semvec/pypi-packages huggingface dataset:
pyg-downloader 0.0.2
python download manager
Requires Python: >=3.6
Keywords: download manager downloader file downloader

# pyg-downloader

Uses multiple parallel connections to download a file.

## Testing
```bash
pytest
```

## Install
```bash
pip install pyg-downloader
```
**OR** if you want to show progress
```bash
pip install pyg-downloader[atpbar]
```

## Usage

### CLI
```bash
pyg-downloader https://download.samplelib.com/mp4/sample-15s.mp4 -o test.mp4 --max-connections 8 --progress
```

### To Download to disk
```py
from pyg_download_manager import DownloadManager

manager = DownloadManager(max_connections=8, show_progress=True)

# Download to disk and returns the path to the file
# destination_path is the path to the folder for download
# filename is the name of the file to save to. Default is the filename from url.
path = manager.download("https://download.samplelib.com/mp4/sample-15s.mp4", destination_path='./', filename=None)
```

### To Download to memory
```py
from pyg_download_manager import DownloadManager

manager = DownloadManager(max_connections=8, show_progress=True)

# Download data to an BytesIO object and returns the object
# task_name is used to log progress. Default is the filename from url.
data = manager.get("https://download.samplelib.com/mp4/sample-15s.mp4", task_name='Test Task')
```
fill-dt-data 0.0.7
A tool to fill column values in Pandas
Requires Python: >=3.8

# Fill Datetime Data

This is a simple python script which fills in data using Pandas in Python using the aggregate mean of the data you wish to fill. 

Importing should look like:

from fill_dt_data import fill_data

The script requires two columns, one labelled exactly "data" (case sensitive) that contains datetime data in YYYYmmdd format and at least one other column that contains data that you wish to replace.

The function takes in a DataFrame, the name of the column that you wish to fill, and the starting and ending dates for the time frame that you wish to fill. It then uses up to the decade of time surrounding your dates and takes the average value for each day in each month, then fills your dates with those averages.