cs.mappings 20181231

Facilities for mappings and objects associated with mappings.

Facilities for mappings and objects associated with mappings.

In particular:

• named_column_tuple(column_names), a function returning a factory for namedtuples subclasses derived from the supplied column names

• named_column_tuples(rows), a function returning a namedtuple factory and an iterable of instances containing the row data

These are used by the csv_import and xl_import functions from cs.csvutils.

Class AttributableList

MRO: builtins.list
An AttributableList maps unimplemented attributes onto the list members and returns you a new AttributableList with the results, ready for a further dereference.

Example:

class C(object): ... def init(self, i): ... self.i = i Cs = [ C(1), C(2), C(3) ] AL = AttributableList( Cs ) print(AL.i) [1, 2, 3]

Class FallbackDict

MRO: collections.defaultdict, builtins.dict
A dictlike object that inherits from another dictlike object; this is a convenience subclass of defaultdict.

Class MappingChain

A mapping interface to a sequence of mappings. It does not support setitem at present; that is expected to be managed via the backing mappings.

Class MethodicalList

MRO: AttributableList, builtins.list
A MethodicalList subclasses a list and maps unimplemented attributes into a callable which calls the corresponding method on each list members and returns you a new MethodicalList with the results, ready for a further dereference.

Example:

n = 1 class C(object): ... def init(self): ... global n ... self.n = n ... n += 1 ... def x(self): ... return self.n ... Cs=[ C(), C(), C() ] ML = MethodicalList( Cs ) print(ML.x()) [1, 2, 3]

Function named_column_tuples(rows, class_name=None, column_names=None, computed=None, preprocess=None, mixin=None)

Process an iterable of data rows, usually with the first row being column names. Returns a generated namedtuple factory and an iterable of instances of the namedtuples for each row.

rows: an iterable of rows, each an iterable of data values. class_name: option class name for the namedtuple class column_names: optional iterable of column names used as the basis for the namedtuple. If this is not provided then the first row from rows is taken to be the column names. computed: optional mapping of str to functions of self preprocess: optional callable to modify CSV rows before they are converted into the namedtuple. It receives a context object an the data row. It should return the row (possibly modified), or None to drop the row. The context object has the following attributes: .cls attribute with the generated namedtuple subclass; this is useful for obtaining things like the column named or column indices; this is None when preprocessing the header row, if any .index attribute with the row's enumeration, which counts from 0 .previous the previously accepted row's namedtuple, or None if there is no previous row mixin: an optional mixin class for the generated namedtuple subclass to provide extra methods or properties

Rows may be flat iterables in the same order as the column names or mappings keyed on the column names.

If the column names contain empty strings they are dropped and the corresponding data row entries are also dropped. This is very common with spreadsheet exports with unused padding columns.

Typical human readable column headings, also common in speadsheet exports, are lowercased and have runs of whitespace or punctuation turned into single underscores; trailing underscores then get dropped.

Basic example::

data1 = [ ... ('a', 'b', 'c'), ... (1, 11, "one"), ... (2, 22, "two"), ... ] cls, rows = named_column_tuples(data1) print(list(rows)) [NamedRow(a=1, b=11, c='one'), NamedRow(a=2, b=22, c='two')]

Human readable column headings::

data1 = [ ... ('Index', 'Value Found', 'Descriptive Text'), ... (1, 11, "one"), ... (2, 22, "two"), ... ] cls, rows = named_column_tuples(data1) print(list(rows)) [NamedRow(index=1, value_found=11, descriptive_text='one'), NamedRow(index=2, value_found=22, descriptive_text='two')]

Rows which are mappings::

data1 = [ ... ('a', 'b', 'c'), ... (1, 11, "one"), ... {'a': 2, 'c': "two", 'b': 22}, ... ] cls, rows = named_column_tuples(data1) print(list(rows)) [NamedRow(a=1, b=11, c='one'), NamedRow(a=2, b=22, c='two')]

CSV export with unused padding columns::

data1 = [ ... ('a', 'b', 'c', '', ''), ... (1, 11, "one"), ... {'a': 2, 'c': "two", 'b': 22}, ... [3, 11, "three", '', 'dropped'], ... ] cls, rows = named_column_tuples(data1, 'CSV_Row') print(list(rows)) [CSV_Row(a=1, b=11, c='one'), CSV_Row(a=2, b=22, c='two'), CSV_Row(a=3, b=11, c='three')]

A mixin class providing a test1 method and a test2 property:

class Mixin(object): ... def test1(self): ... return "test1" ... @property ... def test2(self): ... return "test2" data1 = [ ... ('a', 'b', 'c'), ... (1, 11, "one"), ... {'a': 2, 'c': "two", 'b': 22}, ... ] cls, rows = named_column_tuples(data1, mixin=Mixin) rows = list(rows) rows[0].test1() 'test1' rows[0].test2 'test2'

Function named_row_tuple(*column_names, **kw)

Return a namedtuple subclass factory derived from column_names.

column_names: an iterable of str, such as the heading columns of a CSV export class_name: optional keyword parameter specifying the class name computed: optional keyword parameter providing a mapping of str to functions of self; these strings are available via getitem mixin: an optional mixin class for the generated namedtuple subclass to provide extra methods or properties

The tuple's attributes are computed by converting all runs of nonalphanumerics (as defined by the re module's "\W" sequence) to an underscore, lowercasing and then stripping leading and trailing underscores.

In addition to the normal numeric indices, the tuple may also be indexed by the attribute names or the column names.

The new class has the following additional attributes: attributes_: the attribute names of each tuple in order names_: the originating name strings name_attributes_: the computed attribute names corresponding to the names; there may be empty strings in this list attr_of_: a mapping of column name to attribute name name_of_: a mapping of attribute name to column name index_of_: a mapping of column names and attributes their tuple indices

Examples::

T = named_row_tuple('Column 1', '', 'Column 3', ' Column 4', 'Column 5 ', '', '', class_name='Example') T.attributes_ ['column_1', 'column_3', 'column_4', 'column_5'] row = T('val1', 'dropped', 'val3', 4, 5, 6, 7) row Example(column_1='val1', column_3='val3', column_4=4, column_5=5)

Class SeenSet

A set-like collection with optional backing store file.

Class SeqMapUC_Attrs

A wrapper for a mapping from keys (matching ^[A-Z][A-Z_0-9]*$) to tuples. Attributes matching such a key return the first element of the sequence (and requires the sequence to have exactly on element). An attribute FOOs or FOOes (ending in a literal 's' or 'es', a plural) returns the sequence (FOO must be a key of the mapping).

Class StackableValues

A collection of named stackable values with the latest value available as an attribute.

Note that names conflicting with methods are not available as attributes and must be accessed via getitem. As a matter of practice, in addition to the mapping methods, avoid names which are verbs or which begin with an underscore.

S = StackableValues() print(S) StackableValues() S.push('x', 1) print(S) StackableValues(x=1) print(S.x) 1 S.push('x', 2) print(S.x) 2 S.x = 3 print(S.x) 3 S.pop('x') 3 print(S.x) 1 with S.stack('x', 4): ... print(S.x) ... 4 print(S.x) 1

Class UC_Sequence

MRO: builtins.list
A tuple-of-nodes on which .ATTRs indirection can be done, yielding another tuple-of-nodes or tuple-of-values.