how to perform drop_duplicates with multiple condition in a pandas dataframe

Create a dummy column with a groupby + transform operation.

v = df.groupby(df['Class'].diff().le(0).cumsum())['Data'].transform(' '.join)

Or,

v = df['Data'].groupby(df['Class'].diff().le(0).cumsum()).transform(' '.join) 

This dummy column becomes a factor when deciding what rows are to be dropped.

m = df.assign(Foo=v).duplicated(["Name", "Class", "Data", "Foo"])    
df[~m]

    Class                    Data Name Sr.No
0       1   sri is  a good player  Sri     1
1       2  sri is good in cricket  Sri      
2       3            sri went out  Sri      
3       1    Ram is a good player  Ram     2
4       2  sri is good in cricket  Ram      
5       3            Ram went out  Ram      
9       1   sri is  a good player  Sri     4
10      2  sri is good in cricket  Sri      
11      3            sri went out  Sri      
12      4           sri came back  Sri      

Details

Form groups from the monotonically increasing Class values -

i = df['Class'].diff().le(0).cumsum()
i

0     0
1     0
2     0
3     1
4     1
5     1
6     2
7     2
8     2
9     3
10    3
11    3
12    3
Name: Class, dtype: int64

Use this to group, and transform Data with a str.join operation -

v = df.groupby(i)['Data'].transform(' '.join)

Which is simply a column of joined strings. Finally, assign the dummy column and call duplicated -

m = df.assign(Foo=v).duplicated(["Name", "Class", "Data", "Foo"]) 
m

0     False
1     False
2     False
3     False
4     False
5     False
6      True
7      True
8      True
9     False
10    False
11    False
12    False
dtype: bool