Hugging Face's logo

Datasets:
t4tiana
/
store-sales-time-series-forecasting

Dataset card Viewer Files Community
Dataset Preview
Viewer
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 1 missing columns ({'sales'})

This happened while the csv dataset builder was generating data using

hf://datasets/t4tiana/store-sales-time-series-forecasting/test.csv (at revision 1ac138dfdfafc520bc29831e73373954b1b7bc05)

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 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, 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 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              id: int64
              date: string
              store_nbr: int64
              family: string
              onpromotion: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 818
              to
              {'id': Value(dtype='int64', id=None), 'date': Value(dtype='string', id=None), 'store_nbr': Value(dtype='int64', id=None), 'family': Value(dtype='string', id=None), 'sales': Value(dtype='float64', id=None), 'onpromotion': 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 1321, 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 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 1882, 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 2013, 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 1 missing columns ({'sales'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/t4tiana/store-sales-time-series-forecasting/test.csv (at revision 1ac138dfdfafc520bc29831e73373954b1b7bc05)
              
              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)

Need help to make the dataset viewer work? Open a discussion for direct support.

id
int64
date
string
store_nbr
int64
family
string
sales
float64
onpromotion
int64
0
2013-01-01
1
AUTOMOTIVE
0
0
1
2013-01-01
1
BABY CARE
0
0
2
2013-01-01
1
BEAUTY
0
0
3
2013-01-01
1
BEVERAGES
0
0
4
2013-01-01
1
BOOKS
0
0
5
2013-01-01
1
BREAD/BAKERY
0
0
6
2013-01-01
1
CELEBRATION
0
0
7
2013-01-01
1
CLEANING
0
0
8
2013-01-01
1
DAIRY
0
0
9
2013-01-01
1
DELI
0
0
10
2013-01-01
1
EGGS
0
0
11
2013-01-01
1
FROZEN FOODS
0
0
12
2013-01-01
1
GROCERY I
0
0
13
2013-01-01
1
GROCERY II
0
0
14
2013-01-01
1
HARDWARE
0
0
15
2013-01-01
1
HOME AND KITCHEN I
0
0
16
2013-01-01
1
HOME AND KITCHEN II
0
0
17
2013-01-01
1
HOME APPLIANCES
0
0
18
2013-01-01
1
HOME CARE
0
0
19
2013-01-01
1
LADIESWEAR
0
0
20
2013-01-01
1
LAWN AND GARDEN
0
0
21
2013-01-01
1
LINGERIE
0
0
22
2013-01-01
1
LIQUOR,WINE,BEER
0
0
23
2013-01-01
1
MAGAZINES
0
0
24
2013-01-01
1
MEATS
0
0
25
2013-01-01
1
PERSONAL CARE
0
0
26
2013-01-01
1
PET SUPPLIES
0
0
27
2013-01-01
1
PLAYERS AND ELECTRONICS
0
0
28
2013-01-01
1
POULTRY
0
0
29
2013-01-01
1
PREPARED FOODS
0
0
30
2013-01-01
1
PRODUCE
0
0
31
2013-01-01
1
SCHOOL AND OFFICE SUPPLIES
0
0
32
2013-01-01
1
SEAFOOD
0
0
33
2013-01-01
10
AUTOMOTIVE
0
0
34
2013-01-01
10
BABY CARE
0
0
35
2013-01-01
10
BEAUTY
0
0
36
2013-01-01
10
BEVERAGES
0
0
37
2013-01-01
10
BOOKS
0
0
38
2013-01-01
10
BREAD/BAKERY
0
0
39
2013-01-01
10
CELEBRATION
0
0
40
2013-01-01
10
CLEANING
0
0
41
2013-01-01
10
DAIRY
0
0
42
2013-01-01
10
DELI
0
0
43
2013-01-01
10
EGGS
0
0
44
2013-01-01
10
FROZEN FOODS
0
0
45
2013-01-01
10
GROCERY I
0
0
46
2013-01-01
10
GROCERY II
0
0
47
2013-01-01
10
HARDWARE
0
0
48
2013-01-01
10
HOME AND KITCHEN I
0
0
49
2013-01-01
10
HOME AND KITCHEN II
0
0
50
2013-01-01
10
HOME APPLIANCES
0
0
51
2013-01-01
10
HOME CARE
0
0
52
2013-01-01
10
LADIESWEAR
0
0
53
2013-01-01
10
LAWN AND GARDEN
0
0
54
2013-01-01
10
LINGERIE
0
0
55
2013-01-01
10
LIQUOR,WINE,BEER
0
0
56
2013-01-01
10
MAGAZINES
0
0
57
2013-01-01
10
MEATS
0
0
58
2013-01-01
10
PERSONAL CARE
0
0
59
2013-01-01
10
PET SUPPLIES
0
0
60
2013-01-01
10
PLAYERS AND ELECTRONICS
0
0
61
2013-01-01
10
POULTRY
0
0
62
2013-01-01
10
PREPARED FOODS
0
0
63
2013-01-01
10
PRODUCE
0
0
64
2013-01-01
10
SCHOOL AND OFFICE SUPPLIES
0
0
65
2013-01-01
10
SEAFOOD
0
0
66
2013-01-01
11
AUTOMOTIVE
0
0
67
2013-01-01
11
BABY CARE
0
0
68
2013-01-01
11
BEAUTY
0
0
69
2013-01-01
11
BEVERAGES
0
0
70
2013-01-01
11
BOOKS
0
0
71
2013-01-01
11
BREAD/BAKERY
0
0
72
2013-01-01
11
CELEBRATION
0
0
73
2013-01-01
11
CLEANING
0
0
74
2013-01-01
11
DAIRY
0
0
75
2013-01-01
11
DELI
0
0
76
2013-01-01
11
EGGS
0
0
77
2013-01-01
11
FROZEN FOODS
0
0
78
2013-01-01
11
GROCERY I
0
0
79
2013-01-01
11
GROCERY II
0
0
80
2013-01-01
11
HARDWARE
0
0
81
2013-01-01
11
HOME AND KITCHEN I
0
0
82
2013-01-01
11
HOME AND KITCHEN II
0
0
83
2013-01-01
11
HOME APPLIANCES
0
0
84
2013-01-01
11
HOME CARE
0
0
85
2013-01-01
11
LADIESWEAR
0
0
86
2013-01-01
11
LAWN AND GARDEN
0
0
87
2013-01-01
11
LINGERIE
0
0
88
2013-01-01
11
LIQUOR,WINE,BEER
0
0
89
2013-01-01
11
MAGAZINES
0
0
90
2013-01-01
11
MEATS
0
0
91
2013-01-01
11
PERSONAL CARE
0
0
92
2013-01-01
11
PET SUPPLIES
0
0
93
2013-01-01
11
PLAYERS AND ELECTRONICS
0
0
94
2013-01-01
11
POULTRY
0
0
95
2013-01-01
11
PREPARED FOODS
0
0
96
2013-01-01
11
PRODUCE
0
0
97
2013-01-01
11
SCHOOL AND OFFICE SUPPLIES
0
0
98
2013-01-01
11
SEAFOOD
0
0
99
2013-01-01
12
AUTOMOTIVE
0
0
End of preview.
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

taken from this Kaggle competition:

Dataset Description

In this competition, you will predict sales for the thousands of product families sold at Favorita stores located in Ecuador. The training data includes dates, store and product information, whether that item was being promoted, as well as the sales numbers. Additional files include supplementary information that may be useful in building your models.

File Descriptions and Data Field Information

train.csv
The training data, comprising time series of features store_nbr, family, and onpromotion as well as the target sales.
store_nbr identifies the store at which the products are sold.
family identifies the type of product sold.
sales gives the total sales for a product family at a particular store at a given date. Fractional values are possible since products can be sold in fractional units (1.5 kg of cheese, for instance, as opposed to 1 bag of chips). onpromotion gives the total number of items in a product family that were being promoted at a store at a given date.

test.csv
The test data, having the same features as the training data. You will predict the target sales for the dates in this file.
The dates in the test data are for the 15 days after the last date in the training data.

sample_submission.csv
A sample submission file in the correct format.

stores.csv
Store metadata, including city, state, type, and cluster.
cluster is a grouping of similar stores.

oil.csv
Daily oil price. Includes values during both the train and test data timeframes. (Ecuador is an oil-dependent country and it's economical health is highly vulnerable to shocks in oil prices.)

holidays_events.csv
Holidays and Events, with metadata

NOTE: Pay special attention to the transferred column. A holiday that is transferred officially falls on that calendar day, but was moved to another date by the government. A transferred day is more like a normal day than a holiday. To find the day that it was actually celebrated, look for the corresponding row where type is Transfer. For example, the holiday Independencia de Guayaquil was transferred from 2012-10-09 to 2012-10-12, which means it was celebrated on 2012-10-12. Days that are type Bridge are extra days that are added to a holiday (e.g., to extend the break across a long weekend). These are frequently made up by the type Work Day which is a day not normally scheduled for work (e.g., Saturday) that is meant to payback the Bridge. Additional holidays are days added a regular calendar holiday, for example, as typically happens around Christmas (making Christmas Eve a holiday).

Additional Notes

  • Wages in the public sector are paid every two weeks on the 15 th and on the last day of the month. Supermarket sales could be affected by this.
  • A magnitude 7.8 earthquake struck Ecuador on April 16, 2016. People rallied in relief efforts donating water and other first need products which greatly affected supermarket sales for several weeks after the earthquake.
Downloads last month
1
Edit dataset card