将年作为时间增量单位添加到移动日期的优雅方式 - Pandas
Elegant way to add years as timedelta units to shift dates - Pandas
我有一个如下所示的数据框
df1 = pd.DataFrame({'person_id': [11,11,11,21,21],
'admit_dates': ['03/21/2015', '01/21/2016', '7/20/2018','01/11/2017','12/31/2011'],
'discharge_dates': ['05/09/2015', '01/29/2016', '7/27/2018','01/12/2017','01/31/2016'],
'drug_start_dates': ['05/29/1967', '01/21/1957', '7/27/1959','01/01/1961','12/31/1961'],
'offset':[223,223,223,310,310]})
我想做的是将 years
中的 offset
添加到日期列中。
所以,我试图用 unit=y
或 unit=Y
将偏移量转换为 timedelta
对象,然后移动 admit_dates
df1['offset'] = pd.to_timedelta(df1['offset'],unit='Y') #also tried with `y` (small y)
df1['shifted_date'] = df1['admit_dates'] + df1['offset']
但是,我收到以下错误
ValueError: Units 'M' and 'Y' are no longer supported, as they do not
represent unambiguous timedelta values durations.
还有其他优雅的方法可以将日期移动 years
吗?
您可以做的一件事是从日期中提取年份,并将其添加到偏移量中:
df1 = pd.DataFrame({'person_id': [11,11,11,21,21],
'admit_dates': ['03/21/2015', '01/21/2016', '7/20/2018','01/11/2017','12/31/2011'],
'discharge_dates': ['05/09/2015', '01/29/2016', '7/27/2018','01/12/2017','01/31/2016'],
'drug_start_dates': ['05/29/1967', '01/21/1957', '7/27/1959','01/01/1961','12/31/1961'],
'offset':[10,20,2,31,12]})
df1.admit_dates = pd.to_datetime(df1.admit_dates)
df1["new_year"] = df1.admit_dates.dt.year + df1.offset
df1["date_with_offset"] = pd.to_datetime(pd.DataFrame({"year": df1.new_year,
"month": df1.admit_dates.dt.month,
"day":df1.admit_dates.dt.day}))
问题 - 使用您的原始偏移量,某些日期会导致以下错误:
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2328-01-11 00:00:00
根据 the documentation,pandas 中的最大日期是 2262 年 4 月 11 日(具体来说大约是午夜十二点)。这可能是因为它们以纳秒为单位来保持时间,而这正是这种表示发生越界错误的原因。
pandas 中支持的最大值 Timestamp
是 Timestamp('2262-04-11 23:47:16.854775807')
,因此您无法将 310
年添加到 12/31/2011
,一种可能的方式是使用 python 的日期时间对象,它支持最多 9999
年,所以你应该能够添加 310
年。
from dateutil.relativedelta import relativedelta
df['admit_dates'] = pd.to_datetime(df['admit_dates'])
df['admit_dates'] = df['admit_dates'].dt.date.add(
df['offset'].apply(lambda y: relativedelta(years=y)))
结果:
df
person_id admit_dates discharge_dates drug_start_dates offset
0 11 2238-03-21 05/09/2015 05/29/1967 223
1 11 2239-01-21 01/29/2016 01/21/1957 223
2 11 2241-07-20 7/27/2018 7/27/1959 223
3 21 2327-01-11 01/12/2017 01/01/1961 310
4 21 2321-12-31 01/31/2016 12/31/1961 310
单位 'Y' 和 'M' 自 pandas 0.25.0 起已弃用
但是多亏了 numpy timedelta64,我们可以通过它在 pandas Timedelta
中使用这些单位
import pandas as pd
import numpy as np
# Builds your dataframe
df1 = pd.DataFrame({'person_id': [11,11,11,21,21],
'admit_dates': ['03/21/2015', '01/21/2016', '7/20/2018','01/11/2017','12/31/2011'],
'discharge_dates': ['05/09/2015', '01/29/2016', '7/27/2018','01/12/2017','01/31/2016'],
'drug_start_dates': ['05/29/1967', '01/21/1957', '7/27/1959','01/01/1961','12/31/1961'],
'offset':[223,223,223,310,310]})
>>> df1
person_id admit_dates discharge_dates drug_start_dates offset
0 11 03/21/2015 05/09/2015 05/29/1967 223
1 11 01/21/2016 01/29/2016 01/21/1957 223
2 11 7/20/2018 7/27/2018 7/27/1959 223
3 21 01/11/2017 01/12/2017 01/01/1961 310
4 21 12/31/2011 01/31/2016 12/31/1961 310
>>> df1['shifted_date'] = df1.apply(lambda r: pd.Timedelta(np.timedelta64(r['offset'], 'Y'))+ pd.to_datetime(r['admit_dates']), axis=1)
>>> df1['shifted_date'] = df1['shifted_date'].dt.date
>>> df1
person_id admit_dates discharge_dates drug_start_dates offset shifted_date
0 11 03/21/2015 05/09/2015 05/29/1967 223 2238-03-21
1 11 01/21/2016 01/29/2016 01/21/1957 223 2239-01-21
2 11 7/20/2018 7/27/2018 7/27/1959 223 2241-07-20
....
我有一个如下所示的数据框
df1 = pd.DataFrame({'person_id': [11,11,11,21,21],
'admit_dates': ['03/21/2015', '01/21/2016', '7/20/2018','01/11/2017','12/31/2011'],
'discharge_dates': ['05/09/2015', '01/29/2016', '7/27/2018','01/12/2017','01/31/2016'],
'drug_start_dates': ['05/29/1967', '01/21/1957', '7/27/1959','01/01/1961','12/31/1961'],
'offset':[223,223,223,310,310]})
我想做的是将 years
中的 offset
添加到日期列中。
所以,我试图用 unit=y
或 unit=Y
将偏移量转换为 timedelta
对象,然后移动 admit_dates
df1['offset'] = pd.to_timedelta(df1['offset'],unit='Y') #also tried with `y` (small y)
df1['shifted_date'] = df1['admit_dates'] + df1['offset']
但是,我收到以下错误
ValueError: Units 'M' and 'Y' are no longer supported, as they do not represent unambiguous timedelta values durations.
还有其他优雅的方法可以将日期移动 years
吗?
您可以做的一件事是从日期中提取年份,并将其添加到偏移量中:
df1 = pd.DataFrame({'person_id': [11,11,11,21,21],
'admit_dates': ['03/21/2015', '01/21/2016', '7/20/2018','01/11/2017','12/31/2011'],
'discharge_dates': ['05/09/2015', '01/29/2016', '7/27/2018','01/12/2017','01/31/2016'],
'drug_start_dates': ['05/29/1967', '01/21/1957', '7/27/1959','01/01/1961','12/31/1961'],
'offset':[10,20,2,31,12]})
df1.admit_dates = pd.to_datetime(df1.admit_dates)
df1["new_year"] = df1.admit_dates.dt.year + df1.offset
df1["date_with_offset"] = pd.to_datetime(pd.DataFrame({"year": df1.new_year,
"month": df1.admit_dates.dt.month,
"day":df1.admit_dates.dt.day}))
问题 - 使用您的原始偏移量,某些日期会导致以下错误:
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2328-01-11 00:00:00
根据 the documentation,pandas 中的最大日期是 2262 年 4 月 11 日(具体来说大约是午夜十二点)。这可能是因为它们以纳秒为单位来保持时间,而这正是这种表示发生越界错误的原因。
pandas 中支持的最大值 Timestamp
是 Timestamp('2262-04-11 23:47:16.854775807')
,因此您无法将 310
年添加到 12/31/2011
,一种可能的方式是使用 python 的日期时间对象,它支持最多 9999
年,所以你应该能够添加 310
年。
from dateutil.relativedelta import relativedelta
df['admit_dates'] = pd.to_datetime(df['admit_dates'])
df['admit_dates'] = df['admit_dates'].dt.date.add(
df['offset'].apply(lambda y: relativedelta(years=y)))
结果:
df
person_id admit_dates discharge_dates drug_start_dates offset
0 11 2238-03-21 05/09/2015 05/29/1967 223
1 11 2239-01-21 01/29/2016 01/21/1957 223
2 11 2241-07-20 7/27/2018 7/27/1959 223
3 21 2327-01-11 01/12/2017 01/01/1961 310
4 21 2321-12-31 01/31/2016 12/31/1961 310
单位 'Y' 和 'M' 自 pandas 0.25.0 起已弃用 但是多亏了 numpy timedelta64,我们可以通过它在 pandas Timedelta
中使用这些单位import pandas as pd
import numpy as np
# Builds your dataframe
df1 = pd.DataFrame({'person_id': [11,11,11,21,21],
'admit_dates': ['03/21/2015', '01/21/2016', '7/20/2018','01/11/2017','12/31/2011'],
'discharge_dates': ['05/09/2015', '01/29/2016', '7/27/2018','01/12/2017','01/31/2016'],
'drug_start_dates': ['05/29/1967', '01/21/1957', '7/27/1959','01/01/1961','12/31/1961'],
'offset':[223,223,223,310,310]})
>>> df1
person_id admit_dates discharge_dates drug_start_dates offset
0 11 03/21/2015 05/09/2015 05/29/1967 223
1 11 01/21/2016 01/29/2016 01/21/1957 223
2 11 7/20/2018 7/27/2018 7/27/1959 223
3 21 01/11/2017 01/12/2017 01/01/1961 310
4 21 12/31/2011 01/31/2016 12/31/1961 310
>>> df1['shifted_date'] = df1.apply(lambda r: pd.Timedelta(np.timedelta64(r['offset'], 'Y'))+ pd.to_datetime(r['admit_dates']), axis=1)
>>> df1['shifted_date'] = df1['shifted_date'].dt.date
>>> df1
person_id admit_dates discharge_dates drug_start_dates offset shifted_date
0 11 03/21/2015 05/09/2015 05/29/1967 223 2238-03-21
1 11 01/21/2016 01/29/2016 01/21/1957 223 2239-01-21
2 11 7/20/2018 7/27/2018 7/27/1959 223 2241-07-20
....