Pandas DataFrame行明智比较

Question

I have a pandas DataFrame like following.

       id  label_x label_y
0        1    F    R
1        2    F    F
2        3    F    F
3        4    F    F
4        5    F    F

Now I want to count occurrences of label_x and label_y are equal and not equal. In this case there is only one occurrence of not equal and 4 occurrences of equal.

df = pd.DataFrame({'id' : ["1","2","3","4","5"],
                'label_x'  : ["F","F","F","F","F"], 'label_y' : ["R","F","F","F","F"]})

Answer 1

(df.label_x == df.label_y).value_counts()

Many ways to to that, including the above...

In [43]: (df.label_x == df.label_y).value_counts()
Out[43]:
True     4
False    1
dtype: int64

Answer 2

I came up with this solution. Is that the best one?

def compare(x):
    if x[1] == x[2]:
        return 'yes'
    else:
        return 'no'

df['result'] =  df.apply(compare, axis=1)

df2 = pd.DataFrame({'count' : df.groupby( ["result"] ).size()}).reset_index()