137 lines
4.6 KiB
Python
137 lines
4.6 KiB
Python
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#!/usr/bin/env python
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# pylint: disable=W0212
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import six
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from agate.aggregations import Count
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from agate import utils
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def pivot(self, key=None, pivot=None, aggregation=None, computation=None, default_value=utils.default, key_name=None):
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"""
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Create a new table by grouping the data, aggregating those groups,
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applying a computation, and then organizing the groups into new rows and
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columns.
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This is sometimes called a "crosstab".
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+---------+---------+--------+
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| name | race | gender |
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+=========+=========+========+
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| Joe | white | male |
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+---------+---------+--------+
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| Jane | black | female |
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+---------+---------+--------+
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| Josh | black | male |
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+---------+---------+--------+
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| Jim | asian | female |
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+---------+---------+--------+
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This table can be pivoted with :code:`key` equal to "race" and
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:code:`columns` equal to "gender". The default aggregation is
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:class:`.Count`. This would result in the following table.
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+---------+---------+--------+
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| race | male | female |
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+=========+=========+========+
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| white | 1 | 0 |
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+---------+---------+--------+
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| black | 1 | 1 |
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+---------+---------+--------+
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| asian | 0 | 1 |
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+---------+---------+--------+
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If one or more keys are specified then the resulting table will
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automatically have :code:`row_names` set to those keys.
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See also the related method :meth:`.Table.denormalize`.
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:param key:
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Either the name of a column from the this table to group by, a
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sequence of such column names, a :class:`function` that takes a
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row and returns a value to group by, or :code:`None`, in which case
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there will be only a single row in the output table.
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:param pivot:
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A column name whose unique values will become columns in the new
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table, or :code:`None` in which case there will be a single value
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column in the output table.
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:param aggregation:
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An instance of an :class:`.Aggregation` to perform on each group of
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data in the pivot table. (Each cell is the result of an aggregation
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of the grouped data.)
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If not specified this defaults to :class:`.Count` with no arguments.
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:param computation:
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An optional :class:`.Computation` instance to be applied to the
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aggregated sequence of values before they are transposed into the
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pivot table.
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Use the class name of the aggregation as your column name argument
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when constructing your computation. (This is "Count" if using the
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default value for :code:`aggregation`.)
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:param default_value:
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Value to be used for missing values in the pivot table. Defaults to
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:code:`Decimal(0)`. If performing non-mathematical aggregations you
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may wish to set this to :code:`None`.
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:param key_name:
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A name for the key column in the output table. This is most
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useful when the provided key is a function. This argument is not
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valid when :code:`key` is a sequence.
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:returns:
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A new :class:`.Table`.
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"""
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if key is None:
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key = []
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elif not utils.issequence(key):
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key = [key]
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elif key_name:
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raise ValueError('key_name is not a valid argument when key is a sequence.')
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if aggregation is None:
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aggregation = Count()
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groups = self
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for k in key:
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groups = groups.group_by(k, key_name=key_name)
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aggregation_name = six.text_type(aggregation)
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computation_name = six.text_type(computation) if computation else None
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def apply_computation(table):
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computed = table.compute([
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(computation_name, computation)
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])
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excluded = computed.exclude([aggregation_name])
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return excluded
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if pivot is not None:
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groups = groups.group_by(pivot)
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column_type = aggregation.get_aggregate_data_type(groups)
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table = groups.aggregate([
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(aggregation_name, aggregation)
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])
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pivot_count = len(set(table.columns[pivot].values()))
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if computation is not None:
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column_types = computation.get_computed_data_type(table)
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table = apply_computation(table)
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column_types = [column_type] * pivot_count
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table = table.denormalize(key, pivot, computation_name or aggregation_name, default_value=default_value, column_types=column_types)
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else:
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table = groups.aggregate([
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(aggregation_name, aggregation)
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])
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if computation:
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table = apply_computation(table)
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return table
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