tdnpathviz 0.1.2.17

A python package to visualize npath outputs in python..

tdnpathviz

tdnpathviz is a Python package that provides a set of convenient functions to generate various types of visualizations directly within a Teradata Vantage database. It leverages the in-database capabilities of Teradata Vantage including path analysis and in-database plotting (TD_PLOT) for Teradata SQLE 17.20 and later versions.

Installation

Install the package via pip:

pip install tdnpathviz

Usage

Here are the available functions in the Vantage Visualizer package:

• plot_first_main_paths()
• create_all_pathes_views()
• scatter_plot()
• pair_plot()
• plot_correlation_heatmap()
• plotcurves()
• plotcurvescluster()
• plot_with_background_image()

Example: Plotting First Main Paths

Here is an example of how to use the plot_first_main_paths() function:

from tdnpathviz.visualizations import plot_first_main_paths

# Assume 'myPathAnalysis' is your DataFrame which contains path analysis data

plot_first_main_paths(
    myPathAnalysis=myPathAnalysis,
    path_column='your_path_column',
    id_column='your_id_column',
    nb_paths=15,
    print_query=False,
    font_size=10,
    width=1200,
    height=800,
    weight_column = 'your_weight_column',
    weight_agg = 'count',
    justify='left'
)

Example: Correlation Heatmap

Here is an example of how to use the plot_correlation_heatmap() function to create a correlation heatmap:

from tdnpathviz.visualizations import plot_correlation_heatmap

# Assume 'tddf' is your teradata DataFrame
plot_correlation_heatmap(tddf)

# You can pass additional parameters, e.g., ax for specific matplotlib axis, no_plot if you don't want to display the plot,
# or a title for your plot.
# plot_correlation_heatmap(tddf, ax=your_matplotlib_axis, no_plot=True, title="My Correlation Heatmap")

Example: Scatter Plot

Here is an example of how to use the scatter_plot() function:

from tdnpathviz.visualizations import scatter_plot

# Assume 'tddf' is your teradata DataFrame

scatter_plot(
    tddf=tddf,
    x_col='col1',
    y_col='col2'
)