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csv_metadata_quality: Move scoped imports to global

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According to PEP8 we should avoid scoped imports unless you have a
good reason. Here there are two cases where we do (issn and isbn),
but I will move the others to the global scope.
This commit is contained in:
Alan Orth 2020-10-06 17:11:39 +03:00
parent 826509ddcf
commit 7cfd4c0b59
Signed by: alanorth
GPG Key ID: 0FB860CC9C45B1B9
2 changed files with 8 additions and 10 deletions
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12
csv_metadata_quality/check.py
View File

@ -1,4 +1,9 @@
from datetime import datetime, timedelta
import pandas as pd import pandas as pd
import requests
import requests_cache
from pycountry import languages
def issn(field): def issn(field):
@ -85,7 +90,6 @@ def date(field, field_name):
Prints the date if invalid. Prints the date if invalid.
""" """
from datetime import datetime
if pd.isna(field): if pd.isna(field):
print(f"Missing date ({field_name}).") print(f"Missing date ({field_name}).")
@ -170,8 +174,6 @@ def language(field):
Prints the value if it is invalid. Prints the value if it is invalid.
""" """
from pycountry import languages
# Skip fields with missing values # Skip fields with missing values
if pd.isna(field): if pd.isna(field):
return return
@ -213,10 +215,6 @@ def agrovoc(field, field_name):
Prints a warning if the value is invalid. Prints a warning if the value is invalid.
""" """
from datetime import timedelta
import requests
import requests_cache
# Skip fields with missing values # Skip fields with missing values
if pd.isna(field): if pd.isna(field):
return return

6
csv_metadata_quality/fix.py
View File

@ -1,7 +1,10 @@
import re import re
from unicodedata import normalize
import pandas as pd import pandas as pd
from csv_metadata_quality.util import is_nfc
def whitespace(field, field_name): def whitespace(field, field_name):
"""Fix whitespace issues. """Fix whitespace issues.
@ -212,9 +215,6 @@ def normalize_unicode(field, field_name):
Return normalized string. Return normalized string.
""" """
from csv_metadata_quality.util import is_nfc
from unicodedata import normalize
# Skip fields with missing values # Skip fields with missing values
if pd.isna(field): if pd.isna(field):
return return