Merge pull request 'analyse' (#1) from analyse into main

Reviewed-on: #1

.gitignore

@@ -5,3 +5,4 @@
 /dns-backup-tool.iml
 /venv/
 /.idea/
+/*.db

extract_to_database.py

@@ -0,0 +1,55 @@
+import pandas as pd
+from sqlalchemy import create_engine
+
+# %% Step 1: Load the jsonl dataset in a pandas DataFrame
+results = pd.read_json('results.jsonl', lines=True)
+
+# %% Step 2: Convert the parent column to a DataFrame
+parent_df = results['parent'].apply(pd.Series)
+
+# %% Step 3: Explode the nested array in the ns column into a new DataFrame
+ns_df_ = pd.DataFrame({'ns': parent_df['ns']}).explode('ns').dropna()['ns'].apply(pd.Series)
+
+# %% Step 4: Extract the IPv4 addresses from the nested dictionaries
+ns_df_['ipv4_'] = ns_df_['ipv4'].fillna('').apply(lambda x: [a['ip'] for a in x])
+del ns_df_['ipv4']
+
+# %% Step 5: Extract the IPv6 addresses from the nested dictionaries
+ns_df_['ipv6_'] = ns_df_['ipv6'].fillna('').apply(lambda x: [a['ip'] for a in x])
+del ns_df_['ipv6']
+
+# %% Extract values from results column
+results_df = results['results'].apply(lambda x: x['DNS_LOCAL']).apply(pd.Series)
+
+# %% Prepare the final DataFrames
+
+base_df = results.drop(columns=['parent', 'results']).copy()
+ns_df = ns_df_.explode(['ipv4_', 'ipv6_']).copy()
+mail_df = results_df['MAIL'].explode().dropna().apply(pd.Series).add_prefix('MAIL_')
+web4_df = results_df['WEB4'].explode().dropna().apply(pd.Series).add_prefix('WEB4_')
+web4www_df = results_df['WEB4_www'].explode().dropna().apply(pd.Series).add_prefix('WEB4_www_')
+web6_df = results_df['WEB6'].explode().dropna().apply(pd.Series).add_prefix('WEB6_')
+web6www_df = results_df['WEB6_www'].explode().dropna().apply(pd.Series).add_prefix('WEB6_www_')
+txt_df = results_df['TXT'].explode().dropna().apply(pd.Series).add_prefix('TXT_')
+
+# %% Drop GeoIP columns
+web4_df = web4_df.drop(columns=['WEB4_geoip'])
+web4www_df = web4www_df.drop(columns=['WEB4_www_geoip'])
+web6_df = web6_df.drop(columns=['WEB6_geoip'])
+web6www_df = web6www_df.drop(columns=['WEB6_www_geoip'])
+
+# %% Combine all DataFrames in a SQLite database
+
+date_today = pd.to_datetime('today').strftime('%Y-%m-%d')
+engine = create_engine(f'sqlite:///dns_results_{date_today}.db')
+
+base_df.to_sql('base_df', engine, if_exists='replace', index=True)
+ns_df.to_sql('ns_df', engine, if_exists='replace', index=True)
+mail_df.to_sql('mail_df', engine, if_exists='replace', index=True)
+web4_df.to_sql('web4_df', engine, if_exists='replace', index=True)
+web4www_df.to_sql('web4www_df', engine, if_exists='replace', index=True)
+web6_df.to_sql('web6_df', engine, if_exists='replace', index=True)
+web6www_df.to_sql('web6www_df', engine, if_exists='replace', index=True)
+txt_df.to_sql('txt_df', engine, if_exists='replace', index=True)
+
+print('Data saved to sqlite database.')