polynx 0.1.3

String-powered Polars expression engine with extended DataFrame utilities

Polynx

String-powered Polars expression engine with extended DataFrame utilities.

Table of Contents

• Installation
• Usage
• Features
• Documentation
• Contributing
• License

Installation

Via PyPI (Recommended)

Install using pip:

pip install polynx

From Souce

Clone the repository and install using pip:

git clone https://github.com/LowellWinston/polynx.git
cd polynx
pip install .

Usage

import polynx as plx

• Supports all polars functions

df = plx.DataFrame({
    'A':[1, 2, 3, 4], 
    'B': ['abc', 'bc', 'aaa', None], 
    'C': ['2023-01-01','2021-01-01','2009-11-01','2000-11-11'],
    'E': [1.1, 2.1, 3.5, 0]}
).wc("C.str.to_datetime(format='%Y-%m-%d',time_unit='ns').dt.date()")

df

A	B	C	E
i64	str	date	f64
1	"abc"	2023-01-01	1.1
2	"bc"	2021-01-01	2.1
3	"aaa"	2009-11-01	3.5
4	null	2000-11-11	0.0

• Pandas style query function with chaind comparison, variable substituion in string expression

var1 = 1
var2 = 3
var3 = ['bc']
query_str =  "@var1 <= A < @var2 & C.dt.year() >=2020 & B in @var3"
df.query(query_str)

A	B	C	E
i64	str	date	f64
2	"bc"	2021-01-01	2.1

• String operation

query_str = "B.str.contains('a|b')"
#query_str = " B.str.ends_with('c')"
df.query(query_str)

A	B	C	E
i64	str	date	f64
1	"abc"	2023-01-01	1.1
2	"bc"	2021-01-01	2.1
3	"aaa"	2009-11-01	3.5

• Year month comparison in string format

var5 ='2001-01-01'
var3 = '2020-01-01'
query_str = " @var5 < C < @var3 "
df.query(query_str)

A	B	C	E
i64	str	date	f64
2	"bc"	2021-01-01	2.1

• in and not in

query_str = " B in ['aaa','bc']"
df.query(query_str)

A	B	C	E
i64	str	date	f64
2	"bc"	2021-01-01	2.1
3	"aaa"	2009-11-01	3.5

• Math calcuation

query_str = " A ** 2 - E > 1"
df.query(query_str)

A	B	C	E
i64	str	date	f64
2	"bc"	2021-01-01	2.1
3	"aaa"	2009-11-01	3.5
4	null	2000-11-11	0.0

• Pandas style eval function

query_str = " (A ** 2 - E)/(10-A)"
df.eval(query_str)

A
f64
-0.011111
0.2375
0.785714
2.666667

• negation

query_str = " not (B.is_null() & A.is_not_null())"
df.query(query_str)

A	B	C	E
i64	str	date	f64
1	"abc"	2023-01-01	1.1
2	"bc"	2021-01-01	2.1
3	"aaa"	2009-11-01	3.5

• wc extends with_columns to support multipe statement assignments

df.wc("A.sum().over('B').alias('H'); E.mean().alias('E_mean')")

A	B	C	E	H	E_mean
i64	str	date	f64	i64	f64
1	"abc"	2023-01-01	1.1	1	1.675
2	"bc"	2021-01-01	2.1	2	1.675
3	"aaa"	2009-11-01	3.5	3	1.675
4	null	2000-11-11	0.0	4	1.675

• Extend describe for group by

df.describe(group_keys='B', selected_columns=['A','E']).round()

• Extend group_by to work with string expressions

df.gb('B', " A.sum(); E.mean()")

B	A	E
str	f64	f64
null	4.0	0.0
"aaa"	3.0	3.5
"abc"	1.0	1.1
"bc"	2.0	2.1

• Support lazyframe

df.lazy().wc("E = 1").collect()

A	B	C	E
i64	str	date	i32
1	"abc"	2023-01-01	1
2	"bc"	2021-01-01	1
3	"aaa"	2009-11-01	1
4	null	2000-11-11	1

Features

• String-powered expression engine for Polars DataFrames
• Support for complex mathematical and statistical operations
• Easy-to-use syntax for powerful data manipulation
• Fast and scalable, optimized for large datasets

Documentation

TO BE UPDATED.

Contributing

We welcome contributions! If you'd like to help improve this project, please fork the repository, make your changes, and submit a pull request.

LICENSES

This project is licensed under the MIT License - see the LICENSE file for details.