根据当前行和上一行的数据为数据帧分配新列

Question

I have 2 columns of data called level 1 event and level 2 event.
Both are columns of 1s and zeros.

lev_1 lev_2 lev_2_&_lev_1
0    1    0      0
1    0    0      0
2    1    0      0
3    1    1      1
4    1    0      0

col['lev2_&_lev_1] = 1 if lev_2 of current row and lev_1 of previous row are both 1.
I have achieved this by using for loop.

i = 1  
while i < a.shape[0]:
    if a['lev_1'].iloc[i - 1] == 1 &  a['lev_2'].iloc[i] == 1:
        a['lev_2_&_lev_1'].iloc[i] = 1
    i += 1

I wanted to know a computationally efficient way to do this because my original df is very big.
Thank you!

Answer 1

你要：

(df['lev_2'] & df['lev_1'].shift()).astype(int)

Answer 2

Use np.where and .shift() :

df['lev_2_&_lev_1'] = np.where(df['lev_2'].eq(1) & df['lev_1'].shift().eq(1), 1, 0)

   lev_1  lev_2  lev_2_&_lev_1
0      1      0              0
1      0      0              0
2      1      0              0
3      1      1              1
4      1      0              0

---

Explanation

• df['lev_2'].eq(1) : checks if current row is equal to 1
• df['lev_1'].shift().eq(1) : checks if previous row is equal to 1
• np.where(condition, 1, 0) : if condition is True return 1 else 0