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如何使用所有xticks绘制pandas multiindex dataFrame

[英]How to plot a pandas multiindex dataFrame with all xticks

I have a pandas dataFrame like this: 我有一个像这样的pandas dataFrame:

                     content
date                        
2013-12-18 12:30:00        1
2013-12-19 10:50:00        1
2013-12-24 11:00:00        0
2014-01-02 11:30:00        1
2014-01-03 11:50:00        0
2013-12-17 16:40:00       10
2013-12-18 10:00:00        0
2013-12-11 10:00:00        0
2013-12-18 11:45:00        0
2013-12-11 14:40:00        4
2010-05-25 13:05:00        0
2013-11-18 14:10:00        0
2013-11-27 11:50:00        3
2013-11-13 10:40:00        0
2013-11-20 10:40:00        1
2008-11-04 14:49:00        1
2013-11-18 10:05:00        0
2013-08-27 11:00:00        0
2013-09-18 16:00:00        0
2013-09-27 11:40:00        0

date being the index. 日期是索引。 I reduce the values to months using: 我使用以下方法将值减少到几个月:

dataFrame = dataFrame.groupby([lambda x: x.year, lambda x: x.month]).agg([sum])

which outputs: 哪个输出:

         content
             sum
2006 3        66
     4        65
     5        48
     6        87
     7        37
     8        54
     9        73
     10       74
     11       53
     12       45
2007 1        28
     2        40
     3        95
     4        63
     5        56
     6        66
     7        50
     8        49
     9        18
     10       28

Now when I plot this dataFrame, I want the x-axis show every month/year as a tick. 现在,当我绘制这个dataFrame时,我希望每个月/每年的x轴显示为勾选。 I have tries setting xticks but it doesn't seem to work. 我尝试设置xticks但它似乎不起作用。 How could this be achieved? 怎么能实现这一目标? This is my current plot using dataFrame.plot(): 这是我目前使用dataFrame.plot()的情节:

在此输入图像描述

You can use set_xtick() and set_xticklabels() : 您可以使用set_xtick()set_xticklabels()

idx = pd.date_range("2013-01-01", periods=1000)
val = np.random.rand(1000)
s = pd.Series(val, idx)

g = s.groupby([s.index.year, s.index.month]).mean()

ax = g.plot()
ax.set_xticks(range(len(g)));
ax.set_xticklabels(["%s-%02d" % item for item in g.index.tolist()], rotation=90);

output: 输出:

在此输入图像描述

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