Скачать или смотреть Why plots from Jupyter Notebook and Python code are Different: Understanding Matplotlib Behavior

Discover the reasons behind the differences in saved plots from Jupyter Notebook and Python scripts. Learn how to ensure consistency in your bar charts with Matplotlib!
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Why plots from Jupyter Notebook and Python code are Different

When working with data visualization in Python, especially using libraries like Matplotlib, you may encounter unexpected behavior when saving plots from different environments. One question often arises: Why do the plots saved from a Jupyter notebook differ from those generated in a standalone Python script? If you've recently transitioned your code from a notebook to a Python .py file only to find that your bar charts look different, you're not alone!

In this guide, we will not only address this issue but also provide clarity on how to reproduce consistent plots across both environments.

Understanding the Problem

You might have written a function in both your Jupyter notebook and a .py file that looks the same but produces plots that are visually inconsistent. For instance, you might have the same data and parameters in both instances, yet the appearance, size, or resolution of the plots could vary significantly.

This leads us to investigate and understand the fundamental differences in how Jupyter notebooks and Python scripts handle certain aspects of plotting.

The Function Breakdown

Let’s take a closer look at the core function you’re working with. Here’s a simplified overview of the dict_bar_plot function:

[[See Video to Reveal this Text or Code Snippet]]

Key Components of the Function

Parameter Initialization: Parameters allow customization of width, height, title, and saving behavior of the plot.

Plot Creation: The function creates a bar chart and customizes the rotations and labels of the x-ticks.

Recognizing the Problem Area

The issue arises from the order of operations in your plotting function. As pointed out by the community, a critical point of failure is the placement of the line that sets the figure size:

[[See Video to Reveal this Text or Code Snippet]]

Why is This Important?

The location of this line in your code affects how Matplotlib renders the plot. If you set the figure size parameters after the bar chart has been created (which is the case in your function), it will not have any effect on that specific plot. This could result in different appearances when executing in Jupyter versus running the script due to different default settings in each environment.

Solution: Correcting the Code

To ensure that your plots appear the same across both Jupyter notebooks and standalone Python scripts, it is essential to set the figure size before you create any plots. Here's the corrected version of your function:

[[See Video to Reveal this Text or Code Snippet]]

Final Thoughts

By adjusting the position of the plt.rcParams["figure.figsize"] line, you will ensure your plots are consistent across both platforms. Remember, the order of operations when using libraries like Matplotlib can significantly influence the outcome of your visualizations.

Now that you've seen how to prevent plot discrepancies between your Jupyter notebook and Python script, you can confidently continue your data analysis and visualization journey!