如何在pandas中基于滚动窗口中的多个列查找副本？

Question

样本数据

{"transaction": {"merchant": "merchantA", "amount": 20, "time": "2019-02-13T10:00:00.000Z"}}
{"transaction": {"merchant": "merchantB", "amount": 90, "time": "2019-02-13T11:00:01.000Z"}}
{"transaction": {"merchant": "merchantC", "amount": 90, "time": "2019-02-13T11:00:10.000Z"}}
{"transaction": {"merchant": "merchantD", "amount": 90, "time": "2019-02-13T11:00:20.000Z"}}
{"transaction": {"merchant": "merchantE", "amount": 90, "time": "2019-02-13T11:01:30.000Z"}}
{"transaction": {"merchant": "merchantE", "amount": 90, "time": "2019-02-13T11:02:30.000Z"}}
.
.

我有一些这样的代码

    df = pd.DataFrame()
for line in sys.stdin:
    data = json.loads(line)
    # df1 = pd.DataFrame(data["transaction"], index=[len(df.index)])
    df1 = pd.DataFrame(data["transaction"], index=[data['transaction']['time']])
    df1['time'] = pd.to_datetime(df1['time'])
    df = df.append(df1)
    # df['count'] = df.rolling('2min', on='time', min_periods=1)['amount'].count()

print(df)
print(len(df[df.merchant.eq(data['transaction']['merchant']) & df.amount.eq(data['transaction']['amount'])].index))

电流输出

2019-02-13T10:00:00.000Z  merchantA      20 2019-02-13 10:00:00
2019-02-13T11:00:01.000Z  merchantB      90 2019-02-13 11:00:01
2019-02-13T11:00:10.000Z  merchantC      90 2019-02-13 11:00:10
2019-02-13T11:00:20.000Z  merchantD      90 2019-02-13 11:00:20
2019-02-13T11:01:30.000Z  merchantE      90 2019-02-13 11:01:30
2019-02-13T11:02:30.000Z  merchantE      90 2019-02-13 11:02:30

2

预期产出

2019-02-13T10:00:00.000Z  merchantA      20 2019-02-13 10:00:00
2019-02-13T11:00:01.000Z  merchantB      90 2019-02-13 11:00:01
2019-02-13T11:00:10.000Z  merchantC      90 2019-02-13 11:00:10
2019-02-13T11:00:20.000Z  merchantD      90 2019-02-13 11:00:20
2019-02-13T11:01:30.000Z  merchantE      90 2019-02-13 11:01:30

因为数据是流式传输的。我想检查是否有重复记录（其商户和金额值相同）在两分钟内到达，因此我将其作为丢弃，不对其进行处理。把它打印成副本

我必须使用索引压缩或groupby做些什么吗？但是如何将多个列的数量相等。 或者两列上的一些滚动条件，但找不到任何方法

我错过了什么

谢谢

编辑

#dup = df[df.duplicated(subset=['merchant', 'amount'], keep=False)]
     res = df.loc[(df.merchant == data['transaction']['merchant']) & (df.amount == data['transaction']['amount'])]
        # res['timediff'] = pd.to_timedelta((data['transaction']['time'] - res['time']), unit='T')
        res['timediff'] = (data['transaction']['time'] - res['time'])
        if len(res.index) >1:
           print(res)

所以我试着这样做，如果结果不到120秒，我可以处理它。 但由此产生的df目前以

                      merchant  amount                time       concat          timediff
2019-02-13 11:03:00  merchantF      10 2019-02-13 11:03:00  merchantF10 -1 days +23:59:20
2019-02-13 11:02:20  merchantF      10 2019-02-13 11:02:20  merchantF10          00:00:00

2019-02-13 11:01:30  merchantE      10 2019-02-13 11:01:30  merchantE10 00:01:00
2019-02-13 11:02:00  merchantE      10 2019-02-13 11:02:00  merchantE10 00:00:30
2019-02-13 11:02:30  merchantE      10 2019-02-13 11:02:30  merchantE10 00:00:00

-1天+23:59:20我认为这种格式可以用于获取绝对值

如何将时间转换为可以与120秒进行比较的格式pd.to_deltatime（）对我不起作用，或者我用错了

Answer 1

所以我让它工作，但不支持滚动窗口，因为它不支持字符串类型。熊猫回购协议中也报告并要求提供该功能

我的问题解决方案片段：

    if len(df.index) > 0:
        res = df.loc[(df.merchant == data['transaction']['merchant']) & (df.amount == data['transaction']['amount'])]
        res['timediff'] = (data['transaction']['time'] - res['time']).dt.total_seconds().abs() <= 120
        if res.timediff.any():
            continue
    df = df.append(df1)
print(df)

样本数据：

{"transaction": {"merchant": "merchantA", "amount": 20, "time": "2019-02-13T10:00:00.000Z"}}
{"transaction": {"merchant": "merchantB", "amount": 90, "time": "2019-02-13T11:00:01.000Z"}}
{"transaction": {"merchant": "merchantC", "amount": 10, "time": "2019-02-13T11:00:10.000Z"}}
{"transaction": {"merchant": "merchantD", "amount": 10, "time": "2019-02-13T11:00:20.000Z"}}
{"transaction": {"merchant": "merchantE", "amount": 10, "time": "2019-02-13T11:01:30.000Z"}}
{"transaction": {"merchant": "merchantF", "amount": 10, "time": "2019-02-13T11:03:00.000Z"}}
{"transaction": {"merchant": "merchantE", "amount": 10, "time": "2019-02-13T11:02:00.000Z"}}
{"transaction": {"merchant": "merchantF", "amount": 10, "time": "2019-02-13T11:02:20.000Z"}}
{"transaction": {"merchant": "merchantE", "amount": 10, "time": "2019-02-13T11:02:30.000Z"}}
{"transaction": {"merchant": "merchantF", "amount": 10, "time": "2019-02-13T11:05:20.000Z"}}
{"transaction": {"merchant": "merchantE", "amount": 10, "time": "2019-02-13T11:00:30.000Z"}}

输出：

                      merchant  amount                time
2019-02-13 10:00:00  merchantA      20 2019-02-13 10:00:00
2019-02-13 11:00:01  merchantB      90 2019-02-13 11:00:01
2019-02-13 11:00:10  merchantC      10 2019-02-13 11:00:10
2019-02-13 11:00:20  merchantD      10 2019-02-13 11:00:20
2019-02-13 11:01:30  merchantE      10 2019-02-13 11:01:30
2019-02-13 11:03:00  merchantF      10 2019-02-13 11:03:00
2019-02-13 11:05:20  merchantF      10 2019-02-13 11:05:20