def test_combine_categories(self):
ds = mock.MagicMock()
var_url = 'http://test.crunch.io/api/datasets/123/variables/0001/'
ds.entity.self = 'http://test.crunch.io/api/datasets/123/'
entity_mock = mock.MagicMock()
entity_mock.entity.self = var_url
ds.variables.by.return_value = {
'test': entity_mock
}
combine_categories(ds, 'test', CATEGORY_MAP, 'name', 'alias')
call = ds.variables.create.call_args_list[0][0][0]
recodes_payload = {
"element": "shoji:entity",
"body": {
"name": "name",
"description": "",
"alias": "alias",
"expr": {
"function": "combine_categories",
"args": [
{
"variable": 'http://test.crunch.io/api/datasets/123/variables/0001/'
},
{
"value": [
{
"name": "China",
"id": 1,
"missing": False,
"combined_ids": [2, 3]
},
{
"name": "Other",
"id": 2,
"missing": False,
"combined_ids": [1]
}
]
}
]
}
}
}
assert call == recodes_payload
def main():
assert not invalid_credentials()
# Login.
site = pycrunch.connect(CRUNCH_USER, CRUNCH_PASSWORD, CRUNCH_URL)
assert isinstance(site, pycrunch.shoji.Catalog)
# Create the test dataset.
dataset = site.datasets.create(DATASET_DOC).refresh()
assert isinstance(dataset, pycrunch.shoji.Entity)
try:
# Load initial data.
pycrunch.importing.importer.append_rows(dataset, ROWS)
# Check the initial number of rows.
df = pandaslib.dataframe(dataset)
assert len(df) == len(ROWS) - 1 # excluding the header
# 1. Exclusion Filter Integration Tests
# 1.1 Set a simple exclusion filter.
pycrunch.datasets.exclusion(dataset, 'identity > 5')
df = pandaslib.dataframe(dataset)
assert len(df) == 5
# 1.2 More complex exclusion filters involving a categorical variable.
expr = 'speak_spanish in [32766]'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 10
expr = 'speak_spanish in (32766, 32767)'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 9
expr = 'not (speak_spanish in (1, 2) and operating_system == "Linux")'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 2
# 1.3 Exclusion filters with `has_any`.
expr = 'hobbies.has_any([32766])'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 8
expr = 'not hobbies.has_any([32766])'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 4
expr = 'hobbies.has_any([32766, 32767])'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 7
expr = 'music.has_any([32766])'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 12
expr = 'music.has_any([1])'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 1
expr = 'music.has_any([1, 2])'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 0
# 1.4 Exclusion filters with `has_all`.
expr = 'hobbies.has_all([32767])'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 11
expr = 'not hobbies.has_all([32767])'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 1
expr = 'music.has_all([1])'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 11
expr = 'music.has_all([1]) or music.has_all([2])'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 10
expr = 'not ( music.has_all([1]) or music.has_all([2]) )'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 2
# 1.5 Exclusion filters with `duplicates`.
expr = 'ip_address.duplicates()'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 10
# 1.6 Exclusion filters with `valid` and `missing`.
expr = 'valid(speak_spanish)'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 3
expr = 'not valid(speak_spanish)'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 9
expr = 'missing(speak_spanish)'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 9
expr = 'missing(hobbies)'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 11
expr = 'not missing(hobbies)'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 1
expr = 'valid(hobbies)'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 5
expr = 'not valid(hobbies)'
pycrunch.datasets.exclusion(dataset, expr)
df = pandaslib.dataframe(dataset)
assert len(df) == 7
# 1.7 Clear the exclusion filter.
pycrunch.datasets.exclusion(dataset)
df = pandaslib.dataframe(dataset)
assert len(df) == len(ROWS) - 1 # excluding the header
# 2. Integration Tests for "Transformations".
categories = [
{'id': 1, 'name': 'Nerds', 'numeric_value': 1, 'missing': False},
{'id': 2, 'name': 'Normal Users', 'numeric_value': 2, 'missing': False},
{'id': 3, 'name': 'Hipsters', 'numeric_value': 3, 'missing': False},
{'id': 32767, 'name': 'Unknown', 'numeric_value': None, 'missing': True}
]
rules = [
'operating_system in ("Linux", "Solaris", "Minix", "FreeBSD", "NetBSD")',
'operating_system == "Windows"',
'operating_system == "MacOS"',
'missing(operating_system)'
]
new_var = create_categorical(
ds=dataset,
categories=categories,
rules=rules,
name='Operating System Users',
alias='operating_system_users',
description='Type of Operating System Users'
)
assert isinstance(new_var, pycrunch.shoji.Entity)
new_var.refresh()
assert new_var.body.type == 'categorical'
# Check the data on the new variable.
df = pandaslib.dataframe(dataset)
assert 'operating_system_users' in df
# Check the nerds.
assert len(df[df['operating_system_users'] == 'Nerds']) == 8
assert set(
r['operating_system']
for _, r in df[df['operating_system_users'] == 'Nerds'].iterrows()
) == {'Linux', 'Solaris', 'Minix', 'FreeBSD', 'NetBSD'}
# Check the hipsters.
assert len(df[df['operating_system_users'] == 'Hipsters']) == 1
assert set(
r['operating_system']
for _, r in df[df['operating_system_users'] == 'Hipsters'].iterrows()
) == {'MacOS'}
# Check normal users.
assert len(df[df['operating_system_users'] == 'Normal Users']) == 3
assert set(
r['operating_system']
for _, r in df[df['operating_system_users'] == 'Normal Users'].iterrows()
) == {'Windows'}
# 3. Integration Tests for "Recodes".
# 3.1 combine_categories.
# On a 'categorical' variable.
cat_map = {
1: {
'name': 'Bilingual',
'missing': False,
'combined_ids': [2, 3]
},
2: {
'name': 'Not Bilingual',
'missing': False,
'combined_ids': [1, 4]
},
99: {
'name': 'Unknown',
'missing': True,
'combined_ids': [32766, 32767]
}
}
new_var = combine_categories(
dataset, 'speak_spanish', cat_map, 'Bilingual Person', 'bilingual'
)
assert isinstance(new_var, pycrunch.shoji.Entity)
new_var.refresh()
assert new_var.body.type == 'categorical'
df = pandaslib.dataframe(dataset)
assert 'bilingual' in df
# Check the data in the recoded variable.
assert len(df[df['bilingual'] == 'Bilingual']) == 5
assert set(
int(r['identity'])
for _, r in df[df['bilingual'] == 'Bilingual'].iterrows()
) == {3, 4, 10, 11, 12}
assert len(df[df['bilingual'] == 'Not Bilingual']) == 4
assert set(
int(r['identity'])
for _, r in df[df['bilingual'] == 'Not Bilingual'].iterrows()
) == {1, 2, 5, 6}
assert len(df[df['bilingual'].isnull()]) == 3
assert set(
int(r['identity'])
for _, r in df[df['bilingual'].isnull()].iterrows()
) == {7, 8, 9}
# On a 'categorical_array' variable.
cat_map = {
1: {
'name': 'Interested',
'missing': False,
'combined_ids': [1, 2]
},
2: {
'name': 'Not interested',
'missing': False,
'combined_ids': [3, 4]
},
99: {
'name': 'Unknown',
'missing': True,
'combined_ids': [32766, 32767]
}
}
new_var = combine_categories(
dataset, 'hobbies', cat_map, 'Hobbies (recoded)', 'hobbies_recoded'
)
assert isinstance(new_var, pycrunch.shoji.Entity)
new_var.refresh()
assert new_var.body.type == 'categorical_array'
df = pandaslib.dataframe(dataset)
assert 'hobbies_recoded' in df
# Check the data in the recoded variable.
for _, row in df[['hobbies', 'hobbies_recoded']].iterrows():
hobbies = row['hobbies']
hobbies_rec = row['hobbies_recoded']
assert len(hobbies) == len(hobbies_rec)
for i, value in enumerate(hobbies):
if value in ({'?': 32766}, {'?': 32767}):
assert hobbies_rec[i] == {'?': 99}
elif value in (1, 2):
assert hobbies_rec[i] == 1
elif value in (3, 4):
assert hobbies_rec[i] == 2
# 3.2 combine_responses.
response_map = {
'music_recoded_1': ['music_1', 'music_2'],
'music_recoded_2': ['music_97'],
'music_recoded_3': ['music_98', 'music_99']
}
new_var = combine_responses(
dataset, 'music', response_map, 'Music (alt)', 'music_recoded'
)
assert isinstance(new_var, pycrunch.shoji.Entity)
new_var.refresh()
assert new_var.body.type == 'multiple_response'
df = pandaslib.dataframe(dataset)
assert 'music_recoded' in df
# TODO: Test the data in the recoded variable. Unsure of its meaning.
finally:
dataset.delete()