How to Create Interactive Graphs with Trend Lines Using Checkboxes | Qtdesigner | Python3 | PyQt6

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Dive into the world of interactive data visualization with PyQt6 in Python3! Join us as we explore how to create captivating graphs enriched with trend lines, all controllable through intuitive checkboxes. Whether you're a beginner or seasoned coder, this tutorial series empowers you with the skills to harness the power of PyQt6 and unleash dynamic graphing capabilities using Qtdesigner. Follow along step-by-step as we demystify the process, empowering you to craft visually stunning and interactive graphs for your projects, presentations, or data analysis needs. Let's transform static data into engaging visual narratives together!
We'll walk you through the setup, from creating the initial graph to adding check boxes for each trend line. You'll learn how to link the check boxes to the visibility of the trend lines, allowing users to customize their viewing experience effortlessly.
By the end of this tutorial, you'll have the skills to create engaging and interactive graphs that captivate your audience and convey complex data with clarity. Let's dive in and elevate your data visualization game!
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Опубликовано:

10 сен 2024

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Комментарии : 3

@damianalvarez5266 4 месяца назад

Hello, excellent video was very useful, I have a question, how to set the scale on the y-axis when using the logarithmic y-axis, I try but every time I add a graph this is self-adjusting. I use these lines self.graph.setXRange(0,1000,padding=0) self.graph.setYRange(0.1,10,padding=0) but they only work on linear axes

@albiemer 4 месяца назад

It seems like you're working with inheritance attributes methods, perhaps using functions like setYRange(). In my tutorial, I emphasize the use of compositional attributes methods, specifically designed for beginners. self.orange = self.plot_widget.plot(self.date_orange, self.price_orange, pen=self.orangeline, symbol='o', name='orange') This code snippet effectively plots data points with x-values from self.date_orange and y-values from self.price_orange, following the compositional approach. Regardless of whether you're employing linear or logarithmic scales for the axes, this approach should remain consistent. However, if you're encountering difficulties setting the y-axis scale with a logarithmic scale, I recommend exploring alternative methods or properties within your library or framework that align with the compositional approach.

@damianalvarez5266 4 месяца назад

@@albiemer good morning,thank you for your answer, and i have another question ,how to avoid scientific notation when using logarithmic axis