How to Convert a Given Ordinal Number (From Excel) to a Date

How to convert a given ordinal number (from Excel) to a date

The offset in Excel is the number of days since 1900/01/01, with 1 being the first of January 1900, so add the number of days as a timedelta to 1899/12/31:

from datetime import datetime, timedelta

def from_excel_ordinal(ordinal: float, _epoch0=datetime(1899, 12, 31)) -> datetime:
    if ordinal >= 60:
        ordinal -= 1  # Excel leap year bug, 1900 is not a leap year!
    return (_epoch0 + timedelta(days=ordinal)).replace(microsecond=0)

You have to adjust the ordinal by one day for any date after 1900/02/28; Excel has inherited a leap year bug from Lotus 1-2-3 and treats 1900 as a leap year. The code above returns datetime(1900, 2, 28, 0, 0) for both 59 and 60 to correct for this, with fractional values in the range [59.0 - 61.0) all being a time between 00:00:00.0 and 23:59:59.999999 on that day.

The above also supports serials with a fraction to represent time, but since Excel doesn't support microseconds those are dropped.

Converting Date in excel with Ordinal Notation

You could try the following:

Formula in B1:

=TEXT(A1,"[$-409]mmm d")&IFERROR(INDEX({"st","nd","rd"},ROUNDUP(MATCH(DAY(A1),{1,21,31,2,22,32,3,23},0)/3,0)),"th")&TEXT(A1," yyyy hh:mm AM/PM")

And if you want the commas in there as well:

Formula in C1:

=SUBSTITUTE(TEXT(A1,"[$-409]mmm d, yyyy, hh:mm AM/PM"),",",IFERROR(INDEX({"st,","nd,","rd,"},ROUNDUP(MATCH(DAY(A1),{1,21,31,2,22,32,3,23},0)/3,0)),"th,"),1)

Mind you, this transform a data into text!

Convert date from excel in number format to date format python

from datetime import datetime
excel_date = 42139
dt = datetime.fromordinal(datetime(1900, 1, 1).toordinal() + excel_date - 2)
tt = dt.timetuple()
print(dt)
print(tt)

As mentioned by J.F. Sebastian, this answer only works for any date after 1900/03/01

EDIT: (in answer to @R.K)

If your excel_date is a float number, use this code:

from datetime import datetime

def floatHourToTime(fh):
    hours, hourSeconds = divmod(fh, 1)
    minutes, seconds = divmod(hourSeconds * 60, 1)
    return (
        int(hours),
        int(minutes),
        int(seconds * 60),
    )

excel_date = 42139.23213
dt = datetime.fromordinal(datetime(1900, 1, 1).toordinal() + int(excel_date) - 2)
hour, minute, second = floatHourToTime(excel_date % 1)
dt = dt.replace(hour=hour, minute=minute, second=second)

print(dt)
assert str(dt) == "2015-05-15 00:13:55"

Convert Excel style date with pandas

OK I think the easiest thing is to construct a TimedeltaIndex from the floats and add this to the scalar datetime for 1900,1,1:

In [85]:
import datetime as dt
import pandas as pd
df = pd.DataFrame({'date':[42580.3333333333, 10023]})
df

Out[85]:
           date
0  42580.333333
1  10023.000000

In [86]:
df['real_date'] = pd.TimedeltaIndex(df['date'], unit='d') + dt.datetime(1900,1,1)
df

Out[86]:
           date                  real_date
0  42580.333333 2016-07-31 07:59:59.971200
1  10023.000000 1927-06-12 00:00:00.000000

OK it seems that excel is a bit weird with it's dates thanks @ayhan:

In [89]:
df['real_date'] = pd.TimedeltaIndex(df['date'], unit='d') + dt.datetime(1899, 12, 30)
df

Out[89]:
           date                  real_date
0  42580.333333 2016-07-29 07:59:59.971200
1  10023.000000 1927-06-10 00:00:00.000000

See related: How to convert a python datetime.datetime to excel serial date number

Convert number in Excel time format to string equivalent in python

Update

It seems using the utcfromtimestamp on the datetime function is erroneous(Thanks to @FObersteiner for the comment). You should use fromtimestamp instead. So you can use the below code instead of the code at the bottom. Note that, pytz is deprecated. It seems that zoneinfo can do pytz's job.

from datetime import datetime
import pytz
tzHong = pytz.timezone('Hongkong')
tzGMT = pytz.timezone('Etc/GMT')
dateString = "1645704206000"
date = datetime.fromtimestamp(float(dateString)/1000, tz=tzGMT)
print("Date in Hongkong: " + date.astimezone(tz=tzHong).strftime("%m-%d-%y %I:%M"))
print("Date in GMT: " + date.strftime("%m-%d-%y %I:%M"))

Old Version

It looks like there is a problem in the question related to Excel's natural internal representation. But if you are interested in converting timestamps (with milliseconds) into a string, human-readable date, you can use datetime module in python:

from datetime import datetime
import pytz
tzHong = pytz.timezone('Hongkong')
tzGMT = pytz.timezone('Etc/GMT')
dateString = "1645704206000"
date = datetime.utcfromtimestamp(float(dateString)/1000)
print("Date in Hongkong: " + date.astimezone(tz=tzHong).strftime("%m-%d-%y %I:%M"))
print("Date in GMT: " + date.astimezone(tz=tzGMT).strftime("%m-%d-%y %I:%M"))

Output

Date in Hongkong: 02-24-22 08:03
Date in GMT: 02-24-22 12:03

Also note that the date you are showing (2/24/22 12:03) is in the GMT timezone, not Hongkong. Both time zones are shown in the code above. Make sure which one works for your desired output. Also, the answer provided by this answer is in 12-hour clock format. If you are interested in 24-hour format, just change %I in the code above with %H.

How to convert a column with Excel Serial Dates and regular dates to a pandas datetime?

• All the dates can't be parsed in the same manner
• Load the dataframe
• Cast the dates column as a str if it's not already.
• Use Boolean Indexing to select different date types
  
  • Assuming regular dates contain a /
  • Assuming Excel serial dates do not contain a /
• Fix each dataframe separately based on its datetime type
• Concat the dataframes back together.

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])

display(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

Convert column in excel date format (DDDDD.tttt) to datetime using pandas

Given

# s = df['date']
s

0    42411.0
1    42754.0
Name: 0, dtype: float64

Convert from Excel to datetime using:

s_int = s.astype(int)
# Correcting Excel Leap Year bug.
days = pd.to_timedelta(np.where(s_int > 59, s_int - 1, s_int), unit='D')
secs = pd.to_timedelta(
    ((s - s_int) * 86400.0).round().astype(int), unit='s')

pd.to_datetime('1899/12/31') + days + secs

0   2016-02-11
1   2017-01-19
dtype: datetime64[ns]

Reference.

How to convert a python datetime.datetime to excel serial date number

It appears that the Excel "serial date" format is actually the number of days since 1900-01-00, with a fractional component that's a fraction of a day, based on http://www.cpearson.com/excel/datetime.htm. (I guess that date should actually be considered 1899-12-31, since there's no such thing as a 0th day of a month)

So, it seems like it should be:

def excel_date(date1):
    temp = dt.datetime(1899, 12, 30)    # Note, not 31st Dec but 30th!
    delta = date1 - temp
    return float(delta.days) + (float(delta.seconds) / 86400)

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