向pandas DataFrame添加带有列的行 [英] Add a row with means of columns to pandas DataFrame
问题描述
我有一个熊猫DataFrame，其中包含一些随时间变化的传感器读数，如下所示:
I have a pandas DataFrame consisting of some sensor readings taken over time like this:
diode1 diode2 diode3 diode4
Time
0.530 7 0 10 16
1.218 17 7 14 19
1.895 13 8 16 17
2.570 8 2 16 17
3.240 14 8 17 19
3.910 13 6 17 18
4.594 13 5 16 19
5.265 9 0 12 16
5.948 12 3 16 17
6.632 10 2 15 17
我已经编写了代码，以每列的方式添加另一行:
I have written code to add another row with the means of each column:
# List of the averages for the test.
averages = [df[key].describe()['mean'] for key in df]
indexes = df.index.tolist()
indexes.append('mean')
df.reindex(indexes)
# Adding the mean row to the bottom of the DataFrame
i = 0
for key in df:
df.set_value('mean', key, averages[i])
i += 1
这给了我想要的结果，它是一个像这样的DataFrame:
This gives me the result I want, which is a DataFrame like this:
diode1 diode2 diode3 diode4
Time
0.53 7.0 0.0 10.0 16.0
1.218 17.0 7.0 14.0 19.0
1.895 13.0 8.0 16.0 17.0
2.57 8.0 2.0 16.0 17.0
3.24 14.0 8.0 17.0 19.0
3.91 13.0 6.0 17.0 18.0
4.594 13.0 5.0 16.0 19.0
5.265 9.0 0.0 12.0 16.0
5.948 12.0 3.0 16.0 17.0
6.632 10.0 2.0 15.0 17.0
mean 11.6 4.1 14.9 17.5
但是，我确信这不是添加行的最有效方法.我尝试将append与另存为pandas Series的方法一起使用，但最终结果如下:
However, I am sure that this is not the most efficient way of adding the row. I have tried using append with the means saved as a pandas Series but ended up with something like this:
diode1 diode2 diode3 diode4 mean
0 7.0 0.0 10.0 14.0 NaN
1 9.0 0.0 10.0 15.0 NaN
2 10.0 5.0 14.0 20.0 NaN
3 6.0 0.0 7.0 14.0 NaN
4 7.0 0.0 10.0 15.0 NaN
5 7.0 0.0 8.0 14.0 NaN
6 7.0 0.0 11.0 14.0 NaN
7 7.0 0.0 2.0 11.0 NaN
8 2.0 0.0 4.0 12.0 NaN
9 4.0 0.0 0.0 6.0 NaN
10 NaN NaN NaN NaN [11.6, 4.1, 14.9, 17.5]
我想知道是否有一种更有效的方法将带有索引均值"的行和每一列的平均值添加到熊猫DataFrame的底部.
I was wondering if there was a more efficient means of adding a row with the index 'mean' and the averages of each column to the bottom of a pandas DataFrame.
推荐答案
df.loc['mean'] = df.mean()
结果输出:
diode1 diode2 diode3 diode4
Time
0.53 7.0 0.0 10.0 16.0
1.218 17.0 7.0 14.0 19.0
1.895 13.0 8.0 16.0 17.0
2.57 8.0 2.0 16.0 17.0
3.24 14.0 8.0 17.0 19.0
3.91 13.0 6.0 17.0 18.0
4.594 13.0 5.0 16.0 19.0
5.265 9.0 0.0 12.0 16.0
5.948 12.0 3.0 16.0 17.0
6.632 10.0 2.0 15.0 17.0
mean 11.6 4.1 14.9 17.5
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