Getting an IndexError when trying to run pcolormesh from a pandas DataFrame - pandas

Question

I'm trying to generate a pcolormesh plot from a large dataset, where the rows are in units of hertz, the rows are individual files, and the body is an array of magnitude values per file for each frequency. My DataFrame gets constructed correctly with correct labels, but when I pass it in to pcolormesh, it throws the exception "arrays used as indices must be of integer (or boolean) type". The code I am attaching reflects a conversion of the frequency array to an integer array using .astype(int). Note, if I convert the PSD_array (magnitudes) to integers, it DOES work (but isn't helpful), but it doesn't like it otherwise. I also played around with other pcolormesh generations using decimals as the body of the DataFrame and it worked fine.
Ideas would be lovely, I'll keep working on it.
Code: (Note: specific call file paths redacted).
'''def file_List():
files = [file for file in os.listdir('###)]
file_list = []
for file in files:
file_list += [file]
#print(natsorted(file_list))
#print(len(file_list))
return(natsorted(file_list))
###Using Fast Fourier Transform, take in file list generated from
###file_List program and perform FFT on each file.
###We read the files by adding them to the file directory. Could improve
###by making an overarching program that runs everything with an input
###file directory.
def FFT():
'''
runs through FFT for the files in a file list as determined by the
file_List() program.
'''
file_list = file_List() #runs file_List() program and saves
#the list of files as a variable.
df = pd.DataFrame()
freq_array = np.empty((0,204800))
PSD_array = np.empty((0,204800))
print(len(file_list))
count = 0
for file in file_list:
while count < 10:
file_read = pd.read_csv('###'+file,skiprows=22,sep = '\t')
df = pd.DataFrame(file_read, columns = ['X_Value','Acceleration'])
#print(df.head())
q = df['Acceleration'] #data set input
n = len(df['Acceleration']) #number of data points
dt = 2/len(df['X_Value']) #
f_hat = np.fft.fft(q,n) #Runs FFT
PSD = f_hat * np.conj(f_hat) / n #Power Spectral Density
freq = (1/(dt*n)) * np.arange(n) #Creates x axis of frequencies
freq_array = np.append(freq_array,np.array([freq]),axis=0)
PSD_array = np.append(PSD_array,np.array([PSD]),axis=0)
count += 1
#trans_freq = np.transpose(freq_array)
#trans_PSD = np.transpose(PSD_array)
print(freq_array)
int_freq = freq_array.astype(int)
print(int_freq)
#PSD_int = PSD_array.astype(int)
PSD_df = pd.DataFrame(PSD_array, index = np.arange(len(PSD_array)), columns = int_freq[0])
#print(np.arange(len(PSD_array)))
print(PSD_df)
return(PSD_df)
def heatmap(df):
'''
Constructs a heatmap given an input dataframe
'''
plt.pcolormesh(df)
'''