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如何计算不同熊猫栏中两个日期之间的年数

dataframe datetime pandas python

leofer · 技术社区 · 8 年前

import pandas as pd
import datetime
from dateutil.relativedelta import relativedelta

# the dataframe - id column always starts with year, month and day
df = pd.DataFrame({'id': ['19520630F8', '19680321A5', '19711113E2'],
                   'dte': ['2010-06-02', '2007-08-12', '2013-01-23']})

# create a date string from df['id'] to the format yyyy-mm-dd
dob = (df['id'].str[:4] + '-' +
       df['id'].str[4:6] + '-' +
       df['id'].str[6:8])

# calculate age (years only) at df['dte']
df['age'] = relativedelta(date, dob).years

我收到错误消息:

我不理解我的数据的模糊性,以及在哪里应用这些空的/bool/项… df['dta'] 列if的对象数据类型不是datetime,而是将dob的创建包装在 pd.to_datetime

编辑

          dte          id  age
0  2010-06-02  19520630F8   57
1  2007-08-12  19680321A5   39
2  2013-01-23  19711113E2   41

2 回复 | 直到 8 年前

jezrael 8 年前

我认为需要:

df['age'] = (np.floor((pd.to_datetime(df['dte']) - 
             pd.to_datetime(dob)).dt.days / 365.25)).astype(int)
print (df)
           id         dte  age
0  19520630F8  2010-06-02   57
1  19680321A5  2007-08-12   39
2  19711113E2  2013-01-23   41

将列转换为日期时间并减去:

print (pd.to_datetime(df['dte']) -  pd.to_datetime(dob))
0   21156 days
1   14388 days
2   15047 days
dtype: timedelta64[ns]

print ((pd.to_datetime(df['dte']) -  pd.to_datetime(dob)).dt.days / 365.25)
0    57.921971
1    39.392197
2    41.196441
dtype: float64

最后一个 floor numpy.floor.

print ((np.floor((pd.to_datetime(df['dte']) - pd.to_datetime(dob)).dt.days / 365.25)))
0    57.0
1    39.0
2    41.0
dtype: float64

jpp 8 年前

dateutil.relativedelta datetime 在计算中使用它们之前的序列。

df['id_dte'] = pd.to_datetime(df['id'].str[:8], format='%Y%m%d')
df['dte'] = pd.to_datetime(df['dte'])

df['age'] = [relativedelta(a, b).years for a, b in zip(df['dte'], df['id_dte'])]

print(df)

         dte          id     id_dte  age
0 2010-06-02  19520630F8 1952-06-30   57
1 2007-08-12  19680321A5 1968-03-21   39
2 2013-01-23  19711113E2 1971-11-13   41

不直接属性:

df['age'] = df['dte'].dt.year - df['id_dte'].dt.year

print(df)

         dte          id     id_dte  age
0 2010-06-02  19520630F8 1952-06-30   58
1 2007-08-12  19680321A5 1968-03-21   39
2 2013-01-23  19711113E2 1971-11-13   42

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