SweatStack Python client library
This is the official Python client library for SweatStack.
Documentation can be found here.
Sports follow OpenSportTaxonomy: sweatstack.Sport is the
OST Sport type. See the OpenSportTaxonomy Python guide
for the full Sport API.
Install
uv add "sweatstack[polars]" # analysis with Polars
uv add "sweatstack[pandas]" # analysis with pandas
uv add "sweatstack[arrow]" duckdb # SQL over your data with DuckDB
uv add sweatstack # models only: FastAPI services, webhook consumers
sweatstack[streamlit] and sweatstack[jupyter] include pandas. Extras combine:
sweatstack[polars,arrow] for Polars frames that DuckDB can query.
Choosing your frame library
Every method that returns a collection takes output=. Time-series endpoints return
"pandas", "polars", "arrow" or "bytes"; list endpoints return "models" (default),
"pandas", "polars" or "arrow". When you don't say, time series come back in the frame
library you installed: Polars, then pandas, then Arrow. Set it once to be explicit.
from datetime import date
from pathlib import Path
import polars as pl
import sweatstack
sweatstack.authenticate()
df = sweatstack.get_activity_data(activity_id) # Polars if installed, else pandas
pf = sweatstack.get_activity_data(activity_id, output="polars") # polars.DataFrame
tb = sweatstack.get_activity_data(activity_id, output="arrow") # pyarrow.Table
sweatstack.set_output("polars") # or Client(output="polars")
season = sweatstack.get_longitudinal_data(sports=["cycling"], start=date(2025, 1, 1))
season.group_by("activity_id").agg(pl.col("power").mean())
acts = sweatstack.get_activities(output="polars") # nested fields as structs
acts.unnest("summary").unnest("power").select("id", "mean", "max")
raw = sweatstack.get_longitudinal_data(sports=["cycling"], start=date(2025, 1, 1), output="bytes")
Path("season.parquet").write_bytes(raw) # then query it with DuckDB
No frame carries an index: timestamp, the mean-max metric value and the dailies date
are ordinary first columns on every backend. pandas frames keep float64 dtypes; Polars and
Arrow keep the compact wire dtypes.
Using DuckDB
DuckDB queries every output directly. Its Python API bridges in-memory frames through
pyarrow, so install sweatstack[arrow] for the first two routes.
| Route | Code | Needs |
|---|---|---|
| Arrow table | tb = sweatstack.get_longitudinal_data(..., output="arrow") then duckdb.sql("select ... from tb") |
sweatstack[arrow] |
| Polars frame | pf = sweatstack.get_longitudinal_data(..., output="polars") then duckdb.sql("select ... from pf") |
sweatstack[polars,arrow] |
| Parquet file | Path("season.parquet").write_bytes(sweatstack.get_longitudinal_data(..., output="bytes")) then duckdb.sql("select ... from 'season.parquet'") |
nothing extra |
import duckdb
import sweatstack
from datetime import date
tb = sweatstack.get_longitudinal_data(sports=["cycling"], start=date(2025, 1, 1), output="arrow")
duckdb.sql("""
select sport, count(distinct activity_id) as rides, round(avg(power)) as avg_power
from tb group by sport order by rides desc
""")
Durations arrive as INTERVAL and timestamps as TIMESTAMP WITH TIME ZONE. With an
[arrow]-only install, output defaults to "arrow", so the output= above is optional.
Upgrading from 0.88 and earlier
Four mechanical changes:
- Install line. pandas is an extra now:
uv add "sweatstack[pandas]"(or[polars], or[arrow]for DuckDB). Streamlit and Jupyter extras already include pandas. - Keep pandas frames explicitly. If Polars is also installed, time series now come back
as Polars frames. Add
sweatstack.set_output("pandas")once (orClient(output="pandas")) to keep every frame exactly as before. as_dataframe=True→output="pandas".- Indexes are columns. If you relied on
df.index(.loc[timestamp],.resample(),.plot()), add.set_index("timestamp")(or"power","speed","date") once after the call. The set of columns is unchanged; the former index comes first.
1 maintainer
Metadata
1 maintainer
Release files for sweatstack 0.90.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sweatstack-0.90.0.tar.gz | 314.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sweatstack-0.90.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 399.9 kB
Release files / sweatstack-0.90.0.tar.gz
| Download URL | sweatstack-0.90.0.tar.gz |
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| Size | 314.7 kB |
| Tags | Source |
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Release files / sweatstack-0.90.0-py3-none-any.whl
| Download URL | sweatstack-0.90.0-py3-none-any.whl |
|---|---|
| Size | 85.2 kB |
| Tags | Python 3 |
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