Slavatron Slavatron - 2 months ago 207
Python Question

Pandas - Compute z-score for all columns

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

df2.to_excel("Z-Scores.xlsx")


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.

Answer

Build a list from the columns and remove the column you don't want to calculate the Z score for:

In [66]:
cols = list(df.columns)
cols.remove('ID')
df[cols]

Out[66]:
   Age  BMI  Risk  Factor
0    6   48  19.3       4
1    8   43  20.9     NaN
2    2   39  18.1       3
3    9   41  19.5     NaN
In [68]:
# now iterate over the remaining columns and create a new zscore column
for col in cols:
    col_zscore = col + '_zscore'
    df[col_zscore] = (df[col] - df[col].mean())/df[col].std(ddof=0)
df
Out[68]:
   ID  Age  BMI  Risk  Factor  Age_zscore  BMI_zscore  Risk_zscore  \
0  PT    6   48  19.3       4   -0.093250    1.569614    -0.150946   
1  PT    8   43  20.9     NaN    0.652753    0.074744     1.459148   
2  PT    2   39  18.1       3   -1.585258   -1.121153    -1.358517   
3  PT    9   41  19.5     NaN    1.025755   -0.523205     0.050315   

   Factor_zscore  
0              1  
1            NaN  
2             -1  
3            NaN  
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