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Visit this page for full documentation on simple plots using pyplot.

import numpy as np
import matplotlib.pyplot as plt

# Evenly sampled time from 0s to 10s at 200ms intervals
t = np.arange(0.0, 10.0, 0.2)

# Plotting t at x-axis and sin(t) at y-axis
plt.plot(t, np.sin(t))

# Naming the title and both axis
plt.title('Sinus function')
plt.ylabel('sin(t)')
plt.xlabel('t [s]')

# Need to call the show() function at the end to display myour figure
plt.show()

import numpy as np
import matplotlib.pyplot as plt


# Evenly sampled time at 200ms intervals
t = np.arange(0.0, 5.0, 0.2)

# plot() can plot several lines in the same figure. To seperate the different lines 
# from eachother, we may change the line style and format strings.
# See the plot() documentation for a complete list of line styles and format strings.
# The following lines have red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', label='Linear line')
plt.plot(t, t**2, color='blue', linestyle='none', marker='s', label='Second degree polynom')
plt.plot(t, t**3, 'g^', label='Third degree polynom')

# To describe our plot even more detailed we can draw the labels we previously gave our lines using legend.
plt.legend(loc='upper left')

# The function axis() sets the axis sizes, and takes the argument [xmin, xmax, ymin, ymax]
plt.axis([0, 5, 0, 100])

plt.title('Mulitple polynoms')
plt.show()

More in-depth plot() documentation and legend() documentation.

A very good and more detailed guide on subplots and figures can be found here.

import matplotlib.pyplot as plt
import numpy as np

# Some example data to display
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)

fig, ax = plt.subplots()
fig.suptitle('A single plot')
ax.plot(x, y)

fig, axs = plt.subplots(2)
fig.suptitle('Vertically stacked subplots')
axs[0].plot(x, y)
axs[1].plot(x, -y)

fig, (ax1, ax2) = plt.subplots(2)
fig.suptitle('Vertically stacked subplots')
ax1.plot(x, y)
ax2.plot(x, -y)

fig, (ax1, ax2) = plt.subplots(1, 2)
fig.suptitle('Horizontally stacked subplots')
ax1.plot(x, y)
ax2.plot(x, -y)

axes.flat is not a function, it's an atribute of the numpy.ndarray. ndarray.flat is a 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of Python’s built-in iterator object.

fig, axs = plt.subplots(2, 2)
fig.suptitle('Stacking subplots in two directions')
axs[0, 0].plot(x, y)
axs[0, 0].set_title('Axis [0,0]')
axs[0, 1].plot(x, y, 'tab:orange')
axs[0, 1].set_title('Axis [0,1]')
axs[1, 0].plot(x, -y, 'tab:green')
axs[1, 0].set_title('Axis [1,0]')
axs[1, 1].plot(x, -y, 'tab:red')
axs[1, 1].set_title('Axis [1,1]')


for ax in axs.flat:
    ax.set(xlabel='x-label', ylabel='y-label')

# Hide x labels and tick labels for top plots and y ticks for right plots.
# Try commenting out the next two lines to see what would happen if we did not hide the inner labels and ticks.
for ax in axs.flat:
    ax.label_outer()

To display all figures at once, only call plt.show() at the end of the last figure. As an extra practice, try moving the call or adding more, what happens, and why?

fig1, ax1 = plt.subplots()
fig1.suptitle('A single plot')
ax1.plot(x, y)

fig2, ax2 = plt.subplots()
fig2.suptitle('Another single plot')
ax2.plot(x, y)

plt.show()

plt.figure(1)                # the first figure (now current figure)
plt.subplot(211)             # the first subplot in the first figure (now current subplot)
plt.plot([1, 2, 3])
plt.subplot(212)             # the second subplot in the first figure (new current subplot)
plt.plot([4, 5, 6])

plt.figure(2)                # a second figure (new current figure)
plt.plot([4, 5, 6])          # creates a subplot(111) by default

plt.figure(1)                # figure 1 current; subplot(212) still current
plt.subplot(211)             # make subplot(211) in figure 1 current
plt.title('Easy as 1, 2, 3') # subplot 211 title

A more detailed explanation of the subplot method used above is found in the code explaining Quiver autoscaling vs manually set axes.

More in-depth quiver documentation and functions.

import numpy as np
import matplotlib.pyplot as plt

# X and Y define the arrow locations
X, Y = np.meshgrid(np.arange(0, 2 * np.pi, .2), np.arange(0, 2 * np.pi, .2))

# U and V define the arrow directions, respectively in x- and y-direction
U = np.cos(X)
V = np.sin(Y)

# Call signature: quiver([X, Y], U, V, [C]), where C optionally sets the color
plt.quiver(X, Y, U, V)
plt.title('Simple quiver plot')
plt.show()

The plot autoscaling does not take into account the arrows, so those on the boundaries may reach out of the picture. This is not an easy problem to solve in a perfectly general way. The recommended workaround is to manually set the Axes limits in such a case. An example showing autoscaling vs manually is shown below.

import numpy as np
import matplotlib.pyplot as plt

# X and Y define the arrow locations
# This setup gives us 10 arrows in width and height, as our interval is from -5 to 5 with step 1
X = np.arange(-5, 5, 1)
Y = np.arange(-5, 5, 1)

# U and V define the arrow directions, respectively in x- and y-direction
U, V = np.meshgrid(3*X, 3*Y)

plt.figure()

Argument 121 in subplot() below denotes 1 row, 2 columns, first subplot. sublot(121) is then current.
plt.subplot(121)
plt.quiver(X, Y, U, V)
plt.title('Only autoscaling')

# Argument 122 denotes 1 row, 2 columns, second subplot. Notice that the number of rows and columns has to be equal every time, 
# where as the last number is the position where we want our subplot.
plt.subplot(122)
plt.quiver(X, Y, U, V)
# Here we specify the axes. How much extra space you need depends on the arrow size and direction,
# and must therefore be adapted each time
plt.axis([-6.5, 5.5, -6.5, 5.5])
plt.title('Manually set axes')

plt.show()

Further demos on contour plots and contour labels.

import matplotlib.pyplot as plt
import numpy as np

def f(x, y):
    return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)
 
x = np.linspace(0, 5, 60)
y = np.linspace(0, 5, 50)

X, Y = np.meshgrid(x, y)
Z = f(X, Y)

fig, axs = plt.subplots(1,3)
fig.suptitle('Three versions of the same contour plot')
axs[0].contour(X, Y, Z)
axs[1].contourf(X, Y, Z)
axs[2].contour(X, Y, Z, colors='black')

plt.show()