semvec/pypi-packages huggingface dataset:
maskpw 0.5.5
A simple library to ask the user for a password. Similar to getpass.getpass() but allows to specify a default mask (like '*' instead of blank).
Requires Python: ~=3.4

# Maskpw

A simple library to ask the user for a password. Similar to getpass.getpass() but allows to specify a default mask (like \* instead of blank).

## Features 🧰

-   Customize prompt showed to the user.
-   Use a custom mask to hide the password.

## Installation 📦⬇

Use [pip](https://pip.pypa.io/en/stable/) to install it.

```bash
python3 -m pip install maskpw
```

## Usage 🛠

To use it, just import the `get_password` function and call it with a prompt (`"Password: "` by default):

```python
from maskpw import get_password

if __name__ == "__main__":
    username = input("Username: ")
    password = get_password()
    print(f"Username: {username}; Password: {password}")
```

This will show the following:

```console
$ python test.py
Username: John Doe
Password: *****
Username: John Doe; Password: 12347
```

If you want to change the mask:

```python
from maskpw import get_password

if __name__ == "__main__":
    username = input("Username: ")
    password = get_password(mask="#")
    print(f"Username: {username}; Password: {password}")
```

Now it will look like:

```console
$ python test.py
Username: John Doe
Password: #####
Username: John Doe; Password: 12347
```

## Contributing ✍

[Pull requests](https://github.com/harmony5/maskpw/pulls/new) are welcome. For major changes, [bug fixes](https://github.com/harmony5/maskpw/issues/new?template=bug_report.md&labels=bug&projects=harmony5%2Fmaskpw%2F1) or [feature requests](https://github.com/harmony5/maskpw/issues/new?template=feature_request.md&projects=harmony5%2Fmaskpw%2F1), please open an issue first to discuss what you would like to change.

## License 📜⚖

This project uses the [MIT](https://choosealicense.com/licenses/mit) license.
OpenHowNet 2.0
OpenHowNet
Requires Python: >=3.6

OpenHowNet API is developed by [THUNLP](http://thunlp.org/), which provides a convenient way to search information in HowNet, display sememe trees, calculate word similarity via sememes, etc. You can also visit our [website](https://openhownet.thunlp.org) to enjoy searching and exhibiting sememes of words online.
blackbeard2109 0.2.3
This library is designed for people who need to optimize time in an agile way with an ease of understanding and could
Requires Python: >=3.6

# Blackbeard Library. A python’s life for me!
This library is designed for people who need to optimize time in an agile way with an ease of understanding, as well as dealing with projects under the umbrella of Data Science, including cleaning data frame (including images), visualization and machine learning.

## Overview
This library consists of 3 parts that are the fundamental aspects of Data Science:

### Machine Learning
These functions are for ML-based projects and speed up their execution:
grid_search, models, NN_conv_model, model_train, load_model, save_model, basic_regression_models, regression_errors,print_regress_metrics, print_classif_metrics, split_and_scale, categorical_encoding, automl, captain.

### Visualization
These functions allow us to optimize the execution of the graphs in time:
draw_boxplot, draw_donut_chart, draw_maps, draw_three_countplot, show_roc_curve, sort, pairplot_heatmap, draw_missing_ratio, draw_statistic_values, draw_target_transformation, draw_feat_importance, draw_maps, draw_donut_chart, draw_three_countplot, show_roc_curve.

### Data Cleaning
Using the following functions, we are able to increase the efficiency when it comes down to cleaning data and images:
get_root_path, delete_duplicates, missing_values, save_col_names, Unix_to_UTC, download_csv, split_datetime, max_min, null_to_0, null_to_mean, categorical_numeric, df_information, merge_df, drop_NaN, eleate_column, replace_by_numpy, replace_string, percentage, sort_column, outliers, sales_by_category, month_to_name, columns_dtype,  get_products_that_are_sold_together, frames_from_video, images_dataset_properties, read_images, flip_images, resize_images
edit_images, color2gray, negative_colors_images, monocolor_images, images_load_data, save_images.

## Usage
In the following paragraphs, we are going to describe how you can use Blackbeard:

##  Getting it
To download Blackbeard, either fork this github repo or simply use Pypi via pip.
```sh
pip install blackbeard2109
```

## Using it
Blackbeard was programmed with ease-of-use in mind. First, import Blackbeard and then call any of the functions based on your needs.

If you are a Mac user, consider the following: When you are executing a function for images, by default, it installs the file called .DS_Store on your computer. In order for execute the function correctly, what you have to do is the following: right on click on the images folder that you downloaded, click on a terminal, and execute the following code:  find . -name '.DS_Store' -type f -delete.