Auto EDA.
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G-Look: Auto EDA
glook is a Python library that provides a graphical user interface (GUI) for Automated Exploratory Data Analysis (Auto EDA). With glook, you can easily visualize and analyze your dataset's characteristics, distributions, and relationships.
⚠️ BEFORE INSTALLATION ⚠️
Before installing glook, it's strongly recommended to create a new Python environment to avoid potential conflicts with your current environment.
Creating a New Conda Environment
To create a new conda environment, follow these steps:
-
Install Conda: If you don't have conda installed, you can download and install it from the Anaconda website.
-
Open a Anaconda Prompt: Open a Anaconda Prompt (or Anaconda Terminal) on your system.
-
Create a New Environment: To create a new conda environment, use the following command. Replace
my_env_name
with your desired environment name.
- Support Python versions are > 3.8
conda create --name my_env_name python=3.8
- Activate the Environment: After creating the environment, activate it with the following command:
conda activate my_env_name
OR
Create a New Virtual Environment with venv
If you prefer using Python's built-in venv
module, here's how to create a virtual environment:
- Check Your Python Installation:
Ensure you have Python installed on your system. You can check by running:
- Support Python versions are > 3.8
python --version
- Create a Virtual Environment:
Use the following command to create a new virtual environment. Replace
my_env_name
with your desired environment name.
python -m venv my_env_name
- Activate the Environment: After creating the virtual environment, activate it using the appropriate command for your operating system:
my_env_name\Scripts\activate
Installation
You can install glook using pip:
pip install glook
Usage
Once installed, glook can be launched globally from the command line. Simply type glook
and press enter to start the application.
glook
The glook application GUI will launch, allowing you to perform Auto EDA on your dataset interactively.
Features
- General Data Insights
- Correlation Coefficient Heatmap
Univariate Analysis
- Visualize distributions of individual columns using:
- Histograms
- Box plots
- Q-Q plot
- Statistical Calculations:
Bivariate Analysis
- Explore relationships between two columns using:
- Scatter plots
- Line plots
- Bar plots
- Box plots
- Violin plots
- Strip charts
- Density contours
- Density heatmaps
- Polar plots
- Polar Bar Plot: Display the relationship between two columns as bars in polar coordinates.
- Select x-axis and y-axis columns to visualize their relationship.
Trivariate Analysis
- Analyze relationships between three columns using:
- 3D Scatter plots
- Distplot
- Select three columns to visualize their trivariate relationship.
Supported Formats
glook supports various data formats, including CSV & Excel.
Getting Help
If you encounter any issues or have questions about using glook, please feel free to open an issue on the GitHub repository. We'll be happy to assist you.
License
This project is licensed under the MIT License - see the LICENSE file for details.
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