计算活人与死人的年龄

Question

编辑：

正如有人建议的那样，我提出了一个可验证的例子。 如果您从中取出大熊猫而只是放置原始值而不是数据框值，那么它会完美地工作。

如果您将大熊猫带回去，如下所示，程序将运行并为print（true_age）返回0。

import pandas as pd
import numpy as np
from datetime import datetime

data = np.array([['','bornYear','bornMonth', 'bornDay','diedYear','diedMonth','diedDay'],
                ['Record1',1932,8,17,1980,3,22],
                ['Record2',1950,4,12,1980,3,22]])


df = pd.DataFrame(data=data[1:,1:],
                  index=data[1:,0],
                  columns=data[0,1:])

byear = int(df.iloc[1]['bornYear'])
bmonth = int(df.iloc[1]['bornMonth'])
bday = int(df.iloc[1]['bornDay'])
died_year = df.iloc[1]['diedYear']
died_month = df.iloc[1]['diedMonth']
died_day = df.iloc[1]['diedDay']
now_year = datetime.now().year
now_month = datetime.now().month
now_day = datetime.now().day
age_raw = now_year - byear
true_age = 0


if died_year is not None:
    died_year = int(died_year)
    died_month = int(died_month)
    died_day = int(died_day)

    age_raw = float(died_year) - float(byear)

    if bmonth > died_month:
        if bday > died_day:
            true_age = age_raw - 1
        elif bday < died_day:
            true_age = age_raw
    elif bmonth < died_month:
        true_age = age_raw

print(true_age)

---

原始帖子：

因此，我有一个熊猫数据框，它是一个MySQL查询的结果，该查询搜索一个人的名字，然后返回有关他们的一些信息。 这样的信息之一就是他们的年龄。 该表包含活着的和已故的人。 我正在努力做到这一点，以便如果这个人去世了，它将使用他们的实际年龄（去世时），而不是如果他们还活着的话，那将是多少岁。 如果它们还活着，那么死亡日期的字段为空； 如果它们死了，那么这些字段当然具有值。 这是我已经声明的相关变量：

bmonth = int(storage.iloc[0]['birthMonth'])
bday = int(storage.iloc[0]['birthDay'])
byear = int(storage.iloc[0]['birthYear'])
died_year = storage.iloc[0]['deathYear']
died_month = storage.iloc[0]['deathMonth']
died_day = storage.iloc[0]['deathDay']
now_year = datetime.now().year
now_month = datetime.now().month
now_day = datetime.now().day
age_raw = now_year - byear
true_age = 0

现在，我将其设计为嵌套的if语句，但是我在某个地方出错了。 如果这个人还活着，那么一切都会正常进行。 当我打印年龄时，它会输出正确的年龄。 但是，如果该人已故，则打印的年龄始终为零。 这是嵌套的if语句以及相关的print语句：

#Here are the nested if statements:

    if died_year is None:   

            if bmonth > now_month:
                    if bday > now_day:
                            true_age = age_raw - 1
                    elif bday < now_day:
                            true_age = age_raw
            elif bmonth < now_month:

                    true_age = age_raw

    elif died_year is not None:

            died_year = int(died_year)
            died_month = int(died_month)
            died_day = int(died_day)

            age_raw = died_year - byear

            if bmonth > died_month:
                    if bday > died_day:
                            true_age = age_raw - 1
                    elif bday < died_day:
                            true_age = age_raw
            elif bmonth < died_month:
                    true_age = age_raw

#And now the print statement:

print("DOB: "+str(bmonth)+"/"+str(bday)+"/"+str(byear)+" ("+str(true_age)+" years old)")

另外，我还准备好以下内容，以便在该人死后在输出中返回死亡日期。 它工作正常并且返回正确的日期，所以我知道这些值都是正确的：

    if died_year is not None:
            print("*DECEASED: "+str(died_month)+"/"+str(died_day)+"/"+str(died_year))

注意 ，在满足适当条件之前，我不会将变量dead_year，died_month和dead_day转换为整数。 在if语句之外执行此操作将触发错误，因为不能将null值作为int（）传递。 我觉得我在这里错过了一些非常明显的东西，但也许没有。 另外，如果有人有更好的方法来完成所有这些工作，那么我总是乐于学习如何提高效率。

Answer 1

Pandas对时间序列提供了出色的支持，因此，最好使用适当的工具。 在将列转换为单个Datetime列之后，我们可以对其进行时间算术：

# demo dataframe
df = pd.DataFrame({
    'birthMonth': [5, 2],
    'birthDay': [4, 24],
    'birthYear': [1924, 1997],
    'deathMonth': [3, None],
    'deathDay': [1, None],
    'deathYear': [2008, None]
})

# convert birth dates to datetimes
birth = pd.to_datetime(df[['birthMonth', 'birthDay', 'birthYear']]
                       .rename(columns={'birthMonth': 'month', 'birthDay': 'day', 'birthYear': 'year'}))
# convert death dates to datetimes
death = pd.to_datetime(df[['deathMonth', 'deathDay', 'deathYear']]
                       .rename(columns={'deathMonth':'month', 'deathDay': 'day', 'deathYear': 'year'}))

# calculate age in days, normalizing 'now' to midnight of today
age = (pd.Timestamp.now().normalize() - birth).where(death.isnull(), other=death-birth)

编辑 ：请参阅下面的@ALollz有关时间戳标准化的讨论。

Answer 2

将每个这些值转换为日期时间对象然后执行if / elif过滤要容易得多。

import datetime
bmonth = int(storage.iloc[0]['birthMonth'])
bday = int(storage.iloc[0]['birthDay'])
byear = int(storage.iloc[0]['birthYear'])

died_year = storage.iloc[0]['deathYear']
died_month = storage.iloc[0]['deathMonth']
died_day = storage.iloc[0]['deathDay']

start = datetime.datetime(month = bmonth, day=bday, year=byear)
end =  datetime.datetime(month=died_month, day=died_day, year=died_year)
(start-end).days#returns the difference between the days

您也可以在其中考虑datetime.now() 。

希望有帮助，它将帮助您改善流程。

Answer 3

编辑为最小的示例兼容性

您可以定义一个函数来计算一个人的年龄：

from datetime import date 


def calc_age(row):
    bm = row['bornMonth']
    bd = row['bornDay']
    by = row['bornYear']

    dm = row['diedMonth']
    dd = row['diedDay']
    dy = row['diedYear']

    birth_date = date(*[int(i) for i in (by, bm, bd)])  # suppose that all the parameters is not None
    try:
        end_date = date(*[int(i) for i in (dy, dm, dd)])
    except (TypeError, ValueError):  # if death date is None
        end_date = date.today()

    # is birth date after death date or today; if True == 1, else == 0
    is_next_year = ((end_date.month, end_date.day) < (birth_date.month, birth_date.day))
    age = end_date.year - birth_date.year - is_next_year 
    return age

将此功能应用于行上的数据框：

df.apply(calc_age, axis=1)

如果没有丢失的数据，它将返回以年为单位的所有年龄的pd.Series。 您可以将其连接到数据框：

df['personsAge'] = df.apply(calc_age, axis=1)

然后添加另一列状态并打印结果：

def is_dead(row):
    dm = row['diedMonth']
    dd = row['diedDay']
    dy = row['diedYear']
    try:
        died = date(*[int(i) for i in (dy, dm, dd)])
        return True
    except ValueError:
        return False

df['is_dead'] = df.apply(is_dead, axis=1)

def print_status(row):
    bm = row['bornMonth']
    bd = row['bornDay']
    by = row['bornYear']

    dm = row['diedMonth']
    dd = row['diedDay']
    dy = row['diedYear']
    age = row['personsAge']

    print("DOB: "+str(bm)+"/"+str(bd)+"/"+str(by)+" ("+str(age)+" years old)")
    if row['is_dead']:
        print("*DECEASED: "+str(dm)+"/"+str(dd)+"/"+str(dy))

df.apply(print_status, axis=1)

stdout:
DOB: 8/17/1932 (47 years old)
*DECEASED: 3/22/1980
DOB: 4/12/1950 (68 years old)

如果您不喜欢复制粘贴日期选择，请使用Andrey Portnoy解决方案中的datetime方法替换它。