how to zscore normalize pandas column with nans?

Question

I have a pandas dataframe with a column of real values that I want to zscore normalize:

>> a
array([    nan,  0.0767,  0.4383,  0.7866,  0.8091,  0.195         


        

Answer 1

Another alternative solution to this problem is to fill the NaNs in a DataFrame with the column means when calculating the z-score. This will result in the NaNs being calculated as having a z-score of 0, which can then be masked out using notna on the original df.

You can create a DataFrame of the same dimensions as the original df, containing the z-scores of the original df's values and NaNs in the same places in one line with:

zscore_df = pd.DataFrame(scipy.stats.zscore(df.fillna(df.mean())), index=df.index, columns=df.columns).where(df.notna())

Answer 2

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

Answer 3

I am not sure since when this parameter exists, because I have not been working with python for long. But you can simply use the parameter nan_policy = 'omit' and nans are ignored in the calculation:

a = np.array([np.nan,  0.0767,  0.4383,  0.7866,  0.8091,  0.1954,  0.6307, 0.6599, 0.1065,  0.0508])
ZScore_a = stats.zscore(a,nan_policy='omit')

print(ZScore_a)
[nan -1.14832945  0.07147776  1.24641928  1.3223199  -0.74791154
0.72051236  0.81901449 -1.0478033  -1.23569949]

Answer 4

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