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This is most useful for two subplots (e.g.: fig, (ax1, ax2) = plt.subplots(1, 2) or fig, (ax1, ax2) = plt.subplots(2, 1)). For more subplots, it's more efficient to flatten and iterate through the array of axes.
pyplot.subplots creates a figure and a grid of subplots with a single call, while providing reasonable control over how the individual plots are created. For more advanced use cases you can use GridSpec for a more general subplot layout or Figure.add_subplot for adding subplots at arbitrary locations within the figure.
23 maj 2017 · plt.errorbar can be used to plot x, y, error data (as opposed to the usual plt.plot) import matplotlib.pyplot as plt import numpy as np x = np.array([1, 2, 3, 4, 5]) y = np.power(x, 2) # Effectively y = x**2 e = np.array([1.5, 2.6, 3.7, 4.6, 5.5]) plt.errorbar(x, y, e, linestyle='None', marker='^') plt.show()
subplots() is the recommended method to generate simple subplot arrangements: fig , ( ax1 , ax2 ) = plt . subplots ( 2 , 1 ) fig . suptitle ( 'A tale of 2 subplots' ) ax1 . plot ( x1 , y1 , 'o-' ) ax1 . set_ylabel ( 'Damped oscillation' ) ax2 . plot ( x2 , y2 , '.-' ) ax2 . set_xlabel ( 'time (s)' ) ax2 . set_ylabel ( 'Undamped' ) plt . show ()
Draw 2 plots on top of each other: import matplotlib.pyplot as plt. import numpy as np. #plot 1: x = np.array ( [0, 1, 2, 3]) y = np.array ( [3, 8, 1, 10]) plt.subplot (2, 1, 1) plt.plot (x,y) #plot 2:
Multiple Subplots. Sometimes it is helpful to compare different views of data side by side. To this end, Matplotlib has the concept of subplots: groups of smaller axes that can exist together within a single figure. These subplots might be insets, grids of plots, or other more complicated layouts.
In comparison to plt.subplot(), plt.subplots() is more consistent with Python's conventional 0-based indexing. plt.GridSpec : More Complicated Arrangements ¶ To go beyond a regular grid to subplots that span multiple rows and columns, plt.GridSpec() is the best tool.
