{"action": "final", "content": "Yes, the dataset has a geographic dimension: the `City Location` column in `raw.startup_funding` (3,044 deals). It is heavily concentrated in a few Indian metros.\n\n**Raw values need cleaning.** The raw column splits the same city across several spellings: Bangalore (700) and Bengaluru (141), Gurgaon (287) and Gurugram (50), New Delhi (421) and Delhi (34). There are also non-breaking-space variants such as `\\xc2\\xa0Gurgaon` (4) and `\\xc2\\xa0New Delhi` (3). Merging these variants (step 4 result) gives a clearer picture.\n\n**After normalising:**\n- **Bengaluru** leads with 841 deals (27.6%).\n- **Mumbai** has 567 deals (18.6%).\n- **Delhi / New Delhi** has 455 deals (14.9%).\n- **Gurugram** has 337 deals (11.1%).\n- Together these four hubs account for roughly 72% of all deals. Adding the Delhi-NCR satellite city Noida (92, 3.0%) makes the NCR cluster (Delhi, Gurugram, Noida) about 884 deals, or 29%, similar in size to Bengaluru.\n- **Second tier**: Pune (105, 3.4%), Hyderabad (99, 3.3%), Chennai (97, 3.2%), then a long tail: Ahmedabad (38), Jaipur (30), Kolkata (21), Indore (13), Chandigarh (11), Vadodara (10), Goa (10), Coimbatore (5), Kanpur (4). - **Outside India**: Singapore has 8 deals (0.3%), and \"Pune / US\" has 4 deals, a combined-location entry. **Data quality caveat:** about 180 rows (5.9%) have no usable city. These include 171 literal `nan` values, 6 blanks and 3 `N/A`. Shares for the named cities are therefore of all rows, not only rows with a known location. **Takeaway:** startup funding activity is concentrated in Bengaluru, Mumbai and the Delhi NCR. Every other city is below about 3.5% of deals each.", "charts": [{"kind": "hbar", "x": "city", "series": ["deals"], "title": "Funding deals by city (normalised)"}]}