Pandas to_date returns NaT for all values in my date column

I’m working with a dataframe that shows historical prices for electricity. I’m wanting to convert the ‘Months’ column to datetime. I’m using the to_datetime to do this. However, when I do this, all the dates return as NaT. Specifically, the dataframe goes from this:

    Month  ...    PEA
0  Oct-22  ...  0.249
1  Sep-22  ...  0.021
2  Aug-22  ... -0.048
3  Jul-22  ... -0.053
4  Jun-22  ... -0.032

to looking like this:

   Month  ...    PEA
0    NaT  ...    NaN
1    NaT  ...    NaN
2    NaT  ...    NaN
3    NaT  ...    NaN
4    NaT  ...    NaN

My code looks like this:

import numpy as np
import pandas as pd

inputpath='HistoricalPrices'

dataset=pd.read_csv(inputpath,sep=',', dtype={'a': str},low_memory=False)

print(dataset.head())

dataset = dataset.iloc[::-1].reset_index(drop=True)

dataset['Month']=pd.to_datetime(dataset['Month'], errors='coerce', format='%m-%y')

print(dataset['Month'].dtypes)
print(dataset)

The to_datetime does change the Month column from object to datetime64[ns] but all the values in the Month column return NaT. How do I fix this? Am I doing something wrong within the to_datetime function? I appreciate any help!