Name | kola JSON |
Version |
0.8.0
JSON |
| download |
home_page | None |
Summary | a Python Polars interface to kdb+/q |
upload_time | 2024-03-31 05:17:11 |
maintainer | None |
docs_url | None |
author | None |
requires_python | >=3.8 |
license | None |
keywords |
q
kdb
polars
dataframe
arrow
|
VCS |
|
bugtrack_url |
|
requirements |
No requirements were recorded.
|
Travis-CI |
No Travis.
|
coveralls test coverage |
No coveralls.
|
# kola
a Python [Polars](https://pola-rs.github.io/polars/) Interface to kdb+/q
## Basic Data Type Map
### Deserialization
#### Atom
| k type | n | size | python type | note |
| ----------- | --- | ---- | ----------- | --------------------------- |
| `boolean` | 1 | 1 | `bool` | |
| `guid` | 2 | 16 | `str` | |
| `byte` | 4 | 1 | `int` | |
| `short` | 5 | 2 | `int` | |
| `int` | 6 | 4 | `int` | |
| `long` | 7 | 8 | `int` | |
| `real` | 8 | 4 | `float` | |
| `float` | 9 | 8 | `float` | |
| `char` | 10 | 1 | `str` | |
| `string` | 10 | 1 | `str` | |
| `symbol` | 11 | \* | `str` | |
| `timestamp` | 12 | 8 | `datetime` | |
| `month` | 13 | 4 | `-` | |
| `date` | 14 | 4 | `date` | 0001.01.01 - 9999.12.31 |
| `datetime` | 15 | 8 | `datetime` | |
| `timespan` | 16 | 8 | `timedelta` | |
| `minute` | 17 | 4 | `time` | 00:00 - 23:59 |
| `second` | 18 | 4 | `time` | 00:00:00 - 23:59:59 |
| `time` | 19 | 4 | `time` | 00:00:00.000 - 23:59:59.999 |
#### Composite Data Type
| k type | n | size | python type |
| ---------------- | --- | ---- | ------------------------ |
| `boolean list` | 1 | 1 | `pl.Boolean` |
| `guid list` | 2 | 16 | `pl.List(pl.Binary(16))` |
| `byte list` | 4 | 1 | `pl.Uint8` |
| `short list` | 5 | 2 | `pl.Int16` |
| `int list` | 6 | 4 | `pl.Int32` |
| `long list` | 7 | 8 | `pl.Int64` |
| `real list` | 8 | 4 | `pl.Float32` |
| `float list` | 9 | 8 | `pl.Float64` |
| `char list` | 10 | 1 | `pl.Utf8` |
| `string list` | 10 | 1 | `pl.Utf8` |
| `symbol list` | 11 | \* | `pl.Categorical` |
| `timestamp list` | 12 | 8 | `pl.Datetime` |
| `month list` | 13 | 4 | `-` |
| `date list` | 14 | 4 | `pl.Date` |
| `datetime list` | 15 | 8 | `pl.Datetime` |
| `timespan list` | 16 | 8 | `pl.Duration` |
| `minute list` | 17 | 4 | `pl.Time` |
| `second list` | 18 | 4 | `pl.Time` |
| `time list` | 19 | 4 | `pl.Time` |
| `table` | 98 | \* | `pl.DataFrame` |
| `dictionary` | 99 | \* | `-` |
| `keyed table` | 99 | \* | `pl.DataFrame` |
> performance is impacted by converting guid to string, deserialize the uuid to 16 fixed binary list, use .hex() to convert binary to string if required
> real/float 0n is mapped to Polars null not NaN
> short/int/long 0Nh/i/j, 0Wh/i/j and -0Wh/i/j are mapped to null
```
df.with_columns([
(pl.col("uuid").apply(lambda u: u.hex()))
])
```
### Serialization
#### Basic Data Type
| python type | k type | note |
| ----------- | ----------- | --------------------------- |
| `bool` | `boolean` | |
| `int` | `long` | |
| `float` | `float` | |
| `str` | `symbol` | |
| `bytes` | `string` | |
| `datetime` | `timestamp` | |
| `date` | `date` | 0001.01.01 - 9999.12.31 |
| `datetime` | `datetime` | |
| `timedelta` | `timespan` | |
| `time` | `time` | 00:00:00.000 - 23:59:59.999 |
#### Dictionary, Series and DataFrame
| python type | k type |
| ------------------------ | --------- |
| `dict` | dict |
| `pl.Boolean` | boolean |
| `pl.List(pl.Binary(16))` | guid |
| `pl.Uint8` | byte |
| `pl.Int16` | short |
| `pl.Int32` | int |
| `pl.Int64` | long |
| `pl.Float32` | real |
| `pl.Float64` | float |
| `pl.Utf8` | char |
| `pl.Categorical` | symbol |
| `pl.Datetime` | timestamp |
| `pl.Date` | date |
| `pl.Datetime` | datetime |
| `pl.Duration` | timespan |
| `pl.Time` | time |
| `pl.DataFrame` | table |
> Limited Support for dictionary as arguments, python `string` as keys and Python `Basic Data Types` and `pl.Series` as values.
### Quick Start
#### Create a Connection
```python
import polars as pl
import kola
q = kola.Q('localhost', 1800)
```
#### Connect(Optional)
Automatically connect when querying q process
```python
q.connect()
```
#### Disconnect
Automatically disconnect if any IO error
```python
q.disconnect()
```
#### String Query
```python
q.sync("select from trade where date=last date")
```
#### Functional Query
For functional query, `kola` supports Python [Basic Data Type](#basic-data-type), `pl.Series`, `pl.DataFrame` and Python Dictionary with string keys and Python [Basic Data Type](#basic-data-type) and `pl.Series` values.
```python
from datetime import date, time
q.sync(
".gw.query",
"table",
{
"date": date(2023, 11, 21),
"syms": pl.Series("", ["sym0", "sym1"], pl.Categorical),
# 09:00
"startTime": time(9),
# 11:30
"endTime": time(11, 30),
},
)
```
#### Send DataFrame
```python
# pl_df is a Polars DataFrame
q.sync("upsert", "table", pl_df)
```
```python
# pd_df is a Pandas DataFrame, use pl.DateFrame to cast Pandas DataFrame
q.sync("upsert", "table", pl.DataFrame(pd_df))
```
#### Async Query
```python
# pl_df is a Polars DataFrame
q.asyn("upsert", "table", pl_df)
```
#### Polars Documentations
Refer to
- [User Guide](https://pola-rs.github.io/polars/user-guide/)
- [API Reference](https://pola-rs.github.io/polars/py-polars/html/reference/index.html)
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"description": "# kola\n\na Python [Polars](https://pola-rs.github.io/polars/) Interface to kdb+/q\n\n## Basic Data Type Map\n\n### Deserialization\n\n#### Atom\n\n| k type | n | size | python type | note |\n| ----------- | --- | ---- | ----------- | --------------------------- |\n| `boolean` | 1 | 1 | `bool` | |\n| `guid` | 2 | 16 | `str` | |\n| `byte` | 4 | 1 | `int` | |\n| `short` | 5 | 2 | `int` | |\n| `int` | 6 | 4 | `int` | |\n| `long` | 7 | 8 | `int` | |\n| `real` | 8 | 4 | `float` | |\n| `float` | 9 | 8 | `float` | |\n| `char` | 10 | 1 | `str` | |\n| `string` | 10 | 1 | `str` | |\n| `symbol` | 11 | \\* | `str` | |\n| `timestamp` | 12 | 8 | `datetime` | |\n| `month` | 13 | 4 | `-` | |\n| `date` | 14 | 4 | `date` | 0001.01.01 - 9999.12.31 |\n| `datetime` | 15 | 8 | `datetime` | |\n| `timespan` | 16 | 8 | `timedelta` | |\n| `minute` | 17 | 4 | `time` | 00:00 - 23:59 |\n| `second` | 18 | 4 | `time` | 00:00:00 - 23:59:59 |\n| `time` | 19 | 4 | `time` | 00:00:00.000 - 23:59:59.999 |\n\n#### Composite Data Type\n\n| k type | n | size | python type |\n| ---------------- | --- | ---- | ------------------------ |\n| `boolean list` | 1 | 1 | `pl.Boolean` |\n| `guid list` | 2 | 16 | `pl.List(pl.Binary(16))` |\n| `byte list` | 4 | 1 | `pl.Uint8` |\n| `short list` | 5 | 2 | `pl.Int16` |\n| `int list` | 6 | 4 | `pl.Int32` |\n| `long list` | 7 | 8 | `pl.Int64` |\n| `real list` | 8 | 4 | `pl.Float32` |\n| `float list` | 9 | 8 | `pl.Float64` |\n| `char list` | 10 | 1 | `pl.Utf8` |\n| `string list` | 10 | 1 | `pl.Utf8` |\n| `symbol list` | 11 | \\* | `pl.Categorical` |\n| `timestamp list` | 12 | 8 | `pl.Datetime` |\n| `month list` | 13 | 4 | `-` |\n| `date list` | 14 | 4 | `pl.Date` |\n| `datetime list` | 15 | 8 | `pl.Datetime` |\n| `timespan list` | 16 | 8 | `pl.Duration` |\n| `minute list` | 17 | 4 | `pl.Time` |\n| `second list` | 18 | 4 | `pl.Time` |\n| `time list` | 19 | 4 | `pl.Time` |\n| `table` | 98 | \\* | `pl.DataFrame` |\n| `dictionary` | 99 | \\* | `-` |\n| `keyed table` | 99 | \\* | `pl.DataFrame` |\n\n> performance is impacted by converting guid to string, deserialize the uuid to 16 fixed binary list, use .hex() to convert binary to string if required\n\n> real/float 0n is mapped to Polars null not NaN\n\n> short/int/long 0Nh/i/j, 0Wh/i/j and -0Wh/i/j are mapped to null\n\n```\ndf.with_columns([\n (pl.col(\"uuid\").apply(lambda u: u.hex()))\n ])\n```\n\n### Serialization\n\n#### Basic Data Type\n\n| python type | k type | note |\n| ----------- | ----------- | --------------------------- |\n| `bool` | `boolean` | |\n| `int` | `long` | |\n| `float` | `float` | |\n| `str` | `symbol` | |\n| `bytes` | `string` | |\n| `datetime` | `timestamp` | |\n| `date` | `date` | 0001.01.01 - 9999.12.31 |\n| `datetime` | `datetime` | |\n| `timedelta` | `timespan` | |\n| `time` | `time` | 00:00:00.000 - 23:59:59.999 |\n\n#### Dictionary, Series and DataFrame\n\n| python type | k type |\n| ------------------------ | --------- |\n| `dict` | dict |\n| `pl.Boolean` | boolean |\n| `pl.List(pl.Binary(16))` | guid |\n| `pl.Uint8` | byte |\n| `pl.Int16` | short |\n| `pl.Int32` | int |\n| `pl.Int64` | long |\n| `pl.Float32` | real |\n| `pl.Float64` | float |\n| `pl.Utf8` | char |\n| `pl.Categorical` | symbol |\n| `pl.Datetime` | timestamp |\n| `pl.Date` | date |\n| `pl.Datetime` | datetime |\n| `pl.Duration` | timespan |\n| `pl.Time` | time |\n| `pl.DataFrame` | table |\n\n> Limited Support for dictionary as arguments, python `string` as keys and Python `Basic Data Types` and `pl.Series` as values.\n\n### Quick Start\n\n#### Create a Connection\n\n```python\nimport polars as pl\nimport kola\nq = kola.Q('localhost', 1800)\n```\n\n#### Connect(Optional)\n\nAutomatically connect when querying q process\n\n```python\nq.connect()\n```\n\n#### Disconnect\n\nAutomatically disconnect if any IO error\n\n```python\nq.disconnect()\n```\n\n#### String Query\n\n```python\nq.sync(\"select from trade where date=last date\")\n```\n\n#### Functional Query\n\nFor functional query, `kola` supports Python [Basic Data Type](#basic-data-type), `pl.Series`, `pl.DataFrame` and Python Dictionary with string keys and Python [Basic Data Type](#basic-data-type) and `pl.Series` values.\n\n```python\nfrom datetime import date, time\n\nq.sync(\n \".gw.query\",\n \"table\",\n {\n \"date\": date(2023, 11, 21),\n \"syms\": pl.Series(\"\", [\"sym0\", \"sym1\"], pl.Categorical),\n # 09:00\n \"startTime\": time(9),\n # 11:30\n \"endTime\": time(11, 30),\n },\n)\n```\n\n#### Send DataFrame\n\n```python\n# pl_df is a Polars DataFrame\nq.sync(\"upsert\", \"table\", pl_df)\n```\n\n```python\n# pd_df is a Pandas DataFrame, use pl.DateFrame to cast Pandas DataFrame\nq.sync(\"upsert\", \"table\", pl.DataFrame(pd_df))\n```\n\n#### Async Query\n\n```python\n# pl_df is a Polars DataFrame\nq.asyn(\"upsert\", \"table\", pl_df)\n```\n\n#### Polars Documentations\n\nRefer to\n\n- [User Guide](https://pola-rs.github.io/polars/user-guide/)\n- [API Reference](https://pola-rs.github.io/polars/py-polars/html/reference/index.html)\n\n",
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