README.md

---
pretty_name: "Stephen Curry Game Log (TsFile)"
modality: timeseries
authors: "jawwaadsabree"
task_categories:
- time-series-forecasting
size_categories:
- 1K<n<10K
tags:
- tsfile
- timeseries
- modality:timeseries
- format:tsfile
- NBA
configs:
- config_name: default
  data_files:
  - split: train
    path: "curry_data.tsfile"
---

# Stephen Curry Game Log (TsFile)

This dataset is an Apache TsFile conversion of
[`jawwaadsabree/CurryData`](https://huggingface.co/datasets/jawwaadsabree/CurryData).

Modalities: Time-series.

## Overview

- Per-game NBA stat lines for Stephen Curry across seasons 2009–2016.
- Game statistics (`PTS`, `REB`, `AST`, `MIN`, `FG`/`3PT`/`FT` splits, etc.) plus engineered cyclic date features.
- Each season is a device identified by the `season` TAG.

- Converted observations: 1,204 rows across 1 TsFile file(s)
- Source format: csv

## TsFile schema

- **Time** — source `Date` (epoch nanoseconds), converted to INT64 milliseconds.

| Column | Role | Type | Meaning |
|---|---|---|---|
| `Time` | TIME | INT64 (ms) | sample timestamp |
| `season` | TAG | STRING | season (e.g. 2009_2010) |
| `Result` | FIELD | FLOAT | win/loss |
| `MIN` | FIELD | FLOAT | — |
| `REB` | FIELD | FLOAT | — |
| `AST` | FIELD | FLOAT | — |
| `BLK` | FIELD | FLOAT | — |
| `STL` | FIELD | FLOAT | — |
| `PF` | FIELD | FLOAT | — |
| `TO` | FIELD | FLOAT | — |
| `PTS` | FIELD | FLOAT | points |
| `FG_Made` | FIELD | FLOAT | — |
| `FG_Attempts` | FIELD | FLOAT | — |
| `c_3PT_Made` | FIELD | FLOAT | — |
| `c_3PT_Attempts` | FIELD | FLOAT | — |
| `FT_Made` | FIELD | FLOAT | — |
| `FT_Attempts` | FIELD | FLOAT | — |
| `Opponent_1` | FIELD | FLOAT | — |
| `Opponent_2` | FIELD | FLOAT | — |
| `Opponent_3` | FIELD | FLOAT | — |
| `Opponent_4` | FIELD | FLOAT | — |
| `Opponent_5` | FIELD | FLOAT | — |
| `Opponent_6` | FIELD | FLOAT | — |
| `Opponent_7` | FIELD | FLOAT | — |
| `Opponent_8` | FIELD | FLOAT | — |
| `Opponent_9` | FIELD | FLOAT | — |
| `Opponent_10` | FIELD | FLOAT | — |
| `Opponent_11` | FIELD | FLOAT | — |
| `Opponent_12` | FIELD | FLOAT | — |
| `Opponent_13` | FIELD | FLOAT | — |
| `Opponent_14` | FIELD | FLOAT | — |
| `Opponent_15` | FIELD | FLOAT | — |
| `Opponent_16` | FIELD | FLOAT | — |
| `Opponent_17` | FIELD | FLOAT | — |
| `Opponent_18` | FIELD | FLOAT | — |
| `Opponent_19` | FIELD | FLOAT | — |
| `Opponent_20` | FIELD | FLOAT | — |
| `Opponent_21` | FIELD | FLOAT | — |
| `Opponent_22` | FIELD | FLOAT | — |
| `Opponent_23` | FIELD | FLOAT | — |
| `Opponent_24` | FIELD | FLOAT | — |
| `Opponent_25` | FIELD | FLOAT | — |
| `Opponent_26` | FIELD | FLOAT | — |
| `Opponent_27` | FIELD | FLOAT | — |
| `Opponent_28` | FIELD | FLOAT | — |
| `Opponent_29` | FIELD | FLOAT | — |
| `Opponent_30` | FIELD | FLOAT | — |
| `Is_Home` | FIELD | FLOAT | — |
| `Year` | FIELD | FLOAT | — |
| `Month_Sin` | FIELD | FLOAT | — |
| `Month_Cos` | FIELD | FLOAT | — |
| `Day_Sin` | FIELD | FLOAT | — |
| `Day_Cos` | FIELD | FLOAT | — |
| `Day_of_Week_Sin` | FIELD | FLOAT | — |
| `Day_of_Week_Cos` | FIELD | FLOAT | — |
| `Day_of_Year_Sin` | FIELD | FLOAT | — |
| `Day_of_Year_Cos` | FIELD | FLOAT | — |
| `Is_Playoff` | FIELD | FLOAT | — |
| `Is_Regular_Season` | FIELD | FLOAT | — |
| `Is_Preseason` | FIELD | FLOAT | — |

## Conversion notes

- `season` (from the source file name) is a TAG so each season is a separate device.
- 57 game-stat / engineered-feature columns kept as FLOAT/INT64; no columns dropped.

## Source & license

- Original dataset: https://huggingface.co/datasets/jawwaadsabree/CurryData
- Author / publisher: jawwaadsabree
- License: not declared by the original dataset; please defer to the original

## Usage

Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:

```python
from pathlib import Path
from tsfile import TsFileReader

path = Path("curry_data.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())
```