Description
Describe your issue.
From the attached CSV file, input_data.csv, I import the, say, test_list. Then, I create a pd.Dataframe to store my data in test_df.
from scipy import fft
import numpy as np
test_df = pd.DataFrame(index=range(len(test_list)), columns=['test'])
test_df['test'] = test_list
I want to calculate the FFT transform of the signal in test_df['test']. However, I get different results when I pass as input different types of arguments to the scipy.fft.fft().
Once I pass the signal as a nympy.array I get the plot #1.
Once I pass the signal as a pd.Dataframe I get the plot #2.
Both inputs, np.array and pd.DataFrame contain the same information.
From the problem definition the plot#2 seems to have a better physical interpretation.
However, it is not clear why I get different results.
May be it is a bug?
The code I use is below:
from scipy import fft
import numpy as np
test_df = pd.DataFrame(index=range(len(test_list)), columns=['test'])
test_df['test'] = test_list
########## PLOT 1: from Numpy ##############
to_np = test_df['test'].to_numpy()
print(to_np)
fft_output = fft.fft(to_np)
power = np.abs(fft_output)
freq = fft.fftfreq(len(to_np))
mask = freq >= 0
freq = freq[mask]
power = power[mask]
plt.plot(freq, power)
plt.show()
########## PLOT 2: from pd.DataFrame ##############
to_df = test_df['test'].to_frame()
print(to_df)
fft_output = fft.fft(to_df)
power = np.abs(fft_output)
freq = fft.fftfreq(len(to_df))
mask = freq >= 0
freq = freq[mask]
power = power[mask]
plt.plot(freq, power)
plt.show()
Reproducing Code Example
from scipy import fft
import numpy as np
test_df = pd.DataFrame(index=range(len(test_list)), columns=['test'])
test_df['test'] = test_list
########## PLOT 1: from Numpy ##############
to_np = test_df['test'].to_numpy()
print(to_np)
fft_output = fft.fft(to_np)
power = np.abs(fft_output)
freq = fft.fftfreq(len(to_np))
mask = freq >= 0
freq = freq[mask]
power = power[mask]
plt.plot(freq, power)
plt.show()
########## PLOT 2: from pd.DataFrame ##############
to_df = test_df['test'].to_frame()
print(to_df)
fft_output = fft.fft(to_df)
power = np.abs(fft_output)
freq = fft.fftfreq(len(to_df))
mask = freq >= 0
freq = freq[mask]
power = power[mask]
plt.plot(freq, power)
plt.show()SciPy/NumPy/Python version and system information
1.10.1 1.23.5 sys.version_info(major=3, minor=9, micro=16, releaselevel='final', serial=0)
Build Dependencies:
blas:
detection method: cmake
found: true
include directory: unknown
lib directory: unknown
name: OpenBLAS
openblas configuration: unknown
pc file directory: unknown
version: 0.3.18
lapack:
detection method: cmake
found: true
include directory: unknown
lib directory: unknown
name: OpenBLAS
openblas configuration: unknown
pc file directory: unknown
version: 0.3.18
Compilers:
c:
commands: cc
linker: ld64
name: clang
version: 13.1.6
c++:
commands: c++
linker: ld64
name: clang
version: 13.1.6
cython:
commands: cython
linker: cython
name: cython
version: 0.29.33
fortran:
commands: gfortran
linker: ld64
name: gcc
version: 12.1.0
pythran:
include directory: /private/var/folders/_f/lyvxf0v13gs7984d7sf7j83c0000gn/T/pip-build-env-oi1yv7w9/overlay/lib/python3.9/site-packages/pythran
version: 0.12.1
Machine Information:
build:
cpu: aarch64
endian: little
family: aarch64
system: darwin
cross-compiled: false
host:
cpu: aarch64
endian: little
family: aarch64
system: darwin
Python Information:
path: /private/var/folders/_f/lyvxf0v13gs7984d7sf7j83c0000gn/T/cibw-run-re4zhq8x/cp39-macosx_arm64/build/venv/bin/python
version: '3.9'