Fast-Profiling-UI 0.3

An Exploratory Data Analysis Package for Data Engineers With UI

# Fast_Profiling_UI



Fast_Profiling_UI is an Exploratory Data Analysis (EDA) package designed for data engineers, providing a user-friendly UI to perform various data analysis tasks efficiently. This package leverages `pandas`, `numpy`, and `tkinter` to offer insightful and interactive data analysis.



## Features



- **Overview**: Get a comprehensive summary of your dataset, including statistics such as the number of columns, rows, duplicate rows, missing values, and memory usage.

- **Sample**: View sample data from the dataset, including top and bottom rows.

- **Variables**: Analyze individual columns, displaying statistics such as distinct values, missing values, mean, minimum, maximum, and more.



## Installation



To install the Fast_Profiling_UI package, you need to have Python 3.6 or later installed on your system. You can install the package using `pip`:



```sh

pip install Fast_Profiling_UI

```



## Usage



Here's a quick guide on how to use the Fast_Profiling_UI package.



### Importing the Package



First, import the package in your Python script:



```python

import Fast_Profiling_UI

```



### Loading a DataFrame



Load your data into a pandas DataFrame. For example:



```python

import pandas as pd



# Load your dataset

df = pd.read_csv('your_data.csv')

```



### Performing EDA



Use the `de_analysis` function to start the UI for exploratory data analysis:



```python

Fast_Profiling_UI.de_analysis(df)

```



This will open a UI window with buttons for different analysis options:



- **Overview**: Provides a summary of the dataset.

- **Missing Values**: Analyzes and displays missing values in the dataset.

- **Variable**: Displays detailed statistics for each column in the dataset.

- **Sample**: Allows viewing samples of the dataset (top and bottom rows).



## Detailed Example



Here is a detailed example to help you get started:



```python

import pandas as pd

import Fast_Profiling_UI



# Load your dataset

df = pd.read_csv('your_data.csv')



# Start the EDA UI

Fast_Profiling_UI.de_analysis(df)

```



## Development



### Setting Up the Development Environment



If you want to contribute to the development of Fast_Profiling_UI, follow these steps to set up your development environment:



1. Clone the repository:

    ```sh

    git clone https://github.com/Mukesh-Kumar-Madhur

    cd Fast_Profiling_UI

    ```



2. Create a virtual environment:

    ```sh

    python -m venv venv

    ```



3. Activate the virtual environment:



    - On Windows:

        ```sh

        venv\Scripts\activate

        ```



    - On macOS and Linux:

        ```sh

        source venv/bin/activate

        ```



4. Install the package in development mode:

    ```sh

    pip install -e .

    ```



### Running Tests



Ensure your changes do not break any functionality by running tests. You can add tests in the `tests` directory and run them using a testing framework like `pytest`.



### Code Structure



- `Fast_Profiling_UI/`

    - `__init__.py`: Initializes the package and defines the main `de_analysis` function.

    - `Sample.py`: Defines the `Sample` function for displaying sample data.

    - `Variables.py`: Defines the `Variables` function for analyzing individual columns.

    - `Overview.py`: Defines the `show_overview` function for summarizing the dataset.