Querying data using qSQL with PyKX
This page explains how to query your data with PyKX using qSQL.
Querying tables using qSQL
Create a sample table:
Python
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>>> import pykx as kx
>>> trades = kx.Table(data={
'sym': kx.random.random(100, ['AAPL', 'GOOG', 'MSFT']),
'date': kx.random.random(100, kx.q('2022.01.01') + [0,1,2]),
'price': kx.random.random(100, 1000.0)
})
>>> # Assign the table to a named object in q memory to allow name based query later
>>> kx.q['trades'] = tradesQuery a table, by name, using qSQL:
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>>> kx.q('select from trades')
pykx.Table(pykx.q('
sym date price
------------------------
GOOG 2022.01.02 805.0147
AAPL 2022.01.03 847.6275
AAPL 2022.01.03 329.8159
GOOG 2022.01.02 982.5155
MSFT 2022.01.02 724.9456
..
'))Query a pykx.Table passing it as an argument:
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>>> kx.q('{select from x}', trades)
pykx.Table(pykx.q('
sym date price
------------------------
GOOG 2022.01.02 805.0147
AAPL 2022.01.03 847.6275
AAPL 2022.01.03 329.8159
GOOG 2022.01.02 982.5155
MSFT 2022.01.02 724.9456
..
'))Passing multiple arguments:
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>>> from datetime import date
>>> kx.q('{[x;y] select from trades where date = x, price < y}', date(2022, 1, 2), 500.0)
pykx.Table(pykx.q('
sym date price
------------------------
GOOG 2022.01.02 214.9847
AAPL 2022.01.02 126.2957
AAPL 2022.01.02 184.4151
AAPL 2022.01.02 217.0378
GOOG 2022.01.02 423.6121
..
'))Next steps
Now that you have learnt how to query your data using q you may be interested in other methods for querying your data:
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If you want to query your data in a more Python-first way follow the guide here.
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If you wish to query your data using SQL, you can follow the introduction to this functionality here.
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To learn how to make your queries more performant following the tips and tricks here.
For some further reading, here are some related topics: