[英]Sum of specific rows in a dataframe (Pandas)
I'm given a set of the following data: 我给出了一组以下数据:
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 AE. 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 (eg df.iloc[2:4,:]
), what should I do next? 假设我有我需要的行(例如
df.iloc[2:4,:]
),接下来我该怎么办? 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. 我的假设是我需要创建一个包含每一列总和的单行掩码,但我不知道我是怎么做的。
I know how to do the final step (ie .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。
You can use for select by positions iloc
, sum
and Series.plot.bar
: 您可以使用位置选择
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
: 或者如果想要按索引名称(这里是几周)选择,请使用
loc
:
df.loc[2:4].sum().plot.bar()
Difference is iloc
exclude last position: 区别是
iloc
排除最后位置:
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: 如果还需要按位置过滤列:
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()
All you need to do is call .sum()
on your subset of the data: 您需要做的就是在您的数据子集上调用
.sum()
:
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 week
column (as the sum of week numbers is unlikely to mean anything): 此外,对于绘图,我认为你可以摆脱
week
列(因为周数的总和不太可能意味着什么):
df.iloc[2:4,1:].sum().plot(kind='bar')
# or
df[list('ABCDE')].iloc[2:4].sum().plot(kind='bar')
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