向 Pandas DataFrame 添加一个带有列的行
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Add a row with means of columns to pandas DataFrame
提问by willk
I have a pandas DataFrame consisting of some sensor readings taken over time like this:
我有一个 Pandas DataFrame，其中包含一些随着时间的推移获取的传感器读数，如下所示：
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
I have written code to add another row with the means of each column:
我已经编写了代码来使用每列的方式添加另一行：
# 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
This gives me the result I want, which is a DataFrame like this:
这给了我想要的结果，它是一个像这样的 DataFrame：
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
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:
但是，我确信这不是添加行的最有效方法。我尝试使用 append 方法保存为Pandas系列，但最终得到了这样的结果：
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]
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.
我想知道是否有更有效的方法将带有索引“平均值”的行和每列的平均值添加到 Pandas DataFrame 的底部。
回答by root
Use locfor setting with enlargement:
df.loc['mean'] = df.mean()
The resulting output:
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
