littletable 3.0.2

Python in-memory ORM database

littletable - a Python module to give ORM-like access to a collection of objects

• Introduction
• Optional dependencies
• Importing data from CSV files
• Tabular output
• For More Info
• 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 dicts 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 dicts, they get stored as SimpleNamespaces.

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 Tables
• 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 Tables 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():

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:

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:

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 module, as_html() in Jupyter Notebook, or the tabulate module:

Using table.present() (implemented using rich; present() accepts rich Table keyword args):

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:

from IPython.display import HTML, display
display(HTML(table.as_html()))

Using tabulate:

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.

Sample Demo

Here is a simple littletable data storage/retrieval example:

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())