You need eq with shift:
df['match'] = df.col1.eq(df.col1.shift())
print (df)
col1 match
0 1 False
1 3 False
2 3 True
3 1 False
4 2 False
5 3 False
6 2 False
7 2 True
Or instead eq use ==, but it is a bit slowier in large DataFrame:
df['match'] = df.col1 == df.col1.shift()
print (df)
col1 match
0 1 False
1 3 False
2 3 True
3 1 False
4 2 False
5 3 False
6 2 False
7 2 True
Timings:
import pandas as pd
data={'col1':[1,3,3,1,2,3,2,2]}
df=pd.DataFrame(data,columns=['col1'])
print (df)
#[80000 rows x 1 columns]
df = pd.concat([df]*10000).reset_index(drop=True)
df['match'] = df.col1 == df.col1.shift()
df['match1'] = df.col1.eq(df.col1.shift())
print (df)
In [208]: %timeit df.col1.eq(df.col1.shift())
The slowest run took 4.83 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 933 µs per loop
In [209]: %timeit df.col1 == df.col1.shift()
1000 loops, best of 3: 1 ms per loop