Datasets:
File size: 4,677 Bytes
1837f78 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | ---
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())
```
|