在Python中，比较多列的行差异

Question

I want to perform a row by row comparison over multiple columns. I want a single series, indicating if all entries in a row (over several columns) are the same as the previous row.

Lets say I have the following dataframe

import pandas as pd
df = pd.DataFrame({'A' : [1, 1, 1, 2, 2], 
                   'B' : [2, 2, 3, 3, 3], 
                   'C' : [1, 1, 1, 2, 2]})

I can compare all the rows, of all the columns

>>> df.diff().eq(0)
       A      B      C
0  False  False  False
1   True   True   True
2   True  False   True
3  False   True  False
4   True   True   True

This gives a dataframe comparing each series individually. What I want is the comparison of all columns in one series.

I can achieve this by looping

compare_all = df.diff().eq(0)
compare_tot = compare_all[compare_all.columns[0]]
for c in compare_all.columns[1:]:
    compare_tot = compare_tot & compare_all[c]

This gives

>>> compare_tot
0    False
1     True
2    False
3    False
4     True
dtype: bool

as expected.

Is it possible to achieve this in with a one-liner, that is without the loop?

Answer 1

>>> (df == df.shift()).all(axis=1)
0    False
1     True
2    False
3    False
4     True
dtype: bool

Answer 2

You need all

In [1306]: df.diff().eq(0).all(1)
Out[1306]:
0    False
1     True
2    False
3    False
4     True
dtype: bool