# 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.

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

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

Out:
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 :
# 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:
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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