def _prepare_plot(self, df, values):
"""
convert the data into a pivot table for plotting
with mean as the values, and the two non-data columns
as the index and column values.
Arguments:
df: DataFrame with {vls} column
Returns:
pivot table of {vls},
OrderedDict mapping group labels to dataframe rows
representing them
""".format(vls=self.values_column)
# TODO: should this work on samples without mean?
if values not in df.columns:
raise ValueError("dataframe must have {} column".format(values))
pvt, fgroups, tgroups = pivot_table(df,
values=values,
label_callback=self.label_callback)
# we want to have groups from-first, but without overwriting
# any from-groups with to-groups
groups = fgroups
for key, value in tgroups.items():
if key not in groups:
groups[key] = value
return pvt, groups
def plot(self, dataframe, values=None):
"""
plot a heatplot for dataframe
"""
import numpy as np
kw = {'values': values} if values is not None else {}
pvt, fg, tg = pivot_table(
dataframe, label_callback=self.label_callback, **kw)
f = plt.figure(figsize=np.array(pvt.shape) * 0.3)
hm = heatmap(pvt, cmap=self.cmap, cbar=self.cbar,
xticklabels=True, yticklabels=True)
return f
def test_columns_mismatch(self):
"""
both columns do not need to contain the same values
"""
df = pd.DataFrame(OrderedDict([
(MEAN, [1, 2]),
('pre', ['a', 'b']),
('post', ['c', 'c'])]))
pd.testing.assert_frame_equal(
df.pivot_table(index='pre', columns='post', values=MEAN),
pivot_table(df, 'pre', 'post', MEAN)[0])
def test_basic(self):
"""test the returned table for simple input"""
df =\
pd.DataFrame(OrderedDict([
(MEAN, [1, 2, 1, 2]),
('pre', ['a', 'a', 'b', 'b']),
('post', ['a', 'b', 'a', 'b'])]))
table = pivot_table(df, 'pre', 'post', MEAN)[0]
pd.testing.assert_frame_equal(
table,
df.pivot_table(index='pre', columns='post',
values=MEAN))
def test_duplicate_pathways(self):
"""
when pathways are duplicated, take their mean as pandas does by default
"""
df = pd.DataFrame(OrderedDict([
((MEAN, ''), [1, 2, 1, 2, 3]),
((STD, ''), [1, 2, 3, 1, 3]),
(('pre', 'mtype'), ['a', 'a', 'b', 'b', 'b']),
(('post', 'mtype'), ['a', 'b', 'a', 'b', 'b'])]))
pd.testing.assert_frame_equal(
pd.DataFrame({
'pre: mtype': ['a', 'a', 'b', 'b'],
'post: mtype': ['a', 'b', 'a', 'b'],
MEAN: [1, 2, 1, 2.5]})
.pivot_table(index='pre: mtype',
columns='post: mtype',
values=MEAN),
pivot_table(df, 'pre', 'post', MEAN)[0])
def test_2_level_columns(self):
"""
test handling of multilevel dataframes
the 'index' and 'columns' columns should be collapsed as necessary"""
df = pd.DataFrame(OrderedDict([
((MEAN, ''), [1, 2, 1, 2]),
((STD, ''), [1, 2, 3, 1]),
(('pre', 'mtype'), ['a', 'a', 'b', 'b']),
(('post', 'mtype'), ['a', 'b', 'a', 'b'])]))
table = pivot_table(df, 'pre', 'post', MEAN)[0]
edf =\
pd.DataFrame({
'pre: mtype': ['a', 'a', 'b', 'b'],
'post: mtype': ['a', 'b', 'a', 'b'],
MEAN: [1, 2, 1, 2]})\
.pivot_table(
index='pre: mtype',
columns='post: mtype',
values=MEAN)
print(table, "\n\n", edf)
pd.testing.assert_frame_equal(
table, edf)