semvec/pypi-packages huggingface dataset:
drivescanner 0.0.4
Scan your filesystem to look for files that are a potential GDPR risk
Requires Python: >=3.9

# DriveScanner

DriveScanner is a Python library created by [The Analytics Lab](https://www.theanalyticslab.nl/), which is powered by [Cmotions](https://cmotions.nl/). This library aims to help you with identifying files on your filesystem that could be a potential GDPR threat. To do this, the file contents are scanned, looking for specific information like IBAN, social security numbers (Dutch: BSN), telephone numbers, email addresses, credit card numbers and more. 

## Installation
Install DriveScanner using pip

```bash
pip install drivescanner
```

## Usage
```python
import drivescanner

# set the location of the files you want to scan
# all files in all subdirectories will also be taken into account
file_path = "C:/MyFiles"
file_list = drivescanner.list_all_files(file_path)

# create an overview of all the filetypes on our example drive
drivescanner.extension_stats(file_list)

# if we want we can include/exclude certain extensions
file_list = drivescanner.select_files(file_list, include=["xlsx", "xls", "docx", "doc", "pdf", "ppt", "pptx"], exclude=None)

# now we are ready to scan all the files in the list
resultdict = drivescanner.scan_drive(file_list)

# and calculate the risk score for all scanned files
# there might be some files which gave problems and are not scanned
# your retrieve those in a separate dataframe
df_result, df_not_processed = drivescanner.calculate_severity(resultdict)

# that's it, now you can use and inspect the result any way you like
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.

## License
[GNU General Public License v3.0](https://choosealicense.com/licenses/gpl-3.0/)

## Contributors
Jeanine Schoonemann, Rick Flamand, Sem Frankenberg, Wim Verboom and Wouter van Gils<br>
[Contact us](mailto:info@theanalyticslab.nl)
qpandas 1.0.2
Query Panadas DataFrames with SQL
Requires Python: >=3.10

# qpandas


---


WORK IN PROGRESS


---


Query Panadas DataFrames with SQL


## Install

Currently available on [PyPI](https://pypi.org/project/qpandas/), to install:
```
pip install qpandas
```

## Example

```py
import pandas as pd

import qpandas as qpd
from qpandas import load_births

births = load_births()
assert type(births) == pd.DataFrame
query = """
SELECT b1.date d1
    , b1.births b1
    , b2.date d2
    , b2.births b2
FROM births b1, births b2
"""
df = qpd.mysql(query, locals())  # or .oracle, .spark, etc...
assert type(df) == pd.DataFrame
print(df)
```
```
                                d1      b1                          d2      b2
0       1975-01-01 00:00:00.000000  265775  1975-01-01 00:00:00.000000  265775
1       1975-01-01 00:00:00.000000  265775  1975-02-01 00:00:00.000000  241045
2       1975-01-01 00:00:00.000000  265775  1975-03-01 00:00:00.000000  268849
3       1975-01-01 00:00:00.000000  265775  1975-04-01 00:00:00.000000  247455
4       1975-01-01 00:00:00.000000  265775  1975-05-01 00:00:00.000000  254545
...                            ...     ...                         ...     ...
166459  2012-12-01 00:00:00.000000  340995  2012-08-01 00:00:00.000000  359554
166460  2012-12-01 00:00:00.000000  340995  2012-09-01 00:00:00.000000  361922
166461  2012-12-01 00:00:00.000000  340995  2012-10-01 00:00:00.000000  347625
166462  2012-12-01 00:00:00.000000  340995  2012-11-01 00:00:00.000000  320195
166463  2012-12-01 00:00:00.000000  340995  2012-12-01 00:00:00.000000  340995

[166464 rows x 4 columns]
```

### Example explanation
TODO
kriegspiel 1.1.1
Python Kriegspiel Game
Requires Python: >=3.7

# Python Kriegspiel Game

[![Build Status](https://travis-ci.org/Kriegspiel/ks-game.svg?branch=master)](https://travis-ci.org/Kriegspiel/ks-game)
[![PyPI version](https://badge.fury.io/py/kriegspiel.svg)](https://badge.fury.io/py/kriegspiel)
[![codecov](https://codecov.io/gh/Kriegspiel/ks-game/branch/master/graph/badge.svg)](https://codecov.io/gh/Kriegspiel/ks-game)

---

## Kriegspiel Game Engine

Supported Kriegspiel rules:

1. Berkeley
2. Berkeley + Any

In plan:

1. Crazykrieg
2. Crazykrieg + Any


## How-to

### Install

```bash
pip install kriegspiel
```

### Run tests

```bash
# clone this repo
git clone git@github.com:Kriegspiel/ks-game.git
# activate virtual env (optional)
cd ks-game
python3 -m venv ks-game-env
source ks-game-env/bin/activate
# install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt
# run tests
python -m pytest tests/
```

### Lint

```bash
black -l 128 %FILE%
```
python-storage-timeline-ta-indicators 0.1
Tool to calculate technical analysis indicators in a continuous time series data

# python_storage_timeline_ta_indicators
This library reads, cleans and sorts data acquired from web database. 
Values in a storage expected to be in a json format with a following structure:

```{"time":1684968241256,"value":"{\"r\":[49208.040463174424,730195614842.3989]}"}```

Class data aggregation takes link as an argument to create instance of a database. The data from the database could be updated manualy via ```update``` function.

To calculate ta indicators function ```print_indicators``` should be used that takes next arguments:


```time``` in "2D", "4S" or "W" format that denotes length of even interwals time series will be divided.


```length``` takes int input and stands for number of intervals that will be used to calculate the indicator.


```indicators``` takes one (or multiple) of 'RSI', 'STOCH', 'STOCHRSI', 'ADX', 'MACD', 'WILLIAMS', 'CCI', 'ATR', 'HIGHLOW', 'ULTOSC', 'ULTOSC', 'ROC', 'MA'. If is None
calculates all of listed.

**IMPORTANT**
Some indicators require additional arguments. They should be added to ```print_indicators``` function with names used in ```ta-lib```.

### CODE EXAMPLE ###

```
from dataaggregation.aggregation import data_aggregation

my_data = data_aggregation('thelinkgoeshere')
my_data.get_data()
my_data.print_indicators('D', length = 14, smooth_step=6, indicators = ['RSI'])
```

## Anomaly detection ##

Should only be used with a great understanding of features of data it was trained on. Works best on a volatile time series with high liquidity.  

### CODE EXAMPLE ###

```
from dataaggregation.aggregation import lstm_anomaly_detection

anms = lstm_anomaly_detection(url = 'thelinkgoeshere')
anms.detect_anomalies(threshold=1.25)
```

#### Output example####

![Output sample](https://github.com/C1l1r/python_storage_timeline_ta_indicators/blob/lstm_functional_branch/detected%20anomalies%20sample.png?raw=true)

#### NOW AVALIABLE ON PIP! #####

pip install python_storage_timeline_ta_indicators