how to zscore normalize pandas column with nans?

You could ignore nans using isnan.

z = a                    # initialise array for zscores
z[~np.isnan(a)] = zscore(a[~np.isnan(a)])
pandas.DataFrame({'a':a,'Zscore':z})

     Zscore       a
0       NaN     NaN
1 -1.148329  0.0767
2  0.071478  0.4383
3  1.246419  0.7866
4  1.322320  0.8091
5 -0.747912  0.1954
6  0.720512  0.6307
7  0.819014  0.6599
8 -1.047803  0.1065
9 -1.235699  0.0508

Well the pandas' versions of mean and std will hand the Nan so you could just compute that way (to get the same as scipy zscore I think you need to use ddof=0 on std):

df['zscore'] = (df.a - df.a.mean())/df.a.std(ddof=0)
print df

        a    zscore
0     NaN       NaN
1  0.0767 -1.148329
2  0.4383  0.071478
3  0.7866  1.246419
4  0.8091  1.322320
5  0.1954 -0.747912
6  0.6307  0.720512
7  0.6599  0.819014
8  0.1065 -1.047803
9  0.0508 -1.235699