webhelpers.containers

Container objects, and helpers for lists and dicts.

This would have been called this “collections” except that Python 2 can’t import a top-level module that’s the same name as a module in the current package.

Classes

class webhelpers.containers.Counter

I count the number of occurrences of each value registered with me.

Call the instance to register a value. The result is available as the .result attribute. Example:

>>> counter = Counter()
>>> counter("foo")
>>> counter("bar")
>>> counter("foo")
>>> sorted(counter.result.items())
[('bar', 1), ('foo', 2)]

>> counter.result
{'foo': 2, 'bar': 1}

To see the most frequently-occurring items in order:

>>> counter.get_popular(1)
[(2, 'foo')]
>>> counter.get_popular()
[(2, 'foo'), (1, 'bar')]

Or if you prefer the list in item order:

>>> counter.get_sorted_items()
[('bar', 1), ('foo', 2)]
classmethod correlate(class_, iterable)

Build a Counter from an iterable in one step.

This is the same as adding each item individually.

>>> counter = Counter.correlate(["A", "B", "A"])
>>> counter.result["A"]
2
>>> counter.result["B"]
1

Return the results as as a list of (count, item) pairs, with the most frequently occurring items first.

If max_items is provided, return no more than that many items.

get_sorted_items()

Return the result as a list of (item, count) pairs sorted by item.

class webhelpers.containers.Accumulator

Accumulate a dict of all values for each key.

Call the instance to register a value. The result is available as the .result attribute. Example:

>>> bowling_scores = Accumulator()
>>> bowling_scores("Fred", 0)
>>> bowling_scores("Barney", 10)
>>> bowling_scores("Fred", 1)
>>> bowling_scores("Barney", 9)
>>> sorted(bowling_scores.result.items())
[('Barney', [10, 9]), ('Fred', [0, 1])]

>> bowling_scores.result
{'Fred': [0, 1], 'Barney': [10, 9]}

The values are stored in the order they’re registered.

Alternatives to this class include paste.util. multidict.MultiDict in Ian Bicking’s Paste package.

classmethod correlate(class_, iterable, key)

Create an Accumulator based on several related values.

key is a function to calculate the key for each item, akin to list.sort(key=).

This is the same as adding each item individually.

class webhelpers.containers.UniqueAccumulator

Accumulate a dict of unique values for each key.

The values are stored in an unordered set.

Call the instance to register a value. The result is available as the .result attribute.

class webhelpers.containers.defaultdict(missing_func)

A dict that automatically creates values for missing keys. This is the same as collections.defaultdict in the Python standard library. It’s provided here for Python 2.4, which doesn’t have that class.

When you try to read a key that’s missing, I call missing_func without args to create a value. The result is inserted into the dict and returned. Many Python type constructors can be used as missing_func. Passing list or set creates an empty dict or set. Passing int creates the integer 0. These are useful in the following ways:

>> d = defaultdict(list);  d[ANYTHING].append(SOMEVALUE)
>> d = defaultdict(set);  d[ANYTHING].include(SOMEVALUE)
>> d = defaultdict(int);  d[ANYTHING] += 1
class webhelpers.containers.DumbObject(**kw)

A container for arbitrary attributes.

Usage:

>>> do = DumbObject(a=1, b=2)
>>> do.b
2

Alternatives to this class include collections.namedtuple in Python 2.6, and formencode.declarative.Declarative in Ian Bicking’s FormEncode package. Both alternatives offer more features, but DumbObject shines in its simplicity and lack of dependencies.

Functions

webhelpers.containers.correlate_dicts(dicts, key)

Correlate several dicts under one superdict.

If you have several dicts each with a ‘name’ key, this puts them in a container dict keyed by name.

>>> d1 = {"name": "Fred", "age": 41}
>>> d2 = {"name": "Barney", "age": 31}
>>> flintstones = correlate_dicts([d1, d2], "name")
>>> sorted(flintstones.keys())
['Barney', 'Fred']
>>> flintstones["Fred"]["age"]
41

If you’re having trouble spelling this method correctly, remember: “relate” has one ‘l’. The ‘r’ is doubled because it occurs after a prefix. Thus “correlate”.

webhelpers.containers.correlate_objects(objects, attr)

Correlate several objects under one dict.

If you have several objects each with a ‘name’ attribute, this puts them in a dict keyed by name.

>>> class Flintstone(DumbObject):
...    pass
...
>>> fred = Flintstone(name="Fred", age=41)
>>> barney = Flintstone(name="Barney", age=31)
>>> flintstones = correlate_objects([fred, barney], "name")
>>> sorted(flintstones.keys())
['Barney', 'Fred']
>>> flintstones["Barney"].age
31

If you’re having trouble spelling this method correctly, remember: “relate” has one ‘l’. The ‘r’ is doubled because it occurs after a prefix. Thus “correlate”.

webhelpers.containers.del_quiet(dic, keys)

Delete several keys from a dict, ignoring those that don’t exist.

This modifies the dict in place.

>>> d ={"A": 1, "B": 2, "C": 3}
>>> del_quiet(d, ["A", "C"])
>>> d
{'B': 2}
webhelpers.containers.distribute(lis, columns, direction, fill=None)

Distribute a list into a N-column table (list of lists).

lis is a list of values to distribute.

columns is an int greater than 1, specifying the number of columns in the table.

direction is a string beginning with “H” (horizontal) or “V” (vertical), case insensitive. This affects how values are distributed in the table, as described below.

fill is a value that will be placed in any remaining cells if the data runs out before the last row or column is completed. This must be an immutable value such as None , "", 0, “ ”, etc. If you use a mutable value like [] and later change any cell containing the fill value, all other cells containing the fill value will also be changed.

The return value is a list of lists, where each sublist represents a row in the table. table[0] is the first row. table[0][0] is the first column in the first row. table[0][1] is the second column in the first row.

This can be displayed in an HTML table via the following Mako template:

<table>
% for row in table:
  <tr>
% for cell in row:
    <td>${cell}</td>
% endfor   cell
  </tr>
% endfor   row
</table>

In a horizontal table, each row is filled before going on to the next row. This is the same as dividing the list into chunks:

>>> distribute([1, 2, 3, 4, 5, 6, 7, 8], 3, "H")
[[1, 2, 3], [4, 5, 6], [7, 8, None]]

In a vertical table, the first element of each sublist is filled before going on to the second element. This is useful for displaying an alphabetical list in columns, or when the entire column will be placed in a single <td> with a <br /> between each element:

>>> food = ["apple", "banana", "carrot", "daikon", "egg", "fish", "gelato", "honey"]
>>> table = distribute(food, 3, "V", "")
>>> table
[['apple', 'daikon', 'gelato'], ['banana', 'egg', 'honey'], ['carrot', 'fish', '']]
>>> for row in table:
...    for item in row:
...         print "%-9s" % item,
...    print "."   # To show where the line ends.
...
apple     daikon    gelato    .
banana    egg       honey     .
carrot    fish                .

Alternatives to this function include a NumPy matrix of objects.

webhelpers.containers.except_keys(dic, keys)

Return a copy of the dict without the specified keys.

>>> except_keys({"A": 1, "B": 2, "C": 3}, ["A", "C"])
{'B': 2}
webhelpers.containers.extract_keys(dic, keys)

Return two copies of the dict. The first has only the keys specified. The second has all the other keys from the original dict.

>> extract_keys({"From": "F", "To": "T", "Received", R"}, ["To", "From"]) 
({"From": "F", "To": "T"}, {"Received": "R"})
>>> regular, extra = extract_keys({"From": "F", "To": "T", "Received": "R"}, ["To", "From"]) 
>>> sorted(regular.keys())
['From', 'To']
>>> sorted(extra.keys())
['Received']
webhelpers.containers.only_some_keys(dic, keys)

Return a copy of the dict with only the specified keys present.

dic may be any mapping. The return value is always a Python dict.

>> only_some_keys({"A": 1, "B": 2, "C": 3}, ["A", "C"])
>>> sorted(only_some_keys({"A": 1, "B": 2, "C": 3}, ["A", "C"]).items())
[('A', 1), ('C', 3)]
webhelpers.containers.ordered_items(dic, key_order, other_keys=True, default=<class 'webhelpers.misc.NotGiven'>)

Like dict.iteritems() but with a specified key order.

Arguments:

  • dic is any mapping.
  • key_order is a list of keys. Items will be yielded in this order.
  • other_keys is a boolean.
  • default is a value returned if the key is not in the dict.

This yields the items listed in key_order. If a key does not exist in the dict, yield the default value if specified, otherwise skip the missing key. Afterwards, if other_keys is true, yield the remaining items in an arbitrary order.

Usage:

>>> dic = {"To": "you", "From": "me", "Date": "2008/1/4", "Subject": "X"}
>>> dic["received"] = "..."
>>> order = ["From", "To", "Subject"]
>>> list(ordered_items(dic, order, False))
[('From', 'me'), ('To', 'you'), ('Subject', 'X')]
webhelpers.containers.get_many(d, required=None, optional=None, one_of=None)

Extract values from a dict for unpacking into simple variables.

d is a dict.

required is a list of keys that must be in the dict. The corresponding values will be the first elements in the return list. Raise KeyError if any of the keys are missing.

optional is a list of optional keys. The corresponding values will be appended to the return list, substituting None for missing keys.

one_of is a list of alternative keys. Take the first key that exists and append its value to the list. Raise KeyError if none of the keys exist. This argument will append exactly one value if specified, or will do nothing if not specified.

Example:

uid, action, limit, offset = get_many(request.params, 
    required=['uid', 'action'], optional=['limit', 'offset'])

Contributed by Shazow.

webhelpers.containers.transpose(array)

Turn a list of lists sideways, making columns into rows and vice-versa.

array must be rectangular; i.e., all elements must be the same length. Otherwise the behavior is undefined: you may get IndexError or missing items.

Examples:

>>> transpose([["A", "B", "C"], ["D", "E", "F"]])
[['A', 'D'], ['B', 'E'], ['C', 'F']]
>>> transpose([["A", "B"], ["C", "D"], ["E", "F"]])
[['A', 'C', 'E'], ['B', 'D', 'F']]
>>> transpose([])
[]

Here’s a pictoral view of the first example:

A B C    =>    A D
D E F          B E
               C F

This can be used to turn an HTML table into a group of div columns. An HTML table is row major: it consists of several <tr> rows, each containing several <td> cells. But a <div> layout consists of only one row, each containing an entire subarray. The <div>s have style “float:left”, which makes them appear horizontally. The items within each <div> are placed in their own <div>’s or separated by <br />, which makes them appear vertically. The point is that an HTML table is row major (array[0] is the first row), while a group of div columns is column major (array[0] is the first column). transpose() can be used to switch between the two.

webhelpers.containers.unique(it)

Return a list of unique elements in the iterable, preserving the order.

Usage:

>>> unique([None, "spam", 2, "spam", "A", "spam", "spam", "eggs", "spam"])
[None, 'spam', 2, 'A', 'eggs']

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