"""
Module that contains mapping operations on pandas data structures
"""
from __future__ import division
from future.utils import viewitems
from builtins import zip
import pandas
[docs]def group_dataframe_by_mapping(dataframe, mapping, root_taxon, name_dict=None):
"""
Return a :class:`pandas.DataFrame` filtered by mapping and root taxon, the
values for each column is averaged over all genes mapping to a category.
:param DataFrame dataframe: DataFrame with multindex gene-root
:param dict mapping: dictionary of category->genes
:param str root_taxon: root taxon to group genes
:param dict name_dict: dictionary of category->name
:return DataFrame: DataFrame filtered
"""
data_dict = {}
for category, id_list in viewitems(mapping):
category_name = category
if name_dict is not None:
category_name = name_dict[category_name]
data_dict[category_name] = dataframe.loc[id_list].xs(
root_taxon, level='root'
).mean()
return pandas.DataFrame.from_dict(data_dict, orient='index')
[docs]def calc_coefficient_of_variation(dataframe):
"""
Calculate coefficient of variation for a DataFrame. Uses formula from
`Wikipedia <http://en.wikipedia.org/wiki/Coefficient_of_variation>`_
The formula used is :math:`\\left (1 + \\frac {1}{4n} \\right ) * c_{v}`
where :math:`c_{v} = \\frac {s}{\\bar{x}}`
"""
dataframe_cv = dataframe.std(axis=1) / dataframe.mean(axis=1)
coeff = 1 + (1.0 / (4 * dataframe.count(axis=1)))
return coeff * dataframe_cv
[docs]def make_stat_table(dataframes, roots):
"""
Produces a :class:`pandas.DataFrame` that summarise the supplied
DataFrames. The stats include mean, stdev and coefficient of variation for
each root taxon.
:param iterable dataframes: iterable of DataFrame instances
:param iterable roots: list of root taxa to which each table belongs
:return DataFrame: returns a DataFrame instance
"""
index = []
data = {}
for dataframe, root in zip(dataframes, roots):
index.append((root, 'mean'))
data[(root, 'mean')] = dataframe.mean(axis=1)
index.append((root, 'stdev'))
data[(root, 'stdev')] = dataframe.std(axis=1)
index.append((root, 'c. var'))
data[(root, 'c. var')] = calc_coefficient_of_variation(dataframe)
index = pandas.MultiIndex.from_tuples(sorted(index),
names=('root', 'value'))
return pandas.DataFrame(data, columns=index)
[docs]def concatenate_and_rename_tables(dataframes, roots):
"""
Concatenates a list of :class:`pandas.DataFrame` instances and renames the
columns prepending a string to each column in each table from a list of
prefixes.
:param iterable dataframes: iterable of DataFrame instances
:param iterable roots: list of prefixes to append to the column names of
each DataFrame
:return DataFrame: returns a DataFrame instance
.. todo::
* move to pandas_utils?
"""
renamed = []
for dataframe, root in zip(dataframes, roots):
index = dict((column, root + column) for column in dataframe.columns)
renamed.append(dataframe.rename(columns=index))
return pandas.concat(renamed, axis=1)