Sum values of a coulmn in specific rows in a dataframe-CodePudding

I would like to learn how to specify a "subset-sum" in a dataframe

My dataframe looks like this:

The Data/Time column is the dataframes' index

With

Sum = data['A'].sum()

I get the total sum of column A.

My aim is to sum up a subset of rows only like between 2022-03-18 07:37:51 and 2022-03-18 07:37:55

so that I get a "sum" row:

How can I specify the row numbers to be summed for each column, especially when the index is in datetime format?

CodePudding user response：

data[(data.index >= '2020-01-01 00:00:16') & (data.index <= '2020-01-01 00:00:17')].sum(axis=0)

simply, use axis = 0 for each coulmn sum, and check by a.index >= '2020-01-01 00:00:16' and equivalent for upper bound

if you want to use datetime module:

from datetime import datetime
data[(data.index >= datetime(2020, 1, 1, 0, 0, 16)) & (data.index <= datetime(2020, 1, 1, 0, 0, 17))].sum(axis=0)

CodePudding user response：

If you need a more generic criterion to select rows, not only time ranges then you could work with this:

df = pd.DataFrame([[12,5,7], [13,7,4], [14,7,23], [15,71,9], [16,5,4], [17,55,42]], columns=['T', 'A', 'B'])

print(df)
    T   A   B
0  12   5   7
1  13   7   4
2  14   7  23
3  15  71   9
4  16   5   4
5  17  55  42

range_idx = df['T'].between(13,15) # or another selection
df[range_idx]

    T   A   B
1   13  7   4
2   14  7   23
3   15  71  9

df[range_idx][['A','B']].sum()

Out[42]:
A    85
B    36