Question

如何根据其他列的值同时添加几个新列？我只找到一次添加一行的示例。

我可以添加3个新列，但这似乎没有效率，因为它必须遍历所有行3次。有没有办法遍历DF一次？

import pandas as pd
from decimal import Decimal
d = [
    {'A': 2, 'B': Decimal('628.00')},
    {'A': 1, 'B': Decimal('383.00')},
    {'A': 3, 'B': Decimal('651.00')},
    {'A': 2, 'B': Decimal('575.00')},
    {'A': 4, 'B': Decimal('1114.00')},
]

df = pd.DataFrame(d)

In : df
Out:
   A        B
0  2   628.00
1  1   383.00
2  3   651.00
3  2   575.00
4  4  1114.00

# How to do those in one operation to avoid traversing the DF 3 times
df['C'] = df.apply(lambda row: row['B']-1000, axis=1)
df['D'] = df.apply(lambda row: row['B']*row['B'], axis=1)
df['E'] = df.apply(lambda row: row['B']/2, axis=1)

In : df
Out:
   A        B        C             D       E
0  2   628.00  -372.00   394384.0000  314.00
1  1   383.00  -617.00   146689.0000  191.50
2  3   651.00  -349.00   423801.0000  325.50
3  2   575.00  -425.00   330625.0000  287.50
4  4  1114.00   114.00  1240996.0000  557.00

Answer 1

我不会使用lambda函数。简单的矢量化实现既快又易读。

df['C'] = df['B'] - 1000
df['D'] = df['B'] ** 2
df['E'] = df['B'] / 2

>>> df
   A        B        C             D       E
0  2   628.00  -372.00   394384.0000  314.00
1  1   383.00  -617.00   146689.0000  191.50
2  3   651.00  -349.00   423801.0000  325.50
3  2   575.00  -425.00   330625.0000  287.50
4  4  1114.00   114.00  1240996.0000  557.00

让我们把时间安排在一百万行的数据框上：

df = pd.concat([df for _ in range(200000)], ignore_index=True)
>>> df.shape
(1000000, 2)

>>> %%timeit -n 3
    df['C'] = df.apply(lambda row: row['B'] - 1000, axis=1)
    df['D'] = df.apply(lambda row: row['B'] * row['B'], axis=1)
    df['E'] = df.apply(lambda row: row['B'] / 2, axis=1)
3 loops, best of 3: 1min 20s per loop

>>> %%timeit -n 3
    df['C'] = df['B'] - 1000
    df['D'] = df['B'] ** 2
    df['E'] = df['B'] / 2
3 loops, best of 3: 49.7 s per loop

如果您取消了Decimal类型并使用了浮点数，那么速度明显更快：

d = [
    {'A': 2, 'B': 628.00},
    {'A': 1, 'B': 383.00},
    {'A': 3, 'B': 651.00},
    {'A': 2, 'B': 575.00},
    {'A': 4, 'B': 1114.00}]

df = pd.DataFrame(d)
df = pd.concat([df for _ in range(200000)], ignore_index=True)

>>> %%timeit -n 3
    df['C'] = df['B'] - 1000
    df['D'] = df['B'] ** 2
    df['E'] = df['B'] / 2
3 loops, best of 3: 33.1 ms per loop

>>> df.shape
(1000000, 5)

panda同时根据其他列的值添加几个新列？

1 个答案: