dataanalysts 2.0.0

An advanced Python data analysis library with enhanced cleaning, transformation, and visualization.

DataAnalysts Package

DataAnalysts is a Python library designed to simplify and streamline data analysis tasks, including data cleaning, transformation, and visualization. Whether you're a student, a data analyst, or a researcher, this package is built to handle datasets efficiently and interactively.

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🚀 Key Features

• Data Cleaning:
  • Handle missing values (mean, median, mode strategies).
  • Remove duplicates, manage outliers, and preprocess raw datasets.
• Data Transformation:
  • Scale (standard, min-max, robust) and normalize datasets.
  • Encode categorical data and apply dimensionality reduction (PCA).
• Data Visualization:
  • Generate professional plots: Histogram, Line Plot, Scatter Plot, Heatmap, Pair Plot, Box Plot, Violin Plot.
  • Supports interactive and customizable visualizations.
• Data Loading:
  • Easily load datasets from CSV and Excel files.
• Error Handling:
  • Robust exception handling with clear error messages.
• Interactive Tools:
  • Interactive cleaning, transformation, and plotting tools for hands-on data analysis.

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🛠️ Installation Steps

1. Install the Package from PyPI

To use the library in Google Colab or your local environment, install it directly from PyPI:

pip install dataanalysts

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💡 Usage Examples

1. Import the Library

import dataanalysts as da
import pandas as pd

2. Load Data

df = da.load_csv('data.csv')
df_excel = da.load_excel('data.xlsx', sheet_name='Sheet1')

3. Data Cleaning

df_cleaned = da.clean(df)
df_cleaned_outliers = da.clean(df, handle_outliers=True)
df_interactive_clean = da.interactive_clean(df)

4. Data Transformation

df_transformed = da.transform(df, strategy='standard')
df_pca = da.transform(df_transformed, reduce_dimensionality=True, n_components=3)
df_interactive_transform = da.interactive_transform(df)

5. Data Visualization

da.histogram(df, column='age', bins=30, kde=True)
da.barchart(df, x_col='city', y_col='population')
da.linechart(df, x_col='date', y_col='sales')
da.scatter(df, x_col='height', y_col='weight', hue='gender')
da.heatmap(df)
da.pairplot(df, hue='category')
da.boxplot(df, x_col='region', y_col='sales')
da.violinplot(df, x_col='region', y_col='sales')

6. Interactive Visualization

da.interactive_plot(df)

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🤝 Contributing

Contributions are welcome! Please submit a pull request via our GitHub Repository.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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🛠️ Support

If you encounter any issues, feel free to open an issue on our GitHub Issues page.