duckboat 0.21.0

A SQL-based Python dataframe library for ergonomic interactive data analysis and exploration.

Duckboat

Ugly to some, but gets the job done.

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Duckboat is a SQL-based Python dataframe library for ergonomic interactive data analysis and exploration.

pip install duckboat

Duckboat allows you to chain SQL snippets (meaning you can usually omit select * and from ...) to incrementally and lazily build up complex queries.

Duckboat is a light wrapper around the DuckDB relational API, so expressions are evaluated lazily and optimized by DuckDB prior to execution. The resulting queries are fast, avoiding the need to materialize intermediate tables or perform data transfers. You can leverage all the SQL syntax improvements provided by DuckDB: 1 2 3

Examples

import duckboat as uck

uck.do(
    'https://raw.githubusercontent.com/allisonhorst/palmerpenguins/main/inst/extdata/penguins.csv',
    "where sex = 'female' ",
    'where year > 2008',
    'select *, cast(body_mass_g as double) as grams',
    'select species, island, avg(grams) as avg_grams group by 1,2',
    'select * replace (round(avg_grams, 1) as avg_grams)',
    'order by avg_grams',
)

┌───────────┬───────────┬───────────┐
│  species  │  island   │ avg_grams │
│  varchar  │  varchar  │  double   │
├───────────┼───────────┼───────────┤
│ Adelie    │ Torgersen │    3193.8 │
│ Adelie    │ Dream     │    3357.5 │
│ Adelie    │ Biscoe    │    3446.9 │
│ Chinstrap │ Dream     │    3522.9 │
│ Gentoo    │ Biscoe    │    4786.3 │
└───────────┴───────────┴───────────┘

To and from other data formats

Table implements the Arrow PyCapsule Interface (__arrow_c_stream__), so it interoperates with any Arrow-compatible library---pandas, Polars, PyArrow, cuDF.

import pandas as pd

df = pd.DataFrame({'a': [0]})
t = uck.do(df)
t

┌───────┐
│   a   │
│ int64 │
├───────┤
│     0 │
└───────┘

Translate back to a pandas dataframe:

t.do('pandas')

You can mix duckboat with pandas or polars mid-workflow. Do the heavy lifting in SQL, pop into pandas for fiddly column operations, then come back:

df = t.do('where body_mass_g between 3500 and 4000', 'pandas')
df = df.rename(columns=str.upper)
result = uck.do(df, 'select SPECIES, count(*) as n group by 1')

Chaining expressions

You can chain calls to Table.do():

f = 'select a + 1 as a'
t.do(f).do(f).do(f)

┌───────┐
│   a   │
│ int64 │
├───────┤
│     3 │
└───────┘

Alternatively, Table.do() accepts a sequence of arguments:

t.do(f, f, f)

It also accepts lists of expressions, and will apply them recursively:

fs = [f, f, f]
t.do(fs)

Note, you could also still call this as:

t.do(*fs)

Use lists to group expressions, which Duckboat will apply recursively:

t.do(f, [f], [f, [[f, f], f]])

┌───────┐
│   a   │
│ int64 │
├───────┤
│     6 │
└───────┘

Duckboat will also apply functions:

def foo(x):
    return x.do('select a + 2 as a')

# the following are equivalent
foo(t)
t.do(foo)

Of course, you can mix functions, SQL strings, and lists:

uck.do(df, foo, [f, foo])

┌───────┐
│   a   │
│ int64 │
├───────┤
│     5 │
└───────┘

Joins

Pass a dict to register named tables, then write SQL that references them:

orders = pd.DataFrame({'id': [1, 2, 3], 'customer_id': [10, 20, 10], 'amount': [5.0, 12.0, 8.0]})
customers = pd.DataFrame({'id': [10, 20], 'name': ['Alice', 'Bob']})

uck.do(
    {'orders': orders, 'customers': customers},
    '''
    select c.name, sum(o.amount) as total
    from orders o
    join customers c on o.customer_id = c.id
    group by 1
    ''',
)

┌─────────┬────────┐
│  name   │ total  │
│ varchar │ double │
├─────────┼────────┤
│ Alice   │   13.0 │
│ Bob     │   12.0 │
└─────────┴────────┘

You can also join mid-chain. The current table is always available as _:

store = uck.examples.store()

store['orders'].do(
    'where amount > 10',
    {'customers': store['customers']},
    'join customers on customer_id = customers.id',
    'select name, sum(amount) as total group by 1',
)

Since from _ is always prepended, you can also self-join by aliasing both sides directly:

t.do('as a join _ as b using (hexid)')

Or use uck.rename() to give the current table a name and write full SQL:

t.do(
    uck.rename('trips'),
    'from trips as a join trips as b using (hexid)',
)

Dispatch rules

do() dispatches on the type of each argument.

SQL:

t.do('where x > 5')                # SQL snippet (from _ is prepended)
t.do('queries/transform.sql')      # .sql file path (loaded and executed)

Composition:

t.do(my_func)                      # callable — receives Table, returns Table
t.do([step1, step2, step3])        # list — applied recursively as a pipeline
t.do({'other': other_df})          # dict — registers named tables for next step
t.do(uck.rename('trips'))          # rename — gives _ a name, removes auto-wrap

T-strings (Python 3.14+):

On Python 3.14+, t-strings can replace the dict syntax for joins and inline scalar parameters. See the t-string guide for details.

Output:

t.do('select count(*)', int)       # Python int
t.do('select distinct a', list)    # Python list
t.do('limit 1', dict)              # Python dict
t.do('pandas')                     # Pandas DataFrame
t.do('arrow')                      # PyArrow Table

Display:

t.do('hide')                       # suppress repr (useful for large lazy tables)
t.do('show')                       # re-enable repr

Objects

Table

Table wraps a DuckDB DuckDBPyRelation. The easiest way to create one is through do():

t = uck.do('data.parquet')
t = uck.do(pd.DataFrame({'x': [1, 2, 3]}))
t = uck.do('https://example.com/data.csv')

.do() chains operations and dispatches on argument type (strings, functions, lists, dicts, type conversions). Access the underlying DuckDB relation with t.rel.

Eager evaluation and hide/show

Calling repr() on a Table triggers query evaluation. In Jupyter, this happens when an object is the last expression in a cell. In IDEs like Positron, the variable explorer proactively inspects objects, which can trigger expensive computations.

Use hide() to suppress evaluation:

big = uck.examples.penguins().do('hide')
# Positron's variable explorer will see: <Table(..., _hide=True)>
# instead of evaluating the full query

Call show() (or .do('show')) when you're ready to see results.

Philosophy

Duckboat bets that SQL is already the right language for tabular data manipulation -- you just need a way to compose SQL snippets into pipelines. This results in a mixture of Python and SQL that is semantically similar to Google's Pipe Syntax for SQL.

Strengths:

• Zero new API to learn. If you know SQL, you know duckboat. There are no new method chains, expression builders, or DSLs to memorize.
• Minimal surface area. The library is essentially Table and .do(). The codebase is small and stays out of your way.
• Snippet composability. SQL fragments chain naturally through do(), letting you build complex queries incrementally and interactively.

Tradeoffs:

• No IDE autocomplete on column names. Column references live inside SQL strings, so you don't get tab-completion or type checking. Typos surface at runtime, not in your editor.
• Discoverability. The do() dispatch conventions (int, list, "pandas", "hide", etc.) are terse but must be learned -- they can't be discovered through autocomplete.

Where duckboat fits best:

Duckboat is ideal for interactive exploration and notebook workflows, especially for teams already fluent in SQL. If you need strong static analysis, IDE support, or production-grade type safety, a fluent API like Polars or Ibis may be a better fit. If some operation is easier in another library, duckboat makes it straightforward to translate between them via Pandas, Arrow, or Polars.

Feedback

I'd love to hear any feedback on the approach here, so feel free to reach out through Issues or Discussions.