PHPMem v2.0.1
Version
1.6.45
Uptime
16 days 19 hours 32 minutes 38 seconds
Memory
Total
512MB
Used
12,72MB (2.48%)
Free
499,28MB
Keys
Current
14 060
Total (since start)
40 994
Evictions
0
Reclaimed
760
Expired Unfetched
0
Evicted Unfetched
0
Connections
Current
14 / 1 024 max
Total
191 627
Rejected
0
llm:654f9c558ded0cad5f1ebd6be548303ec0047295e792e8ff529faceed444dbe9
Edit
Yes, the dataset has a geographic dimension in the `Location` column, present in all five monthly tables (January–May 2022). The 446 funding rounds are heavily concentrated in a few Indian metros, with a long tail of single-row locations.
**Concentration**
- Bangalore is the largest location with 124 rows (27.8%), followed by Mumbai with 62 (13.9%), Gurgaon with 48 (10.8%), New Delhi with 31 (7.0%) and Chennai with 27 (6.1%).
- Those five locations together account for 292 of 446 rows, about 65%.
- Next come Noida (15), Hyderabad (14), Pune (12) and Bengaluru (10). Each of the remaining locations has 6 rows or fewer.
- Most of the long tail is single-row locations. It includes overseas places such as San Francisco (6 rows), Delaware, Newyork, Tokyo, London, Amsterdam, Santa Clara and Singapore.
**Data quality issues that affect the picture**
- **Spelling variants split the counts.** Bangalore (124), Bengaluru (10) and Banglore (6) are the same city. Combined, that is 140 rows, about 31% of the data, so Bangalore is understated if you take the labels at face value.
- **Other variants.** "New delhi" (2) duplicates "New Delhi" (31), "Mumba" (1) duplicates "Mumbai" (62), and "Ahemdabad" (1) duplicates "Ahmedabad" (5).
- **Blank locations.** 20 rows (4.5%) have no location.
- **Misaligned values.** The long tail contains entries that are not places, such as "Healthcare,Wellness" and "Retail". These are probably misaligned rows, mostly from the May table, which the dataset notes flag as having misaligned or blank rows.
- **Mixed granularity.** Some values are cities, some are states ("Haryana", "Delaware", "Maryland"), and some are neighbourhoods or regions ("Kormangala", "Small Towns, Andhra"). Rolling up to city or state would need cleaning first.
Cleaning the labels would make the concentration in Bangalore, Mumbai and the Delhi NCR (Gurgaon, New Delhi, Noida, Faridabad) even stronger than shown above.
The query results above show these counts. The first table is a clean top-25 by row count.
{"action":"final","content":"placeholder"}
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Correction: please disregard the stray block above; the answer is the text before it.