as.numeric(res$mean) Will do the job. As per your comment, so Forecast is the actual forecast and it is not always just the mean. Consider the following example using a data set taken from here library(forecast) births <- scan(“http://robjhyndman.com/tsdldata/data/nybirths.dat”) birthstimeseries <- ts(births, frequency=12, start=c(1946,1)) fit <- auto.arima(birthstimeseries) res <- forecast(fit, 12) plot(res) As you can see … Read more
You need to join those two dataframes to perform multiplication of two columns. merged_df = segmentallocation.merge(second,on=[‘year’,’month’],how=’left’,suffixes=[”,’_second’]) for c in interested_columns: merged_df[‘allocation’+str(c)] = merged_df[‘%of allocation’+str(c)] * merged_df[c] merged_df year month segment x y z k %of allocationx %of allocationy %of allocationz %of allocationk x_second y_second z_second k_second allocationx allocationy allocationz allocationk 0 2018 FEB A 2094663 … Read more
I recommend you use numpy arrays for this, that will be the easiest way. Either convert your tuples to arrays or just store them as arrays. If you subtract numpy arrays, they will be subtracted elementwise. The same statement is true for squaring (which, btw, in Python is not ^2 but **2). You will then … Read more