Datetime64 Summing Issues

Question

Can someone help with this Datetime64 sum issue? I am reading the date from a CSV.

Code:

import pandas as pd
import numpy as np
import scipy
from scipy.stats import norm
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date

pd.options.display.float_format = '{:,.4f}'.format

# Inputs and Parameters
filename = 'SPY_quotedata.csv'

# Black-Scholes European-Options Gamma
def calcGammaEx(S, K, vol, T, r, q, optType, OI):
    if T == 0 or vol == 0:
        return 0

    dp = (np.log(S/K) + (r - q + 0.5*vol**2)*T) / (vol*np.sqrt(T))
    dm = dp - vol*np.sqrt(T) 

    if optType == 'call':
        gamma = np.exp(-q*T) * norm.pdf(dp) / (S * vol * np.sqrt(T))
        return OI * 100 * S * S * 0.01 * gamma 
    else: # Gamma is same for calls and puts. This is just to cross-check
        gamma = K * np.exp(-r*T) * norm.pdf(dm) / (S * S * vol * np.sqrt(T))
        return OI * 100 * S * S * 0.01 * gamma 

def isThirdFriday(d):
    return d.weekday() == 4 and 15 <= d.day <= 21

# This assumes the CBOE file format hasn't been edited, i.e. table beginds at line 4
optionsFile = open("SPY_quotedata.csv")
optionsFileData = optionsFile.readlines()
optionsFile.close()

# Get SPY Spot
spotLine = optionsFileData[1]
spotPrice = float(spotLine.split('Last:')[1].split(',')[0])
fromStrike = 0.8 * spotPrice
toStrike = 1.2 * spotPrice

# Get Today's Date
dateLine = optionsFileData[2]
todayDate = dateLine.split('Date: ')[1].split(' ')
day1=todayDate[1].split(',')

year=int(todayDate[2])
month=todayDate[0]
day=int(day1[0])

todayDate = datetime.strptime(month,'%B')
todayDate = todayDate.replace(day=day, year=year)

# Get SPY Options Data
df = pd.read_csv(filename, sep=",", header=None, skiprows=4)
df.columns = ['ExpirationDate','Calls','CallLastSale','CallNet','CallBid','CallAsk','CallVol',
              'CallIV','CallDelta','CallGamma','CallOpenInt','StrikePrice','Puts','PutLastSale',
              'PutNet','PutBid','PutAsk','PutVol','PutIV','PutDelta','PutGamma','PutOpenInt']

df['ExpirationDate'] = pd.to_datetime(df['ExpirationDate'], format='%a %b %d %Y')
df['ExpirationDate'] = df['ExpirationDate'] + timedelta(hours=16)
df['StrikePrice'] = df['StrikePrice'].astype(float)
df['CallIV'] = df['CallIV'].astype(float)
df['PutIV'] = df['PutIV'].astype(float)
df['CallGamma'] = df['CallGamma'].astype(float)
df['PutGamma'] = df['PutGamma'].astype(float)
df['CallOpenInt'] = df['CallOpenInt'].astype(float)
df['PutOpenInt'] = df['PutOpenInt'].astype(float)


# ---=== CALCULATE SPOT GAMMA ===---
# Gamma Exposure = Unit Gamma * Open Interest * Contract Size * Spot Price 
# To further convert into 'per 1% move' quantity, multiply by 1% of spotPrice
df['CallGEX'] = df['CallGamma'] * df['CallOpenInt'] * 100 * spotPrice * spotPrice * 0.01
df['PutGEX'] = df['PutGamma'] * df['PutOpenInt'] * 100 * spotPrice * spotPrice * 0.01 * -1

df['TotalGamma'] = (df.CallGEX + df.PutGEX) / 10**9
dfAgg = df.groupby(['StrikePrice']).sum()
strikes = dfAgg.index.values

C:\Users\v_ichase\Desktop\New folder (3)>py "C:\Users\v_ichase\Desktop\New folder (3)\Gex.py"
Traceback (most recent call last):
  File "C:\Users\v_ichase\Desktop\New folder (3)\Gex.py", line 78, in <module>
    dfAgg = df.groupby(['StrikePrice']).sum()
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\v_ichase\AppData\Roaming\Python\Python311\site-packages\pandas\core\groupby\groupby.py", line 3053, in sum
    result = self._agg_general(
             ^^^^^^^^^^^^^^^^^^
  File "C:\Users\v_ichase\AppData\Roaming\Python\Python311\site-packages\pandas\core\groupby\groupby.py", line 1835, in _agg_general
    result = self._cython_agg_general(
             ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\v_ichase\AppData\Roaming\Python\Python311\site-packages\pandas\core\groupby\groupby.py", line 1926, in _cython_agg_general
    new_mgr = data.grouped_reduce(array_func)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\v_ichase\AppData\Roaming\Python\Python311\site-packages\pandas\core\internals\managers.py", line 1431, in grouped_reduce
    applied = blk.apply(func)
              ^^^^^^^^^^^^^^^
  File "C:\Users\v_ichase\AppData\Roaming\Python\Python311\site-packages\pandas\core\internals\blocks.py", line 366, in apply
    result = func(self.values, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\v_ichase\AppData\Roaming\Python\Python311\site-packages\pandas\core\groupby\groupby.py", line 1902, in array_func
    result = self.grouper._cython_operation(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\v_ichase\AppData\Roaming\Python\Python311\site-packages\pandas\core\groupby\ops.py", line 815, in _cython_operation
    return cy_op.cython_operation(
           ^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\v_ichase\AppData\Roaming\Python\Python311\site-packages\pandas\core\groupby\ops.py", line 525, in cython_operation
    return values._groupby_op(
           ^^^^^^^^^^^^^^^^^^^
  File "C:\Users\v_ichase\AppData\Roaming\Python\Python311\site-packages\pandas\core\arrays\datetimelike.py", line 1637, in _groupby_op
    raise TypeError(f"datetime64 type does not support {how} operations")
TypeError: datetime64 type does not support sum operations

C:\Users\v_ichase\Desktop\New folder (3)>py "C:\Users\v_ichase\Desktop\New folder (3)\Gex.py"`

enter image description here

Uninstalled and reinstalled Python versions, currently running 3.11.6