querexfuzz 2.0.4

A flexible query engine for pandas DataFrames with SQL, regex, date, and fuzzy matching.

Querexfuzz

A flexible query engine for pandas DataFrames. querexfuzz lets you filter and search your data using a unified syntax that combines SQL-like where clauses, regular expressions, natural date ranges, and fuzzy matching — all in a single query string.

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Core Features

• Unified query language: combine where, regex (~ / !), date filters (@), and fuzzy matching (#) in one string.
• DataFrame native: attaches a .querex() method (and .q() alias) directly to DataFrame instances.
• Auto-configuration: querexfuzz_from_df inspects column types and sets sensible defaults automatically.
• Fast fuzzy search: powered by skimmatch; matcher built once per DataFrame and cached for the lifetime of the engine.
• Configurable: via YAML file, keyword arguments, or both.

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Installation

pip install querexfuzz

For development:

git clone https://github.com/mynl/querexfuzz.git
cd querexfuzz
pip install -e .[test]

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Quickstart

Auto-configure from a DataFrame

The simplest path — querexfuzz_from_df inspects column types and wires everything up:

import pandas as pd
from querexfuzz import querexfuzz_from_df

df = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'age': [25, 30, 35, 40, 45],
    'city': ['Amsterdam', 'Berlin', 'Copenhagen', 'Berlin', 'Amsterdam'],
    'registered_date': pd.to_datetime([
        '2025-08-10', '2025-06-15', '2024-01-20', '2025-08-25', '2025-07-30'
    ])
})

querexfuzz_from_df(df)        # attaches .querex() and .q() to df in-place

result = df.querex("where city == 'Berlin'")
result = df.q("top 5 # ams")  # short alias

Explicit configuration

from querexfuzz import Querexfuzz

engine = Querexfuzz(
    base_cols=['name', 'city', 'registered_date', 'age'],
    date_fields=['registered_date'],
    default_date_field='registered_date',
    bang_field='name',
    recent_field='registered_date',
    fuzzy=dict(fields=['name', 'city'], limit=50, score_col_name='score'),
)
engine.attach_to(df)

result = df.querex("recent top 10 where age > 30 # berlin")

From a YAML config file

engine = Querexfuzz(config_path='config.yml')
engine = Querexfuzz(config_path='config.yml', fuzzy={'limit': 200})  # with overrides

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Query Syntax

Clauses are order-sensitive and must appear in this sequence (all optional):

[verbose] [recent] [top N | bottom N] [select cols]
[field ~ regex | ! term] [where expr] [order by cols] [@ date_spec] [# fuzzy_term]

An empty query returns all base columns for all rows.

where — SQL-like filter

df.querex("where city == 'Amsterdam' and age > 30")

! / ~ — Regex

df.querex("! ^[AB]")            # regex on bang_field (default regex target)
df.querex("name ~ ^[AB]")       # regex on named column

@ — Date range

Units: c calendar year, y year, q quarter, m month, w week, d day, h hour.

df.querex("@m-3")                        # last 3 months (default date field)
df.querex("@registered_date m-28:6")     # 28 to 6 months ago on named field
df.querex("@y-1")                        # last year

# — Fuzzy matching

Must be the last clause. Results are sorted by score descending.

df.querex("# berlin")
df.querex("where age > 30 # ams")        # filter first, then fuzzy over full data

select — Column projection

Syntax	Meaning
(default)	base columns
select *	base columns
select **	all columns
select a, b	named columns
select *, a	base columns plus a
select *, -a	base columns minus a (- or ! prefix)
select **, -a	all columns minus a

top / bottom / recent / order by

df.querex("top 10")
df.querex("bottom 5")
df.querex("recent")                       # sort by recent_field descending
df.querex("order by age")
df.querex("order by -age, name")          # - prefix = descending

Combining clauses

df.querex("top 5 recent where city == 'Berlin' @m-3 select name, age # bob")

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Fuzzy matching and caching

The fuzzy matcher (skimmatch) is built once per attached DataFrame and cached on the engine. Repeat fuzzy queries against the same DataFrame pay only the cost of matcher.query().

When where, regex, or date pre-filters are present, the matcher still runs over the full DataFrame and results are intersected with the pre-filtered rows (5× over-fetch to compensate for the narrower valid set).

If the DataFrame's contents change between queries, re-attach with mutable=True:

engine.attach_to(df, mutable=True)   # rebuilds matcher on every fuzzy call

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Versions

2.0.3 (current)

Fuzzy caching refactor. Matcher built once per attached DataFrame and cached by id(df) on the engine — repeat queries skip all data preparation. Multiple DataFrames per engine each get an independent cache entry. attach_to(mutable=True) opt-in for DataFrames whose contents change.

2.0.2

Performance pass on execute_query: lazy DataFrame copy (copy only when date-column type coercion is needed, after prior filters have already reduced the frame); initial fuzzy matcher caching.

2.0.1

Code review fixes: method renamed .querex() / .q(); importlib.metadata version; Pydantic config corrections; parser and test suite overhaul; tzlocal dependency added.

2.0.0

Major rewrite. Lark-based grammar parser, Pydantic configuration, skimmatch fuzzy backend (replacing rustfuzz), src/ layout.

0.1.0

Initial release.