semvec/pypi-packages huggingface dataset:
pyg-npy 0.0.9
Utilities for data manipulation including creation of DAGs and tables
Requires Python: >=3.6

# pyg-npy

pip install from https://pypi.org/project/pyg-npy/

conda install from https://anaconda.org/yoavgit/pyg-npy

A quick utility to save dataframes as npy files. 

It supports append and checks lightly on column names matching and index.

For simple read/write/append, it is about 5-10 times faster than parquet writing or pystore. 

```
import numpy as np; import pandas as pd
from pyg_npy import pd_to_npy, pd_read_npy
import pystore
import datetime

pystore.set_path("c:/temp/pystore")
store = pystore.store('mydatastore')
collection = store.collection('NASDAQ')
arr = np.random.normal(0,1,(100,10))
df = pd.DataFrame(arr, columns = list('abcdefghij'))
dates = [datetime.datetime(2020,1,1) + datetime.timedelta(i) for i in range(-10000,0)]
ts = pd.DataFrame(np.random.normal(0,1,(10000,10)), dates, columns = list('abcdefghij'))

### write
%timeit collection.write('TEST', df, overwrite = True)
19.5 ms ± 1.97 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit df.to_parquet('c:/temp/test.parquet')
9.53 ms ± 650 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit pd_to_npy(df, 'c:/temp/test.npy')
947 µs ± 38.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)


### read
%timeit collection.item('TEST').to_pandas()
7.7 ms ± 171 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit pd.read_parquet('c:/temp/test.parquet')
2.85 ms ± 54.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit pd_read_npy('c:/temp/test.npy')
847 µs ± 39.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

### append
# because we need to append data with increasing index value, we use this:

collection.write('TEST2', ts.iloc[:100], overwrite = True)
%time len([collection.append('TEST2', ts.iloc[i*100: i*100+100], npartitions=2) for i in range(1,100)])
Wall time: 12.1 s

pd_to_npy(ts.iloc[:100], 'c:/temp/test.npy')
%time len([pd_to_npy(ts.iloc[i*100: i*100+100], 'c:/temp/test.npy', 'a', True) for i in range(1,100)])
Wall time: 2.14 s

```
microyc 0.1.2
yandex.cloud minimalistic sdk
Requires Python: >=3.8, <4
Keywords: yandex,yandex.cloud

# microyc

minimalistic sdk for operating with yandex.cloud

project currently abandonned for nanoyc project, but still works fine

## Installation

Just as simple as `pip install microyc`

## Authorization

for Amazon services autorize like with boto3

Read more at https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html

### Use .aws file

store it in ~/.aws with credentials~/.aws/credentials

### Enviromental variables (recomended)

`AWS_ACCESS_KEY_ID` - The access key for your AWS account.
`AWS_SECRET_ACCESS_KEY` - The secret key for your AWS account.

to get this key generate a new static key for your service account at yandex cloud console


## using Object storage

predefine `BUCKET` environment variable with name of bucket

    from microyc import Bucket

    b=Bucket()  # or b=Bucket(BucketName='my-bucket')
    b.upload('my_file.txt')

## more 

u can user FifoQuequest, start virtual machines, use DocumentDatabase  and even suicide a turned virtual machine

check `objects.py` file

## revision history

0.0.6 fixed fifo recv() issue
0.0.5 Bug fixes and Bucket.upload_unique
0.0.4 added FifoQueue
IMDbPlayer 1.0.4
A simple script to play movies and TV shows from IMDb using 2embed
Requires Python: >=3.6

# 2EmbedPlayer

*A simple Python script that plays movies and TV shows using IMDb and 2embed.*

## Features
- **Simple**: Easy command-line arguments
- **Large library**: Supports a large library frequently updated by 2embed
- **Integratable**: Easily integrate into popular launchers, like Alfred

## Quick Start
Install using pip:
```
pip install IMDbPlayer
```
If you like, you may also use it by downloading and running `__main.py__`

## Usage
2EmbedPlayer works simply - you can use it with a few command-line arguments.
- `IMDbPlayer name` is used for playing movies.
- `IMDbPlayer name season episode` is used for TV shows.

## Additional Configuration
2EmbedPlayer can be integrated into Alfred on macOS. This allows easy access to playing movies and TV shows by simply searching, rather than opening a terminal.
### Prerequisites 
- As of 2023, this requires the Alfred Powerpack, which can be purchased from https://www.alfredapp.com/powerpack/
- The source of 2EmbedPlayer

### Installation
1. Create a workflow in Alfred
2. Add the keyword, e.g. `play`
3. Set `Argument Required`
4. Add `Run Script`
5. Set the contents to `python3 *location of __main__.py* '{query}'`
6. Set inputs to `with input as {query}`
7. Save and use!

This should relatively easily integrate 2EmbedPlayer into macOS, and this may be possible with other launchers -- I haven't tried others.