使用直方图的Matplotlib/Pandas错误

Question

我有问题从熊猫系列对象制作直方图,我不明白为什么它不起作用.代码之前运行良好,但现在却没有.

这是我的一些代码(具体来说,我正在尝试制作直方图的pandas系列对象):

type(dfj2_MARKET1['VSPD2_perc'])

输出结果: pandas.core.series.Series

这是我的绘图代码:

fig, axes = plt.subplots(1, 7, figsize=(30,4))
axes[0].hist(dfj2_MARKET1['VSPD1_perc'],alpha=0.9, color='blue')
axes[0].grid(True)
axes[0].set_title(MARKET1 + '  5-40 km / h')

错误信息:

    AttributeError                            Traceback (most recent call last)
    <ipython-input-75-3810c361db30> in <module>()
      1 fig, axes = plt.subplots(1, 7, figsize=(30,4))
      2 
    ----> 3 axes[1].hist(dfj2_MARKET1['VSPD2_perc'],alpha=0.9, color='blue')
      4 axes[1].grid(True)
      5 axes[1].set_xlabel('Time spent [%]')

    C:\Python27\lib\site-packages\matplotlib\axes.pyc in hist(self, x, bins, range, normed,          weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label,    stacked, **kwargs)
   8322             # this will automatically overwrite bins,
   8323             # so that each histogram uses the same bins
-> 8324             m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
   8325             m = m.astype(float) # causes problems later if it's an int
   8326             if mlast is None:

    C:\Python27\lib\site-packages\numpy\lib\function_base.pyc in histogram(a, bins, range,     normed, weights, density)
    158         if (mn > mx):
    159             raise AttributeError(
--> 160                 'max must be larger than min in range parameter.')
    161 
    162     if not iterable(bins):

AttributeError: max must be larger than min in range parameter.

Answer 1

当您在系列中具有NaN值时,会发生此错误.可能是这样吗？

histmatplotlib 的功能不能很好地处理这些NaN .例如:

s = pd.Series([1,2,3,2,2,3,5,2,3,2,np.nan])
fig, ax = plt.subplots()
ax.hist(s, alpha=0.9, color='blue')

产生相同的错误AttributeError: max must be larger than min in range parameter.一个选项是例如在绘图之前移除NaN.这将有效:

ax.hist(s.dropna(), alpha=0.9, color='blue')

另一种选择是使用大熊猫hist在你的系列,并提供方法axes[0]的ax关键字:

dfj2_MARKET1['VSPD1_perc'].hist(ax=axes[0], alpha=0.9, color='blue')