Pandas: 创建条件列

Question

我正在尝试根据 A 列和 B 列中的值创建 C 列，条件如下：

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)

离我有多远？

Answer 1

使用向量化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)

Answer 2

我想你会想要这样的东西

df['C'] = df.apply(lambda x: x.A * x.B if x.A > 0.5 else x.A, 1)

完整示例：

import pandas as pd
import numpy as np

N = 10
data = np.asarray([np.random.rand(N).tolist(), list(range(N))]).T
df = pd.DataFrame(data, columns=['A', 'B'])

df['C'] = df.apply(lambda x: x.A * x.B if x.A > 0.5 else x.A, 1)

Answer 3

我确定之前提供的解决方案更好，但我用第三种方式解决了。数据集很小，所以现在就可以了。

乘法 = df['A'] * df['B'] df['C'] = multiply.where(df['A'] < 5000, 其他=df['A'])

Pandas: 创建条件列

Pandas: Conditional column creating

python

conditional

pandas