Comparing previous row values in Pandas DataFrame

import pandas as pd
data={'col1':[1,3,3,1,2,3,2,2]}
df=pd.DataFrame(data,columns=['col1'])
print df


         col1  
    0     1          
    1     3          
    2     3          
    3     1          
    4     2          
    5     3          
    6     2          
    7     2

I have the following Pandas DataFrame and I want to create another column that compares the previous row of col1 to see if they are equal. What would be the best way to do this? It would be like the following DataFrame. Thanks

    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

Solution

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