pandas 如何在matplotlib的两个y轴图表中对齐条形和线条？
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How to align the bar and line in matplotlib two y-axes chart?
提问by bigbug
I have a pandas df as below:
我有一个Pandas df 如下：
>>> df
sales net_pft sales_gr net_pft_gr
STK_ID RPT_Date
600809 20120331 22.1401 4.9253 0.1824 -0.0268
20120630 38.1565 7.8684 0.3181 0.1947
20120930 52.5098 12.4338 0.4735 0.7573
20121231 64.7876 13.2731 0.4435 0.7005
20130331 27.9517 7.5182 0.2625 0.5264
20130630 40.6460 9.8572 0.0652 0.2528
20130930 53.0501 11.8605 0.0103 -0.0461
Then df[['sales','net_pft']].unstack('STK_ID').plot(kind='bar', use_index=True)create bar chart.
然后df[['sales','net_pft']].unstack('STK_ID').plot(kind='bar', use_index=True)创建条形图。
And df[['sales_gr','net_pft_gr']].plot(kind='line', use_index=True)create line chart:
并df[['sales_gr','net_pft_gr']].plot(kind='line', use_index=True)创建折线图：
Now I want to put them together in a chart of two y-axes, using twinx().
现在我想使用 twinx() 将它们放在两个 y 轴的图表中。
import matplotlib.pyplot as plt
fig = plt.figure()
ax = df[['sales','net_pft']].unstack('STK_ID').plot(kind='bar', use_index=True)
ax2 = ax.twinx()
ax2.plot(df[['sales_gr','net_pft_gr']].values, linestyle='-', marker='o', linewidth=2.0)
The result is like this :
结果是这样的：
My issues are:
我的问题是：
- How to shift the line to align with the bar at the same x-tickers ?
- How to let the left and right y_axis tickers aligned at the same line?
- 如何移动线以与相同 x-tickers 的条对齐？
- 如何让左右 y_axis 代码在同一条线上对齐？
采纳答案by CT Zhu
Just change the final line to:
只需将最后一行更改为：
ax2.plot(ax.get_xticks(),
df[['sales_gr','net_pft_gr']].values,
linestyle='-',
marker='o', linewidth=2.0)
You will be all set.
你会一切就绪。
I don't quite get your second question. The 1st and 2nd y axis are of different scale, what do you mean by aligning them to the same line? They can't be aligned to the same grid line (yes you can but the right axis will look ugly, having values like 0.687 and alike). Anyway, you can do:
我不太明白你的第二个问题。第一个和第二个 y 轴的比例不同，将它们对齐到同一条线是什么意思？它们不能与同一条网格线对齐（是的，您可以，但右轴看起来很难看，其值类似于 0.687 等）。无论如何，你可以这样做：
ax.set_ylim((-10, 80.))
to align them, and the plot now looks ugly:
对齐它们，情节现在看起来很难看：
