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, 0.1954, 0.6307,
0.6599, 0.1065, 0.0508])
>> df = pandas.DataFrame({"a": a})
The problem is that a single nan value makes all the array nan:
>> from scipy.stats import zscore
>> zscore(df["a"])
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
What's the correct way to apply zscore (or an equivalent function not from scipy) to a column of a pandas dataframe and have it ignore the nan values? I'd like it to be same dimension as original column with np.nan for values that can't be normalized
edit: maybe the best solution is to use scipy.stats.nanmean and scipy.stats.nanstd? I don't see why the degrees of freedom need to be changed for std for this purpose:
zscore = lambda x: (x - scipy.stats.nanmean(x)) / scipy.stats.nanstd(x)
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