ts-viz 0.1.3

Visualization utilities for time series analysis including outlier detection, trendlines, and correlation analysis.

TS-Viz: Time Series Visualization Library

ts-viz is a Python package for visualizing and analyzing time series data, with built-in tools for:

• Outlier detection
• Histograms with statistical markers
• Time series plots with rolling averages
• Feature correlation scatter plots with trendlines

Built using pandas, matplotlib, and seaborn, this library simplifies exploratory data analysis (EDA) for time-series-heavy applications.

🧩 Features

• ✅ Plot histograms with mean and ±3 standard deviation lines
• ✅ Detect and visualize outliers for a given feature by month
• ✅ Plot time series trends with outlier and date-range highlights
• ✅ Explore feature-to-feature correlation with outlier and trend detection

🚀 Usage Examples

1. 📊 Histogram with Statistical Markers

from ts_viz import plot_histogram_with_markers
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
plot_histogram_with_markers(df, 'your_column_name', ax)
plt.show()

2. 🧯 Outlier Detection (Optional Month-Year Filter)

from ts_viz import identify_and_plot_outliers

fig, ax = plt.subplots()
results = identify_and_plot_outliers(
    data=df,
    feature_name='your_column',
    date_column='timestamp',
    month_year='2023-11',  # optional, can be None
    ax=ax
)
plt.show()

# Access results:
print(results['total_outliers'], results['percent_outliers'])

3. 📈 Line Plot Over Time

from ts_viz import plot_feature_line_graph

plot = plot_feature_line_graph(
    data=df,
    feature_name='temperature',
    date_column='timestamp',
    highlight_month_year='2023-11'
)
plot.show()

4. 📅 Line Plot for Custom Date Ranges with Highlights

from ts_viz import plot_feature_line_graph_date_range

plot = plot_feature_line_graph_date_range(
    data=df,
    feature_name='pressure',
    date_column='timestamp',
    start_date='2023-01-01',
    end_date='2023-06-30',
    highlight_ranges=[
        ('2023-02-01', '2023-02-10', 'Batch A'),
        ('2023-05-15', '2023-05-20', 'Batch B')
    ]
)
plot.show()

5. 🔗 Correlation Between Two Features

from ts_viz import plot_feature_correlation

plot = plot_feature_correlation(
    data=df,
    feature_x='temperature',
    feature_y='pressure',
    date_column='timestamp',
    highlight_month_year='2023-03'
)
plot.show()

📦 Installation

pip3 install ts-viz

🧰 Requirements

• Python >= 3.11
• pandas >= 2.1.0
• numpy >= 1.26.0
• matplotlib
• seaborn
• datetime

📄 License

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

👤 Author

Created by Chitra Kumar Sai Chenuri Venkata. Contributions welcome!

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request