对日志表进行矢量化处理,以确定最新的可用性

我有一个包含更改的日志表。符号+表示添加,符号-表示删除。

import pandas as pd

history = pd.DataFrame({
    "First": 
        ["X","X", "Y", "Y", "X", "X", "Y", "Z"],
    "Last": 
         ["Y", "X", "Y", "Y", "X", "X", "Y", "A"],
    "Change":
        ["+", "+", "-", "+", "-", "+", "+", "-"],
    "Date": 
        ["2022-05-01", "2024-05-01", "2024-06-01", "2024-06-01",
         "2024-05-03", "2024-05-02", "2024-06-02", "2024-06-01"]
})
    
history = history.sort_values(by=["Date", "Change"])
# sort needed to process the entries chronologically

这会产生

  First Last Change        Date
0     X    Y      +  2022-05-01
1     X    X      +  2024-05-01
5     X    X      +  2024-05-02
4     X    X      -  2024-05-03
3     Y    Y      +  2024-06-01
2     Y    Y      -  2024-06-01
7     Z    A      -  2024-06-01
6     Y    Y      +  2024-06-02

在下一步中,我只想显示当前可用的内容。

• 最后一个可用的符号必须是+,项目才能可用。
  • 可用:+、++、+-+、-+、--+等。
  • 不可用:-、++-、+--等。
• 项是第一列和最后一列的组合
• 按日期和更改进行排序

我使用迭代来构建这个逻辑,这是非常缓慢的。大体上

latest = {}
item_columns = [
    "First",
    "Last",
]  

for _, row in history.iterrows():
    key = tuple(row[column] for column in item_columns)
    if row["Change"] == "+":
        latest[key] = row
    elif row["Change"] == "-" and key in latest:
        del latest[key]

available = pd.DataFrame(latest.keys(), columns=item_columns)

这会产生可用的项目

  First Last
0     X    Y
1     Y    Y

问题是大表格的循环很慢,例如20秒以下

latest = {}
item_columns = [
    "First",
    "Last",
]  

duplicated = pd.concat([history.iloc[[1]]] * 50000, ignore_index=True)
history = pd.concat([history, duplicated], ignore_index=True)
for _, row in history.iterrows():
    key = tuple(row[column] for column in item_columns)
    if row["Change"] == "+":
        latest[key] = row
    elif row["Change"] == "-" and key in latest:
        del latest[key]

available = pd.DataFrame(latest.keys(), columns=item_columns)

有办法加快速度吗?