PolyY 0.1.1

A Multi Y axis Plotting Library based on Plotly

MultiYPlot: A Flexible Multi Y-Axis (A Plotly based Library)

MultiYPlot is a lightweight and intuitive Python library built on top of Plotly Graph Objects, designed to simplify the creation of multi–Y-axis interactive charts.With PolyY, you can easily visualize multiple datasets with different scales on the same figure — without losing clarity, interactivity, or control. The library provides a clean object-oriented interface to build, customize, and update complex figures in just a few lines of code.

Key Features

🔹 Multi Y-Axis Support: Effortlessly plot multiple series with independent Y-axes while maintaining alignment and scale integrity. 🔹 Built on Plotly Graph Objects: Leverages Plotly’s powerful graph_objects module for high-quality, interactive visualization. 🔹 Full Interactivity: Zoom, pan, hover, and toggle traces directly in the figure — no static images or re-renders needed. 🔹 Fine Figure Control: Access and modify each trace, axis, and layout component with full Plotly compatibility. 🔹 Dynamic Trace Management: Add, update, or restyle traces after creation — ideal for data exploration and dashboard integration.

Example 1: Energy Industry Data

import polyY.plotly as plot
import pandas as pd

elect = pd.read_csv(r"data\electricity_consumption_data.csv")
x = elect.timestamp
ys = ['power_kwh', 'voltage_v', 'current_a', 'temperature_c', 'reactive_power_kvar']
clrs = ["pink", "magenta", "green", "purple", "orange"]

figure = plot.MakeFigure("Power Consumption Metrics", "plotly_dark")
for i in range(5):
    figure.add_trace(x, elect[ys[i]].to_list(), name=ys[i], kind="line", color=clrs[i])
figure.get_figure().update_layout(width=1500, height=800)

Example 2: Oil and Gas Data

import polyY.plotly as plot
import pandas as pd

data = pd.read_csv(r"data\oil and gas.txt", sep="\t")
x = data.Time_Days
ys = ['Gas_Volume', 'Water_Volume_', 'Casing_Pressure_', 'Active_Pressure_', 'Line_Pressure_', 'Calculated_Sandface_Pressure_']
clrs = ["olive", "blue", "magenta", "red", "orange", "green"]

figure = plot.MakeFigure("Oil and Gas Production Metrics", "none")
for i in range(6):
    figure.add_trace(x, data[ys[i]].to_list(), name=ys[i], kind="line", color=clrs[i])
figure.get_figure().update_layout(width=1500, height=800)

Example 3: Weather and Forecast Data

import polyY.plotly as plot
import pandas as pd

data = pd.read_csv(r"data\weather_data_500.csv")
x = data.timestamp
ys = ['humidity_%', 'wind_speed_m_s', 'rainfall_mm', 'solar_radiation_w_m2', 'pressure_hpa', 'temperature_c']
clrs = ["blue", "orange", "green", "magenta", "red", "olive"]

figure = plot.MakeFigure("Weather and Forecast Metrics", "ggplot2")
for i in range(6):
    figure.add_trace(x, data[ys[i]].to_list(), name=ys[i], kind="line", color=clrs[i])
figure.get_figure().update_layout(width=1500, height=800)

Example 4: Combo Chart

import polyY.plotly as plot
import pandas as pd

data = pd.read_csv(r"data\oil and gas.txt", sep="\t")
x = data.Time_Days
ys = ['Gas_Volume', 'Water_Volume_', 'Casing_Pressure_', 'Active_Pressure_']
clrs = ["olive", "blue", "magenta", "red"]
types = ["area", "area", "line", "scatter"]

figure = plot.MakeFigure("Combo Chart Example", "none")
for i in range(4):
    figure.add_trace(x, data[ys[i]].to_list(), name=ys[i], kind=types[i], color=clrs[i])
figure.get_figure().update_layout(width=1500, height=800)