# Easy Insight (One Click - Easy Exploratory Data Analysis)
**Easy Insight** is a simple library designed for exploratory data analysis (EDA) 📊. It provides an easy way to inspect and analyze datasets, helping you to quickly understand the structure and contents of your data 📈.
## Features
- 🔍 Basic data inspection
- ❓ Missing values analysis
- 📊 Univariate analysis for numerical and categorical features
- 🔗 Bivariate analysis for understanding relationships between variables
- 🌐 Multivariate analysis for comprehensive insights
## Installation ⚙️
You can install Easy Insight using [Poetry](https://python-poetry.org/) or `pip`.
### Using Poetry
1. Install Poetry if you haven't already:
```bash
curl -sSL https://install.python-poetry.org | python3 -
```
2. Then run:
```bash
poetry add easy-insight
```
### Using pip
```bash
pip install easy-insight
```
## Usage 🚀
Here's a quick example of how to use Easy Insight for exploratory data analysis on a DataFrame `df`:
```python
import pandas as pd
from easy_insight.eda_tools.basic_data_inspection import DataInspector, DataTypeInspectionStrategy, SummaryStatisticsInspectionStrategy
from easy_insight.eda_tools.missing_values_analysis import SimpleMissingValuesAnalysis
from easy_insight.eda_tools.univariate_analysis import UnivariateAnalyzer, NumericalUnivariateAnalysis, CategoricalUnivariateAnalysis
from easy_insight.eda_tools.bivariate_analysis import BivariateAnalyzer, NumericalVsNumericalAnalysisStrategy, CategoricalVsNumericalAnalysisStrategy
from easy_insight.eda_tools.multivariate_analysis import SimpleMultivariateAnalysis
```
## Load your DataFrame (example) 📂
```python
df = pd.read_csv('your_dataset.csv')
```
## Quick Automated EDA ⚡
### For Quick automated EDA
```python
from easy_insight.eda_tools.utility import quick_eda
quick_eda(df, perform_data_inspection=True, perform_missing_values_analysis=True,
perform_univariate_analysis=True, perform_bivariate_analysis=True, perform_multivariate_analysis=True)
```
## For Quick but Customized EDA 🛠️
### Data Inspection
```python
data_inspector = DataInspector(DataTypeInspectionStrategy())
data_inspector.evaluate_inspection(df)
```
### Set strategy to Summary Statistics
```python
data_inspector.set_strategy(SummaryStatisticsInspectionStrategy())
data_inspector.evaluate_inspection(df)
```
### Missing Values Analysis❓
```python
missing_values_analysis = SimpleMissingValuesAnalysis()
missing_values_analysis.analyze(df)
```
### Univariate Analysis 📊
```python
univariate_analyzer = UnivariateAnalyzer(NumericalUnivariateAnalysis())
numerical_columns = df.select_dtypes(include=[int, float]).columns
for feature in numerical_columns:
univariate_analyzer.execute_analysis(df, feature=feature)
```
### Bivariate Analysis 🔗
```python
bivariate_analysis = BivariateAnalyzer(CategoricalVsNumericalAnalysisStrategy())
bivariate_analysis.execute_analysis(df, "department", "annual_salary")
```
### Multivariate Analysis 🌐
```python
multivariate_analysis = SimpleMultivariateAnalysis()
multivariate_analysis.analyze(df)
```
## Contributing 🤝
Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or bugs you encounter.
## License 📝
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Author ✍️
Durgesh Rathod - [durgeshrathod.777@gmail.com](mailto:durgeshrathod.777@gmail.com)
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