datap-rs-client
Python client for a DataPress dataset
server, backed by the native Rust client (datapress-client) via PyO3.
Requests are plain Python dicts; responses come back as dicts. Structured
queries can optionally be decoded into a pyarrow.Table.
Install
pip install datap-rs-client # core
pip install datap-rs-client[arrow] # + pyarrow for query_arrow()
This project standardises on
uv:uv pip install datap-rs-client[arrow].
Usage
from datap_rs_client import DataPressClient
client = DataPressClient("http://127.0.0.1:8000")
client.datasets()
# ['accidents']
client.count("accidents", predicates=[{"col": "Severity", "op": "gte", "val": 3}])
# 123456
rows = client.query(
"accidents",
columns=["State", "Severity"],
predicates=[{"col": "Severity", "op": "gte", "val": 3}],
page_size=1000,
)
rows["page"], len(rows["data"])
# (1, 1000)
# Arrow (requires the [arrow] extra)
table = client.query_arrow("accidents", columns=["State", "Severity"], page_size=100_000)
table.num_rows
Authentication
client = DataPressClient(
"http://127.0.0.1:8000",
bearer_token="…", # servers with auth enabled
admin_token="…", # required by reload()
)
SQL
client.sql("SELECT State, COUNT(*) AS n FROM accidents GROUP BY State", max_rows=100)
DataFrames
query_arrow(...) returns a pyarrow.Table (install the [arrow] extra).
Arrow is the zero-copy interchange format for every popular dataframe
library, so a single query feeds them all:
from datap_rs_client import DataPressClient
client = DataPressClient("http://127.0.0.1:8000")
table = client.query_arrow(
"accidents",
columns=["State", "Severity"],
predicates=[{"col": "Severity", "op": "gte", "val": 3}],
page_size=1_000_000,
)
Polars
import polars as pl
# Zero-copy from the Arrow table.
df = pl.from_arrow(table)
df.group_by("State").len().sort("len", descending=True)
pandas
import pandas as pd # noqa: F401 (pyarrow drives the conversion)
# Arrow-backed dtypes (recommended) …
df = table.to_pandas(types_mapper=pd.ArrowDtype)
# … or classic NumPy-backed dtypes:
df = table.to_pandas()
df.groupby("State")["Severity"].mean()
DuckDB
import duckdb
# DuckDB queries the Arrow table in place — no copy, no temp files.
duckdb.sql("SELECT State, COUNT(*) AS n FROM table GROUP BY State ORDER BY n DESC")
PySpark
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
# Spark has no direct Arrow-table constructor; go via pandas (Arrow-accelerated).
sdf = spark.createDataFrame(table.to_pandas())
sdf.groupBy("State").count().orderBy("count", ascending=False).show()
DataFusion
from datafusion import SessionContext
ctx = SessionContext()
df = ctx.from_arrow(table)
df.aggregate([df["State"]], [df["Severity"].mean()])
PyArrow / Arrow ecosystem
# The result is already a pyarrow.Table.
table.column("Severity").combine_chunks()
table.to_batches() # -> list[pyarrow.RecordBatch]
table.to_pydict() # -> dict[str, list]
Anything implementing the Arrow C Data Interface (Polars, DuckDB, DataFusion, Vaex, cuDF, …) can consume the table directly. For libraries without an Arrow constructor,
table.to_pandas()is the universal fallback.
Relationship to datap-rs
datap-rs ships the server; datap-rs-client is a standalone client.
They are independent packages — install whichever you need.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for datap-rs-client 0.8.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| datap_rs_client-0.8.6.tar.gz | 75.5 kB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| datap_rs_client-0.8.6-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| datap_rs_client-0.8.6-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| datap_rs_client-0.8.6-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| datap_rs_client-0.8.6-cp39-abi3-macosx_11_0_arm64.whl | CPython 3.9 | abi3 | macOS 11.0+ ARM64 | Details |
Total release size: 7.8 MB
Release files / datap_rs_client-0.8.6.tar.gz
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| Size | 75.5 kB |
| Tags | Source |
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| Size | 1.9 MB |
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| Download URL | datap_rs_client-0.8.6-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl |
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| Size | 1.9 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ ARM64 abi3 |
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| Download URL | datap_rs_client-0.8.6-cp39-abi3-macosx_11_0_arm64.whl |
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