A data model based on in-memory sqlite
Project description
SQLDataModel
SQLDataModel is a speedy & lightweight data model with no external dependencies for quickly fetching and storing your tabular data
Installation
Use the package manager pip to install SQLDataModel.
python -m pip install SQLDataModel
Usage
from SQLDataModel import SQLDataModel
# create a SQLDataModel object from any valid source:
sdm = SQLDataModel.from_csv('data.csv')
# manipulate it
sdm_sliced = sdm[2:7]
# view it as a table
print(sdm)
| country | region | check | total | report date | |
|---|---|---|---|---|---|
| 1 | US | West | Yes | 2016 | 2023-08-23 13:11:43 |
| 2 | US | West | No | 1996 | 2023-08-23 13:11:43 |
| 3 | US | West | Yes | 1296 | 2023-08-23 13:11:43 |
| 4 | US | West | No | 2392 | 2023-08-23 13:11:43 |
| 5 | US | Northeast | Yes | 1233 | 2023-08-23 13:11:43 |
| 6 | US | Northeast | No | 3177 | 2023-08-23 13:11:43 |
| 7 | US | Midwest | Yes | 1200 | 2023-08-23 13:11:43 |
| 8 | US | Midwest | No | 2749 | 2023-08-23 13:11:43 |
| 9 | US | Midwest | Yes | 1551 | 2023-08-23 13:11:43 |
# group by columns:
sdm_group = sdm.group_by('region','check')
print(sdm_group)
| idx | region | check | count |
|---|---|---|---|
| 1 | Midwest | Yes | 2 |
| 2 | West | No | 2 |
| 3 | West | Yes | 2 |
| 4 | Midwest | No | 1 |
| 5 | Northeast | No | 1 |
| 6 | Northeast | Yes | 1 |
# loop through it:
for row in sdm.iter_rows():
print(row)
# or save it for later as csv:
sdm.to_csv('data.csv')
# or to sqlite database:
sdm.to_sql('data', 'sqlite.db')
# and get it back again as a new model:
sdm_new = SQLDataModel.from_sql('select * from data', 'sqlite.db')
Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
License
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