change figure size and figure format in matplotlib
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Okay, let's dive deep into how to change the figure size and figure format in Matplotlib. We'll cover the concepts, options, best practices, and plenty of code examples.
*I. Understanding the Basics: Figures, Axes, and DPI*
Before we manipulate sizes and formats, it's crucial to understand Matplotlib's underlying structure:
*Figure:* The overall window or page that your plot lives in. Think of it as the canvas. A figure can contain multiple subplots or individual axes.
*Axes:* A single plot or graph within the figure. An axes object is where you actually draw your data, set labels, titles, and other elements. A figure can have one or more axes.
*DPI (Dots Per Inch):* A measure of the resolution or pixel density of an image. A higher DPI means a sharper image, but also a larger file size. In Matplotlib, DPI affects the size of the figure when it's displayed on screen or saved to a file.
*II. Setting the Figure Size*
There are a few main ways to control the figure size in Matplotlib:
1. *Using `plt.figure()` before plotting:*
This is the most common and generally recommended approach. You create a `Figure` object with the desired dimensions before you start plotting your data.
`figsize` is a tuple `(width, height)` in *inches*. This is crucial! Matplotlib interprets these dimensions as inches, and the DPI will then determine the actual number of pixels in the output image.
2. *Using `plt.subplots()`:*
This function is especially useful when you want to create multiple subplots within a single figure. It returns both the `Figure` object and an array of `Axes` objects.
`plt.subplots(nrows, ncols, figsize=(width, height))` creates a figure and a grid of subplots. `nrows` and `ncols` specify the number of rows and columns of subplots. `figsize` still controls the overall figure size in inches.
3. *Using the `Figure` object directly:*
You can create a `Figure` object first, ...
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