FFT of exponentially decaying sinusoidal function - matplotlib

Question

I have a set of simulation data to which I want to perform an FFT. I am using matplotlib to do this. However, the FFT is looking strange, so I don't know if I am missing something in my code. Would appreciate any help.
Original data:
time-varying data
FFT:
FFT
Code for the FFT calculation:
import numpy as np
import matplotlib.pyplot as plt
import scipy.fftpack as fftpack
data = pd.read_csv('table.txt',header=0,sep="\t")
fig, ax = plt.subplots()
mz_res=data[['mz ()']].to_numpy()
time=data[['# t (s)']].to_numpy()
ax.plot(time[:300],mz_res[:300])
ax.set_title("Time-varying mz component")
ax.set_xlabel('time')
ax.set_ylabel('mz amplitude')
fft_res=fftpack.fft(mz_res[:300])
power=np.abs(fft_res)
frequencies=fftpack.fftfreq(fft_res.size)
fig2, ax_fft=plt.subplots()
ax_fft.plot(frequencies[:150],power[:150]) // taking just half of the frequency range
I am just plotting the first 300 datapoints because the rest is not important.
Am I doing something wrong here? I was expecting single frequency peaks not what I got. Thanks!
Link for the input file:
Pastebin
EDIT
Turns out the mistake was in the conversion of the dataframe to a numpy array. For a reason I have yet to understand, if I convert a dataframe to a numpy array it is converted as an array of arrays, i.e., each element of the resulting array is itself an array of a single element. When I change the code to:
mz_res=data['mz ()'].to_numpy()
so that it is a conversion from a pandas series to a numpy array, then the FFT behaves as expected and I get single frequency peaks from the FFT.
So I just put this here in case someone else finds it useful. Lesson learned: the conversion from a pandas series to a numpy array yields a different result than the conversion from a pandas dataframe.

Answer 1

Solution:
Using the conversion from pandas series to numpy array instead of pandas dataframe to numpy array.
Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.fftpack as fftpack
data = pd.read_csv('table.txt',header=0,sep="\t")
fig, ax = plt.subplots()
mz_res=data['mz ()'].to_numpy() #series to array
time=data[['# t (s)']].to_numpy() #dataframe to array
ax.plot(time,mz_res)
ax.set_title("Time-varying mz component")
ax.set_xlabel('time')
ax.set_ylabel('mz amplitude')
fft_res=fftpack.fft(mz_res)
power=np.abs(fft_res)
frequencies=fftpack.fftfreq(fft_res.size)
indices=np.where(frequencies>0)
freq_pos=frequencies[indices]
power_pos=power[indices]
fig2, ax_fft=plt.subplots()
ax_fft.plot(freq_pos,power_pos) # taking just half of the frequency range
ax_fft.set_title("FFT")
ax_fft.set_xlabel('Frequency (Hz)')
ax_fft.set_ylabel('FFT Amplitude')
ax_fft.set_yscale('linear')
Yields:
Time-dependence
FFT