Matplotlib：结合两个地块，以一个 - 显示x轴在两个不同单位在同一时间

Question

这里是我的data_df

col_C  col_A  col_B col_D 
----------------------------------- 
8/21/2017 4  1.0 736562.0 
8/28/2017 5  2.0 736569.0 
9/4/2017 6  4.0 736576.0 
9/11/2017 7  5.0 736583.0 
9/18/2017 8  6.0 736590.0 
10/2/2017 10  7.0 736604.0 
2/5/2018 28  125.0 736730.0 

我有下面的代码，其绘出了几个数据点和它们的拟合曲线。

xi = np.array(data_df[['col_A']]) 
yi = np.array(data_df[['col_B']]) 

plt.figure() 
plt.scatter(xi, yi, color = 'r') 

x = np.linspace(0, 30, 30) 
y= np.exp(x*0.172) 
plt.plot(x, y, color = 'g', label = 'exp(t)') 
plt.legend()  
plt.show() 

我也有另一段代码，该曲线图与相应的日期作为x轴的数据点。

import matplotlib 
data_df['col_D'] = data_df['col_C'] \ 
    .apply(lambda x: matplotlib.dates.datestr2num(x)) 
plt.plot_date(data_df[['col_D']], data_df[['col_B']]) 

plt.show() 

现在我尝试这两个地块合并成一个。所以x轴可以同时显示[0, 5, 10 ... 30]和[2017-09, 2017-10 ..., 2018-02]。以下是结合代码：

xi = np.array(data_df[['col_A']]) 
yi = np.array(data_df[['col_B']]) 
plt.figure() 
plt.scatter(xi, yi, color = 'r') 

x = np.linspace(0, 30, 30) 
y= np.exp(x*0.172) 
plt.plot(x, y, color = 'g', label = 'exp(t)') 
plt.legend() 

data_df['col_C'] = data_df['col_D'] \ 
    .apply(lambda x: matplotlib.dates.datestr2num(x)) 
plt.plot_date(data_df[['col_C']], data_df[['col_B']]) 
plt.show() 

然而，合并后的代码给我下面的错误：

---

ValueError        Traceback (most recent call last) 
/usr/local/lib/python3.4/dist-packages/IPython/core/formatters.py in __call__(self, obj) 
    339     pass 
    340    else: 
--> 341     return printer(obj) 
    342    # Finally look for special method names 
    343    method = get_real_method(obj, self.print_method) 

/usr/local/lib/python3.4/dist-packages/IPython/core/pylabtools.py in <lambda>(fig) 
    236 
    237  if 'png' in formats: 
--> 238   png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 
    239  if 'retina' in formats or 'png2x' in formats: 
    240   png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) 

/usr/local/lib/python3.4/dist-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 
    120 
    121  bytes_io = BytesIO() 
--> 122  fig.canvas.print_figure(bytes_io, **kw) 
    123  data = bytes_io.getvalue() 
    124  if fmt == 'svg': 

/usr/local/lib/python3.4/dist-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 
    2190      orientation=orientation, 
    2191      dryrun=True, 
-> 2192      **kwargs) 
    2193     renderer = self.figure._cachedRenderer 
    2194     bbox_inches = self.figure.get_tightbbox(renderer) 

/usr/local/lib/python3.4/dist-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 
    543 
    544  def print_png(self, filename_or_obj, *args, **kwargs): 
--> 545   FigureCanvasAgg.draw(self) 
    546   renderer = self.get_renderer() 
    547   original_dpi = renderer.dpi 

/usr/local/lib/python3.4/dist-packages/matplotlib/backends/backend_agg.py in draw(self) 
    462 
    463   try: 
--> 464    self.figure.draw(self.renderer) 
    465   finally: 
    466    RendererAgg.lock.release() 

/usr/local/lib/python3.4/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 
    61  def draw_wrapper(artist, renderer, *args, **kwargs): 
    62   before(artist, renderer) 
---> 63   draw(artist, renderer, *args, **kwargs) 
    64   after(artist, renderer) 
    65 

/usr/local/lib/python3.4/dist-packages/matplotlib/figure.py in draw(self, renderer) 
    1141 
    1142    mimage._draw_list_compositing_images(
-> 1143     renderer, self, dsu, self.suppressComposite) 
    1144 
    1145    renderer.close_group('figure') 

/usr/local/lib/python3.4/dist-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, dsu, suppress_composite) 
    137  if not_composite or not has_images: 
    138   for zorder, a in dsu: 
--> 139    a.draw(renderer) 
    140  else: 
    141   # Composite any adjacent images together 

/usr/local/lib/python3.4/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 
    61  def draw_wrapper(artist, renderer, *args, **kwargs): 
    62   before(artist, renderer) 
---> 63   draw(artist, renderer, *args, **kwargs) 
    64   after(artist, renderer) 
    65 

/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_base.py in draw(self, renderer, inframe) 
    2407    renderer.stop_rasterizing() 
    2408 
-> 2409   mimage._draw_list_compositing_images(renderer, self, dsu) 
    2410 
    2411   renderer.close_group('axes') 

/usr/local/lib/python3.4/dist-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, dsu, suppress_composite) 
    137  if not_composite or not has_images: 
    138   for zorder, a in dsu: 
--> 139    a.draw(renderer) 
    140  else: 
    141   # Composite any adjacent images together 

/usr/local/lib/python3.4/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 
    61  def draw_wrapper(artist, renderer, *args, **kwargs): 
    62   before(artist, renderer) 
---> 63   draw(artist, renderer, *args, **kwargs) 
    64   after(artist, renderer) 
    65 

/usr/local/lib/python3.4/dist-packages/matplotlib/axis.py in draw(self, renderer, *args, **kwargs) 
    1134   renderer.open_group(__name__) 
    1135 
-> 1136   ticks_to_draw = self._update_ticks(renderer) 
    1137   ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw, 
    1138                 renderer) 

/usr/local/lib/python3.4/dist-packages/matplotlib/axis.py in _update_ticks(self, renderer) 
    967 
    968   interval = self.get_view_interval() 
--> 969   tick_tups = [t for t in self.iter_ticks()] 
    970   if self._smart_bounds: 
    971    # handle inverted limits 

/usr/local/lib/python3.4/dist-packages/matplotlib/axis.py in <listcomp>(.0) 
    967 
    968   interval = self.get_view_interval() 
--> 969   tick_tups = [t for t in self.iter_ticks()] 
    970   if self._smart_bounds: 
    971    # handle inverted limits 

/usr/local/lib/python3.4/dist-packages/matplotlib/axis.py in iter_ticks(self) 
    910   Iterate through all of the major and minor ticks. 
    911   """ 
--> 912   majorLocs = self.major.locator() 
    913   majorTicks = self.get_major_ticks(len(majorLocs)) 
    914   self.major.formatter.set_locs(majorLocs) 

/usr/local/lib/python3.4/dist-packages/matplotlib/dates.py in __call__(self) 
    981  def __call__(self): 
    982   'Return the locations of the ticks' 
--> 983   self.refresh() 
    984   return self._locator() 
    985 

/usr/local/lib/python3.4/dist-packages/matplotlib/dates.py in refresh(self) 
    1001  def refresh(self): 
    1002   'Refresh internal information based on current limits.' 
-> 1003   dmin, dmax = self.viewlim_to_dt() 
    1004   self._locator = self.get_locator(dmin, dmax) 
    1005 

/usr/local/lib/python3.4/dist-packages/matplotlib/dates.py in viewlim_to_dt(self) 
    758    vmin, vmax = vmax, vmin 
    759 
--> 760   return num2date(vmin, self.tz), num2date(vmax, self.tz) 
    761 
    762  def _get_unit(self): 

/usr/local/lib/python3.4/dist-packages/matplotlib/dates.py in num2date(x, tz) 
    399   tz = _get_rc_timezone() 
    400  if not cbook.iterable(x): 
--> 401   return _from_ordinalf(x, tz) 
    402  else: 
    403   x = np.asarray(x) 

/usr/local/lib/python3.4/dist-packages/matplotlib/dates.py in _from_ordinalf(x, tz) 
    252 
    253  ix = int(x) 
--> 254  dt = datetime.datetime.fromordinal(ix).replace(tzinfo=UTC) 
    255 
    256  remainder = float(x) - ix 

ValueError: ordinal must be >= 1 

<matplotlib.figure.Figure at 0x7f65c6f98f28> 

任何想法，什么是错的？是否有可能在x轴上同时显示[0, 5, 10 ... 30]和[2017-09, 2017-10 ..., 2018-02]？谢谢！你有

Answer 1

的problemis的轴范围为一个情节大致是0到30，而其他情节大致是736562到736730（这是日期的数字表示。

一个选项是使用双轴和一个范围映射到另一个。根据所述映射然后设置双轴的极限。

u = u"""col_C  col_A  col_B col_D 
8/21/2017 4  1.0 736562.0 
8/28/2017 5  2.0 736569.0 
9/4/2017 6  4.0 736576.0 
9/11/2017 7  5.0 736583.0 
9/18/2017 8  6.0 736590.0 
10/2/2017 10  7.0 736604.0 
2/5/2018 28  125.0 736730.0""" 

import io 
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import matplotlib.dates 

df = pd.read_csv(io.StringIO(u), delim_whitespace=True) 

xi = np.array(df[['col_A']]) 
yi = np.array(df[['col_B']]) 

fig, ax = plt.subplots() 
ax.scatter(xi, yi, color = 'r') 

x = np.linspace(0, 30, 30) 
y= np.exp(x*0.172) 
ax.plot(x, y, color = 'g', label = 'exp(t)') 
ax.legend()  

ax2 = ax.twiny() 

df['col_D'] = df['col_C'].apply(lambda x: matplotlib.dates.datestr2num(x)) 
ax2.plot_date(df[['col_D']], df[['col_B']]) 

f = lambda x: (df['col_D'].iloc[-1]- df['col_D'].iloc[0])/ \ 
       (xi[-1]-xi[0])*(x-xi[0])+df['col_D'].iloc[0] 

ax2.set_xlim(f(np.array(ax.get_xlim()))) 


plt.show() 

你可以增加alpha=0到第二PLO如果你喜欢，可以隐藏它。