1. matplotlib入门

Matplotlib提供了两种方法来作图：状态接口和面向对象。

  1. #  状态接口是通过pyplot模块来实现的，matplotlib会追踪绘图环境的当前状态
  2. #  这种方法适合快速画一些简单的图，但是对于多图和多轴会不方便
  3.  In[2]: x = [-3, 5, 7]
  4.         y = [10, 2, 5]
  6.         plt.figure(figsize=(15,3))
  7.         plt.plot(x, y)
  8.         plt.xlim(0, 10)
  9.         plt.ylim(-3, 8)
  10.         plt.xlabel('X Axis')
  11.         plt.ylabel('Y axis')
  12.         plt.title('Line Plot')
  13.         plt.suptitle('Figure Title', size=20, y=1.03)
  14. Out[2]: Text(0.5,1.03,'Figure Title')

1. matplotlib入门 - 图1

  1. #  面向对象的方法更易懂，修改的是哪个对象非常清晰
  2. #  而且代码更加pythonic，与pandas的交互方式更相似
  3.  In[3]: fig, ax = plt.subplots(figsize=(15,3))
  4.         ax.plot(x, y)
  5.         ax.set_xlim(0, 10)
  6.         ax.set_ylim(-3, 8)
  7.         ax.set_xlabel('X axis')
  8.         ax.set_ylabel('Y axis')
  9.         ax.set_title('Line Plot')
  10.         fig.suptitle('Figure Title', size=20, y=1.03)
  11. Out[3]: Text(0.5,1.03,'Figure Title')

1. matplotlib入门 - 图2

  1. #  用subplots函数创建一个带有一个Axes的Figure
  2.  In[4]: fig, ax = plt.subplots(nrows=1, ncols=1)

1. matplotlib入门 - 图3

  1. #  查看fig和ax的数据类型
  2.  In[5]: type(fig)
  3. Out[5]: matplotlib.figure.Figure
  5.  In[6]: type(ax)
  6. Out[6]: matplotlib.axes._subplots.AxesSubplot
  1. #  查询Figure的大小，并放大
  2.  In[7]: fig.get_size_inches()
  3. Out[7]: array([ 6.,  4.])
  5.  In[8]: fig.set_size_inches(14, 4)
  6.         fig
  7. Out[8]:

1. matplotlib入门 - 图4

  1. #  axes属性可以获取Figure的所有Axes
  2.  In[9]: fig.axes
  3. Out[9]: [<matplotlib.axes._subplots.AxesSubplot at 0x1134202b0>]
  1. #  判断Axes列表中的第一个元素和之前定义的ax是否相同
  2.  In[10]: fig.axes[0] is ax
  3. Out[10]: True
  1. #  用浮点数显示不同的灰度
  2.  In[11]: fig.set_facecolor('.9')
  3.          ax.set_facecolor('.7')
  4.          fig
  5. Out[11]:

1. matplotlib入门 - 图5

  1. #  检查Axes的子元素，每个基本的图都有四个spine和两个axis
  2. #  spine是数据边界，即四条边
  3. #  x和y轴对象包含了更多的绘图对象，比如刻度、标签
  4.  In[12]: ax_children = ax.get_children()
  5.          ax_children         
  6. Out[12]: [<matplotlib.spines.Spine at 0x11145b358>,
  7.           <matplotlib.spines.Spine at 0x11145b0f0>,
  8.           <matplotlib.spines.Spine at 0x11145ae80>,
  9.           <matplotlib.spines.Spine at 0x11145ac50>,
  10.           <matplotlib.axis.XAxis at 0x11145aa90>,
  11.           <matplotlib.axis.YAxis at 0x110fa8d30>,
  12.           ...]
  1. #  通过spines属性直接访问spines
  2. >>> spines = ax.spines
  3. >>> spines
  4. OrderedDict([('left', <matplotlib.spines.Spine at 0x11279e320>),
  5.              ('right', <matplotlib.spines.Spine at 0x11279e0b8>),
  6.              ('bottom', <matplotlib.spines.Spine at 0x11279e048>),
  7.              ('top', <matplotlib.spines.Spine at 0x1127eb5c0>)])
  1. #  选中左边，改变它的位置和宽度，使底边不可见
  2.  In[13]: spine_left = spines['left']
  3.          spine_left.set_position(('outward', -100))
  4.          spine_left.set_linewidth(5)
  6.          spine_bottom = spines['bottom']
  7.          spine_bottom.set_visible(False)
  8.          fig
  9. Out[13]:

1. matplotlib入门 - 图6

  1. #  通过属性xaxis和yaxis可以修改属性，有些属性也可以通过Axes对象直接修改
  2.  In[14]: ax.xaxis.grid(True, which='major', linewidth=2, color='black', linestyle='--')
  3.          ax.xaxis.set_ticks([.2, .4, .55, .93])
  4.          ax.xaxis.set_label_text('X Axis', family='Verdana', fontsize=15)
  6.          ax.set_ylabel('Y Axis', family='Calibri', fontsize=20)
  7.          ax.set_yticks([.1, .9])
  8.          ax.set_yticklabels(['point 1', 'point 9'], rotation=45)
  9.          fig
  10. Out[14]:

1. matplotlib入门 - 图7

原理

  1. #  plt.subplots函数返回的是一个元组
  2.  In[22]: plot_objects = plt.subplots()
  4.  In[23]: type(plot_objects)
  5. Out[23]: tuple
  7.  In[24]: fig = plot_objects[0]
  8.          ax = plot_objects[1]
  1. #  如果创建了多个轴，则元组的第二个元素是一个包含所有轴的NumPy数组
  2.  In[25]: plot_objects = plt.subplots(2, 4, figsize=(14, 4))

1. matplotlib入门 - 图8

  1.  In[26]: plot_objects[1]
  2. Out[26]: array([[<matplotlib.axes._subplots.AxesSubplot object at 0x113eefa20>,
  3.                  <matplotlib.axes._subplots.AxesSubplot object at 0x113f7ccc0>,
  4.                  <matplotlib.axes._subplots.AxesSubplot object at 0x11413ed68>,
  5.                  <matplotlib.axes._subplots.AxesSubplot object at 0x114213e48>],
  6.                 [<matplotlib.axes._subplots.AxesSubplot object at 0x11424ce80>,
  7.                  <matplotlib.axes._subplots.AxesSubplot object at 0x1142807b8>,
  8.                  <matplotlib.axes._subplots.AxesSubplot object at 0x1142b8898>,
  9.                  <matplotlib.axes._subplots.AxesSubplot object at 0x1142f2898>]], dtype=object)
  1. #  一些属性和与其等价的get方法
  2.  In[27]: fig.get_axes() == fig.axes
  3. Out[27]: True
  5.  In[29]: fig.axes == fig.get_axes()
  6. Out[29]: True
  8.  In[30]: ax.xaxis == ax.get_xaxis()
  9. Out[30]: True
  11.  In[31]: ax.yaxis == ax.get_yaxis()
  12. Out[31]: True

更多

  1. #  查询xaxis的所有属性
  2.  In[15]: ax.xaxis.properties()
  3. Out[15]: 
  4. {'agg_filter': None,
  5.  'alpha': None,
  6.  'animated': False,
  7.  'children': [Text(0.5,22.2,'X Axis'),
  8.   Text(1,23.2,''),
  9.   <matplotlib.axis.XTick at 0x113371fd0>,
  10.   <matplotlib.axis.XTick at 0x113514240>,
  11.   <matplotlib.axis.XTick at 0x1136387b8>,
  12.   <matplotlib.axis.XTick at 0x113638f60>],
  13.  'clip_box': TransformedBbox(Bbox([[0.0, 0.0], [1.0, 1.0]]), CompositeGenericTransform(CompositeGenericTransform(BboxTransformTo(Bbox([[0.0, 0.0], [1.0, 1.0]])), Affine2D(array([[ 1.,  0.,  0.],
  14.         [ 0.,  1.,  0.],
  15.         [ 0.,  0.,  1.]]))), BboxTransformTo(TransformedBbox(Bbox([[0.125, 0.125], [0.9, 0.88]]), BboxTransformTo(TransformedBbox(Bbox([[0.0, 0.0], [14.0, 4.0]]), Affine2D(array([[ 72.,   0.,   0.],
  16.         [  0.,  72.,   0.],
  17.         [  0.,   0.,   1.]])))))))),
  18.  'clip_on': True,
  19.  'clip_path': None,
  20.  'contains': None,
  21.  'data_interval': array([ inf, -inf]),
  22.  'figure': <matplotlib.figure.Figure at 0x11332abe0>,
  23.  'gid': None,
  24.  'gridlines': <a list of 4 Line2D gridline objects>,
  25.  'label': Text(0.5,22.2,'X Axis'),
  26.  'label_position': 'bottom',
  27.  'label_text': 'X Axis',
  28.  'major_formatter': <matplotlib.ticker.ScalarFormatter at 0x113543780>,
  29.  'major_locator': <matplotlib.ticker.FixedLocator at 0x113648ba8>,
  30.  'major_ticks': [<matplotlib.axis.XTick at 0x113371fd0>,
  31.   <matplotlib.axis.XTick at 0x113514240>,
  32.   <matplotlib.axis.XTick at 0x1136387b8>,
  33.   <matplotlib.axis.XTick at 0x113638f60>],
  34.  'majorticklabels': <a list of 4 Text major ticklabel objects>,
  35.  'majorticklines': <a list of 8 Line2D ticklines objects>,
  36.  'majorticklocs': array([ 0.2 ,  0.4 ,  0.55,  0.93]),
  37.  'minor_formatter': <matplotlib.ticker.NullFormatter at 0x11341a518>,
  38.  'minor_locator': <matplotlib.ticker.NullLocator at 0x113624198>,
  39.  'minor_ticks': [],
  40.  'minorticklabels': <a list of 0 Text minor ticklabel objects>,
  41.  'minorticklines': <a list of 0 Line2D ticklines objects>,
  42.  'minorticklocs': [],
  43.  'minpos': inf,
  44.  'offset_text': Text(1,23.2,''),
  45.  'path_effects': [],
  46.  'picker': None,
  47.  'pickradius': 15,
  48.  'rasterized': None,
  49.  'scale': 'linear',
  50.  'sketch_params': None,
  51.  'smart_bounds': False,
  52.  'snap': None,
  53.  'tick_padding': 3.5,
  54.  'tick_space': 26,
  55.  'ticklabels': <a list of 4 Text major ticklabel objects>,
  56.  'ticklines': <a list of 8 Line2D ticklines objects>,
  57.  'ticklocs': array([ 0.2 ,  0.4 ,  0.55,  0.93]),
  58.  'ticks_direction': array(['out', 'out', 'out', 'out'],
  59.        dtype='<U3'),
  60.  'ticks_position': 'bottom',
  61.  'transform': IdentityTransform(),
  62.  'transformed_clip_path_and_affine': (None, None),
  63.  'units': None,
  64.  'url': None,
  65.  'view_interval': array([ 0.,  1.]),
  66.  'visible': True,
  67.  'zorder': 1.5}