如何在python中查找连续出现的带有条件的值
我在熊猫中有以下数据框
code tank date time no_operation_flag
123 1 01-01-2019 00:00:00 1
123 1 01-01-2019 00:30:00 1
123 1 01-01-2019 01:00:00 0
123 1 01-01-2019 01:30:00 1
123 1 01-01-2019 02:00:00 1
123 1 01-01-2019 02:30:00 1
123 1 01-01-2019 03:00:00 1
123 1 01-01-2019 03:30:00 1
123 1 01-01-2019 04:00:00 1
123 1 01-01-2019 05:00:00 1
123 1 01-01-2019 14:00:00 1
123 1 01-01-2019 14:30:00 1
123 1 01-01-2019 15:00:00 1
123 1 01-01-2019 15:30:00 1
123 1 01-01-2019 16:00:00 1
123 1 01-01-2019 16:30:00 1
123 2 02-01-2019 00:00:00 1
123 2 02-01-2019 00:30:00 0
123 2 02-01-2019 01:00:00 0
123 2 02-01-2019 01:30:00 0
123 2 02-01-2019 02:00:00 1
123 2 02-01-2019 02:30:00 1
123 2 02-01-2019 03:00:00 1
123 2 03-01-2019 03:30:00 1
123 2 03-01-2019 04:00:00 1
123 1 03-01-2019 14:00:00 1
123 2 03-01-2019 15:00:00 1
123 2 03-01-2019 00:30:00 1
123 2 04-01-2019 11:00:00 1
123 2 04-01-2019 11:30:00 0
123 2 04-01-2019 12:00:00 1
123 2 04-01-2019 13:30:00 1
123 2 05-01-2019 03:00:00 1
123 2 05-01-2019 03:30:00 1
123 2 05-01-2019 04:00:00 1
我想要做的是no_operation_flag在坦克级别和日级别标记连续 1 超过 5 次，但时间应该是连续的（时间是半小时级别）。Dataframe 已经在容器、日期和时间级别进行了排序。
动漫人物浏览 138回答 3
3回答
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守着星空守着你
我认为这是一种非常具有前瞻性且有些肮脏的方式，但很容易理解。对于行循环，4 行后的检查时间是 2 小时远。（如果 1 为真）检查所有对应的五个值df['no_operation_flag']都是 1。（如果 2 为真）将 1 放入对应的 5 个值中df['final_flag']。# make col with zerodf['final_flag'] = 0for i in range(1, len(df)-4): j = i + 4 dt1 = df['date'].iloc[i]+' '+df['time'].iloc[i] ts1 = pd.to_datetime(dt1) dt2 = df['date'].iloc[j]+' '+df['time'].iloc[j] ts2 = pd.to_datetime(dt2) # timedelta is 2 hours? if ts2 - ts1 == datetime.timedelta(hours=2, minutes=0): # all of no_operation_flag == 1? if (df['no_operation_flag'].iloc[i:j+1] == 1).all(): df['final_flag'].iloc[i:j+1] = 1 -
慕尼黑5688855
您可以使用这样的解决方案，仅使用新助手过滤每个组的连续日期时间，DataFrame并添加所有缺少的日期merge时间，最后添加新列：df['datetimes'] = pd.to_datetime(df['date'].astype(str) + ' ' + df['time'].astype(str))df1 = (df.set_index('datetimes') .groupby(['code','tank', 'date'])['no_operation_flag'] .resample('30T') .first() .reset_index())shifted1 = df1.groupby(['code','tank', 'date'])['no_operation_flag'].shift()g1 = df1['no_operation_flag'].ne(shifted1).cumsum()mask1 = g1.map(g1.value_counts()).gt(5) & df1['no_operation_flag'].eq(1)df1['final_flag'] = mask1.astype(int)#print (df1.head(40))df = df.merge(df1[['code','tank','datetimes','final_flag']]).drop('datetimes', axis=1)print (df) code tank date time no_operation_flag final_flag0 123 1 01-01-2019 00:00:00 1 01 123 1 01-01-2019 00:30:00 1 02 123 1 01-01-2019 01:00:00 0 03 123 1 01-01-2019 01:30:00 1 14 123 1 01-01-2019 02:00:00 1 15 123 1 01-01-2019 02:30:00 1 16 123 1 01-01-2019 03:00:00 1 17 123 1 01-01-2019 03:30:00 1 18 123 1 01-01-2019 04:00:00 1 19 123 1 01-01-2019 05:00:00 1 010 123 1 01-01-2019 14:00:00 1 111 123 1 01-01-2019 14:30:00 1 112 123 1 01-01-2019 15:00:00 1 113 123 1 01-01-2019 15:30:00 1 114 123 1 01-01-2019 16:00:00 1 115 123 1 01-01-2019 16:30:00 1 116 123 2 02-01-2019 00:00:00 1 017 123 2 02-01-2019 00:30:00 0 018 123 2 02-01-2019 01:00:00 0 019 123 2 02-01-2019 01:30:00 0 020 123 2 02-01-2019 02:00:00 1 021 123 2 02-01-2019 02:30:00 1 022 123 2 02-01-2019 03:00:00 1 023 123 2 03-01-2019 03:30:00 1 024 123 2 03-01-2019 04:00:00 1 025 123 1 03-01-2019 14:00:00 1 026 123 2 03-01-2019 15:00:00 1 027 123 2 03-01-2019 00:30:00 1 028 123 2 04-01-2019 11:00:00 1 029 123 2 04-01-2019 11:30:00 0 030 123 2 04-01-2019 12:00:00 1 031 123 2 04-01-2019 13:30:00 1 032 123 2 05-01-2019 03:00:00 1 033 123 2 05-01-2019 03:30:00 1 034 123 2 05-01-2019 04:00:00 1 0 -
江户川乱折腾
利用：df['final_flag'] = ( df.groupby([df['no_operation_flag'].ne(1).cumsum(), 'tank', 'date', pd.to_datetime(df['time'].astype(str)) .diff() .ne(pd.Timedelta(minutes = 30)) .cumsum(), 'no_operation_flag'])['no_operation_flag'] .transform('size') .gt(5) .view('uint8') )print(df)输出 code tank date time no_operation_flag final_flag0 123 1 01-01-2019 00:00:00 1 01 123 1 01-01-2019 00:30:00 1 02 123 1 01-01-2019 01:00:00 0 03 123 1 01-01-2019 01:30:00 1 14 123 1 01-01-2019 02:00:00 1 15 123 1 01-01-2019 02:30:00 1 16 123 1 01-01-2019 03:00:00 1 17 123 1 01-01-2019 03:30:00 1 18 123 1 01-01-2019 04:00:00 1 19 123 1 01-01-2019 05:00:00 1 010 123 1 01-01-2019 14:00:00 1 111 123 1 01-01-2019 14:30:00 1 112 123 1 01-01-2019 15:00:00 1 113 123 1 01-01-2019 15:30:00 1 114 123 1 01-01-2019 16:00:00 1 115 123 1 01-01-2019 16:30:00 1 116 123 2 02-01-2019 00:00:00 1 017 123 2 02-01-2019 00:30:00 0 018 123 2 02-01-2019 01:00:00 0 019 123 2 02-01-2019 01:30:00 0 020 123 2 02-01-2019 02:00:00 1 021 123 2 02-01-2019 02:30:00 1 022 123 2 02-01-2019 03:00:00 1 023 123 2 03-01-2019 03:30:00 1 024 123 2 03-01-2019 04:00:00 1 025 123 1 03-01-2019 14:00:00 1 026 123 2 03-01-2019 15:00:00 1 027 123 2 03-01-2019 00:30:00 1 028 123 2 04-01-2019 11:00:00 1 029 123 2 04-01-2019 11:30:00 0 030 123 2 04-01-2019 12:00:00 1 031 123 2 04-01-2019 13:30:00 1 032 123 2 05-01-2019 03:00:00 1 033 123 2 05-01-2019 03:30:00 1 0
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