Using pandas and datetime in Jupyter to see during what hours no sales were ever made (on any day)

So I have sales data that I'm trying to analyze. I have datetime data ["Order Date Time"] and I'd like to see the most common hours for sales but more importantly I'd like to see what minutes have NO sales.

I have been spinning my wheels for a while and I can't get my brain around a solution. Any help is greatly appreciated.

I import the data:

df = pd.read_excel ('Audit Period.xlsx')
print (df)

I clean up the data:

# Remove all columns except `applieddate` and null rows
time_df = df[df["Order Date Time"].notnull()]

# Ensure the index is still sequential
time_df = time_df[["Order Date Time"]].reset_index(drop=True)

# Select the first 10 rows
time_df.head(10)

I convert to datetime and I look at the month totals:

# Convert applieddate to datetime
time_df = time_df.copy()
time_df["Order Date Time"] = time_df["Order Date Time"].apply(pd.to_datetime)
time_df = time_df.set_index(time_df["Order Date Time"])

# Group by month
grouped = time_df.resample("M").count()
time_df = pd.DataFrame({"count": grouped.values.flatten()}, index=grouped.index)
time_df.head(10)

I try to group by hour but that gives me totals per day/hour rather than totals per hour like every order ever at noon, etc:

# Group by hour
grouped = time_df.resample("2H").count()
time_df = pd.DataFrame({"count": grouped.values.flatten()}, index=grouped.index)
time_df.head(10)

And that is where I'm stuck. I'm trying to integrate the below suggestions but can't quite get a grasp on them yet. Any help would be appreciated.