I am not sure what you are trying to achieve with that agg() function. Assuming 'first' refers to the first quantile and 'last' to the last quantile and you want to calculate some statistics per column, I suggest you do the following:
Get your data:
import yfinance as yf
import pandas as pd
n = yf.download('^nsei', period= '5d', interval= '1m')
Resample your data:
Note: your result is the same as when you resample with n.resample('5T').first() but this means every value in the dataframe
equals the first value from the 5 minute interval consisting of 5
values. A more logical resampling method is to use the mean() or
sum() function as shown below.
If this is data on stock prices it makes more sense to use mean():
resampled_df = n.resample('5T').mean()
To remove resampled hours that are outside of the working stock hours you have 2 options.
Option 1: drop na values:
filtered_df = resampled_df.dropna()
Note: this will not work if you use sum() since the result won't contain missing values but zeros.
Option 2 filter based on start and end hour
Get minimum and maximum time of day where data is available as datetime.time object:
start = n.index.min().time() # 09:15 as datetime.time object
end = n.index.max().time() # 15:29 as datetime.time object
Filter dataframe based on start and end times:
filtered_df = resampled_df.between_time(start, end)
Get the statistics:
statistics = filtered_df.describe()
statistics
Note that describe() will not contain the sum, so in order to add it you could do:
statistics = pd.concat([statistics, filtered_df.agg(['sum'])])
statistics
Output:
