# 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.
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"description": "# Fast_Profiling_UI\r\n\r\nFast_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.\r\n\r\n## Features\r\n\r\n- **Overview**: Get a comprehensive summary of your dataset, including statistics such as the number of columns, rows, duplicate rows, missing values, and memory usage.\r\n- **Sample**: View sample data from the dataset, including top and bottom rows.\r\n- **Variables**: Analyze individual columns, displaying statistics such as distinct values, missing values, mean, minimum, maximum, and more.\r\n\r\n## Installation\r\n\r\nTo 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`:\r\n\r\n```sh\r\npip install Fast_Profiling_UI\r\n```\r\n\r\n## Usage\r\n\r\nHere's a quick guide on how to use the Fast_Profiling_UI package.\r\n\r\n### Importing the Package\r\n\r\nFirst, import the package in your Python script:\r\n\r\n```python\r\nimport Fast_Profiling_UI\r\n```\r\n\r\n### Loading a DataFrame\r\n\r\nLoad your data into a pandas DataFrame. For example:\r\n\r\n```python\r\nimport pandas as pd\r\n\r\n# Load your dataset\r\ndf = pd.read_csv('your_data.csv')\r\n```\r\n\r\n### Performing EDA\r\n\r\nUse the `de_analysis` function to start the UI for exploratory data analysis:\r\n\r\n```python\r\nFast_Profiling_UI.de_analysis(df)\r\n```\r\n\r\nThis will open a UI window with buttons for different analysis options:\r\n\r\n- **Overview**: Provides a summary of the dataset.\r\n- **Missing Values**: Analyzes and displays missing values in the dataset.\r\n- **Variable**: Displays detailed statistics for each column in the dataset.\r\n- **Sample**: Allows viewing samples of the dataset (top and bottom rows).\r\n\r\n## Detailed Example\r\n\r\nHere is a detailed example to help you get started:\r\n\r\n```python\r\nimport pandas as pd\r\nimport Fast_Profiling_UI\r\n\r\n# Load your dataset\r\ndf = pd.read_csv('your_data.csv')\r\n\r\n# Start the EDA UI\r\nFast_Profiling_UI.de_analysis(df)\r\n```\r\n\r\n## Development\r\n\r\n### Setting Up the Development Environment\r\n\r\nIf you want to contribute to the development of Fast_Profiling_UI, follow these steps to set up your development environment:\r\n\r\n1. Clone the repository:\r\n ```sh\r\n git clone https://github.com/Mukesh-Kumar-Madhur\r\n cd Fast_Profiling_UI\r\n ```\r\n\r\n2. Create a virtual environment:\r\n ```sh\r\n python -m venv venv\r\n ```\r\n\r\n3. Activate the virtual environment:\r\n\r\n - On Windows:\r\n ```sh\r\n venv\\Scripts\\activate\r\n ```\r\n\r\n - On macOS and Linux:\r\n ```sh\r\n source venv/bin/activate\r\n ```\r\n\r\n4. Install the package in development mode:\r\n ```sh\r\n pip install -e .\r\n ```\r\n\r\n### Running Tests\r\n\r\nEnsure 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`.\r\n\r\n### Code Structure\r\n\r\n- `Fast_Profiling_UI/`\r\n - `__init__.py`: Initializes the package and defines the main `de_analysis` function.\r\n - `Sample.py`: Defines the `Sample` function for displaying sample data.\r\n - `Variables.py`: Defines the `Variables` function for analyzing individual columns.\r\n - `Overview.py`: Defines the `show_overview` function for summarizing the dataset.\r\n",
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