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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteChoose the plotting method based on what your NumPy array represents: use ax.plot() for paired x-y values, and ax.imshow() for a matrix, image, or two-dimensional field. Matplotlib’s documented workflow is to create a figure and axes, plot on the axes, then display the result when your environment requires it.
Plot one-dimensional data as an x-y series
Use Axes.plot() when each x value corresponds to a y value. This example creates a sine curve from two NumPy arrays:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlabel("x")
ax.set_ylabel("sin(x)")
ax.set_title("Sine curve")
plt.show()plt.subplots() returns a Figure, which contains the overall figure, and an Axes, the region where the data is plotted. The Matplotlib quick start guide describes pyplot.subplots as the simplest way to create a figure with an axes. Using methods such as ax.plot() and ax.set_title() keeps plotting and labeling attached to the axes you are working with.
If you provide only y values to ax.plot(y), their positions are used as x coordinates. Use this convenience form when the horizontal axis is sample position; provide an x array when the horizontal values have a different meaning, such as time or distance.
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Call plt.show() in a script or other environment where you need to open or display the figure explicitly. Some notebook and interactive environments display figures automatically, so the call may not be needed there.
Display a matrix or image array
Use imshow() when the array represents image pixels or values arranged on a two-dimensional grid—not a list of paired x-y observations. A scalar matrix has shape (M, N); RGB and RGBA image arrays have shapes (M, N, 3) and (M, N, 4), respectively, as documented in the imshow API reference.
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fig, ax = plt.subplots()
image = ax.imshow(matrix, cmap="viridis")
fig.colorbar(image, ax=ax, label="value")
ax.set_title("Matrix values")
plt.show()For a scalar matrix, values are normalized and mapped to display colors using a colormap. The matrix itself does not contain those colors. RGB and RGBA arrays instead provide color channels directly. If the matrix represents grayscale intensity, use a grayscale colormap such as "gray"; set vmin and vmax when the intended data scale calls for explicit display limits.
Set image orientation and coordinates deliberately
By default, imshow() places pixel centers at integer coordinates, with the origin at the center of pixel (0, 0). The array’s row and column indices therefore determine the default axes; they may not be the physical coordinates your data represents.
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- Set
originto control whether the first row appears at the top or bottom. - Set
extentwhen the axes should show meaningful data bounds rather than pixel-index coordinates. - Choose
interpolationfor the visual treatment you need. Resizing an image for display can change its appearance through smoothing or aliasing.
For example, if a grid covers known horizontal and vertical bounds, supply those bounds with extent so the axes communicate the real coordinate range. The imshow API reference and Matplotlib’s image-rendering documentation describe these image-display options.
Plot multiple arrays in comparable panels
Create a grid of axes with plt.subplots(rows, columns), then plot each array on its corresponding axes. For example, two side-by-side line plots can share an x-axis:
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fig, axs = plt.subplots(1, 2, sharex=True)
axs[0].plot(x, y1)
axs[0].set_title("Series 1")
axs[1].plot(x, y2)
axs[1].set_title("Series 2")
fig.tight_layout()
plt.show()Matplotlib supports shared axes set to True or "all", "row", and "col", as well as independent axes. Shared scales can make comparisons clearer when values use comparable ranges; independent scales may be more appropriate when they do not. The subplots API reference documents these options and the returned axes structure.
Be aware that the type and shape of axs depend on the requested layout and the squeeze setting: a single panel can return one Axes, while multiple panels can return a one-dimensional or two-dimensional collection. Index the axes to match the layout you requested.
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Choose the plotting method by data meaning
| What the array represents | Method | What to consider |
|---|---|---|
| Paired x and y observations | ax.plot(x, y) |
Supply x values when the horizontal coordinate is meaningful; label both axes. |
| One-dimensional y observations indexed by position | ax.plot(y) |
The horizontal coordinates represent sample positions. |
| Two-dimensional scalar field or matrix | ax.imshow(array) |
Choose a colormap and consider explicit limits, origin, and coordinate bounds. |
| RGB or RGBA image pixels | ax.imshow(array) |
The final dimension carries three or four color channels. |
These methods answer different visual questions: plot() shows a relationship between observations, while imshow() shows values arranged across a grid or image. Matplotlib’s quick start guide, imshow reference, subplots reference, and image tutorial cover the corresponding APIs.
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