| Name | easy_insight JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | A simple library for easy exploratory data analysis |
| upload_time | 2024-10-30 20:29:30 |
| maintainer | None |
| docs_url | None |
| author | Durgesh Rathod |
| requires_python | <4.0,>=3.10 |
| license | None |
| keywords |
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| VCS |
|
| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# 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)
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"description": "\n# Easy Insight (One click - Easy Exploratory Data Analysis)\n\n**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.\n\n## Features\n\n- Basic data inspection\n- Missing values analysis\n- Univariate analysis for numerical and categorical features\n- Bivariate analysis for understanding relationships between variables\n- Multivariate analysis for comprehensive insights\n\n## Installation\n\nYou can install Easy Insight using [Poetry](https://python-poetry.org/) or `pip`. \n\n## Using Poetry\n\n1. Install Poetry if you haven't already:\n\n ```bash\n curl -sSL https://install.python-poetry.org | python3 -\n ```\n\n2. Then run:\n\n ```bash\n poetry add easy-insight\n ```\n\n## Using pip\n\n```bash\npip install easy-insight\n```\n\n## Usage\n\nHere's a quick example of how to use Easy Insight for exploratory data analysis on a DataFrame `df`:\n\n```python\nimport pandas as pd\n\nfrom easy_insight.eda_tools.basic_data_inspection import DataInspector, DataTypeInspectionStrategy, SummaryStatisticsInspectionStrategy\n\nfrom easy_insight.eda_tools.missing_values_analysis import SimpleMissingValuesAnalysis\n\nfrom easy_insight.eda_tools.univariate_analysis import UnivariateAnalyzer, NumericalUnivariateAnalysis, CategoricalUnivariateAnalysis\n\nfrom easy_insight.eda_tools.bivariate_analysis import BivariateAnalyzer, NumericalVsNumericalAnalysisStrategy, CategoricalVsNumericalAnalysisStrategy\n\nfrom easy_insight.eda_tools.multivariate_analysis import SimpleMultivariateAnalysis\n```\n\n## Load your DataFrame (example)\n```\ndf = pd.read_csv('your_dataset.csv')\n```\n## Quick Automated EDA\n\n\n### For Quick automated EDA\n\n```python\nfrom easy_insight.eda_tools.utility import quick_eda\n\nquick_eda(df, perform_data_inspection=True, perform_missing_values_analysis=True,\n perform_univariate_analysis=True, perform_bivariate_analysis=True, perform_multivariate_analysis=True)\n```\n## For Quick but customized EDA\n\n### Data Inspection\n```\ndata_inspector = DataInspector(DataTypeInspectionStrategy())\ndata_inspector.evaluate_inspection(df)\n```\n\n### Set strategy to summary statistics\n\n```\ndata_inspector.set_strategy(SummaryStatisticsInspectionStrategy())\ndata_inspector.evaluate_inspection(df)\n```\n\n### Missing Values Analysis\n\n```\nmissing_values_analysis = SimpleMissingValuesAnalysis()\nmissing_values_analysis.analyze(df)\n```\n\n### Univariate Analysis\n```\nunivariate_analyzer = UnivariateAnalyzer(NumericalUnivariateAnalysis())\nnumerical_columns = df.select_dtypes(include=[int, float]).columns\nfor feature in numerical_columns:\n univariate_analyzer.execute_analysis(df, feature=feature)\n```\n\n### Bivariate Analysis\n```\nbivariate_analysis = BivariateAnalyzer(CategoricalVsNumericalAnalysisStrategy())\nbivariate_analysis.execute_analysis(df, \"department\", \"annual_salary\")\n```\n\n### Multivariate Analysis\n```\nmultivariate_analysis = SimpleMultivariateAnalysis()\nmultivariate_analysis.analyze(df)\n```\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or bugs you encounter.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n## Author\n\nDurgesh Rathod - [durgeshrathod.777@gmail.com](mailto:durgeshrathod.777@gmail.com)\n",
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