semvec/pypi-packages huggingface dataset:
LoIpGen 0.0.2
Simple Lorem Ipsum Generator
Requires Python: >=3.8

# LoIpGen

This is a simple package to generate Lorem Ipsum text with as many characters as you want

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install LoIpGen

```bash
pip install LoIpGen
```

## Usage

```python
from LoIpGen.LoIpGen import generate_lorem
print(generate_lorem(100))
```

## Author
NoemyT

## License
[MIT](https://choosealicense.com/licenses/mit/)
win-all-env 1.1.3
windows env
Requires Python: >=3.12.3
Keywords: win-env

# win_all_env 1.1.3

This is a library that allows you to display and set Windows system environment variables and user environment variables.

## Description

This module uses the winreg module to edit the location of environment variables.

## Usage

### env_type

#### ```win_env.user```

Specifies the user environment variables.

#### ```win_env.systems```

Specifies the system environment variables.

#### ```win_env.custom```

Think of it as a carry-over variable.
It's stored in a Registry so it can be easily seen from outside.

Registry location: ```HKEY_CURRENT_USER\Software\python-lib\3.12\win_env\custom_env```

#### ```win_env.volatile``` (beta)

Specifies a volatile environment variable.
There seems to be a bug in Windows so it may not work properly.

### write_mode

#### 1

  Write it in REG_SZ format, not in list format, which indicates that you haven't specified a location.

#### 2

  Specify 2 to specify location or list format, written as REG_EXPAND_SZ.

### Multiple Placement Commands

#### ```win_env.open(env_type)```

You can use either a variable or with.

Available commands

##### ```list(only=None)```

Same as list command

##### ```get(key)```

Same as get command

##### ```set(key, value, write_mode=1)```

Same as set command

##### ```dels(key)```

Same as dels command

##### ```add(key, value, write_mode=1)```

Same as add command

### Single command

#### ```win_env.list(env_type, only=None)```
  Lists the contents of the environment variables.

Only args: False will return a list of just the values, True will return just the keys, None will return both.

#### only

False returns a list of only the values. True returns only the keys.
defolt = None

#### ```win_env.get(env_type, key)```

Displays the contents of the environment variable named key.

#### ```win_env.set(env_type, key, value, write_mode)```

Write the environment variables.

Note: Run as administrator to write system environment variables.

#### ```win_env.all_list()```

Returns all environment variables as a dictionary+list.

#### ```win_env.dels(env_type, key)```

Removes an environment variable. Returns Env NotFoundError of the environment variable does not exist.

Note: Admin privileges are required for system environment variables.

#### ```win_env.add(env_type, key, value, write_mode)```


Adds the environment variable if it doesn't exist, raises an EnvError if it does exist.

## How to install

```pip install git+https://github.com/akino11/win_env.git```

in the command prompt.
h1b-processor 1.73
A package for cleaning data frames.
Requires Python: >=3.6

# Data Cleaner

Data Cleaner 🧹 is a Python package created to simplify cleaning data frames from DoL Performance Data. As an international student juggling campus jobs, adjusting to a new culture, and pushing through a master’s program, life can get super busy. 🏫🌍

That’s why I built this package—to help others save time ⏳ and focus on what really matters: landing that dream job 🎯. With this tool, you can easily type in the job you’re aiming for and get a detailed comparison of companies, roles, and locations that match your needs.

At the heart of it 💙, this package is all about cutting down the hours spent cleaning through massive datasets so you can focus on making informed, life-changing decisions 🔑 for your future! 🚀
PS: Experienced folks can work their way too because H1b's are issued to everyone.

## Installation

You can install the package using pip:

```bash
pip install h1b-processor

from clean_h1b import data_cleaner

# Create an instance of data_cleaner
processor = data_cleaner(keywords = 'data')

# Process the DataFrame
processed_df = processor.process()

# Display the processed DataFrame
print(processed_df)

#if you want csv, then:
processed_df.to_csv('outputfile.csv')

# Display the processed DataFrame
print(processed_df) #optional

```
Some examples are:

Physician:
![image](https://github.com/user-attachments/assets/d6975628-c5a9-400e-b036-4c0c0eccae36)

Attorney:
![image](https://github.com/user-attachments/assets/39e8b788-9611-4d4c-b351-438c623465a2)

Software Engineer:
![image](https://github.com/user-attachments/assets/16d465dd-86f1-40b7-9676-42a32a3d34d6)

Financial Analyst:
![image](https://github.com/user-attachments/assets/ca1bce41-9ba9-46d6-866d-3f18bffefd28)

Psychologist:
![image](https://github.com/user-attachments/assets/e0ab969d-c602-40ef-a8b3-0552f05a924f)

Experienced Folks, listen up 📢 🏫 🌍

Looking at becoming Partner? :
![image](https://github.com/user-attachments/assets/da558123-71dd-4318-a59c-fbb7d434262c)
climate-hurricane-analysis 0.1.2
A package for hurricane analysis using SST data.
Requires Python: >=3.6

# Introduction

This Python extension provides tools to analyze the impact of sea surface temperatures on hurricane intensity using Accumulated Cyclone Energy (ACE) as a metric. It includes optimization algorithms to improve prediction accuracy by tuning parameters such as the number of Principal Components (PCs), lag months, and analog years.

The extension is designed for researchers, students, and weather enthusiasts interested in understanding the relationship between climate factors and hurricane development.

# Features

- Reads in ACE and Explained Variance Ratio values
- Creates a vector for each year of their PCs and corresponding lag months

# Requirements/Installation
Download sst.mon.mean.nc from NOAA: https://downloads.psl.noaa.gov/Datasets/noaa.oisst.v2.highres/


# Usage
```
from hurricane_analysis.pca_calculator import load_sst_data, extract_sst_for_region, process_monthly_sst_data, calculate_pcs_and_evr
from hurricane_analysis import calculate_start_date, optimize_pc_analog_and_lag_configuration, read_ace_data

import pandas as pd
```
Functions:

calculate_pcs_and_evr(file_path, lat_min, lat_max, lon_min, lon_max)  
- Parameters define the region you want to analyze and calculates PCs and EVR values for it.  
- Outputs SST and EVR directory with csv files necessary for analysis

read_ace_data(ace_file_path)  
- Returns a pandas DataFrame that contains ace data from a given CSV

optimize_pc_analog_and_lag_configuration(data_directory, evr_directory, ace_data, target_year, target_month)  
- Parameters define which data you want to use and which month and year you want to analyze  
- Returns best_num_pcs, best_num_analogs, best_lag_months, highest_skil, best_ace_fcst, best_top_analogs
py-anansi 0.0.0.0
`anansi` (`ANother ANSI`) is yet antoher python library for ANSI escape characters.
Requires Python: >=3.7
Keywords: ansi, color, escape, terminal

# anansi
`anansi` (`ANother ANSI`) is yet antoher python library for ANSI escape characters. The name "anansi" was inspired by the Akan folktale character Anansi.

# Install
```bash
python3 -m pip install py-anansi --upgrade
```

# Usage

## Colors
```python
from anansi.font.colors import background as bg
from anansi.font.colors import foreground as fg

print(fg.red+bg.blue + "Hello" + bg.off + " World!" + fg.off)
```

## Effects
```python
from anansi.font import effects as eff

print(eff.underline + "Hello" + eff.underline_off+eff.blink + " World!" + eff.blink_off)
```

# License
MIT