{"id":4983,"date":"2020-11-11T12:06:20","date_gmt":"2020-11-11T06:36:20","guid":{"rendered":"http:\/\/www.pythonpool.com\/?p=4983"},"modified":"2026-07-13T12:28:30","modified_gmt":"2026-07-13T06:58:30","slug":"matplotlib-quiver","status":"publish","type":"post","link":"https:\/\/www.pythonpool.com\/matplotlib-quiver\/","title":{"rendered":"Matplotlib Quiver: Vector Fields, Scale, Angles, and Color"},"content":{"rendered":"

Quick answer:<\/strong> Use Matplotlib quiver() to draw arrows from x and y locations with U and V vector components. Set scale, angles, units, and color deliberately so the field communicates direction and magnitude instead of only filling the plot with arrows.<\/p>\n

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A useful quiver plot keeps vector components, arrow scale, coordinate angles, and the key understandable together.<\/figcaption><\/figure>\n

A Matplotlib quiver plot draws arrows to show direction and magnitude across points. It is useful for vector fields, gradients, wind maps, velocity diagrams, force directions, image flow, and any grid-based chart where an arrow communicates more than a dot or line. Quiver plots are particularly useful for vector fields; PyWake Library for Wind Farm Modeling<\/a> applies wind vectors to wake and wind-farm modeling.<\/p>\n

The central call is ax.quiver(X, Y, U, V)<\/code>. The X<\/code> and Y<\/code> arrays give arrow positions. The U<\/code> and V<\/code> arrays give horizontal and vertical arrow components. Those arrays usually share the same shape, often created with np.meshgrid()<\/code>.<\/p>\n

The official Matplotlib references for Axes.quiver()<\/a>, pyplot.quiver()<\/a>, and quiverkey()<\/a> explain the plotting API. NumPy’s meshgrid()<\/a> documentation explains the grid construction.<\/p>\n

The most common mistake is passing arrays with mismatched shapes. If X<\/code> and Y<\/code> form a five-by-five grid, the U<\/code> and V<\/code> components should also describe that same five-by-five set of positions. Print shapes before plotting if the arrows appear missing or misaligned.<\/p>\n

Scaling also matters. Quiver arrows are drawn in display units by default, so their visual length depends on the scale<\/code>, scale_units<\/code>, and angles<\/code> settings. For data-coordinate arrows, set those arguments deliberately and test a small chart before adding styling.<\/p>\n

Arrow density matters as much as styling. A grid with hundreds of arrows can hide the pattern it is supposed to explain. Downsample the grid, plot every second or third point, or make a larger figure before adding color and labels. A clear sparse field is usually more useful than a dense plot where arrowheads overlap.<\/p>\n

Quiver plots also work well with background plots, but the layers need planning. A heatmap or pseudocolor plot can show magnitude while arrows show direction. In that case, set alpha, z-order, and colors so the arrows remain readable against the background.<\/p>\n

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