Estimate power spectral density of time series DF using scipy.welch

Question

I have a pandas DF with datetime index with spacing = 200ms and corresponding values for each index as shown

print(filtered)

2016-07-14 16:31:19.000 -0.010054
2016-07-14 16:31:19.200 -0.011849
2016-07-14 16:31:19.400 -0.009564
2016-07-14 16:31:19.600 -0.001077

[20038 rows x 1 columns]

I want to compute the power spectral density using scipy.welch function.

f,pxx =welch(filtered.values.flatten(),5)        

But when I run this line of code the power density array pxx is nan

In [897]: pxx
Out[897]: 

array([ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,

What is the proper way to run the welch estimation on a time series dataframe and where might I find information on what causes the welch function to output nan?

Answer 1

f,pxx =welch(filtered.values.flatten(),5)        

works fine on my side, make sure you have no missing values in your DF and your dtypes are correct (values are floats) first.

this should work

filtered = filtered.astype(float)
filtered = filtered.dropna()
f,pxx =welch(filtered.values.flatten(),5)