PHPMem v2.0.1
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1.6.45
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16 days 19 hours 36 minutes 13 seconds
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512MB
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llm:ae120614b8943c01b7a6af08fe6202bd415cf90902b3fee461737c3c854e8389
Edit
Yes, the dataset has a geographic dimension: a `Location` column (city or region of the startup's headquarters) in each of the five monthly tables, January to May 2022. I pooled all 446 funding-round rows across the tables. The data is heavily concentrated in a few Indian metros and has messy location values.
**Concentration**
- Bangalore is the largest single location with 124 rows (27.8%). Mumbai has 62 (13.9%), Gurgaon 48 (10.8%), New Delhi 31 (7.0%) and Chennai 27 (6.1%).
- Those five spellings add up to 292 rows, about 65% of the data.
- Noida (15), Hyderabad (14) and Pune (12) follow. After that the distribution is a long tail of locations with 1–6 rows each.
**Spelling variants understate the real concentration**
- Bangalore also appears as "Bengaluru" (10), "Banglore" (6) and "Kormangala" (1, a Bangalore neighbourhood). Together that is about 141 rows, roughly 31.6% of the data.
- New Delhi (31) plus "New delhi" (2) gives 33 rows.
- The Delhi NCR cluster is Gurgaon (48), New Delhi (33), Noida (15) and Faridabad (3), about 99 rows. Haryana (5) is a state-level entry that probably overlaps with it.
- Other variants include "Mumba" and "Ahemdabad" next to Ahmedabad.
**International presence**
- A small share of startups are located abroad: San Francisco (6), Delaware (3), New York (spelled "Newyork", 3), Tokyo (2), London (2), and single rows for places like Singapore, Amsterdam, Chicago, Palo Alto and Israel.
- Together this is a small minority, so the data is overwhelmingly India-focused.
**Data quality**
- 20 rows (4.5%) have a blank location.
- The location column also holds values that clearly belong to other fields, such as "Healthcare,Wellness" and "Retail". This is consistent with the misaligned rows noted for the May table.
- Some entries are truncated or odd ("Vitznau, Luzern,", "Small Towns, Andhra", "Tiruchirappall").
- Location values are free text with no standardisation, so any city-level analysis should first normalise spellings.
The tables above show the rank-ordered counts and percentages. A cleaned Bangalore total or NCR grouping would need an explicit mapping of the variants.