使用Pandas API for Python，对于每个时间戳，我想计算每个时间戳在最近48小时内在帐户中看到的唯一设备的数量。
这是我的数据：
╔═════════════════════╦══════════╦═══════════╗
║ timestamp ║ device ║ accountid ║
╠═════════════════════╬══════════╬═══════════╣
║ 2018-10-29 18:52:30 ║ d1ed6e6 ║ DhHUXPw ║
║ 2018-11-01 18:52:30 ║ d123ff96 ║ zgffRDY ║
║ 2018-11-01 20:53:30 ║ e322ff96 ║ zgffRDY ║
║ 2018-11-02 21:33:30 ║ g133gf42 ║ zgffRDY ║
║ 2018-11-15 18:52:30 ║ d123ff96 ║ awfdsct ║
║ 2018-11-17 08:25:30 ║ d123ff96 ║ awfdsct ║
╚═════════════════════╩══════════╩═══════════╝
我除了输出看起来像这样。本质上，对于zgffRDY上的帐户2018-11-02 21:33:30，我们在过去48小时内看到了3个唯一设备，而在2018-11-01 18:52:30，我们只看到了1个设备（是当前设备）
╔═════════════════════╦══════════╦═══════════╦═══════════════════════════╗
║ timestamp ║ device ║ accountid ║ last_48hour_device_count ║
╠═════════════════════╬══════════╬═══════════╬═══════════════════════════╣
║ 2018-10-29 18:52:30 ║ d1ed6e6 ║ DhHUXPw ║ 1 ║
║ 2018-11-01 18:52:30 ║ d123ff96 ║ zgffRDY ║ 1 ║
║ 2018-11-01 20:53:30 ║ e322ff96 ║ zgffRDY ║ 2 ║
║ 2018-11-02 21:33:30 ║ g133gf42 ║ zgffRDY ║ 3 ║
║ 2018-11-15 18:52:30 ║ d123ff96 ║ awfdsct ║ 1 ║
║ 2018-11-16 08:25:30 ║ d123ff96 ║ awfdsct ║ 1 ║
╚═════════════════════╩══════════╩═══════════╩═══════════════════════════╝
我当前的代码如下所示。
count_list = []
for idx, row in df.iterrows():
account = row['accountid']
earliest = row['timestamp'] - pd.to_timedelta('48 hours')
current_time = row['timestamp']
filtered_data = df.query('timestamp >= @earliest and '
'timestamp < @current_time and '
'accountid == @account')
device_cnt = len(set(filtered_data['device']))
count_list.append(device_cnt)
df['last_48hour_device_count'] = count_list
我得到正确的输出，但是我的代码运行得太慢了，并且我有一个包含大量观察结果的数据集。
您知道解决此问题的更好方法吗？