In [156]: fig = plt.figure()

In [157]: ax1 = fig.add_subplot(211)

In [158]: ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.3])

In [159]: ax1
Out[159]: <matplotlib.axes.Subplot instance at 0xd54b26c>

In [160]: print(fig.axes)
[<matplotlib.axes.Subplot instance at 0xd54b26c>, <matplotlib.axes.Axes instance at 0xd3f0b2c> 

for ax in fig.axes:
    ax.grid(True) 

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as lines

fig = plt.figure()

l1 = lines.Line2D([0, 1], [0, 1], transform=fig.transFigure, figure=fig)
l2 = lines.Line2D([0, 1], [1, 0], transform=fig.transFigure, figure=fig)
fig.lines.extend([l1, l2])

plt.show() 

　

　ここには図が含まれるアーティストの要約があります

Figure attribute	Description
axes	Axesインスタンスのリスト（Subplotを含む）
patch	矩形の背景
images	Imageパッチのリスト - 生のピクセル表示に役立ちます
legends	Figure Legendインスタンスのリスト（Axes.legendsとは異なる）
lines	Line2Dインスタンスのリスト（めったに使われません、Axes.linesを参照）
patches	図パッチのリスト（めったに使われない、Axes.patchesを参照）
texts	リストの図Textインスタンス

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ax = fig.add_subplot(111)
rect = ax.patch  # a Rectangle instance
rect.set_facecolor('green') 

In [213]: x, y = np.random.rand(2, 100)

In [214]: line, = ax.plot(x, y, '-', color='blue', linewidth=2) 

In [229]: print(ax.lines)
[<matplotlib.lines.Line2D instance at 0xd378b0c>] 

In [233]: n, bins, rectangles = ax.hist(np.random.randn(1000), 50, facecolor='yellow')

In [234]: rectangles
Out[234]: <a list of 50 Patch objects>

In [235]: print(len(ax.patches)) 

In [262]: fig, ax = plt.subplots()

# create a rectangle instance
In [263]: rect = matplotlib.patches.Rectangle( (1,1), width=5, height=12)

# by default the axes instance is None
In [264]: print(rect.get_axes())
None

# and the transformation instance is set to the "identity transform"
In [265]: print(rect.get_transform())
<Affine object at 0x13695544>

# now we add the Rectangle to the Axes
In [266]: ax.add_patch(rect)

# and notice that the ax.add_patch method has set the axes
# instance
In [267]: print(rect.get_axes())
Axes(0.125,0.1;0.775x0.8)

# and the transformation has been set too
In [268]: print(rect.get_transform())
<Affine object at 0x15009ca4>

# the default axes transformation is ax.transData
In [269]: print(ax.transData)
<Affine object at 0x15009ca4>

# notice that the xlimits of the Axes have not been changed
In [270]: print(ax.get_xlim())
(0.0, 1.0)

# but the data limits have been updated to encompass the rectangle
In [271]: print(ax.dataLim.bounds)
(1.0, 1.0, 5.0, 12.0)

# we can manually invoke the auto-scaling machinery
In [272]: ax.autoscale_view()

# and now the xlim are updated to encompass the rectangle
In [273]: print(ax.get_xlim())
(1.0, 6.0)

# we have to manually force a figure draw
In [274]: ax.figure.canvas.draw() 

for label in ax.get_xticklabels():
    label.set_color('orange') 

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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as lines

fig, ax = plt.subplots()
axis = ax.xaxis
axis.get_ticklocs()

plt.show() 

　

axis.get_ticklabels() 

axis.get_ticklines() 

axis.get_ticklines()
get_major_ticks() 

axis.get_ticklines(minor=True)
get_minor_ticks() 

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as lines

fig, ax = plt.subplots()
axis = ax.xaxis
# a = axis.get_ticklocs()
# print(a)

axis.get_ticklines(minor=True)

# Here is a summary of some of the useful accessor methods of the ``Axis``
# (these have corresponding setters where useful, such as
# set_major_formatter)
#
# ======================  =========================================================
# Accessor method         Description
# ======================  =========================================================
# get_scale               The scale of the axis, e.g., 'log' or 'linear'
# get_view_interval       The interval instance of the axis view limits
# get_data_interval       The interval instance of the axis data limits
# get_gridlines           A list of grid lines for the Axis
# get_label               The axis label - a Text instance
# get_ticklabels          A list of Text instances - keyword minor=True|False
# get_ticklines           A list of Line2D instances - keyword minor=True|False
# get_ticklocs            A list of Tick locations - keyword minor=True|False
# get_major_locator       The matplotlib.ticker.Locator instance for major ticks
# get_major_formatter     The matplotlib.ticker.Formatter instance for major ticks
# get_minor_locator       The matplotlib.ticker.Locator instance for minor ticks
# get_minor_formatter     The matplotlib.ticker.Formatter instance for minor ticks
# get_major_ticks         A list of Tick instances for major ticks
# get_minor_ticks         A list of Tick instances for minor ticks
# grid                    Turn the grid on or off for the major or minor ticks
# ======================  =========================================================
#
# Here is an example, not recommended for its beauty, which customizes
# the axes and tick properties

# plt.figure creates a matplotlib.figure.Figure instance
# fig = plt.figure()
rect = fig.patch  # a rectangle instance
rect.set_facecolor('lightgoldenrodyellow')

ax1 = fig.add_axes([0.3, 0.3, 0.4, 0.4])
rect = ax1.patch
rect.set_facecolor('lightslategray')

for label in ax1.xaxis.get_ticklabels():
    # label is a Text instance
    label.set_color('red')
    label.set_rotation(45)
    label.set_fontsize(16)

for line in ax1.yaxis.get_ticklines():
    # line is a Line2D instance
    line.set_color('green')
    line.set_markersize(25)
    line.set_markeredgewidth(3)

plt.show() 

　

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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as lines
import matplotlib.ticker as ticker

# Fixing random state for reproducibility
np.random.seed(19680801)

fig, ax = plt.subplots()
ax.plot(100*np.random.rand(20))

formatter = ticker.FormatStrFormatter('$%1.2f')
ax.yaxis.set_major_formatter(formatter)

for tick in ax.yaxis.get_major_ticks():
    tick.label1On = False
    tick.label2On = True
    tick.label2.set_color('green')

plt.show() 

　

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