semvec/pypi-packages huggingface dataset:
js-dependency-audit 0.0.1
A python library to check for vulnerabilities in yarn.lock files.
Requires Python: >=3.8
Keywords: js, npm, security, yarn

# JS-DEPENDENCY-AUDIT

[![PyPI - License](https://img.shields.io/pypi/l/js_dependency_audit)](https://pypi.org/project/js-dependency-audit/)
[![PyPI - Python Version](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fjeromediaz%2Fjs-dependency-audit%2Frefs%2Fheads%2Fmain%2Fpyproject.toml)](https://pypi.org/project/js_dependency_audit/)
[![PyPI - Version](https://img.shields.io/pypi/v/js_dependency_audit)](https://pypi.org/project/js-dependency-audit/)

A library to help perform a security audit check using a yarn (v1) lock file.

Not intended to be used as a standalone tool, but as part of a system
periodically checking for vulnerabilities.

## Usage

```python
from js_dependency_audit.lock_file_content import LockFileContent
from js_dependency_audit.security_audit_request import request_security_audit

lock_file_content = LockFileContent.from_yarn_file("files/yarn.lock")
audit_data = request_security_audit(lock_file_content)
```

## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/jeromediaz/js-dependency-audit/blob/main/LICENSE) file for details.
ipo 2.0.0
Infix piping operator
Requires Python: >=3.8

ipo: Infix piping operator

## Demo
```python
from ipo import ipo, opi, head, write, p

# Ipo makes data flows much easier to follow.
ipo([5, 2, 3, 1, 4]) | sorted                     | opi  # [1, 2, 3, 4, 5]
# Ipo comes with a shorthand for partial application:
#   p(head)(3) is equivalent to functools.partial(head, 3)
ipo([5, 2, 3, 1, 4]) | p(head)(3)          | list | opi  # [5, 2, 3]
ipo([5, 2, 3, 1, 4]) | p(head)(3) | sorted        | opi  # [2, 3, 5]
ipo([5, 2, 3, 1, 4]) | sorted | p(head)(3) | list | opi  # [1, 2, 3]
# Much more readable than the original!
#   list(itertools.islice(sorted([5, 2, 3, 1, 4]), 3))

# You can also pipe to print directly…
ipo([5, 2, 3, 1, 4]) | sorted | p(head)(3) | list | print  # [1, 2, 3]
# …or to ipo's write, which prints each line of an iterable on its own line.
ipo([5, 2, 3, 1, 4]) | sorted | p(head)(3) | list | write  # 1\n2\n3
```

```python
from itertools import starmap
from ipo import from_csv, skip, to_csv, before, write, p

# Ipo is ideally suited for working with CSV data.
ipo("""
#Voltage,Current
12,1
8,2
220,6
""".strip().split()) | from_csv | p(skip)(1) | \
	p(starmap)(lambda v, a: (v, a, float(v) * float(a))) | \
	to_csv | p(before)(["#Voltage,Current,Power"]) | write
# #Voltage,Current,Power
# 12,1,12.0
# 8,2,16.0
# 220,6,1320.0
```

```python
from ipo import read, write, p

# Most ipo chains that work over iterable data are lazy by default. You can load huge files,
# process them and write the results to another file.
with open("bigfile.txt") as f_in, open("processed.txt") as f_out:
	read(file=f_in) | p(map)(some_preprocessing) | \
	p(map)(some_advanced_function) | \
	p(map)(some_formatting) | p(write)(file=f_out)
```