pandas 熊猫：生成并绘制平均值
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pandas: generate and plot average
提问by vkontori
I have a pandas dataframe like:
我有一个Pandas数据框，如：
In [61]: df = DataFrame(np.random.rand(3,4), index=['art','mcf','mesa'],
columns=['pol1','pol2','pol3','pol4'])
In [62]: df
Out[62]:
pol1 pol2 pol3 pol4
art 0.661592 0.479202 0.700451 0.345085
mcf 0.235517 0.665981 0.778774 0.610344
mesa 0.838396 0.035648 0.424047 0.866920
and I want to generate a row with the average for the policies across benchmarks and then plot it.
我想用跨基准的策略的平均值生成一行，然后绘制它。
Currently, the way I do this is:
目前，我这样做的方式是：
df = df.T
df['average'] = df.apply(average, axis=1)
df = df.T
df.plot(kind='bar')
Is there an elegant way to avoid the double transposition?
有没有一种优雅的方法来避免双重换位？
I tried:
我试过：
df.append(DataFrame(df.apply(average)).T)
df.plot(kind='bar')
This will append the correct values but does not update the index properly and the graph is messed up.
这将附加正确的值，但不会正确更新索引，并且图表会混乱。
A clarification. The result of the code with the double transposition is this: 
This is what I want. To show both the benchmarks and the average of the policies, not just the average. I was just curious if I can do it better.
澄清。双转位的代码结果是这样的：这就是我想要的。显示基准和策略的平均值，而不仅仅是平均值。我只是好奇我是否可以做得更好。
Note that the legend is usually messed up. For a fix:
请注意，图例通常是乱七八糟的。修复：
ax = df.plot(kind='bar')
ax.legend(patches, list(df.columns), loc='best')
回答by bmu
You can simply use the instance method meanof the DataFrameand than plot the results. There is no need for transposition.
您可以简单地使用 的实例方法mean，DataFrame然后绘制结果。不需要转位。
In [14]: df.mean()
Out[14]:
pol1 0.578502
pol2 0.393610
pol3 0.634424
pol4 0.607450
In [15]: df.mean().plot(kind='bar')
Out[15]: <matplotlib.axes.AxesSubplot at 0x4a327d0>
Update
更新
If you want to plot the bars of all columns and the mean you can appendthe mean:
如果您想绘制所有列的条形图和平均值，您可以append使用平均值：
In [95]: average = df.mean()
In [96]: average.name = 'average'
In [97]: df = df.append(average)
In [98]: df
Out[98]:
pol1 pol2 pol3 pol4
art 0.661592 0.479202 0.700451 0.345085
mcf 0.235517 0.665981 0.778774 0.610344
mesa 0.838396 0.035648 0.424047 0.866920
average 0.578502 0.393610 0.634424 0.607450
In [99]: df.plot(kind='bar')
Out[99]: <matplotlib.axes.AxesSubplot at 0x52f4390>
If your layout doesn't fit in to the subplot tight_layoutwill adjust the matplotlib parameters.
如果您的布局不适合子图，tight_layout则将调整 matplotlib 参数。
