semvec/pypi-packages huggingface dataset:
datai 1.5.3
A library for data visualization and cleaning
Requires Python: >=3.7


# Datai

Welcome to **Datai**. This library provides tools to visualize data easily, clean datasets, and automatically generate appropriate plots for your data. Whether you're a beginner or an experienced data scientist, this library aims to simplify the data exploration and visualization process.

## Features

- **Data Visualization**: Easily create bar charts, scatter plots, histograms, and more with simple functions.
- **Data Cleaning**: Clean and preprocess your data with built-in utilities for handling missing values, normalizing data, and more.
- **Auto Plotting**: Automatically generate the most suitable plot based on your dataset's characteristics.
- **Example Datasets**: Load popular datasets like Iris, Titanic, and more for quick experimentation and testing.

## Installation

To install the library, use pip:

```bash
pip install datai
```

## Basic Usage

### 1. Visualize Data

Create simple visualizations with just a few lines of code:

```python
# Example: Create a bar chart

from datai.visualization import Examples
Examples.barchart()
```

### 2. Clean Data

Easily clean and preprocess your data:

```python
# Data cleaning
import seaborn as sns
from datai.data_cleaning import DataCleaning

# Load the Iris dataset
iris_data = sns.load_dataset('iris')

# Clean the data
cleaner = DataCleaning(iris_data)
cleaned_data = cleaner.get_cleaned_data()
```

### 3. Auto Plot

Automatically generate a plot based on your dataset:

```python
# Auto plot a dataset
import seaborn as sns
from datai.auto_plot import AutoPlot

tips = sns.load_dataset('tips')

AutoPlot.auto_plot(tips)
```

You can also plot a chart of your choice:

```python
x_data = tips['total_bill']
y_data = tips['tip']
AutoPlot.plot_scatter_plot(x_data, y_data)
```

### 4. Gift

Here's an interesting feature in the library. You can animate your name:

```python
from datai.gift import Gift
Gift.myName("Datai")
```

## Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue if you have any suggestions or improvements.

## Contact

For any questions or feedback, please reach out at [m.ans.cs@outlook.com](mailto:m.ans.cs@outlook.com).

Thank you for using **Datai**! We hope it makes your data analysis journey easier and more enjoyable.
```
docs-maker 1.0.0.dev4
Documents Maker
Requires Python: <4.0,>=3.9

# docs-maker

docs-maker — это удобный и мощный инструмент для автоматизации создания документов. С его помощью можно быстро и легко генерировать различные документы, используя шаблоны и интеграции. Подходит как для личных, так и для профессиональных проектов, где требуется гибкость и масштабируемость в создании документации.
Основные возможности:

- Генерация документов на основе пользовательских шаблонов.
- Поддержка различных форматов вывода.
- Интуитивно понятный графический интерфейс.
- Легкость в интеграции с другими системами и приложениями.

## Быстрый старт:
### Сборка
Для начала выполните сборку проекта:
~~~
poetry build
~~~

### Установка
Чтобы установить docs-maker, выполните команду:
~~~
pip install .
~~~
### Запуск
После установки запустите программу:
~~~
docs-maker
~~~
Для получения справки по командам и возможностям используйте:
~~~
docs-maker --help
~~~

### Запуск тестов
Чтобы запустить тесты, выполните команду:
~~~
python -m pytest tests
~~~

### Графический интерфейс (GUI)
Для работы с графическим интерфейсом программы можно использовать одну из команд:
~~~
docs-maker gui
~~~
или

~~~
python -m docs_maker gui
~~~
или
~~~
python -m docs_maker_gui
~~~
lucie 1.0.2
A package that will automatically determine the dataset scheme/format for a UCI dataset, and import it to the best of its ability. Preserves the UCI Python import API. Please see https://github.com/ArnaoutLab/Lucie
Requires Python: >=3.8

# Lucie

A Python package that will automatically import un-importable datasets on the UCI ML repository, by using heuristics to figure out how each dataset is likely to be laid out. Our package name "Lucie" is inspired by its function: loading UCI repos

Preserves the UCIML API to the best of its ability, with one minor change (see below). 

Succeeds on 95.4% of the top 250 datasets, and includes manually cleaned datasets to allow all top 100 datasets to be imported. 

GitHub: https://github.com/ArnaoutLab/Lucie

PyPI Package: `pip install lucie`

ArXiV Paper: 

## Usage and API Changes

For previously importable datasets, we have one minor change: instead of returning the dataset itself, this program will return a tuple, with the first element indicating the dataset format, and the second representing the dataset. 

Example:
```python
from lucie import fetch_ucirepo 
```
```diff
# fetch dataset 
- iris = fetch_ucirepo(id=53)
+ iris = fetch_ucirepo(id=53)[1]
```
```python
# data (as pandas dataframes) 
X = iris.data.features 
y = iris.data.targets 
  
# metadata 
print(iris.metadata) 

# variable information 
print(iris.variables) 
```

For datasets that were previously not importable, but are importable via `lucie`, we return a dictionary, with keys representing filenames and values representing the associated Pandas dataframe. For example:

```python
from lucie import fetch_ucirepo
diabetes = fetch_ucirepo(name='diabetes')
print(diabetes)

"""
output is:
('custom',
 {'diabetes_complete.tsv':
  0      04-21-1991   9:09  33  009
  1      04-21-1991   9:09  34  013
  2      04-21-1991  17:08  62  119
  3      04-21-1991  17:08  33  007
  ...
 }
)
"""

print(diabetes[1]['diabetes_complete.tsv'])

"""
output is:
0      04-21-1991   9:09  33  009
1      04-21-1991   9:09  34  013
2      04-21-1991  17:08  62  119
3      04-21-1991  17:08  33  007
4      04-21-1991  22:51  48  123
...    ...          ...   .. ...
[29329 rows x 4 columns]
"""
```

In some rare cases, we instead return a JSON dictionary representing the directory structure. However, in practice we did not encounter this in any of the top 250 datasets. If you would like to see how that API would work, we have created a bogus dataset to test this functionality. You can try it by running this script: 

```python
from lucie import extract_and_get_path

extract_and_get_path('bogus_sets/bogus_json.zip') # bogus json dataset
```