples and Sequences 5.4. Sets 5.5. Dictionaries 5.6. Looping Techniques 5.7. More on Conditions 5.8. Comparing Sequences and Other Types

© Copyright 1990-2013, Python Software Foundation.

The Python Software Foundation is a non-profit corporation. Please donate. Last updated on Oct 26, 2013. Found a bug?

Created using Sphinx 1.0.7.    

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def AfisMenu(): print (chr(10)) print (" 1. Sir Vid ") print (" 2. Adaug el la sf. sir ") print (" 3. Inserare pe o poz sir ") print (" 4. Sterge el. aflat pe o poz ") print (" 5. Sterge el. aflate pe poz. prec [] ") print (" 6. Inloc. un subsir cu alt subsir ") print (" 7. Prime in int poz. date [p,q] ") print (" 8. Impare in poz. date [p,q] ") print (" 9. Suma el. din poz. date [p,q] ") print (" 10. Cmmdc() din poz. date [p,q] ") print (" 11. Maxim() din poz. date [p,q] ") print (" 12. Filtreaza prime ") print (" 13. Filtreaza negativ ") print (" 14. Refac ultima operatie ") print (" * Stop ") def Init(): return [] # Vid [] !!! def Adaug(X,x): Y=X[:] Y.append(x) return Y # return X+[x] def Insert(X,x,p): Y=X[:] Y.insert(p-1,x) return Y def Remove(X,p): Y=X[:] print (" Se sterge ",Y[p-1]) Y.remove(Y[p-1]) return Y def RemoveFrom(X,p,q): Y=X[:] for i in range(p-1,q): Y=Remove(Y,p) # print (" Se sterge ",Y[p-1]) # Y.remove(Y[p-1]) return Y

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def Ss(Mess): s=[] while (True): x=int(input(Mess)) if (x): s+=[x] else: return s def FindReplace(X,F,R): Y=X[:] p=Y.index(F[0]) # print (p,X[p]) s=Y[p:p+len(F)] # print (s) # print (R) Y=RemoveFrom(Y,p+1,p+len(F)) # print (Y) for i in range(0,len(R)): print (" Se ins. ", R[i]) Y.insert(p+i,R[i]) # print (Y) return Y def Prim(p): if (p<0): p=-p; if (p<2): return False d=2; while d*d<=p and p%d: d+=1; return d*d>p def PrimeInt(Y,p,q): sP=[] for i in range(p-1,q): if Prim(Y[i]): sP+=[Y[i]] #print (Y[i]) return sP def Impar(p): return p%2>0 def Imp(X,p,q): sI=[] for i in range(p-1,q): if Impar(X[i]): sI+=[X[i]] return sI def Sum(X,p,q):

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sS=0 for i in range(p-1,q): sS+=X[i] return sS def Cmmdc(a,b): # if (b==0): print (a,b) # else: print (a,b,a%b) if (b==0): return a else: return Cmmdc(b,a%b) def Cmmdc_(X,p,q): cmmdc=X[p-1] for i in range(p,q): cmmdc=Cmmdc(cmmdc,X[i]) return cmmdc def Maxim(X,p,q): m=X[p-1] for i in range(p,q): if (X[i]>m): m=X[i] return m def FiltrPr(X): Y=X[:] i=0 while i<len(Y): # print (Y[i], "...") if Prim(Y[i]): # print (" Ramane ",Y[i]) i+=1 else: print (" Eliminat Neprim ",Y[i]) Y.remove(Y[i]) return Y def FiltrNeg(Y): X=Y[:] i=0 while i<len(X): # print (X[i], "...") if X[i]<0: # print (" Ramane ",X[i])

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i+=1 else: print (" Eliminat Neneg.",X[i]) X.remove(X[i]) return X print (" Invatare numere ... ") X=Init() U=[] while (True): AfisMenu() o=int(input(" Ce doriti : ")) if o==1: X=Init(); U.append(X) elif o==2: U.append(X); X=Adaug(X,int(input(" x:"))) elif o==3: U.append(X); X=Insert(X,int(input(" x:")),int(input(" p:"))) elif o==4: U.append(X); X=Remove(X,int(input(" p:"))) elif o==5: U.append(X); X=RemoveFrom(X,int(input(" p:")),int(input(" q:"))) elif o==6: U.append(X); X=FindReplace(X,Ss(" f:"),Ss(" r:")) # Y=X; .... refac : X=Y elif o==7: print(" Prime:",PrimeInt(X,int(input(" p:")),int(input(" q:")))) elif o==8: print(" Imp:",Imp(X,int(input(" p:")),int(input(" q:")))) elif o==9: print(" Sum:",Sum(X,int(input(" p:")),int(input(" q:")))) elif o==10: print(" Cmmdc:",Cmmdc_(X,int(input(" p:")),int(input(" q:")))) elif o==11: print(" Max:",Maxim(X,int(input(" p:")),int(input(" q:")))) elif o==12: U.append(X); X=FiltrPr(X) elif o==13: U.append(X); X=FiltrNeg(X) elif o==14: if U: X=U.pop() else: print (" No Back Way!") else: break print (" X = ",X) print (" U = ",U) # Se poate scoate! Nu trebuie neaparat!

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print ("Stop", X, " ... -_- ...", U)

 

  def FindReplace(X,F,R): Y=X[:] p=Y.index(F[0]) # print (p,X[p]) s=Y[p:p+len(F)] # print (s) # print (R) Y=RemoveFrom(Y,p+1,p+len(F)) # print (Y) for i in range(0,len(R)): print (" Se ins. ", R[i]) Y.insert(p+i,R[i]) # print (Y) return Y