I have a dataframe containing a single column of IDs and all other columns are numerical values for which I want to compute z-scores. Here's a subsection of it:
ID Age BMI Risk Factor
PT 6 48 19.3 4
PT 8 43 20.9 NaN
PT 2 39 18.1 3
PT 9 41 19.5 NaN
Some of my columns contain NaN values which I do not want to include into the z-score calculations so I intend to use a solution offered to this question: how to zscore normalize pandas column with nans?
df['zscore'] = (df.a - df.a.mean())/df.a.std(ddof=0)
I'm interested in applying this solution to all of my columns except the ID column to produce a new dataframe which I can save as an Excel file using
So basically; how can I compute z-scores for each column (ignoring NaN values) and push everything into a new dataframe?
SIDENOTE: there is a concept in pandas called "indexing" which intimidates me because I do not understand it well. If indexing is a crucial part of solving this problem, please dumb down your explanation of indexing.