def test_user_agent(self):
user = '******'
pw = 'supersecret'
site = 'https://test.crunch.io/api/'
from pycrunch.version import __version__ as pycrunch_v
from requests.sessions import Session
with mock.patch.object(Session, 'send') as mock_send:
scrunch.connect(user, pw, site)
prep_req = mock_send.call_args[0][0]
assert prep_req.headers['user-agent'] == 'scrunch/%s (pycrunch/%s)' % (scrunch.__version__, pycrunch_v)
def test_move_dataset(self):
fo = get_user(username2)
fo_site = connect(fo.email, password2, HOST)
# These two datasets are created by the default logged user
_ds1 = site.datasets.create(
shoji_entity_wrapper({'name': 'test_move_dataset1'})).refresh()
_ds2 = site.datasets.create(
shoji_entity_wrapper({'name': 'test_move_dataset2'})).refresh()
# This dataset is created and owned by the other user
_ds4 = fo_site.datasets.create(
shoji_entity_wrapper({
'name': 'test_move_dataset4',
'owner': fo.url
})).refresh()
ds1 = get_dataset(_ds1.body.id)
ds2 = get_dataset(_ds2.body.id)
ds4 = get_dataset(_ds4.body.id, connection=fo_site)
ds2.add_user(fo, edit=True)
# Create a hierarchy A -> B
pa = new_project("test_move_dataset_A")
pa.move_here([ds1]) # Put ds1 in A
pb = pa.create_project("test_move_dataset_B")
pa.add_user(fo, edit=True)
# Move ds4 to B as the other user
fo_pa = get_project(pa.name, fo_site)
fo_pa.place(ds4, path="| %s" % pb.name)
pb.resource.refresh()
self.assertItemsEqual(
pb.resource.index.keys(),
# Only ds4 here
[_ds4.self])
fo_ds1 = get_dataset(_ds1.body.id, connection=fo_site)
fo_pa.place(fo_ds1, path="| %s" % pb.name)
pb.resource.refresh()
self.assertItemsEqual(pb.resource.index.keys(), [_ds1.self, _ds4.self])
pa.place(ds2, path="| %s" % pb.name)
pb.resource.refresh()
self.assertItemsEqual(pb.resource.index.keys(),
[_ds1.self, _ds2.self, _ds4.self])
self.assertEqual(ds2.resource.project.self, pb.url)
from pycrunch.shoji import Catalog
from fixtures import NEWS_DATASET
from scrunch import connect
from scrunch import get_dataset
from scrunch.datasets import Variable
from scrunch.exceptions import InvalidPathError
from scrunch.folders import Folder
HOST = os.environ['SCRUNCH_HOST']
username = os.environ['SCRUNCH_USER']
password = os.environ['SCRUNCH_PASS']
site = connect(username, password, HOST)
def setup_folders(ds):
sess = ds.session
ds.settings.edit(variable_folders=True)
ds.variables.create({
'element': 'shoji:entity',
'body': {'name': 'testvar1', 'type': 'numeric'}
})
ds.variables.create({
'element': 'shoji:entity',
'body': {'name': 'testvar2', 'type': 'numeric'}
})
ds.variables.create({
'element': 'shoji:entity',
# coding: utf-8
from getpass import getpass
from scrunch import connect
from scrunch.datasets import get_dataset, create_dataset
username = raw_input("Enter email: ")
password = getpass("Enter password for %s: " % username)
site = connect(username, password, site_url='https://alpha.crunch.io/api/')
new_ds = create_dataset(
'Test dataset', {
"catvar": {
'name':
'categorical variable',
'type':
'categorical',
'categories': [{
'id': 1,
'name': 'yes',
'numeric_value': 1,
'missing': False
}, {
'id': 2,
'name': 'no',
'numeric_value': 2,
'missing': False
}, {
'id': 3,
'name': 'Not asked',
def main():
assert not invalid_credentials()
assert pandaslib, 'Pandas library not installed'
# Login.
site = connect(CRUNCH_USER, CRUNCH_PASSWORD, CRUNCH_URL)
assert isinstance(site, pycrunch.shoji.Catalog)
# Create the test dataset.
dataset_resource = site.datasets.create(DATASET_DOC).refresh()
assert isinstance(dataset_resource, pycrunch.shoji.Entity)
dataset = StreamingDataset(dataset_resource)
try:
# Load initial data.
pycrunch.importing.importer.append_rows(dataset.resource, ROWS)
# refresh dataset instance, so size is updated
dataset.resource.refresh()
# Check the initial number of rows.
df = pandaslib.dataframe(dataset.resource)
assert len(df) == len(ROWS) - 1 # excluding the header
assert dataset.size.rows == len(df)
assert dataset.size.unfiltered_rows == len(df)
# Also check number of columns
columns = DATASET_DOC['body']['table']['metadata'].__len__()
assert dataset.size.columns == columns
# 0. Manipulate metadata
# 0.1 Start by updating the missing rules for the `identity` variable
identity_missing_rules = {"not asked": 9999, "skipped": 9998}
assert dataset['identity'].missing_rules == {}
dataset['identity'].set_missing_rules(identity_missing_rules)
assert dataset['identity'].missing_rules == identity_missing_rules
# 0.2 Try setting and unsetting the geodata view
location = dataset['location']
geodata = get_geodata('UK Regions')
assert 'geodata' not in location.view
# Set geodata using Entity object
location.set_geodata_view(geodata, feature_key='EER13NM')
assert 'geodata' in location.view
location.unset_geodata_view()
assert 'geodata' not in location.view
# Set geodata using url
location.set_geodata_view(geodata.self, feature_key='EER13NM')
assert 'geodata' in location.view
location.unset_geodata_view()
assert 'geodata' not in location.view
# Set geodata using name
location.set_geodata_view('UK Regions', feature_key='EER13NM')
assert 'geodata' in location.view
location.unset_geodata_view()
assert 'geodata' not in location.view
# 1. Exclusion Filter Integration Tests
# 1.1 Set a simple exclusion filter.
dataset.exclude('identity > 5')
df = pandaslib.dataframe(dataset.resource)
assert len(df) == 5
assert not any(r['identity'] > 5 for _, r in df.iterrows())
# 1.2 More complex exclusion filters involving a categorical variable.
expr = 'speak_spanish in [32766]'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity' and row[4] != 32766
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
expr = 'speak_spanish in (32766, 32767)'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and row[4] not in (32766, 32767)
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert not isnan(row['speak_spanish'])
expr = 'not (speak_spanish in (1, 2) and operating_system == "Linux")'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and row[4] in (1, 2) and row[2] == 'Linux'
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert row['speak_spanish'] in \
('I speak Spanish primarily',
'I speak both Spanish and English equally')
assert row['operating_system'] == 'Linux'
# 1.3 Exclusion filters with `any`.
expr = 'hobbies.any([32766])'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and 32766 not in row[5:9]
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert {'?': 32766} not in row['hobbies']
expr = 'not hobbies.any([32766])'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and 32766 in row[5:9]
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert {'?': 32766} in row['hobbies']
expr = 'hobbies.any([32766, 32767])'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity'
and 32766 not in row[5:9] and 32767 not in row[5:9]
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert {'?': 32766} not in row['hobbies'] and \
{'?': 32767} not in row['hobbies']
expr = 'music.any([32766])'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and 32766 not in row[9:14]
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert {'?': 32766} not in row['music']
expr = 'music.any([1])'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and 1 not in row[9:14]
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert 1 not in row['music']
expr = 'music.any([1, 2])'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity'
and 1 not in row[9:14] and 2 not in row[9:14]
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert 1 not in row['music'] and 2 not in row['music']
# 1.4 Exclusion filters with `all`.
expr = 'hobbies.all([32767])'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity'
and row[5:9] != [32767, 32767, 32767, 32767]
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert row['hobbies'] != [{
'?': 32767
}, {
'?': 32767
}, {
'?': 32767
}, {
'?': 32767
}]
expr = 'not hobbies.all([32767])'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity'
and row[5:9] == [32767, 32767, 32767, 32767]
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert row['hobbies'] == [{
'?': 32767
}, {
'?': 32767
}, {
'?': 32767
}, {
'?': 32767
}]
expr = 'music.all([1])'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and row[9:14] != [1, 1, 1, 1, 1]
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert row['music'] != [1, 1, 1, 1, 1]
expr = 'music.all([1]) or music.all([2])'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity' and (
row[9:14] != [1, 1, 1, 1, 1] and row[9:14] != [2, 2, 2, 2, 2])
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert row['music'] != [1, 1, 1, 1, 1] and \
row['music'] != [2, 2, 2, 2, 2]
expr = 'not ( music.all([1]) or music.all([2]) )'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity' and (
row[9:14] == [1, 1, 1, 1, 1] or row[9:14] == [2, 2, 2, 2, 2])
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert row['music'] == [1, 1, 1, 1, 1] or \
row['music'] == [2, 2, 2, 2, 2]
# 1.5 Exclusion filters with `duplicates`.
expr = 'ip_address.duplicates()'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
seen_ip_addresses = []
for _, row in df.iterrows():
assert row['ip_address'] not in seen_ip_addresses
seen_ip_addresses.append(row['ip_address'])
# 1.6 Exclusion filters with `valid` and `missing`.
expr = 'valid(speak_spanish)'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and row[4] in (32766, 32767)
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert isnan(row['speak_spanish'])
expr = 'not valid(speak_spanish)'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and row[4] not in (32766, 32767)
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert not isnan(row['speak_spanish'])
expr = 'missing(speak_spanish)'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and row[4] not in (32766, 32767)
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert not isnan(row['speak_spanish'])
expr = 'missing(hobbies)'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity' and (
row[5:9] != [32766, 32766, 32766, 32766]
and row[5:9] != [32767, 32767, 32767, 32767])
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert row['hobbies'] != [{'?': 32766}, {'?': 32766},
{'?': 32766}, {'?': 32766}] \
and row['hobbies'] != [{'?': 32767}, {'?': 32767},
{'?': 32767}, {'?': 32767}]
expr = 'not missing(hobbies)'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity' and (
row[5:9] == [32766, 32766, 32766, 32766]
or row[5:9] == [32767, 32767, 32767, 32767])
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert row['hobbies'] == [{'?': 32766}, {'?': 32766},
{'?': 32766}, {'?': 32766}] \
or row['hobbies'] == [{'?': 32767}, {'?': 32767},
{'?': 32767}, {'?': 32767}]
expr = 'valid(hobbies)'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity' and (
32766 in row[5:9] or 32767 in row[5:9])
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert {'?': 32766} in row['hobbies'] or \
{'?': 32767} in row['hobbies']
expr = 'not valid(hobbies)'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity' and (
32766 not in row[5:9] and 32767 not in row[5:9])
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert {'?': 32766} not in row['hobbies'] and \
{'?': 32767} not in row['hobbies']
# 1.7 Exclusion filter that refers to a subvariable by alias.
expr = 'hobbies_1 == 4'
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS if row[0] != 'identity' and row[5] != 4
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
assert row['hobbies'][0] != 4
# 1.8 Complex exclusion filters (multiple rules)
expr = (
'(religion != 1 and (not valid(speak_spanish) or speak_spanish >= 1)) '
'or (religion == 1 and speak_spanish == 2) '
'or (religion == 3 and speak_spanish == 4)')
dataset.exclude(expr)
# 1.9 Exclusion filters using date variables.
dt_str = '2014-12-30T00:00:00+00:00'
dt = isodate.parse_datetime(dt_str)
expr = 'registration_time < "%s"' % dt_str
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and isodate.parse_datetime(row[3]) >= dt
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
dt_str = '2015-01-01T00:00:00+00:00'
dt = isodate.parse_datetime(dt_str)
expr = 'registration_time >= "%s"' % dt_str
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and isodate.parse_datetime(row[3]) < dt
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
dt_str = '2014-05-10T00:00:00+00:00'
dt = isodate.parse_datetime(dt_str)
expr = 'registration_time == "%s"' % dt_str
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and isodate.parse_datetime(row[3]) != dt
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
dt_str = '2014-05-10T00:00:00+00:00'
dt = isodate.parse_datetime(dt_str)
expr = 'not(registration_time == "%s")' % dt_str
dataset.exclude(expr)
df = pandaslib.dataframe(dataset.resource)
valid_ids = [
row[0] for row in ROWS
if row[0] != 'identity' and isodate.parse_datetime(row[3]) == dt
]
assert len(df) == len(valid_ids)
for _, row in df.iterrows():
assert row['identity'] in valid_ids
# 1.10 Clear the exclusion filter.
dataset.exclude()
df = pandaslib.dataframe(dataset.resource)
assert len(df) == len(ROWS) - 1 # excluding the header
# 2. Integration Tests for "Transformations".
categories = [{
'id':
1,
'name':
'Nerds',
'numeric_value':
1,
'missing':
False,
'case':
'operating_system in ("Linux", "Solaris", "Minix", "FreeBSD", "NetBSD")',
}, {
'id': 2,
'name': 'Normal Users',
'numeric_value': 2,
'missing': False,
'case': 'operating_system == "Windows"',
}, {
'id': 3,
'name': 'Hipsters',
'numeric_value': 3,
'missing': False,
'case': 'operating_system == "MacOS"',
}, {
'id': 32767,
'name': 'Unknown',
'numeric_value': None,
'missing': True,
'case': 'missing(operating_system)'
}]
new_var = dataset.create_single_response(
categories=categories,
name='Operating System Users',
alias='operating_system_users',
description='Type of Operating System Users')
assert isinstance(new_var, Variable)
assert new_var.type == 'categorical'
# Check the data on the new variable.
df = pandaslib.dataframe(dataset.resource)
assert 'operating_system_users' in df
# Check the nerds.
assert set(r['operating_system'] for _, r in df[
df['operating_system_users'] == 'Nerds'].iterrows()) == {
'Linux', 'Solaris', 'Minix', 'FreeBSD', 'NetBSD'
}
# Check the hipsters.
assert set(r['operating_system']
for _, r in df[df['operating_system_users'] ==
'Hipsters'].iterrows()) == {'MacOS'}
# Check normal users.
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: [2, 3], 2: [1, 4], 99: [32766, 32767]}
cat_names = {1: 'Bilingual', 2: 'Not Bilingual', 99: 'Unknown'}
new_var = dataset.combine_categorical('speak_spanish',
map=cat_map,
categories=cat_names,
name='Bilingual Person',
alias='bilingual',
missing=[99])
assert isinstance(new_var, Variable)
assert new_var.type == 'categorical'
df = pandaslib.dataframe(dataset.resource)
assert 'bilingual' in df
# Check the data in the recoded variable.
bilingual_ids = set(row[0] for row in ROWS
if row[0] != 'identity' and row[4] in (2, 3))
assert set(
int(r['identity']) for _, r in df[
df['bilingual'] == 'Bilingual'].iterrows()) == bilingual_ids
non_bilingual_ids = set(row[0] for row in ROWS
if row[0] != 'identity' and row[4] in (1, 4))
assert set(
int(r['identity'])
for _, r in df[df['bilingual'] ==
'Not Bilingual'].iterrows()) == non_bilingual_ids
bilingual_null_ids = set(
row[0] for row in ROWS
if row[0] != 'identity' and row[4] in (32766, 32767))
assert set(
int(r['identity']) for _, r in df[
df['bilingual'].isnull()].iterrows()) == bilingual_null_ids
# On a 'categorical_array' variable.
cat_map = {1: [1, 2], 2: [3, 4], 99: [32766, 32767]}
cat_names = {
1: 'Interested',
2: 'Not interested',
99: 'Unknown',
}
new_var = dataset.combine_categorical('hobbies',
map=cat_map,
categories=cat_names,
name='Hobbies (recoded)',
alias='hobbies_recoded',
missing=[99])
assert isinstance(new_var, Variable)
assert new_var.type == 'categorical_array'
df = pandaslib.dataframe(dataset.resource)
assert 'hobbies_recoded' in df
# Check the data in the recoded variable.
for _, row in df.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.
cat_map = {1: [1, 2], 2: [97], 3: [98, 99]}
cat_names = {
1: 'music_recoded_1',
2: 'music_recoded_2',
3: 'music_recoded_3'
}
new_var = dataset.combine_multiple_response('music',
map=cat_map,
categories=cat_names,
name='Music (alt)',
alias='music_recoded')
assert isinstance(new_var, Variable)
assert new_var.type == 'multiple_response'
df = pandaslib.dataframe(dataset.resource)
assert 'music_recoded' in df
# TODO: Test the data in the recoded variable. Unsure of its meaning.
finally:
dataset.resource.delete()
This example shows a more complex example using different recodes and combines
to create new variables.
"""
from examples import NEWS_DATASET, NEWS_DATASET_ROWS, mr_in
from getpass import getpass
from scrunch import connect
from scrunch.datasets import create_dataset
HOST = 'https://alpha.crunch.io'
username = raw_input("Enter email: ")
password = getpass("Enter password for %s: " % username)
site = connect(username, password, site_url='%s/api/' % HOST)
# Create a dataset for usage
dataset = create_dataset("Recodes example", NEWS_DATASET)
print("Dataset %s created" % dataset.id)
# Add data rows
total = dataset.stream_rows(NEWS_DATASET_ROWS)
dataset.push_rows(total)
# Recode a new single response variable
agerange = dataset.create_categorical([{
'id': 1,
'name': 'Underage',
'case': 'age < 18'
}, {