问题
I have the following data frame:
>>> import pandas as pd
>>> import numpy as np
>>> df_test = pd.DataFrame({'id': [100, 101, 102, 103, 104], 'drive': ['4WD', None, '4WD', None, '2WD']})
>>> print(df_test)
id drive
0 100 4WD
1 101 None
2 102 4WD
3 103 None
4 104 2WD
And I would like to make a new column is_4x4, that would be equal to 0, when drive is None, or drive is 2WD. In other cases, I would like the column to be equal to 1.
I am using the following code, but the result is not as I expected:
>>> df_test['is_4x4'] = np.where(pd.isnull(df_test['drive']) | df_test['drive'] == '2WD', 0, 1)
>>> print(df_test)
id drive is_4x4
0 100 4WD 1
1 101 None 1
2 102 4WD 1
3 103 None 1
4 104 2WD 1
My desired output is following:
id drive is_4x4
0 100 4WD 1
1 101 None 0
2 102 4WD 1
3 103 None 0
4 104 2WD 0
Please, could you help me, what I am doing wrong? Why is my code not working?
回答1:
Add parentheses because priority precedence of |
operator (bitwise OR):
df_test['is_4x4'] = np.where(pd.isnull(df_test['drive']) | (df_test['drive'] == '2WD'), 0, 1)
Or use Series.eq:
df_test['is_4x4'] = np.where(df_test['drive'].isna() | df_test['drive'].eq('2WD'), 0, 1)
You can check docs - 6.16. Operator precedence where see |
have higher priority as ==
:
Operator Description
lambda Lambda expression
if – else Conditional expression
or Boolean OR
and Boolean AND
not x Boolean NOT
in, not in, is, is not, Comparisons, including membership tests
<, <=, >, >=, !=, == and identity tests
| Bitwise OR
^ Bitwise XOR
& Bitwise AND
(expressions...), [expressions...], Binding or tuple display, list display,
{key: value...}, {expressions...} dictionary display, set display
来源:https://stackoverflow.com/questions/60524372/python-why-is-np-where-not-working-with-two-conditions