Yes. The data splits naturally on four dimensions, but each needs its labels cleaned first, because the same category is spelled several ways. **1. Funding stage (investment type)** - Two types dominate: Private Equity (1,356 deals) and Seed Funding (1,355). - A long tail of seed/angel variants (e.g. "Seed/ Angel Funding", "Seed Round", "Seed") adds about 179 deals once merged. Variants such as "Seed\nFunding" carry stray whitespace or escape characters. - The later stages are small: Debt Funding (25), Series A (24), B (20), C (14) and D (12). - A clean grouping would be Seed/Angel, Private Equity, Venture Series A–D, and Debt. **2. Geography (city)** - Four clusters account for most deals: Bangalore, Mumbai, New Delhi and Gurgaon. - Bangalore (700) and Bengaluru (141) are the same city, which gives about 841 deals combined. - Gurgaon and Gurugram merge the same way, to about 337 (the card's Gurgaon count of 287 plus 50, plus a few with a stray non-breaking space). - New Delhi (421) and Delhi (34) also overlap. - A second tier is Pune, Hyderabad, Chennai, Noida and Ahmedabad. - Some values are not usable cities: "nan" (171), blank, "N/A", and multi-city entries such as "Pune / US" or "Bangalore / SFO". These should be a separate "unknown / multi-location" group. **3. Industry vertical** - Consumer Internet (941) and Technology (478) are the largest groups. - eCommerce is split across "eCommerce" (186), "ECommerce" (61) and "E-Commerce" (29), about 276 combined, which would make it the third-largest group. - Healthcare (70), Finance (62), Logistics (32), Education (24) and Food & Beverage (23) follow. - The card lists about 903 distinct verticals, so a top-N grouping with an "Other" bucket works better than using the raw values. The 171 "nan" rows form their own unclassified group. **4. Time (year from the date)** - 2015 has 935 deals, 2016 has 993, 2017 has 687, 2018 has 310, 2019 has 111 and 2020 has 7. - Years work as natural cohorts. The sharp fall after 2016 could reflect a real slowdown, thinner reporting in later years, or both. The data alone does not say which. **Practical segmentation:** group by stage × top-tier city × consolidated vertical, with year as a trend axis. The same 171 rows are "nan" in both city and vertical, so they likely form one under-documented group that should be set aside rather than forced into a category. **Caveat:** the "with_amount" count equals the deal count for every investment type. That suggests the amount column is filled with text placeholders rather than true blanks, so check it before segmenting by deal size. I did not examine the amount values here. The figures come from the three grouped-count queries on `raw.startup_funding` (investment type, city and vertical, and year).