Fix #70: stop calling logging.basicConfig() at import time

gtfparse called logging.basicConfig(level=logging.INFO) at module import
in three modules (read_gtf, attribute_parsing, create_missing_features).
As a library this is an anti-pattern: it configures the root logger on
behalf of the importing application, adding a handler and forcing the
INFO level onto everyone's root logger — hijacking the host app's logging.

Remove the basicConfig() calls and route the remaining log statements
through the module-level `logger` (logging.getLogger(__name__)) instead
of the root-logger convenience functions logging.info(...), which would
otherwise implicitly re-trigger basicConfig().

Configuring handlers/levels is now left entirely to the application, per
the standard library logging guidance for libraries.

Claude-Session: https://claude.ai/code/session_014fNxSBm5zvdsDNwN3nhmpS

Author: iskandr

gtfparse/attribute_parsing.py

@@ -14,7 +14,6 @@
 from collections import OrderedDict
 from sys import intern
 
-logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
@@ -103,5 +102,5 @@ def expand_attribute_strings(attribute_strings, quote_char="'", missing_value=""
                 extra_columns[column_name] = column
                 column_order.append(column_name)
 
-    logging.info("Extracted GTF attributes: %s" % column_order)
+    logger.info("Extracted GTF attributes: %s" % column_order)
     return OrderedDict((column_name, extra_columns[column_name]) for column_name in column_order)

gtfparse/create_missing_features.py

@@ -15,7 +15,6 @@
 
 import pandas as pd
 
-logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
@@ -58,9 +57,9 @@ def create_missing_features(dataframe, unique_keys={}, extra_columns={}, missing
 
     for feature_name, groupby_key in unique_keys.items():
         if feature_name in existing_features:
-            logging.info("Feature '%s' already exists in GTF data" % feature_name)
+            logger.info("Feature '%s' already exists in GTF data" % feature_name)
             continue
-        logging.info("Creating rows for missing feature '%s'" % feature_name)
+        logger.info("Creating rows for missing feature '%s'" % feature_name)
 
         # don't include rows where the groupby key was missing
         missing = pd.Series([x is None or x == "" for x in dataframe[groupby_key]])

gtfparse/read_gtf.py

@@ -19,7 +19,6 @@
 from .attribute_parsing import expand_attribute_strings
 from .parsing_error import ParsingError
 
-logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
@@ -406,10 +405,10 @@ def wrapped_fn(x):
             # the 2nd column is the transcript_biotype (otherwise, it's the
             # gene_biotype)
             if "gene_biotype" not in column_names:
-                logging.info("Using column 'source' to replace missing 'gene_biotype'")
+                logger.info("Using column 'source' to replace missing 'gene_biotype'")
                 result_df["gene_biotype"] = result_df["source"]
             if "transcript_biotype" not in column_names:
-                logging.info("Using column 'source' to replace missing 'transcript_biotype'")
+                logger.info("Using column 'source' to replace missing 'transcript_biotype'")
                 result_df["transcript_biotype"] = result_df["source"]
 
     if usecols is not None: