数据框中特定行的总和（Pandas）
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Sum of specific rows in a dataframe (Pandas)
提问by Fares K. A.
I'm given a set of the following data:
我得到了一组以下数据：
week A B C D E
1 243 857 393 621 194
2 644 576 534 792 207
3 946 252 453 547 436
4 560 100 864 663 949
5 712 734 308 385 303
I'm asked to find the sum of each column for specified rows/a specified number of weeks, and then plot those numbers onto a bar chart to compare A-E.
我被要求找到指定行/指定周数的每列的总和，然后将这些数字绘制到条形图上以比较 AE。
Assuming I have the rows I need (e.g. df.iloc[2:4,:]), what should I do next? My assumption is that I need to create a mask with a single row that includes the sum of each column, but I'm not sure how I go about doing that.
假设我有我需要的行（例如df.iloc[2:4,:]），接下来我应该做什么？我的假设是我需要创建一个包含每列总和的单行掩码，但我不确定如何去做。
I know how to do the final step (i.e. .plot(kind='bar'), I just need to know what the middle step is to obtain the sums I need.
我知道如何做最后一步（即.plot(kind='bar'），我只需要知道中间步骤是什么以获得我需要的总和。
采纳答案by jezrael
You can use for select by positions iloc, sumand Series.plot.bar:
您可以使用按位置选择iloc，sum和Series.plot.bar：
df.iloc[2:4].sum().plot.bar()
Or if want select by names of index (here weeks) use loc:
或者，如果要按索引名称（此处为周）进行选择，请使用loc：
df.loc[2:4].sum().plot.bar()
Difference is ilocexclude last position:
差异是iloc排除最后一个位置：
print (df.loc[2:4])
A B C D E
week
2 644 576 534 792 207
3 946 252 453 547 436
4 560 100 864 663 949
print (df.iloc[2:4])
A B C D E
week
3 946 252 453 547 436
4 560 100 864 663 949
And if need also filter columns by positions:
如果需要还按位置过滤列：
df.iloc[2:4, :4].sum().plot.bar()
And by names (weeks):
并按名称（周）：
df.loc[2:4, list('ABCD')].sum().plot.bar()
回答by sacuL
All you need to do is call .sum()on your subset of the data:
您需要做的就是调用.sum()您的数据子集：
df.iloc[2:4,:].sum()
Returns:
返回：
week 7
A 1506
B 352
C 1317
D 1210
E 1385
dtype: int64
Furthermore, for plotting, I think you can probably get rid of the weekcolumn (as the sum of week numbers is unlikely to mean anything):
此外，对于绘图，我认为您可以去掉该week列（因为周数的总和不太可能有任何意义）：
df.iloc[2:4,1:].sum().plot(kind='bar')
# or
df[list('ABCDE')].iloc[2:4].sum().plot(kind='bar')
