JSON File Parsing

json-file-parsing

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.

Summary

Parse and extract structured fields from JSON files distributed across a directory tree, aggregating results into a tabular format. This skill is essential when source data is curated in JSON format with nested schema (like MIBiG cluster annotations) and you need to index or summarize a particular field across all records.

When to use

When you have JSON-formatted curation data organized in a directory structure (e.g., the MIBiG data directory) and need to extract a specific nested field (e.g., cluster.status) across all entries to build an index, summary table, or quality report.

When NOT to use

• JSON files are already pre-indexed or aggregated into a single database or API endpoint — use direct database/API query instead.
• Target field is deeply nested or conditional on complex schema variants — may require custom schema mapping rather than simple dot-notation extraction.
• File count is extremely large (>1M files) and directory I/O becomes a bottleneck — consider batch processing or stream-based parsing.

Inputs

• Directory tree containing JSON files (e.g., github.com/mibig-secmet/mibig-json data directory)
• Target nested field path as a dot-notation string (e.g., 'cluster.status')

Outputs

• Tabular data file (CSV) with columns: entry_id, extracted_field_value
• Aggregated summary indexed by entry and field value

How to apply

Recursively scan the target directory for all JSON files. For each file, parse the JSON structure and navigate to the target nested field using dot notation (e.g., cluster.status). Extract the field value along with the entry identifier. Aggregate all extracted records into a structured table (with columns for entry_id and the target field). Export the aggregated table to CSV or other tabular format. Validate the output by spot-checking a sample of entries against the original JSON files to confirm field extraction accuracy and absence of parsing errors.

Related tools

• Python json module (Parse JSON structure and navigate nested fields)
• pandas (Aggregate parsed records into tabular data and export to CSV)

Examples

import json, os, pandas as pd; records = []; [records.append({'entry_id': f.split('.')[0], 'cluster_status': json.load(open(os.path.join('data', f))).get('cluster', {}).get('status')}) for f in os.listdir('data') if f.endswith('.json')]; pd.DataFrame(records).to_csv('entry_status_index.csv', index=False)

Evaluation signals

• Output CSV contains no blank rows and row count matches total JSON files scanned
• Spot-check: randomly select 5–10 entries from output and verify extracted field values match the original JSON files
• No parsing errors or exceptions logged during directory traversal and JSON deserialization
• All expected entry identifiers are present in the output; no entries silently dropped
• CSV schema matches specification: entry_id column is unique and non-null; extracted_field_value column contains valid values for the field type (e.g., status is a recognized categorical value)

Limitations

• Assumes well-formed JSON and consistent nested schema across all files; malformed or schema-variant entries may cause parsing errors or silent omissions.
• Dot-notation field extraction is brittle if schema evolves or if field is conditionally present; requires manual schema validation before applying this skill at scale.
• No changelog or version tracking in the MIBiG repository was found, so field semantics or structure may change without notice between data pulls.

Evidence

• [readme] The current datasets for [MIBiG] live in the data directory, entry status is now tracked via the cluster.status field.: "The current datasets for [MIBiG] live in the data directory, entry status is now tracked via the cluster.status field."
• [other] MIBiG curation data is maintained in JSON format with entry status tracked through the cluster.status field within each annotation record.: "MIBiG curation data is maintained in JSON format with entry status tracked through the cluster.status field"
• [other] Recursively scan the data directory for all JSON files. Parse each JSON file and extract the cluster.status field value for each entry. Aggregate entries into a table grouped by status.: "Recursively scan the data directory for all JSON files. 2. Parse each JSON file and extract the cluster.status field value for each entry. 3. Aggregate entries into a table"
• [other] Python (json module for parsing JSON files), pandas or equivalent tabular data library: "Python (json module for parsing JSON files), pandas or equivalent tabular data library"