easy_insight 1.0.0
A simple library for easy exploratory data analysis
Requires Python: <4.0,>=3.10


# 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)
```
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
```
data_inspector = DataInspector(DataTypeInspectionStrategy())
data_inspector.evaluate_inspection(df)
```

### Set strategy to summary statistics

```
data_inspector.set_strategy(SummaryStatisticsInspectionStrategy())
data_inspector.evaluate_inspection(df)
```

### Missing Values Analysis

```
missing_values_analysis = SimpleMissingValuesAnalysis()
missing_values_analysis.analyze(df)
```

### Univariate Analysis
```
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
```
bivariate_analysis = BivariateAnalyzer(CategoricalVsNumericalAnalysisStrategy())
bivariate_analysis.execute_analysis(df, "department", "annual_salary")
```

### Multivariate Analysis
```
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)