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generating histograms for countries more efficient

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frankknoll
2023-02-16 14:59:43 +01:00
parent ef7c36d567
commit 0ffc5881b6
8 changed files with 139 additions and 136 deletions
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100
src/HistogramDescriptionTableFactoryTest.py
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@@ -6,7 +6,7 @@ import pandas as pd
class HistogramDescriptionTableFactoryTest(unittest.TestCase):
def test_createHistogramDescriptionTable(self):
def test_createGlobalHistogramDescriptionTable(self):
# Given
dictByBatchcodeTable = TestHelper.createDataFrame(
columns = ['SYMPTOM_COUNT_BY_VAX_LOT'],
@@ -93,3 +93,101 @@ class HistogramDescriptionTableFactoryTest(unittest.TestCase):
'FD1921',
'015M20A'])),
check_like = True)
def test_createHistogramDescriptionTable4Countries(self):
# Given
dictByBatchcodeTable = TestHelper.createDataFrame(
columns = ['SYMPTOM_COUNT_BY_VAX_LOT', 'COUNTRY'],
data = [ [{"Blood pressure orthostatic abnormal": 5, "Chest discomfort": 1}, 'Country A'],
[{"Blood pressure orthostatic abnormal": 5, "Chest discomfort": 1}, 'Country A'],
[{"Blood pressure orthostatic abnormal": 5, "Chest discomfort": 1}, 'Country A'],
[{"Chest discomfort": 2}, 'Country A'],
[{"Chest discomfort": 2}, 'Country A'],
[{"Chest discomfort": 2}, 'Country A']
],
index = pd.MultiIndex.from_tuples(
names = ['VAX_LOT_EXPLODED', 'VAX_LOT1', 'VAX_LOT2', 'VAX_LOT3'],
tuples = [['1808982', '1808982', 'EW0175', 'FD1921'],
['EW0175', '1808982', 'EW0175', 'FD1921'],
['FD1921', '1808982', 'EW0175', 'FD1921'],
['015M20A', '015M20A', '1808982', 'nan'],
['1808982', '015M20A', '1808982', 'nan'],
['nan', '015M20A', '1808982', 'nan']]))
# When
histogramDescriptionTable = HistogramDescriptionTableFactory.createHistogramDescriptionTable(dictByBatchcodeTable)
# Then
assert_frame_equal(
histogramDescriptionTable,
TestHelper.createDataFrame(
columns = ['HISTOGRAM_DESCRIPTION', 'COUNTRY'],
data = [ [
{
"batchcode": "1808982",
"histograms": [
{
"batchcodes": ["1808982", "EW0175", "FD1921"],
"histogram": {
"Blood pressure orthostatic abnormal": 5,
"Chest discomfort": 1}
},
{
"batchcodes": ["015M20A", "1808982"],
"histogram": {"Chest discomfort": 2}
}
]
},
'Country A'
],
[
{
"batchcode": "EW0175",
"histograms": [
{
"batchcodes": ["1808982", "EW0175", "FD1921"],
"histogram": {
"Blood pressure orthostatic abnormal": 5,
"Chest discomfort": 1}
}
]
},
'Country A'
],
[
{
"batchcode": "FD1921",
"histograms": [
{
"batchcodes": ["1808982", "EW0175", "FD1921"],
"histogram": {
"Blood pressure orthostatic abnormal": 5,
"Chest discomfort": 1}
}
]
},
'Country A'
],
[
{
"batchcode": "015M20A",
"histograms": [
{
"batchcodes": ["015M20A", "1808982"],
"histogram": {"Chest discomfort": 2}
}
]
},
'Country A'
]
],
index = pd.Index(
name = 'VAX_LOT',
data = [
'1808982',
'EW0175',
'FD1921',
'015M20A'])),
check_like = True)