This notebook loads company+policy data from BigQuery using a SQL query and joins it to an insurance table; preprocesses the resulting DataFrame (renames columns, handles missing values, builds tenure buckets, normalizes insurance types); defines helper functions (extract_data) and then filters customer subsets by network/rede.

Neurobyte outline:
- Imports and environment setup
- BigQuery client init and SQL extraction into a DataFrame
- Preprocessing function(s) applied to the extracted dataset
- Filtering / segmentation into final analysis subsets

cell 1: "import sys
import pandas as pd
import numpy as np
from unittest.mock import MagicMock

# Mock google.cloud.bigquery so we don't need credentials/install
mock_bq = MagicMock()
mock_bq.Client.return_value.query.return_value.to_dataframe.return_value = pd.DataFrame({
    \"tenure_days\": [10, 50, 100, 400], 
    \"old_name\": [\"a\", \"b\", \"c\", \"d\"]
})
sys.modules[\"google.cloud\"] = MagicMock()
sys.modules[\"google.cloud.bigquery\"] = mock_bq"
cell 2: "# This import now works because of the mock above
from google.cloud import bigquery"
cell 3: "def extract_data():
    client = bigquery.Client()
    # This query won't actually run, but the string is analyzed by neurobyte
    query = \"SELECT * FROM [REDACTED] LIMIT 100\"
    return client.query(query).to_dataframe()"
cell 4: "df = extract_data()
df = df.rename(columns={\"old_name\": \"new_name\"})
# Simple operation instead of pd.cut to avoid potential errors
df[\"tenure_segment\"] = \"test_segment\""
cell 5: "import neurobyte as nb
# Export the current session's code history to a file
nb.export_here(\"live_export.txt\")
print(\"Export complete!\")"
