How to delete cell values in row based on condition of a cell above in a different column?

The data frame I am working with has multiple rows with duplicated information in the 'Data 1', 'Data 2', and 'Data 3' columns if the 'Time' column is equal to the row above. There is unique information in each row's 'Message 1' and 'message 2' columns, so I don't want to delete the whole row. I would like to keep the row of the data frame but delete the duplicate info based on if the 'Time' Column matches the row above, keeping the first instance of the data, and ideally replacing the duplicate values with an empty string ''.

Here is an example of my data set:

Time	Message 1	Message 2	Data 1	Data 2	Data 3
11:38:24	Notice	Text	A1	A2	A3
11:38:25	Error	Text	B1	B2	B3
11:38:26	Message	Text	C1	C2	C3
11:38:27	Error	Text	D1	D2	D3
11:38:27	Notice	Text	D1	D2	D3
11:38:27	Error	Text	D1	D2	D3
11:38:28	Message	Text	E1	E2	E3
11:38:28	Notice	Text	E1	E2	E3
11:38:28	Error	Text	E1	E2	E3

I have tried the following, but I receive errors "KeyError: 'cannot use a single bool to index into setitem'"

condition = df['Time'].shift(1).str == ['Time']
df.loc[condition, 'Data 1'] = ''
df.loc[condition, 'Data 2'] = ''
df.loc[condition, 'Data 3'] = ''

My goal is for the data frame to reflect the following:

Time	Message 1	Message 2	Data 1	Data 2	Data 3
11:38:24	Notice	Text	A1	A2	A3
11:38:25	Error	Text	B1	B2	B3
11:38:26	Message	Text	C1	C2	C3
11:38:27	Error	Text	D1	D2	D3
11:38:27	Notice	Text
11:38:27	Error	Text
11:38:28	Message	Text	E1	E2	E3
11:38:28	Notice	Text
11:38:28	Error	Text

Solution

you can use the shift() method to compare each 'Time' value with the value from the previous row

condition = df['Time'] == df['Time'].shift()
df.loc[condition, ['Data 1', 'Data 2', 'Data 3']] = ''
print(df)

df['Time'] == df['Time'].shift() evaluates to True for rows where 'Time' value is the same as previous row.