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https://github.com/ptmcg/littletable
An in-memory database of Python objects, searchable using quasi-SQL API
https://github.com/ptmcg/littletable
data-analysis-python database python
Last synced: about 2 months ago
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An in-memory database of Python objects, searchable using quasi-SQL API
- Host: GitHub
- URL: https://github.com/ptmcg/littletable
- Owner: ptmcg
- License: mit
- Created: 2018-01-30T07:14:31.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-07-29T17:25:08.000Z (about 2 months ago)
- Last Synced: 2024-07-30T15:11:19.027Z (about 2 months ago)
- Topics: data-analysis-python, database, python
- Language: Python
- Homepage:
- Size: 9.25 MB
- Stars: 156
- Watchers: 10
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES
- License: LICENSE
Awesome Lists containing this project
README
# littletable - a Python module to give ORM-like access to a collection of objects
[](https://travis-ci.org/ptmcg/littletable) [](https://mybinder.org/v2/gh/ptmcg/littletable/master)
- [Introduction](#introduction)
- [Optional dependencies](#optional-dependencies)
- [Importing data from CSV files](#importing-data-from-csv-files)
- [Tabular output](#tabular-output)
- [For More Info](#for-more-info)
- [Sample Demo](#sample-demo)
Introduction
------------
The `littletable` module provides a low-overhead, schema-less, in-memory database access to a collection
of user objects. `littletable` Tables will accept Python `dict`s or any user-defined object type, including:
- `namedtuples` and `typing.NamedTuples`
- `dataclasses`
- `types.SimpleNamespaces`
- `attrs` classes
- `PyDantic` data models
- `traitlets`
`littletable` infers the Table's "columns" from those objects' `__dict__`, `__slots__`, or `_fields` mappings to access
object attributes.
If populated with Python `dict`s, they get stored as `SimpleNamespace`s.
In addition to basic ORM-style insert/remove/query/delete access to the contents of a `Table`, `littletable` offers:
* simple indexing for improved retrieval performance, and optional enforcing key uniqueness
* access to objects using indexed attributes
* direct import/export to CSV, TSV, JSON, and Excel .xlsx files
* clean tabular output for data presentation
* simplified joins using `"+"` operator syntax between annotated `Table`s
* the result of any query or join is a new first-class `littletable` `Table`
* simple full-text search against multi-word text attributes
* access like a standard Python list to the records in a `Table`, including indexing/slicing, `iter`, `zip`, `len`, `groupby`, etc.
* access like a standard Python `dict` to attributes with a unique index, or like a standard Python `defaultdict(list)` to attributes with a non-unique index
`littletable` `Table`s do not require an upfront schema definition, but simply work off of the attributes in
the stored values, and those referenced in any query parameters.
Optional dependencies
---------------------
The base `littletable` code has no dependencies outside of the Python stdlib. However, some operations
require additional package installs:
| operation | additional install required |
|-----------------------------|--------------------------------------------------------------------|
| `Table.present` | `rich` |
| `Table.excel_import/export` | `openpyxl` (plus `defusedxml` or `lxml`, `defusedxml` recommended) |
| `Table.as_dataframe` | `pandas` |
Importing data from CSV files
-----------------------------
You can easily import a CSV file into a `Table` using `Table.csv_import()`:
```python
import littletable as lt
t = lt.Table().csv_import("my_data.csv")
# or
t = lt.csv_import("my_data.csv")
```
In place of a local file name, you can also specify an HTTP url:
```python
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"
names = ["sepal-length", "sepal-width", "petal-length", "petal-width", "class"]
iris_table = Table('iris').csv_import(url, fieldnames=names)
```
You can also directly import CSV data as a string:
```python
catalog = Table("catalog")
catalog_data = """\
sku,description,unitofmeas,unitprice
BRDSD-001,Bird seed,LB,3
BBS-001,Steel BB's,LB,5
MGNT-001,Magnet,EA,8"""
catalog.csv_import(catalog_data, transforms={'unitprice': int})
```
Data can also be directly imported from compressed .zip, .gz, and .xz files.
Files containing JSON-formatted records can be similarly imported using `json_import()`.
Tabular output
--------------
To produce a nice tabular output for a table, you can use the embedded support for
the [rich](https://github.com/willmcgugan/rich) module, `as_html()` in [Jupyter Notebook](https://jupyter.org/),
or the [tabulate](https://github.com/astanin/python-tabulate) module:
Using `table.present()` (implemented using `rich`; `present()` accepts `rich` `Table` keyword args):
```python
table(title_str).present(fields=["col1", "col2", "col3"])
or
table.select("col1 col2 col3")(title_str).present(caption="caption text",
caption_justify="right")
```
Using `Jupyter Notebook`:
```python
from IPython.display import HTML, display
display(HTML(table.as_html()))
```
Using `tabulate`:
```python
from tabulate import tabulate
print(tabulate((vars(rec) for rec in table), headers="keys"))
```
For More Info
-------------
Extended "getting started" notes at [how_to_use_littletable.md](https://github.com/ptmcg/littletable/blob/master/how_to_use_littletable.md).
Sample Demo
-----------
Here is a simple `littletable` data storage/retrieval example:
```python
from littletable import Table
customers = Table('customers')
customers.create_index("id", unique=True)
customers.csv_import("""\
id,name
0010,George Jetson
0020,Wile E. Coyote
0030,Jonny Quest
""")
catalog = Table('catalog')
catalog.create_index("sku", unique=True)
catalog.insert({"sku": "ANVIL-001", "descr": "1000lb anvil", "unitofmeas": "EA","unitprice": 100})
catalog.insert({"sku": "BRDSD-001", "descr": "Bird seed", "unitofmeas": "LB","unitprice": 3})
catalog.insert({"sku": "MAGNT-001", "descr": "Magnet", "unitofmeas": "EA","unitprice": 8})
catalog.insert({"sku": "MAGLS-001", "descr": "Magnifying glass", "unitofmeas": "EA","unitprice": 12})
wishitems = Table('wishitems')
wishitems.create_index("custid")
wishitems.create_index("sku")
# easy to import CSV data from a string or file
wishitems.csv_import("""\
custid,sku
0020,ANVIL-001
0020,BRDSD-001
0020,MAGNT-001
0030,MAGNT-001
0030,MAGLS-001
""")
# print a particular customer name
# (unique indexes will return a single item; non-unique
# indexes will return a new Table of all matching items)
print(customers.by.id["0030"].name)
# see all customer names
for name in customers.all.name:
print(name)
# print all items sold by the pound
for item in catalog.where(unitofmeas="LB"):
print(item.sku, item.descr)
# print all items that cost more than 10
for item in catalog.where(lambda o: o.unitprice > 10):
print(item.sku, item.descr, item.unitprice)
# join tables to create queryable wishlists collection
wishlists = customers.join_on("id") + wishitems.join_on("custid") + catalog.join_on("sku")
# print all wishlist items with price > 10 (can use Table.gt comparator instead of lambda)
bigticketitems = wishlists().where(unitprice=Table.gt(10))
for item in bigticketitems:
print(item)
# list all wishlist items in descending order by price
for item in wishlists().sort("unitprice desc"):
print(item)
# print output as a nicely-formatted table
wishlists().sort("unitprice desc")("Wishlists").present()
# print output as an HTML table
print(wishlists().sort("unitprice desc")("Wishlists").as_html())
# print output as a Markdown table
print(wishlists().sort("unitprice desc")("Wishlists").as_markdown())
```