Python Pandas: Get index of multiple rows which column matches certain value

Question

Given a DataFrame with the columns xk and yk, we want to find the indexes of the DataFrame in which the values for xk and yk ==0.

I have it working perfectly fine for just the one column but I cant get it working for both

b = (df[df['xk'] ==0]).index.tolist()

How would I do it for xk and yk at the same time.

Answer 1

I think you can check if all values are True in compared subset ['xk', 'yk'] by all:

b = df[(df[['xk', 'yk']] == 0).all(1)].index.tolist()

Another solution is add second condition with &:

b = (df[(df['xk']  == 0) & (df['yk'] == 0)].index.tolist())

Sample:

df = pd.DataFrame({'xk':[0,2,3],
                   'yk':[0,5,0],
                   'aa':[0,1,0]})

print (df)
   aa  xk  yk
0   0   0   0
1   1   2   5
2   0   3   0

b = df[(df[['xk', 'yk']] == 0).all(1)].index.tolist()
print (b)
[0]

b1 = (df[(df['xk']  == 0) & (df['yk'] == 0)].index.tolist())
print (b1)
[0]

Second solution is faster:

#length of df = 3k
df = pd.concat([df]*1000).reset_index(drop=True)

In [294]: %timeit df[(df[['xk', 'yk']] == 0).all(1)].index.tolist()
1000 loops, best of 3: 1.21 ms per loop

In [295]: %timeit (df[(df['xk']  == 0) & (df['yk'] == 0)].index.tolist())
1000 loops, best of 3: 828 µs per loop