| Name | nanos JSON |
| Version |
0.1.8
JSON |
| download |
| home_page | https://github.com/aleosd/nanos |
| Summary | Collection of small utility-functions |
| upload_time | 2025-08-28 13:53:29 |
| maintainer | None |
| docs_url | None |
| author | Alex |
| requires_python | <4.0,>=3.10 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Nanos
*Nanos* is a collection of small but handy Python utilities: different
functions, classes and mixins. The library has zero dependencies and relies
only on built-in Python modules.
Complete documentation: https://nanos.readthedocs.io/en/latest/index.html
## Features
* **Data Processing** - Utilities for working with data structures (chunking, ID mapping, empty value handling)
* **Date & Time** - Helper functions for common date operations and time measurements
* **Formatting** - Human-readable formatting for data types (file sizes, etc.)
* **Logging** - Simple logging setup and convenient LoggerMixin
* **Zero Dependencies** - Works with just the Python standard library
## Installation
Library is available on PyPI and can be installed using pip:
```bash
pip install nanos
```
## Quick Examples
### Format file sizes
```python
from nanos import fmt
print(fmt.size(1024)) # 1.00 KiB
print(fmt.size(1572864)) # 1.50 MiB
print(fmt.size(3.5 * 10**9)) # 3.26 GiB
```
### Measure execution time
```python
import time
from nanos import time as ntime
with ntime.Timer() as t:
time.sleep(1.5)
print(t.elapsed) # 1.5033...
print(t) # 0:00:01.50
```
### Date helpers
```python
from nanos import dt
import datetime
# Get tomorrow's date in UTC
tomorrow = dt.tomorrow()
# Get yesterday's start/end timestamps
day_start = dt.yesterday_start()
day_end = dt.yesterday_end()
```
### Data processing
```python
from nanos import data
# Split a list into chunks
chunks = data.chunker(range(10), 3) # [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
# Convert a list of objects to a dict indexed by ID
users = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]
users_by_id = data.idfy(users) # {1: {"id": 1, "name": "Alice"}, 2: {"id": 2, "name": "Bob"}}
# Remove empty values from nested data
cleaned = data.remove_empty_members({"user": {"name": "Alice", "bio": ""}}) # {"user": {"name": "Alice"}}
```
### Simple logging setup
```python
from nanos import logging
# Get a pre-configured logger with console output
logger = logging.get_simple_logger(name="myapp")
logger.info("Application started") # 2023-05-01T12:34:56 myapp INFO Application started
# Use LoggerMixin in your classes
class MyService(logging.LoggerMixin):
def process(self):
self.logger.debug("Processing started") # mypackage.MyService DEBUG Processing started
```
## Type Hinting
Nanos is fully typed and includes a `py.typed` marker file for better IDE support.
## License
The library is released under the [Apache License 2.0](LICENSE)
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