semvec/pypi-packages huggingface dataset:
bp-data-grid 1.0.8
Show data in data grid
Requires Python: >=3.8.10

# Data Grid

**Data Grid** is a Python package that allows you to effortlessly present tabular data in a visually appealing format. With built-in features such as client-side filtering, pagination, and export options, it provides a comprehensive solution for displaying and manipulating data.

## Features

- **Tabular Data Display**: Easily present data in a grid-like structure for clear and organized visualization.
- **Client-Side Filtering**: Enable users to quickly search and filter data within the grid, improving data exploration and analysis.
- **Pagination:** Divide large datasets into manageable chunks, enabling smooth navigation and enhanced performance.
- **Export Options**: Provide users with the ability to export grid data to various formats such as CSV or Excel, facilitating data sharing and external analysis.
- **Data Masking**: Ensure data security by incorporating data masking capabilities during export, safeguarding sensitive information from unauthorized access.

## Installation

You can install the **Data Grid** package from PyPI using pip:

```shell
pip install bp-data-grid
```

## Usage

To use the Data Grid component in your Streamlit application, follow these steps:

1. Import the necessary modules:

```shell
import data_grid as dg
```

1. Add the **Data Grid** component to your application:

```shell
dg.data_grid(
    title="Results",
    rows=rows,
    columns=columns,
)
```

1. Run your Streamlit application:

```shell
streamlit run your_app.py
```

### Example

```shell
import data_grid as dg

columns = [
  {
    "field": "id",
    "headerName": "ID",
    "width": 90
  },
  {
    "field": "firstName",
    "headerName": "First Name",
    "width": 150,
    "editable": True
  },
  {
    "field": "lastName",
    "headerName": "Last Name",
    "width": 150,
    "editable": True
  },
  {
    "field": "age",
    "headerName": "Age",
    "type": "number",
    "width": 110,
    "editable": True
  }
]

rows = [
    {"id": 1, "lastName": "Snow", "firstName": "Jon", "age": 35},
    {"id": 2, "lastName": "Lannister", "firstName": "Cersei", "age": 42},
    {"id": 3, "lastName": "Lannister", "firstName": "Jaime", "age": 45},
    {"id": 4, "lastName": "Stark", "firstName": "Arya", "age": 16},
    {"id": 5, "lastName": "Targaryen", "firstName": "Daenerys", "age": 90},
    {"id": 6, "lastName": "Melisandre", "firstName": "Tom", "age": 150},
    {"id": 7, "lastName": "Clifford", "firstName": "Ferrara", "age": 44},
    {"id": 8, "lastName": "Frances", "firstName": "Rossini", "age": 36},
    {"id": 9, "lastName": "Roxie", "firstName": "Harvey", "age": 65},
]

dg.data_grid(
     title="Results",
     rows=rows,
     columns=columns,
     key="data_grid",
)

```

## Screenshot

![Screenshot](https://i.ibb.co/NsfwfY6/eintrag-Streamlit.png "Screen Shot")
execy 0.1.9
The ececy exec_time decorator is a utility that measures the execution time of a function. It can be used to easily track and log the time taken by a function to execute, both for synchronous and asynchronous functions.
Requires Python: >=3.8

# Execy
[![Upload Python Package](https://github.com/hglong16/execy/actions/workflows/python-publishing.yml/badge.svg)](https://github.com/hglong16/execy/actions/workflows/python-publishing.yml)

The execy `@exec_time` decorator is a utility that measures the execution time of a function. It can be used to easily track and log the time taken by a function to execute, both for synchronous and asynchronous functions.

## Table of Contents

- [Execy](#execy)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [Usage](#usage)

## Installation

```bash
pip install execy
```

## Usage

To use the exec_time decorator, follow these steps:
```python
from execy import exec_time
import asyncio
import time

@exec_time
def sync_function():
    # Simulate a time-consuming synchronous task
    time.sleep(2)

@exec_time
async def async_function():
    # Simulate a time-consuming asynchronous task
    await asyncio.sleep(2)

# Call the decorated synchronous function
sync_function()

# Call the decorated asynchronous function
asyncio.run(async_function())
```

The result will be

```bash
Function 'sync_function' executed in 2.0001089572906494 seconds.
Function 'async_function' executed in 2.001432180404663 seconds.

```