Scipy FFT Frequency Analysis of very noisy signal

Question

I have noisy data for which I want to calculate frequency and amplitude. The samples were collected every 1/100th sec. From trends, I believe frequency to be ~ 0.3

When I use numpy fft module, I end up getting very high frequency (36.32 /sec) which is clearly not correct. I tried to filter the data with pandas rolling_mean to remove the noise before fft, but that too didn't work.

import pandas as pd
from numpy import fft
import numpy as np
import matplotlib.pyplot as plt

Moisture_mean_x = pd.read_excel("signal.xlsx", header = None)
Moisture_mean_x = pd.rolling_mean(Moisture_mean_x, 10) # doesn't helps
Moisture_mean_x = Moisture_mean_x.dropna()
Moisture_mean_x = Moisture_mean_x -Moisture_mean_x.mean()
frate = 100. #/sec           
Hn = fft.fft(Moisture_mean_x)
freqs = fft.fftfreq(len(Hn), 1/frate)
idx = np.argmax(np.abs(Hn))
freq_in_hertz = freqs[idx]

Can someone guide me how to fix this?

Answer 1

You are right there is something wrong. One needs to explictiy ask pandas for the zeroth column:

Hn = np.fft.fft(Moisture_mean_x[0])

Else something wrong happen, which you can see by the fact that the FFT result was not symetric, which should be the case for real input.