Data Transformation Task
Budget: $30 – $250 USD
I need help to transform a large options pricing data file (15GB) into a new format with specific column pairings.
Requirements:
- Pair specific columns (to be described)
- Output in CSV format
Ideal Skills:
- Experience with large datasets
- Proficiency in data manipulation tools (e.g., Python, Pandas)
- Attention to detail and accuracy
The data is organized like this:
[QUOTE_UNIXTIME], [QUOTE_READTIME], [QUOTE_DATE], [QUOTE_TIME_HOURS], [UNDERLYING_LAST], [EXPIRE_DATE], [EXPIRE_UNIX], [DTE], [C_DELTA], [C_GAMMA], [C_VEGA], [C_THETA], [C_RHO], [C_IV], [C_VOLUME], [C_LAST], [C_SIZE], [C_BID], [C_ASK], [STRIKE], [P_BID], [P_ASK], [P_SIZE], [P_LAST], [P_DELTA], [P_GAMMA], [P_VEGA], [P_THETA], [P_RHO], [P_IV], [P_VOLUME], [STRIKE_DISTANCE], [STRIKE_DISTANCE_PCT]
1662039010, 2022-09-01 09:30, 2022-09-01, 9.500000, 392.930000, 2022-09-02, 1662148800, 1.270000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, , 0.000000, 203.200000, 2 x 10, 192.270000, 193.400000, 200.000000, 0.010000, 0.010000, 0 x 6162, 0.010000, 0.000000, 0.000050, 0.000680, -0.010790, -0.000090, 2.823160, 0.000000, 192.900000, 0.491000
1662039010, 2022-09-01 09:30, 2022-09-01, 9.500000, 392.930000, 2022-09-02, 1662148800, 1.270000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, , 0.000000, 198.220000, 20 x 20, 187.480000, 188.210000, 205.000000, 0.010000, 0.010000, 0 x 3920, 0.010000, -0.000400, 0.000050, 0.000900, -0.010280, 0.000000, 2.718560, 0.000000, 187.900000, 0.478000
1662039010, 2022-09-01 09:30, 2022-09-01, 9.500000, 392.930000, 2022-09-02, 1662148800, 1.270000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, , , 0.000000, 20 x 20, 182.490000, 183.210000, 210.000000, 0.010000, 0.010000, 0 x 6098, 0.020000, -0.000020, 0.000030, 0.000790, -0.010220, -0.000440, 2.619070, 0.000000, 182.900000, 0.466000
1662039010, 2022-09-01 09:30, 2022-09-01, 9.500000, 392.930000, 2022-09-02, 1662148800, 1.270000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, , , 0.000000, 20 x 20, 177.490000, 178.210000, 215.000000, 0.010000, 0.010000, 0 x 4438, 0.010000, -0.000080, -0.000010, 0.000550, -0.009940, -0.000350, 2.525050, 0.000000, 177.900000, 0.453000
1662039010, 2022-09-01 09:30, 2022-09-01, 9.500000, 392.930000, 2022-09-02, 1662148800, 1.270000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, , , 0.000000, 20 x 20, 172.490000, 173.210000, 220.000000, 0.010000, 0.010000, 0 x 5232, 0.010000, -0.001000, 0.000060, 0.000240, -0.009990, 0.000000, 2.435170, 0.000000, 172.900000, 0.440000
Requirements:
- Pair specific columns (to be described)
- Output in CSV format
Ideal Skills:
- Experience with large datasets
- Proficiency in data manipulation tools (e.g., Python, Pandas)
- Attention to detail and accuracy
The data is organized like this:
[QUOTE_UNIXTIME], [QUOTE_READTIME], [QUOTE_DATE], [QUOTE_TIME_HOURS], [UNDERLYING_LAST], [EXPIRE_DATE], [EXPIRE_UNIX], [DTE], [C_DELTA], [C_GAMMA], [C_VEGA], [C_THETA], [C_RHO], [C_IV], [C_VOLUME], [C_LAST], [C_SIZE], [C_BID], [C_ASK], [STRIKE], [P_BID], [P_ASK], [P_SIZE], [P_LAST], [P_DELTA], [P_GAMMA], [P_VEGA], [P_THETA], [P_RHO], [P_IV], [P_VOLUME], [STRIKE_DISTANCE], [STRIKE_DISTANCE_PCT]
1662039010, 2022-09-01 09:30, 2022-09-01, 9.500000, 392.930000, 2022-09-02, 1662148800, 1.270000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, , 0.000000, 203.200000, 2 x 10, 192.270000, 193.400000, 200.000000, 0.010000, 0.010000, 0 x 6162, 0.010000, 0.000000, 0.000050, 0.000680, -0.010790, -0.000090, 2.823160, 0.000000, 192.900000, 0.491000
1662039010, 2022-09-01 09:30, 2022-09-01, 9.500000, 392.930000, 2022-09-02, 1662148800, 1.270000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, , 0.000000, 198.220000, 20 x 20, 187.480000, 188.210000, 205.000000, 0.010000, 0.010000, 0 x 3920, 0.010000, -0.000400, 0.000050, 0.000900, -0.010280, 0.000000, 2.718560, 0.000000, 187.900000, 0.478000
1662039010, 2022-09-01 09:30, 2022-09-01, 9.500000, 392.930000, 2022-09-02, 1662148800, 1.270000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, , , 0.000000, 20 x 20, 182.490000, 183.210000, 210.000000, 0.010000, 0.010000, 0 x 6098, 0.020000, -0.000020, 0.000030, 0.000790, -0.010220, -0.000440, 2.619070, 0.000000, 182.900000, 0.466000
1662039010, 2022-09-01 09:30, 2022-09-01, 9.500000, 392.930000, 2022-09-02, 1662148800, 1.270000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, , , 0.000000, 20 x 20, 177.490000, 178.210000, 215.000000, 0.010000, 0.010000, 0 x 4438, 0.010000, -0.000080, -0.000010, 0.000550, -0.009940, -0.000350, 2.525050, 0.000000, 177.900000, 0.453000
1662039010, 2022-09-01 09:30, 2022-09-01, 9.500000, 392.930000, 2022-09-02, 1662148800, 1.270000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, , , 0.000000, 20 x 20, 172.490000, 173.210000, 220.000000, 0.010000, 0.010000, 0 x 5232, 0.010000, -0.001000, 0.000060, 0.000240, -0.009990, 0.000000, 2.435170, 0.000000, 172.900000, 0.440000