[FIXED] Sum values of a coulmn in specific rows in a dataframe

Issue

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?

Solution

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)

Answered By - MoRe