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ZennyKenny
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CDNOW

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The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 4 new columns ({'cds', 'users_id', 'date', 'amt'}) and 6 missing columns ({'age', 'gender', 'zone', 'age_category', 'state', 'id'}).

This happened while the csv dataset builder was generating data using

hf://datasets/ZennyKenny/CDNOW/purchases.csv (at revision 3400bd38e52a012e691bcddd168504187edbfa55)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              users_id: int64
              date: string
              cds: int64
              amt: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 694
              to
              {'id': Value(dtype='int64', id=None), 'zone': Value(dtype='string', id=None), 'state': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'age_category': Value(dtype='string', id=None), 'age': Value(dtype='int64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1438, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 4 new columns ({'cds', 'users_id', 'date', 'amt'}) and 6 missing columns ({'age', 'gender', 'zone', 'age_category', 'state', 'id'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/ZennyKenny/CDNOW/purchases.csv (at revision 3400bd38e52a012e691bcddd168504187edbfa55)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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id int64	zone string	state string	gender string	age_category string	age int64
1	Pacific	Oregon	M	young	26
2	Eastern	New Jersey	M	medium	36
3	Central	Minnesota	M	young	17
4	Eastern	Michigan	M	medium	56
5	Eastern	New Jersey	M	medium	46
6	Mountain	New Mexico	M	medium	35
7	Central	Nebraska	F	null	30
8	Eastern	Indiana	M	young	20
9	Mountain	Colorado	M	medium	45
10	Eastern	Ohio	F	medium	40
11	Eastern	New York	F	medium	40
12	Eastern	North Carolina	F	medium	51
13	Eastern	North Carolina	M	young	28
14	Pacific	California	F	medium	55
15	Central	Louisiana	M	young	22
16	Mountain	New Mexico	F	null	30
17	Eastern	Kentucky	F	medium	37
18	Central	Louisiana	F	young	24
19	Central	Texas	M	medium	53
20	Eastern	Maryland	M	young	17
21	Central	Illinois	F	medium	43
22	Central	Louisiana	F	medium	38
23	Central	North Dakota	F	young	25
24	Central	Illinois	M	medium	37
25	Central	Alabama	F	young	20
26	Eastern	Maryland	F	medium	41
27	Eastern	Florida	M	medium	36
28	Eastern	Massachusetts	F	young	28
29	Central	Texas	F	medium	44
30	Mountain	New Mexico	F	young	21
31	Mountain	Arizona	F	young	21
32	Eastern	Massachusetts	M	young	28
33	Central	Iowa	F	young	29
34	Eastern	New York	F	medium	48
35	Central	Missouri	M	medium	56
36	Central	Oklahoma	M	medium	40
37	Central	Oklahoma	F	medium	46
38	Eastern	Ohio	M	medium	46
39	Central	Alabama	F	young	21
40	Eastern	Pennsylvania	F	medium	57
41	Eastern	North Carolina	M	medium	42
42	Central	Illinois	F	young	19
43	Eastern	Michigan	F	medium	49
44	Eastern	Indiana	M	null	30
45	Mountain	Arizona	F	medium	34
46	Mountain	Arizona	F	medium	35
47	Eastern	New York	M	young	28
48	Mountain	Colorado	M	medium	34
49	Mountain	Colorado	F	null	30
50	Central	Illinois	M	old	62
51	Central	Illinois	M	null	30
52	Eastern	North Carolina	M	medium	39
53	Central	Arkansas	M	null	30
54	Pacific	Oregon	F	young	25
55	Central	Iowa	M	medium	31
56	Mountain	Arizona	F	medium	40
57	Mountain	Arizona	F	medium	35
58	Mountain	New Mexico	M	medium	56
59	Central	Tennessee	F	young	24
60	Eastern	New Jersey	F	medium	50
61	Eastern	New York	M	medium	53
62	Central	Minnesota	M	old	63
63	Eastern	New York	F	young	24
64	Central	Louisiana	F	old	76
65	Eastern	New Jersey	F	old	67
66	Pacific	Oregon	M	medium	31
67	Eastern	Delaware	M	medium	35
68	Eastern	Florida	F	young	26
69	Central	Tennessee	M	medium	52
70	Central	Wisconsin	F	medium	42
71	Central	Texas	M	young	23
72	Pacific	California	F	young	29
73	Eastern	New Jersey	M	medium	57
74	Eastern	New Jersey	F	medium	41
75	Pacific	California	M	medium	45
76	Eastern	Pennsylvania	M	young	18
77	Eastern	Indiana	F	medium	56
78	Central	Minnesota	F	medium	48
79	Eastern	Michigan	F	medium	34
80	Mountain	Idaho	M	medium	37
81	Central	Texas	F	old	61
82	Central	Missouri	F	young	19
83	Central	Texas	F	medium	44
84	Eastern	New York	F	young	20
85	Eastern	Pennsylvania	F	medium	50
86	Pacific	Nevada	F	young	25
87	Central	Oklahoma	M	young	18
88	Central	Minnesota	M	medium	59
89	Mountain	Colorado	M	medium	38
90	Central	Illinois	M	medium	41
91	Mountain	Colorado	M	medium	36
92	Eastern	Massachusetts	M	medium	46
93	Mountain	Idaho	F	medium	40
94	Mountain	Colorado	F	medium	46
95	Eastern	New York	M	medium	33
96	Mountain	Colorado	M	medium	47
97	Eastern	Pennsylvania	F	medium	50
98	Eastern	Kentucky	M	medium	31
99	Eastern	North Carolina	M	medium	37
100	Mountain	Arizona	F	young	25

End of preview.

Dataset Card for CDNOW Dataset

Dataset Summary

The CDNOW dataset is a well-known dataset in customer analytics and predictive modeling, especially used in the context of Customer Lifetime Value (CLV) estimation and Buy Till You Die (BTYD) models. This dataset consists of detailed transaction logs from a CD retailer (CDNOW) for a sample of customers, along with anonymized demographic data.

It is often used in research and teaching materials for modeling purchasing behavior, including techniques like Pareto/NBD, BG/NBD, and Gamma-Gamma models.

Dataset Structure

The dataset is composed of two main CSV files:

`purchases.csv`

Column	Description
users_id	Unique ID for each customer
date	Date of purchase (YYYY-MM-DD format)
cds	Number of CDs bought in a single purchase
amt	Total dollar amount spent in the purchase

`customers.csv`

Column	Description
id	Customer ID (matches `users_id` in purchases)
zone	U.S. sales region
state	Customer's state
gender	Gender of the customer
age_category	Categorical label for customer's age group
age	Customer's age in years

Supported Tasks and Benchmarks

Customer Segmentation
Churn Prediction
CLV Modeling
RFM Analysis
Marketing Attribution

This dataset is suitable for supervised and unsupervised learning tasks related to customer behavior analysis.

Languages

Not language-dependent; numeric and categorical data only.

Source Information

The CDNOW dataset was originally made publicly available as part of a research project on CLV modeling by:

Fader, Peter S., and Bruce GS Hardie. "Customer-Base Valuation in a Contractual Setting: The Perils of Ignoring Heterogeneity." Marketing Science 29.1 (2010): 85-93.

It has since been widely used in textbooks and academic courses on marketing analytics, such as:

"Customer Centricity" by Peter Fader
"Data Science for Business" by Provost and Fawcett

Citation

If you use this dataset, consider citing the following:

@article{fader2010customer, title={Customer-Base Valuation in a Contractual Setting: The Perils of Ignoring Heterogeneity}, author={Fader, Peter S and Hardie, Bruce GS}, journal={Marketing Science}, volume={29}, number={1}, pages={85--93}, year={2010}, publisher={INFORMS} }

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