matplot-object-tutorial








common use cases for matplotlib (object API)

This is a collection of code snippets to cover most common use cases of using matplotlib library

In [1]:
import numpy as np

TOC:

1. Initialize

In [ ]:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(3,2) # fig is Figure instance, axes is 2d list of axes

1.a Iterate over axes

In [24]:
fig, axes = plt.subplots(1,1)
axes
Out[24]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fbdba7c5990>
In [25]:
fig, axes = plt.subplots(1,2)
axes
Out[25]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fbdbb414f50>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7fbdbb3b94d0>],
      dtype=object)
In [26]:
fig, axes = plt.subplots(2,1)
axes
Out[26]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fbdbb2284d0>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7fbdbacde4d0>],
      dtype=object)
In [27]:
fig, axes = plt.subplots(2,2)
axes
Out[27]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fbdbb28d850>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbdbae3e890>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbdbb145090>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbdbb150850>]],
      dtype=object)
In [7]:
for ax in axes.flat:
    print(ax)

AxesSubplot(0.125,0.657941;0.352273x0.222059)
AxesSubplot(0.547727,0.657941;0.352273x0.222059)
AxesSubplot(0.125,0.391471;0.352273x0.222059)
AxesSubplot(0.547727,0.391471;0.352273x0.222059)
AxesSubplot(0.125,0.125;0.352273x0.222059)
AxesSubplot(0.547727,0.125;0.352273x0.222059)
In [ ]:

2. Plot some data

In [10]:
data = np.random.rand(20)*10
fig, axes = plt.subplots(3,2) 
axes[0][0].plot(data)
plt.show()

3. show an image

In [12]:
img = np.random.rand(30, 30, 3)
fig, axes = plt.subplots(3,2) 
axes[0][0].imshow(img)    # changed
plt.show()

4. plot size

In [15]:
fig, axes = plt.subplots(3,2, figsize=(10,10))     # changed
data = np.random.rand(20)*10
axes[0][0].plot(data)
plt.show()

5. multiple data plotting

In [19]:
data1 = np.random.rand(20)*10
data2 = np.random.rand(20)*10  
data3 = np.random.rand(20)*10
data4 = np.random.rand(20)*10
fig, axes = plt.subplots(1,2)
axes[0].plot(data1)
axes[0].plot(data2)    # changed
axes[1].plot(data3)    # changed
axes[1].plot(data4)    # changed
plt.show()
In [21]:
data1 = np.random.rand(20)*10
data2 = np.random.rand(20)*10  
data3 = np.random.rand(20)*10
data4 = np.random.rand(20)*10
fig, axes = plt.subplots(2,1)
axes[0].plot(data1)
axes[0].plot(data2)    # changed
axes[1].plot(data3)    # changed
axes[1].plot(data4)    # changed
plt.show()

6. set plot color

In [23]:
data1 = np.random.rand(20)*10
data2 = np.random.rand(20)*10
fig, axes = plt.subplots(1,1)
axes.plot(data1, color='g')    # changed
axes.plot(data2, color='r')    # changed
plt.show()

7. plot legend

In [29]:
data1 = np.random.rand(20)*10
data2 = np.random.rand(20)*10
fig, axes = plt.subplots(2,1)
axes[0].plot(data1, color='g', label='data1')    # changed
axes[0].plot(data2, color='r', label='data2')    # changed
axes[0].legend()    # changed
plt.show()

8. axis turn on/off

In [34]:
data1 = np.random.rand(20)*10
data2 = np.random.rand(20)*10
fig, axes = plt.subplots(1,1)
axes.plot(data1, color='g', label='data1')
axes.plot(data2, color='r', label='data2')
axes.set_xticks([])    # changed
axes.set_yticks([])    # changed
axes.legend()
plt.show()

9. major ticks

In [35]:
data1 = np.random.rand(20)*10
data2 = np.random.rand(20)*10
fig, axes = plt.subplots(1,1)
axes.plot(data1, color='g', label='data1')
axes.plot(data2, color='r', label='data2')
axes.set_xticks(np.linspace(0, 20, 4, endpoint=False))    # changed
# plt.yticks([])
axes.legend()
plt.show()

10. minor ticks

In [37]:
data1 = np.random.rand(20)*10
data2 = np.random.rand(20)*10
fig, axes = plt.subplots(1,1)
axes.plot(data1, color='g', label='data1')
axes.plot(data2, color='r', label='data2')
axes.set_xticks(np.linspace(0, 20, 4, endpoint=False))
axes.xaxis.set_minor_locator(plt.MultipleLocator(1))    # changed
axes.legend()
plt.show()

11. grid

In [47]:
data1 = np.random.rand(20)*10
fig, axes = plt.subplots(1,1)
axes.plot(data1, color='g', label='data1')
axes.set_xticks(np.linspace(0, 20, 4, endpoint=False))
axes.xaxis.set_minor_locator(plt.MultipleLocator(1))
axes.legend()
axes.grid(True, which='both', axis='both')    # changed
plt.show()

12. Axis limits

In [43]:
data1 = np.random.rand(20)*10
fig, axes = plt.subplots(1,1)
axes.xaxis.set_minor_locator(plt.MultipleLocator(1))
axes.plot(data1, color='g', label='data1')
axes.set_xticks(np.linspace(0, 20, 4, endpoint=False))
axes.set_xlim(0, 50)    # changed
axes.set_ylim(0, 20)    # changed
axes.legend()
axes.grid(True, which='both')
plt.show()
In [ ]:
In [ ]: