当索引是 PeriodIndex 时,如何在 apply/lambda 函数中访问索引值?
How to access index value in a apply/lambda function when index is a PeriodIndex?
访问apply/lambda组合中的索引值时,我使用了name
参数。
但是在周期索引的情况下,它似乎不起作用。
在下面的代码中,我正在计算给定行的完成率,考虑 4 小时的时间段。
import pandas as pd
p4h = pd.period_range(start='2020-02-01 00:00', end='2020-02-04 00:00', freq='4h')
p1h = pd.period_range(start='2020-02-01 00:00', end='2020-02-04 00:00', freq='1h')
df = p1h.to_series()
p4h_st_as_series = p4h.start_time.to_series()
df['OpenPI'] = df.apply(lambda x:
p4h.to_series().loc[p4h_st_as_series.index <=
x.start_time].index[-1])
completion = df.apply(lambda row: ((row.name.end_time - row['OpenPI'].start_time)
/(row['OpenPI'].end_time - row['OpenPI'].start_time)))
结果:
>>> AttributeError: 'Period' object has no attribute 'name'
请问有人知道吗?
感谢您的帮助!最佳,
下面的工作代码。
我忘记了丢失的 axis=1
。
import pandas as pd
p4h = pd.period_range(start='2020-02-01 00:00', end='2020-02-04 00:00', freq='4h', name='p4h')
p1h = pd.period_range(start='2020-02-01 00:00', end='2020-02-04 00:00', freq='1h', name='p1h')
df = p1h.to_frame()
p4h_st_as_series = p4h.start_time.to_series()
df['OpenPI'] = df.apply(lambda x:
p4h.to_series().loc[p4h_st_as_series.index <=
x.name.start_time].index[-1], axis=1)
completion = df.apply(lambda row: ((row.name.end_time - row.OpenPI.start_time)
/(row.OpenPI.end_time - row.OpenPI.start_time)), axis=1)
访问apply/lambda组合中的索引值时,我使用了name
参数。
但是在周期索引的情况下,它似乎不起作用。
在下面的代码中,我正在计算给定行的完成率,考虑 4 小时的时间段。
import pandas as pd
p4h = pd.period_range(start='2020-02-01 00:00', end='2020-02-04 00:00', freq='4h')
p1h = pd.period_range(start='2020-02-01 00:00', end='2020-02-04 00:00', freq='1h')
df = p1h.to_series()
p4h_st_as_series = p4h.start_time.to_series()
df['OpenPI'] = df.apply(lambda x:
p4h.to_series().loc[p4h_st_as_series.index <=
x.start_time].index[-1])
completion = df.apply(lambda row: ((row.name.end_time - row['OpenPI'].start_time)
/(row['OpenPI'].end_time - row['OpenPI'].start_time)))
结果:
>>> AttributeError: 'Period' object has no attribute 'name'
请问有人知道吗?
感谢您的帮助!最佳,
下面的工作代码。
我忘记了丢失的 axis=1
。
import pandas as pd
p4h = pd.period_range(start='2020-02-01 00:00', end='2020-02-04 00:00', freq='4h', name='p4h')
p1h = pd.period_range(start='2020-02-01 00:00', end='2020-02-04 00:00', freq='1h', name='p1h')
df = p1h.to_frame()
p4h_st_as_series = p4h.start_time.to_series()
df['OpenPI'] = df.apply(lambda x:
p4h.to_series().loc[p4h_st_as_series.index <=
x.name.start_time].index[-1], axis=1)
completion = df.apply(lambda row: ((row.name.end_time - row.OpenPI.start_time)
/(row.OpenPI.end_time - row.OpenPI.start_time)), axis=1)