python - 合并 2 个带时间戳的 numpy 数组

Question

我有两个 numpy ndarrays - 每个都有自己的时间戳维度。我想将它们合并在一起。但是它们的时间戳的间隔不一定相同。这是我的意思的一个例子:

Array 1: names =  ['timestamp', 'value']
a1 = [(1531000000, 0), (1532000000, 1), (1533000000, 2), (1534000000, 3)]

Array 2: names =  ['timestamp', 'color']
a2 = [(1531500000, "blue"), (1532000000, "black"), (1533500000, "green"), (1534000000, "red")]

Resulting Array: names =  ['timestamp', 'value', 'color']
a3 = [(1531000000, 0, nan), (1531500000, nan, "blue"), (1532000000, 1, "black"), (1533000000, 2, nan), (1533500000, nan, "green"), (1534000000, 3, "red")]

Answer 1

使用 Pandas，您可以执行外部合并，然后进行排序。这是很自然的，因为在 Pandas 框架中使用了 NumPy 数组。

import pandas as pd

res = pd.merge(df1, df2, how='outer').sort_values('timestamp').values.tolist()

结果

[[1531000000, 0.0, nan],
 [1531500000, nan, 'blue'],
 [1532000000, 1.0, 'black'],
 [1533000000, 2.0, nan],
 [1533500000, nan, 'green'],
 [1534000000, 3.0, 'red']]

设置

names =  ['timestamp', 'value']
a1 = [(1531000000, 0), (1532000000, 1), (1533000000, 2), (1534000000, 3)]
df1 = pd.DataFrame(a1, columns=names)

names =  ['timestamp', 'color']
a2 = [(1531500000, "blue"), (1532000000, "black"), (1533500000, "green"), (1534000000, "red")]
df2 = pd.DataFrame(a2, columns=names)