如何将Excel序列日期和常规日期的列转换为pandas日期时间

Question

我有一个数据框架，其中有一些生日的常规日期与Excel序列日期混合，如下所示：

09/01/2020 12:00:00 AM
05/15/1985 12:00:00 AM
06/07/2013 12:00:00 AM
33233
26299
29428

我尝试了这个答案的解决方案，所有Excel序列格式的日期都被删除了，同时保留了那些正常日期格式的日期。

这是我的代码：

import pandas as pd
import xlrd
import numpy as np
from numpy import *
from numpy.core import *
import os
import datetime
from datetime import datetime, timedelta
import glob
def from_excel_ordinal(ordinal, _epoch0=datetime(1899, 12, 31)):
if ordinal >= 60:
ordinal -= 1  # Excel leap year bug, 1900 is not a leap year!
return (_epoch0 + timedelta(days=ordinal)).replace(microsecond=0)
path = 'C:\Input'
os.chdir(path)
filelist = glob.glob('*BLAH*.xlsx')  
filename = os.fsdecode(filelist[0])
df = pd.read_excel(filename, sheet_name = 'Blah Blah') 
m = df['Birthday'].astype(str).str.isdigit()
df.loc[m, 'Birthday'] = df.loc[m, 'Birthday'].astype(int).apply(from_excel_ordinal)
df['Birthday'] = pd.to_datetime(df['Birthday'], errors = 'coerce')

我不确定我在哪里出了问题，因为代码不应该像现在这样把生日都删掉。

Answer 1

• 不能以相同的方式解析所有日期
• 加载数据帧
• 如果尚未将dates列强制转换为str列，请将其强制转换为
• 使用布尔索引选择不同的日期类型
  • 假设常规日期包含/
  • 假设Excel序列日期不包含/
• 根据日期时间类型分别修复每个数据帧
• 将数据帧重新拼接在一起

import pandas as pd
from datetime import datetime
# load data
df = pd.DataFrame({'dates': ['09/01/2020', '05/15/1985', '06/07/2013', '33233', '26299', '29428']})
# display(df)
dates
0  09/01/2020
1  05/15/1985
2  06/07/2013
3       33233
4       26299
5       29428
# set the column type as a str if it isn't already
df.dates = df.dates.astype('str')
# create a date mask based on the string containing a /
date_mask = df.dates.str.contains('/')
# split the dates out for excel
df_excel = df[~date_mask].copy()
# split the regular dates out
df_reg = df[date_mask].copy()
# convert reg dates to datetime
df_reg.dates = pd.to_datetime(df_reg.dates)
# convert excel dates to datetime; the column needs to be cast as ints
df_excel.dates = pd.TimedeltaIndex(df_excel.dates.astype(int), unit='d') + datetime(1900, 1, 1)
# combine the dataframes
df = pd.concat([df_reg, df_excel])

显示器(df(

dates
0 2020-09-01
1 1985-05-15
2 2013-06-07
3 1990-12-28
4 1972-01-03
5 1980-07-28

Answer 2

pd。TimedeltaIndex(dates_in_excel_sterial_format，单位='d'(+pd.datetime(1900,1,1(

演示：

> dates_in_excel_serial_format = [29428]
> pd.TimedeltaIndex(dates_in_excel_serial_format, unit='d') + pd.datetime(1900,1,1)
< DatetimeIndex(['1980-07-28'], dtype='datetime64[ns]', freq=None)