內存錯誤而繪製數據框（matplotlib）

Question

我使用的是熊貓與Jupyter筆記本電腦，並試圖繪製一個小的數據幀：

，當我插入以下行：

df9.plot(x='Time', y='Pressure mean') 

我收到以下錯誤：

--------------------------------------------------------------------------- 
MemoryError        Traceback (most recent call last) 
<ipython-input-8-c789b8162a1a> in <module>() 
----> 1 df9.plot(x='Time', y='Pressure mean') 

C:\Anaconda3\lib\site-packages\pandas\tools\plotting.py in __call__(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds) 
    3735       fontsize=fontsize, colormap=colormap, table=table, 
    3736       yerr=yerr, xerr=xerr, secondary_y=secondary_y, 
-> 3737       sort_columns=sort_columns, **kwds) 
    3738  __call__.__doc__ = plot_frame.__doc__ 
    3739 

C:\Anaconda3\lib\site-packages\pandas\tools\plotting.py in plot_frame(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds) 
    2609     yerr=yerr, xerr=xerr, 
    2610     secondary_y=secondary_y, sort_columns=sort_columns, 
-> 2611     **kwds) 
    2612 
    2613 

C:\Anaconda3\lib\site-packages\pandas\tools\plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds) 
    2436   plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds) 
    2437 
-> 2438  plot_obj.generate() 
    2439  plot_obj.draw() 
    2440  return plot_obj.result 

C:\Anaconda3\lib\site-packages\pandas\tools\plotting.py in generate(self) 
    1029 
    1030   for ax in self.axes: 
-> 1031    self._post_plot_logic_common(ax, self.data) 
    1032    self._post_plot_logic(ax, self.data) 
    1033 

C:\Anaconda3\lib\site-packages\pandas\tools\plotting.py in _post_plot_logic_common(self, ax, data) 
    1157     ax.set_xticklabels(xticklabels) 
    1158    self._apply_axis_properties(ax.xaxis, rot=self.rot, 
-> 1159           fontsize=self.fontsize) 
    1160    self._apply_axis_properties(ax.yaxis, fontsize=self.fontsize) 
    1161   elif self.orientation == 'horizontal': 

C:\Anaconda3\lib\site-packages\pandas\tools\plotting.py in _apply_axis_properties(self, axis, rot, fontsize) 
    1205 
    1206  def _apply_axis_properties(self, axis, rot=None, fontsize=None): 
-> 1207   labels = axis.get_majorticklabels() + axis.get_minorticklabels() 
    1208   for label in labels: 
    1209    if rot is not None: 

C:\Anaconda3\lib\site-packages\matplotlib\axis.py in get_majorticklabels(self) 
    1159  def get_majorticklabels(self): 
    1160   'Return a list of Text instances for the major ticklabels' 
-> 1161   ticks = self.get_major_ticks() 
    1162   labels1 = [tick.label1 for tick in ticks if tick.label1On] 
    1163   labels2 = [tick.label2 for tick in ticks if tick.label2On] 

C:\Anaconda3\lib\site-packages\matplotlib\axis.py in get_major_ticks(self, numticks) 
    1288   'get the tick instances; grow as necessary' 
    1289   if numticks is None: 
-> 1290    numticks = len(self.get_major_locator()()) 
    1291   if len(self.majorTicks) < numticks: 
    1292    # update the new tick label properties from the old 

C:\Anaconda3\lib\site-packages\pandas\tseries\converter.py in __call__(self) 
    876    vmin, vmax = vmax, vmin 
    877   if self.isdynamic: 
--> 878    locs = self._get_default_locs(vmin, vmax) 
    879   else: # pragma: no cover 
    880    base = self.base 

C:\Anaconda3\lib\site-packages\pandas\tseries\converter.py in _get_default_locs(self, vmin, vmax) 
    857 
    858   if self.plot_obj.date_axis_info is None: 
--> 859    self.plot_obj.date_axis_info = self.finder(vmin, vmax, self.freq) 
    860 
    861   locator = self.plot_obj.date_axis_info 

C:\Anaconda3\lib\site-packages\pandas\tseries\converter.py in _daily_finder(vmin, vmax, freq) 
    481      Period(ordinal=int(vmax), freq=freq)) 
    482  span = vmax.ordinal - vmin.ordinal + 1 
--> 483  dates_ = PeriodIndex(start=vmin, end=vmax, freq=freq) 
    484  # Initialize the output 
    485  info = np.zeros(span, 

C:\Anaconda3\lib\site-packages\pandas\tseries\period.py in __new__(cls, data, ordinal, freq, start, end, periods, copy, name, tz, **kwargs) 
    186    else: 
    187     data, freq = cls._generate_range(start, end, periods, 
--> 188             freq, kwargs) 
    189   else: 
    190    ordinal, freq = cls._from_arraylike(data, freq, tz) 

C:\Anaconda3\lib\site-packages\pandas\tseries\period.py in _generate_range(cls, start, end, periods, freq, fields) 
    200     raise ValueError('Can either instantiate from fields ' 
    201         'or endpoints, but not both') 
--> 202    subarr, freq = _get_ordinal_range(start, end, periods, freq) 
    203   elif field_count > 0: 
    204    subarr, freq = _range_from_fields(freq=freq, **fields) 

C:\Anaconda3\lib\site-packages\pandas\tseries\period.py in _get_ordinal_range(start, end, periods, freq, mult) 
    1026        dtype=np.int64) 
    1027  else: 
-> 1028   data = np.arange(start.ordinal, end.ordinal + 1, mult, dtype=np.int64) 
    1029 
    1030  return data, freq 

MemoryError: 

問題是什麼？我無法弄清楚。 謝謝！

Answer 1

該問題源於TimedeltaIndex（或timedelta）用於時間列。據報道：https://github.com/pydata/pandas/issues/8711 尚未給我們解決方案。

作爲替代解決方案，我邀請您將數據轉換爲DateTime或DateTimeIndex。假設YourDate包含您的觀察的開始日期。

df9.index = pd.DatetimeIndex(pd.datetime.strptime(YourDate,'%d.%m.%Y %H:%M:%S') 
          +df9['Time']) 
df9.plot(y='Pressure mean') 

請注意，如果您少於24小時，它將只繪製小時數。

編輯（2016-11-07）： 我現在可以使用timedelta作爲索引並正確繪圖。這是我如何進行（假設我有浮點數表示小時）：

converter = {'Time[h]' : lambda x: pd.to_timedelta(float(x),unit='h')}#converts float to timedelta 
df = pd.read_csv(fpath, sep='\t', 
       skiprows=len(comments),#header 
       names=dt.keys(),#you need of course your own dtype 
       dtype=dt,#you need of course your own dtype 
       encoding='latin-1',#European data... 
       skipinitialspace=True, 
       converters=converter) 
df = df.set_index('Time[h]')#time column to index. 

Answer 2

正如Wli提到的那樣，這是一個尚未解決的錯誤。但作爲解決方法，這對我來說很有效。 -

plt.plot(s.index,s.values)