Datasets:

t22000t
/

bike-sharing-tabular

instant int64	dteday string	season int64	mnth int64	hr int64	weekday int64	workingday int64	weathersit int64	temp float64	atemp float64	hum float64	windspeed float64	casual int64	registered int64	cnt int64
1	2011-01-01	1	1	0	6	0	1	0.24	0.2879	0.81	0	3	13	16
2	2011-01-01	1	1	1	6	0	1	0.22	0.2727	0.8	0	8	32	40
3	2011-01-01	1	1	2	6	0	1	0.22	0.2727	0.8	0	5	27	32
4	2011-01-01	1	1	3	6	0	1	0.24	0.2879	0.75	0	3	10	13
5	2011-01-01	1	1	4	6	0	1	0.24	0.2879	0.75	0	0	1	1
6	2011-01-01	1	1	5	6	0	2	0.24	0.2576	0.75	0.0896	0	1	1
7	2011-01-01	1	1	6	6	0	1	0.22	0.2727	0.8	0	2	0	2
8	2011-01-01	1	1	7	6	0	1	0.2	0.2576	0.86	0	1	2	3
9	2011-01-01	1	1	8	6	0	1	0.24	0.2879	0.75	0	1	7	8
10	2011-01-01	1	1	9	6	0	1	0.32	0.3485	0.76	0	8	6	14
11	2011-01-01	1	1	10	6	0	1	0.38	0.3939	0.76	0.2537	12	24	36
12	2011-01-01	1	1	11	6	0	1	0.36	0.3333	0.81	0.2836	26	30	56
13	2011-01-01	1	1	12	6	0	1	0.42	0.4242	0.77	0.2836	29	55	84
14	2011-01-01	1	1	13	6	0	2	0.46	0.4545	0.72	0.2985	47	47	94
15	2011-01-01	1	1	14	6	0	2	0.46	0.4545	0.72	0.2836	35	71	106
16	2011-01-01	1	1	15	6	0	2	0.44	0.4394	0.77	0.2985	40	70	110
17	2011-01-01	1	1	16	6	0	2	0.42	0.4242	0.82	0.2985	41	52	93
18	2011-01-01	1	1	17	6	0	2	0.44	0.4394	0.82	0.2836	15	52	67
19	2011-01-01	1	1	18	6	0	3	0.42	0.4242	0.88	0.2537	9	26	35
20	2011-01-01	1	1	19	6	0	3	0.42	0.4242	0.88	0.2537	6	31	37
21	2011-01-01	1	1	20	6	0	2	0.4	0.4091	0.87	0.2537	11	25	36
22	2011-01-01	1	1	21	6	0	2	0.4	0.4091	0.87	0.194	3	31	34
23	2011-01-01	1	1	22	6	0	2	0.4	0.4091	0.94	0.2239	11	17	28
24	2011-01-01	1	1	23	6	0	2	0.46	0.4545	0.88	0.2985	15	24	39
25	2011-01-02	1	1	0	0	0	2	0.46	0.4545	0.88	0.2985	4	13	17
26	2011-01-02	1	1	1	0	0	2	0.44	0.4394	0.94	0.2537	1	16	17
27	2011-01-02	1	1	2	0	0	2	0.42	0.4242	1	0.2836	1	8	9
28	2011-01-02	1	1	3	0	0	2	0.46	0.4545	0.94	0.194	2	4	6
29	2011-01-02	1	1	4	0	0	2	0.46	0.4545	0.94	0.194	2	1	3
30	2011-01-02	1	1	6	0	0	3	0.42	0.4242	0.77	0.2985	0	2	2
31	2011-01-02	1	1	7	0	0	2	0.4	0.4091	0.76	0.194	0	1	1
32	2011-01-02	1	1	8	0	0	3	0.4	0.4091	0.71	0.2239	0	8	8
33	2011-01-02	1	1	9	0	0	2	0.38	0.3939	0.76	0.2239	1	19	20
34	2011-01-02	1	1	10	0	0	2	0.36	0.3485	0.81	0.2239	7	46	53
35	2011-01-02	1	1	11	0	0	2	0.36	0.3333	0.71	0.2537	16	54	70
36	2011-01-02	1	1	12	0	0	2	0.36	0.3333	0.66	0.2985	20	73	93
37	2011-01-02	1	1	13	0	0	2	0.36	0.3485	0.66	0.1343	11	64	75
38	2011-01-02	1	1	14	0	0	3	0.36	0.3485	0.76	0.194	4	55	59
39	2011-01-02	1	1	15	0	0	3	0.34	0.3333	0.81	0.1642	19	55	74
40	2011-01-02	1	1	16	0	0	3	0.34	0.3333	0.71	0.1642	9	67	76
41	2011-01-02	1	1	17	0	0	1	0.34	0.3333	0.57	0.194	7	58	65
42	2011-01-02	1	1	18	0	0	2	0.36	0.3333	0.46	0.3284	10	43	53
43	2011-01-02	1	1	19	0	0	1	0.32	0.2879	0.42	0.4478	1	29	30
44	2011-01-02	1	1	20	0	0	1	0.3	0.2727	0.39	0.3582	5	17	22
45	2011-01-02	1	1	21	0	0	1	0.26	0.2273	0.44	0.3284	11	20	31
46	2011-01-02	1	1	22	0	0	1	0.24	0.2121	0.44	0.2985	0	9	9
47	2011-01-02	1	1	23	0	0	1	0.22	0.2273	0.47	0.1642	0	8	8
48	2011-01-03	1	1	0	1	1	1	0.22	0.197	0.44	0.3582	0	5	5
49	2011-01-03	1	1	1	1	1	1	0.2	0.1667	0.44	0.4179	0	2	2
50	2011-01-03	1	1	4	1	1	1	0.16	0.1364	0.47	0.3881	0	1	1
51	2011-01-03	1	1	5	1	1	1	0.16	0.1364	0.47	0.2836	0	3	3
52	2011-01-03	1	1	6	1	1	1	0.14	0.1061	0.5	0.3881	0	30	30
53	2011-01-03	1	1	7	1	1	1	0.14	0.1364	0.5	0.194	1	63	64
54	2011-01-03	1	1	8	1	1	1	0.14	0.1212	0.5	0.2836	1	153	154
55	2011-01-03	1	1	9	1	1	1	0.16	0.1364	0.43	0.3881	7	81	88
56	2011-01-03	1	1	10	1	1	1	0.18	0.1667	0.43	0.2537	11	33	44
57	2011-01-03	1	1	11	1	1	1	0.2	0.1818	0.4	0.3284	10	41	51
58	2011-01-03	1	1	12	1	1	1	0.22	0.2121	0.35	0.2985	13	48	61
59	2011-01-03	1	1	13	1	1	1	0.24	0.2121	0.35	0.2836	8	53	61
60	2011-01-03	1	1	14	1	1	1	0.26	0.2424	0.3	0.2836	11	66	77
61	2011-01-03	1	1	15	1	1	1	0.26	0.2424	0.3	0.2537	14	58	72
62	2011-01-03	1	1	16	1	1	1	0.26	0.2424	0.3	0.2537	9	67	76
63	2011-01-03	1	1	17	1	1	1	0.24	0.2273	0.3	0.2239	11	146	157
64	2011-01-03	1	1	18	1	1	1	0.24	0.2576	0.32	0.1045	9	148	157
65	2011-01-03	1	1	19	1	1	1	0.2	0.2576	0.47	0	8	102	110
66	2011-01-03	1	1	20	1	1	1	0.2	0.2273	0.47	0.1045	3	49	52
67	2011-01-03	1	1	21	1	1	1	0.18	0.197	0.64	0.1343	3	49	52
68	2011-01-03	1	1	22	1	1	1	0.14	0.1515	0.69	0.1343	0	20	20
69	2011-01-03	1	1	23	1	1	1	0.18	0.2121	0.55	0.1045	1	11	12
70	2011-01-04	1	1	0	2	1	1	0.16	0.1818	0.55	0.1045	0	5	5
71	2011-01-04	1	1	1	2	1	1	0.16	0.1818	0.59	0.1045	0	2	2
72	2011-01-04	1	1	2	2	1	1	0.14	0.1515	0.63	0.1343	0	1	1
73	2011-01-04	1	1	4	2	1	1	0.14	0.1818	0.63	0.0896	0	2	2
74	2011-01-04	1	1	5	2	1	1	0.12	0.1515	0.68	0.1045	0	4	4
75	2011-01-04	1	1	6	2	1	1	0.12	0.1515	0.74	0.1045	0	36	36
76	2011-01-04	1	1	7	2	1	1	0.12	0.1515	0.74	0.1343	2	92	94
77	2011-01-04	1	1	8	2	1	1	0.14	0.1515	0.69	0.1642	2	177	179
78	2011-01-04	1	1	9	2	1	1	0.16	0.1515	0.64	0.2239	2	98	100
79	2011-01-04	1	1	10	2	1	2	0.16	0.1364	0.69	0.3284	5	37	42
80	2011-01-04	1	1	11	2	1	1	0.22	0.2121	0.51	0.2985	7	50	57
81	2011-01-04	1	1	12	2	1	1	0.22	0.2273	0.51	0.1642	12	66	78
82	2011-01-04	1	1	13	2	1	1	0.24	0.2273	0.56	0.194	18	79	97
83	2011-01-04	1	1	14	2	1	1	0.26	0.2576	0.52	0.2239	9	54	63
84	2011-01-04	1	1	15	2	1	1	0.28	0.2727	0.52	0.2537	17	48	65
85	2011-01-04	1	1	16	2	1	1	0.3	0.2879	0.49	0.2537	15	68	83
86	2011-01-04	1	1	17	2	1	1	0.28	0.2727	0.48	0.2239	10	202	212
87	2011-01-04	1	1	18	2	1	1	0.26	0.2576	0.48	0.194	3	179	182
88	2011-01-04	1	1	19	2	1	1	0.24	0.2576	0.48	0.1045	2	110	112
89	2011-01-04	1	1	20	2	1	1	0.24	0.2576	0.48	0.1045	1	53	54
90	2011-01-04	1	1	21	2	1	1	0.22	0.2727	0.64	0	0	48	48
91	2011-01-04	1	1	22	2	1	1	0.22	0.2576	0.64	0.0896	1	34	35
92	2011-01-04	1	1	23	2	1	1	0.2	0.2273	0.69	0.0896	2	9	11
93	2011-01-05	1	1	0	3	1	1	0.2	0.2576	0.64	0	0	6	6
94	2011-01-05	1	1	1	3	1	1	0.16	0.197	0.74	0.0896	0	6	6
95	2011-01-05	1	1	2	3	1	1	0.16	0.197	0.74	0.0896	0	2	2
96	2011-01-05	1	1	4	3	1	1	0.24	0.2273	0.48	0.2239	0	2	2
97	2011-01-05	1	1	5	3	1	1	0.22	0.2273	0.47	0.1642	0	3	3
98	2011-01-05	1	1	6	3	1	1	0.2	0.197	0.47	0.2239	0	33	33
99	2011-01-05	1	1	7	3	1	1	0.18	0.1818	0.43	0.194	1	87	88
100	2011-01-05	1	1	8	3	1	1	0.2	0.1818	0.4	0.2985	3	192	195

End of preview. Expand in Data Studio

Bike Sharing Demand - Hourly (Poisson)

A ready-to-use copy of the UCI Bike Sharing Dataset (hourly granularity, 17,379 × 17), accompanied by baseline metrics from an 8-architecture tabular modelling pipeline for direct comparison.

Originally collected and published by Fanaee-T & Gama (2014). Source: UCI ML Repository id 275.

At a glance

Field	Value
Rows	17,379 hourly observations
Time range	Jan 2011 - Dec 2012
Columns	17 (16 features + 1 target)
Target	`cnt` (hourly bike rental count)
Target range	1 - 977
Target mean / median	189 / 142
Distribution family	Poisson (count data)
Continuous features	7 (temperature, humidity, wind, time features)
Categorical features	5 (season, weather, day-of-week, holiday, working day)
Missing values	none

Suggested distribution family

cnt is a non-negative count, so a Poisson family with log link is the natural choice. The tabular-data-modelling-pipeline ships a ready-made config: configs/example_bike_sharing.py.

How to use

from datasets import load_dataset
ds = load_dataset("t22000t/bike-sharing-tabular", split="train")
print(ds[0])

Or plain pandas:

import pandas as pd
df = pd.read_csv("hf://datasets/t22000t/bike-sharing-tabular/hour.csv")
print(df.shape, df["cnt"].describe())

Or via the modelling pipeline:

git clone https://github.com/timothy22000/tabular_data_modelling_pipeline
cd tabular_data_modelling_pipeline
pip install -e ".[all]"

python scripts/download_data.py --dataset bike_sharing
python train.py \
    --config configs/example_bike_sharing.py \
    --input data/bike_sharing.csv

Feature dictionary

Feature	Type	Description
`instant`	int	Record id (drop before training)
`dteday`	date	Date string (drop - use `yr`/`mnth` instead)
`season`	cat	1=spring, 2=summer, 3=fall, 4=winter
`yr`	int	0=2011, 1=2012
`mnth`	int	1-12
`hr`	int	Hour of day (0-23)
`holiday`	cat	0/1
`weekday`	cat	0=Sunday ... 6=Saturday
`workingday`	cat	1 if working day, 0 otherwise
`weathersit`	cat	1=clear, 2=mist, 3=light rain/snow, 4=heavy precipitation
`temp`	float	Normalised temperature in Celsius (divided by 41)
`atemp`	float	Normalised "feels-like" temperature (divided by 50)
`hum`	float	Normalised humidity (divided by 100)
`windspeed`	float	Normalised wind speed (divided by 67)
`casual`	int	Leakage - non-registered user count (excluded from features)
`registered`	int	Leakage - registered user count (excluded from features)
`cnt`	int	Target - total rentals (`casual + registered`)

casual and registered sum to cnt and must be excluded from the feature set. The shipped config does this.

Baseline metrics (8-architecture pipeline)

Baseline metrics will be filled in here once the model collection lands at t22000t/bike-sharing-tabular-models.

Splits

Single CSV - 17,379 hourly rows from Jan 2011 to Dec 2012. The pipeline does its own 80/20 random split (deterministic with seed=42). For a more realistic time-series split, set DatasetConfig.split_col to a column you construct (e.g. "before/after 2012-09").

Personal and sensitive information

None. Each row is a count of aggregated hourly bike rentals from the Capital Bikeshare system in Washington, DC. No individual rider data.

License and attribution

CC BY 4.0. Original publication:

Fanaee-T, Hadi, and Gama, Joao. Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence (2014): pp. 1-15, Springer Berlin Heidelberg.

UCI ML Repository link: https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset

Citation

@article{fanaee2014event,
  title   = {Event labeling combining ensemble detectors and background knowledge},
  author  = {Fanaee-T, Hadi and Gama, Jo{\~a}o},
  journal = {Progress in Artificial Intelligence},
  pages   = {1--15},
  year    = {2014},
  publisher = {Springer Berlin Heidelberg}
}

@software{tabular_data_modelling_pipeline,
  author = {Mun, Timothy},
  title  = {tabular-data-modelling-pipeline},
  url    = {https://github.com/timothy22000/tabular_data_modelling_pipeline},
  year   = {2026}
}