Question

我正在尝试创建像这样的pandas数据框：

           x2        x3
0    3.536220  0.681269
1    0.681269  3.536220
2   -0.402380  2.303833
3    2.303833 -0.402380
4    2.032329  3.334412
5    3.334412  2.032329
6    0.371338  5.879732
. . .

因此x2是一列随机数，x3的值为x2中的行0和1，交换的值为2和3，依此类推。我目前的代码是这样的：

import numpy as np
import pandas as pd
x2 = pd.Series(np.random.normal(loc = 2, scale = 2.5, size = 1000))
x3 = pd.Series([x2[i + 1] if i % 2 == 0 else x2[i - 1] for i in range(1000)])
df = pd.DataFrame({'x2': x2, 'x3': x3})

我想知道是否有更快或更优雅的方式，特别是如果我想要有很多行（例如100万？）或反复这样做（例如蒙特卡罗模拟）？

Answer 1

而不是

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你可以使用

[x2[i + 1] if i % 2 == 0 else x2[i - 1] for i in range(1000)]

对于长度为1000的序列，使用def swap(arr): result = np.empty_like(arr) result[::2] = arr[1::2] result[1::2] = arr[::2] return result的速度提高了3000倍以上：

swap

In [84]: %timeit [x2[i + 1] if i % 2 == 0 else x2[i - 1] for i in range(1000)]
100 loops, best of 3: 12.7 ms per loop

In [98]: %timeit  swap(x2.values)
100000 loops, best of 3: 3.82 µs per loop

打印

import numpy as np
import pandas as pd
np.random.seed(2017)
x2 = pd.Series(np.random.normal(loc = 2, scale = 2.5, size = 1000))
x3 = [x2[i + 1] if i % 2 == 0 else x2[i - 1] for i in range(1000)]

def swap(arr):
    result = np.empty_like(arr)
    result[::2] = arr[1::2]
    result[1::2] = arr[::2]
    return result

df = pd.DataFrame({'x2': x2, 'x3': x3, 'x4': swap(x2.values)})
print(df.head())

Pandas：创建新列，交换其他行的值

1 个答案: