# DataLib: Python Data Analysis Library
## Overview
DataLib is a comprehensive Python library designed to simplify data manipulation, statistical analysis, visualization, and machine learning tasks. It provides an intuitive and powerful set of tools for data scientists, researchers, and analysts.
## Features
### Data Manipulation
- CSV file loading and saving
- Data filtering
- Missing value handling
- Data normalization
### Statistical Analysis
- Descriptive statistics
- Correlation analysis
- T-tests
- Chi-square tests
### Data Visualization
- Bar plots
- Histograms
- Scatter plots
- Correlation heatmaps
### Advanced Analysis
- Linear and Polynomial Regression
- Classification Algorithms (KNN, Decision Trees)
- Clustering (K-means)
- Dimensionality Reduction (PCA)
## Installation
```bash
pip install datalib
```
## Quick Examples
### Data Manipulation
```python
from datalib.data_manipulation import DataManipulation
# Load CSV
df = DataManipulation.load_csv('data.csv')
# Filter data
filtered_df = DataManipulation.filter_data(df, {'age': lambda x: x > 25})
```
### Statistical Analysis
```python
from datalib.statistics import StatisticalAnalysis
# Calculate descriptive stats
stats = StatisticalAnalysis.descriptive_stats(df['column'])
# Correlation matrix
corr_matrix = StatisticalAnalysis.correlation(df)
```
### Visualization
```python
from datalib.visualization import DataVisualization
# Create bar plot
DataVisualization.bar_plot(df, 'category', 'value')
# Scatter plot
DataVisualization.scatter_plot(df, 'x_column', 'y_column')
```
## Contributing
Contributions are welcome! Please check our GitHub repository for guidelines.
## License
This project is licensed under the MIT License.
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"description": "# DataLib: Python Data Analysis Library\r\n\r\n## Overview\r\n\r\nDataLib is a comprehensive Python library designed to simplify data manipulation, statistical analysis, visualization, and machine learning tasks. It provides an intuitive and powerful set of tools for data scientists, researchers, and analysts.\r\n\r\n## Features\r\n\r\n### Data Manipulation\r\n- CSV file loading and saving\r\n- Data filtering\r\n- Missing value handling\r\n- Data normalization\r\n\r\n### Statistical Analysis\r\n- Descriptive statistics\r\n- Correlation analysis\r\n- T-tests\r\n- Chi-square tests\r\n\r\n### Data Visualization\r\n- Bar plots\r\n- Histograms\r\n- Scatter plots\r\n- Correlation heatmaps\r\n\r\n### Advanced Analysis\r\n- Linear and Polynomial Regression\r\n- Classification Algorithms (KNN, Decision Trees)\r\n- Clustering (K-means)\r\n- Dimensionality Reduction (PCA)\r\n\r\n## Installation\r\n\r\n```bash\r\npip install datalib\r\n```\r\n\r\n## Quick Examples\r\n\r\n### Data Manipulation\r\n```python\r\nfrom datalib.data_manipulation import DataManipulation\r\n\r\n# Load CSV\r\ndf = DataManipulation.load_csv('data.csv')\r\n\r\n# Filter data\r\nfiltered_df = DataManipulation.filter_data(df, {'age': lambda x: x > 25})\r\n```\r\n\r\n### Statistical Analysis\r\n```python\r\nfrom datalib.statistics import StatisticalAnalysis\r\n\r\n# Calculate descriptive stats\r\nstats = StatisticalAnalysis.descriptive_stats(df['column'])\r\n\r\n# Correlation matrix\r\ncorr_matrix = StatisticalAnalysis.correlation(df)\r\n```\r\n\r\n### Visualization\r\n```python\r\nfrom datalib.visualization import DataVisualization\r\n\r\n# Create bar plot\r\nDataVisualization.bar_plot(df, 'category', 'value')\r\n\r\n# Scatter plot\r\nDataVisualization.scatter_plot(df, 'x_column', 'y_column')\r\n```\r\n\r\n## Contributing\r\n\r\nContributions are welcome! Please check our GitHub repository for guidelines.\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License.\r\n",
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