我正在尝试根据 A 列和 B 列中的值创建 C 列，并满足以下条件:
if A < 5000: C = A * B
else: C = A
下面给出语法错误:
df['C'] = df.apply(lambda x (x['A'] * x['B)'] if x['A'] < 5000 else x = x['A']),axis=1)
我离你有多远？
请您参考如下方法:
使用矢量化 numpy.where :
df['C'] = np.where(df['A'] < 5000, df['A'] * df['B'], df['A'])
性能:
np.random.seed(2019)
N = 1000
data = np.asarray([np.random.rand(N).tolist(), list(range(N))]).T
df = pd.DataFrame(data, columns=['A', 'B'])
In [56]: %timeit df['C'] = np.where(df['A'] < 5000, df['A'] * df['B'], df['A'])
536 µs ± 47.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [57]: %timeit df['C'] = df.apply(lambda x: x.A * x.B if x.A > 0.5 else x.A, 1)
30.9 ms ± 597 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
N = 100000
data = np.asarray([np.random.rand(N).tolist(), list(range(N))]).T
df = pd.DataFrame(data, columns=['A', 'B'])
In [59]: %timeit df['C'] = np.where(df['A'] < 5000, df['A'] * df['B'], df['A'])
1.29 ms ± 23.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [60]: %timeit df['C'] = df.apply(lambda x: x.A * x.B if x.A > 0.5 else x.A, 1)
3.32 s ± 374 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
