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Convert time zone date column to timestamp format

I have column containing dates in format as seen here....
2021-09-02 06:00:10.474000+00:00
However, I need to convert this column into a 13 numbered timestamp.
I have tried...
df['date_timestamp'] = df[['date']].apply(lambda x: x[0].timestamp(), axis=1).astype(int)
...but this is not producing a 13 numbered timestamp, just 10 numbers instead.
How can get it to spit a 13 numbered timestamp?

you parse to datetime, take the int64 representation and divide that by 1e6 to get Unix time in milliseconds since the epoch (1970-01-01 UTC). Ex:
import numpy as np
import pandas as pd
# string to datetime
s = pd.to_datetime(["2021-09-02 06:00:10.474000+00:00"])
# datetime to Unix time in milliseconds
unix = s.view(np.int64)/1e6
print(unix[0])
# 1630562410473.9998
The standard int64 representation is nanoseconds; so divide by 1e3 if you need microseconds.

Converting Julian to calendar date using pandas

I am trying to convert Julian codes to calendar dates in pandas using :
pd.to_datetime(43390, unit = 'D', origin = 'Julian')
This is giving me ValueError: origin Julian cannot be converted to a Timestamp

You need to set origin = 'julian'
pd.to_datetime(43390, unit = 'D', origin = 'julian')
but this number (43390) throws
OutOfBoundsDatetime: 43390 is Out of Bounds for origin='julian'
because the bounds are from 2333836 to 2547339
(Timestamp('1677-09-21 12:00:00') to Timestamp('2262-04-11 12:00:00'))

Method 1 - using Julian for origin didn't work
Method 2 - using excel start date to calculate other dates. All other date values will be referenced from excel default start date.
Finally this worked for me.
pd.to_datetime(43390, unit = 'D', origin=pd.Timestamp("30-12-1899"))

Below code works only for 6 digit julian value. It also handles the calendar date for leap and non-leap years.
A Julian date is considered as "CYYDDD". Where C represents century, YY represents Year and DDD represents total days which are then further defined in Days and Months.
import pandas as pd
from datetime import datetime
jul_date = '120075'
add_days = int(jul_date[3:6])
cal_date = pd.to_datetime(datetime.strptime(str(19+int(jul_date[0:1]))+jul_date[1:3]+'-01-01','%Y-%m-%d'))-timedelta(1)+pd.DateOffset(days= add_days)
print(cal_date.strftime('%Y-%m-%d'))
output: 2020-03-15
without timedelta(1): 2020-03-16
Here datetime.strptime function is being used to cast date type from string to date.
%Y represents year in 4 digit (1980)
%m & %d represents month and day in digits.
strftime('%Y-%m-%d') is used to remove timestamp from the date.
timedelta(1) :- It's used to minus one day from the date because we've concatenated year with '01-01'. so when total no's of days being split to days and months, one day will not be extra.

Python convert from ordinal time with milliseconds [duplicate]

I just started moving from Matlab to Python 2.7 and I have some trouble reading my .mat-files. Time information is stored in Matlab's datenum format. For those who are not familiar with it:
A serial date number represents a calendar date as the number of days that has passed since a fixed base date. In MATLAB, serial date number 1 is January 1, 0000.
MATLAB also uses serial time to represent fractions of days beginning at midnight; for example, 6 p.m. equals 0.75 serial days. So the string '31-Oct-2003, 6:00 PM' in MATLAB is date number 731885.75.
(taken from the Matlab documentation)
I would like to convert this to Pythons time format and I found this tutorial. In short, the author states that
If you parse this using python's datetime.fromordinal(731965.04835648148) then the result might look reasonable [...]
(before any further conversions), which doesn't work for me, since datetime.fromordinal expects an integer:
>>> datetime.fromordinal(731965.04835648148)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: integer argument expected, got float
While I could just round them down for daily data, I actually need to import minutely time series. Does anyone have a solution for this problem? I would like to avoid reformatting my .mat files since there's a lot of them and my colleagues need to work with them as well.
If it helps, someone else asked for the other way round. Sadly, I'm too new to Python to really understand what is happening there.
/edit (2012-11-01): This has been fixed in the tutorial posted above.

You link to the solution, it has a small issue. It is this:
python_datetime = datetime.fromordinal(int(matlab_datenum)) + timedelta(days=matlab_datenum%1) - timedelta(days = 366)
a longer explanation can be found here

Using pandas, you can convert a whole array of datenum values with fractional parts:
import numpy as np
import pandas as pd
datenums = np.array([737125, 737124.8, 737124.6, 737124.4, 737124.2, 737124])
timestamps = pd.to_datetime(datenums-719529, unit='D')
The value 719529 is the datenum value of the Unix epoch start (1970-01-01), which is the default origin for pd.to_datetime().
I used the following Matlab code to set this up:
datenum('1970-01-01') % gives 719529
datenums = datenum('06-Mar-2018') - linspace(0,1,6) % test data
datestr(datenums) % human readable format

Just in case it's useful to others, here is a full example of loading time series data from a Matlab mat file, converting a vector of Matlab datenums to a list of datetime objects using carlosdc's answer (defined as a function), and then plotting as time series with Pandas:
from scipy.io import loadmat
import pandas as pd
import datetime as dt
import urllib
# In Matlab, I created this sample 20-day time series:
# t = datenum(2013,8,15,17,11,31) + [0:0.1:20];
# x = sin(t)
# y = cos(t)
# plot(t,x)
# datetick
# save sine.mat
urllib.urlretrieve('http://geoport.whoi.edu/data/sine.mat','sine.mat');
# If you don't use squeeze_me = True, then Pandas doesn't like
# the arrays in the dictionary, because they look like an arrays
# of 1-element arrays. squeeze_me=True fixes that.
mat_dict = loadmat('sine.mat',squeeze_me=True)
# make a new dictionary with just dependent variables we want
# (we handle the time variable separately, below)
my_dict = { k: mat_dict[k] for k in ['x','y']}
def matlab2datetime(matlab_datenum):
day = dt.datetime.fromordinal(int(matlab_datenum))
dayfrac = dt.timedelta(days=matlab_datenum%1) - dt.timedelta(days = 366)
return day + dayfrac
# convert Matlab variable "t" into list of python datetime objects
my_dict['date_time'] = [matlab2datetime(tval) for tval in mat_dict['t']]
# print df
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 201 entries, 2013-08-15 17:11:30.999997 to 2013-09-04 17:11:30.999997
Data columns (total 2 columns):
x 201 non-null values
y 201 non-null values
dtypes: float64(2)
# plot with Pandas
df = pd.DataFrame(my_dict)
df = df.set_index('date_time')
df.plot()

Here's a way to convert these using numpy.datetime64, rather than datetime.
origin = np.datetime64('0000-01-01', 'D') - np.timedelta64(1, 'D')
date = serdate * np.timedelta64(1, 'D') + origin
This works for serdate either a single integer or an integer array.

Just building on and adding to previous comments. The key is in the day counting as carried out by the method toordinal and constructor fromordinal in the class datetime and related subclasses. For example, from the Python Library Reference for 2.7, one reads that fromordinal
Return the date corresponding to the proleptic Gregorian ordinal, where January 1 of year 1 has ordinal 1. ValueError is raised unless 1 <= ordinal <= date.max.toordinal().
However, year 0 AD is still one (leap) year to count in, so there are still 366 days that need to be taken into account. (Leap year it was, like 2016 that is exactly 504 four-year cycles ago.)
These are two functions that I have been using for similar purposes:
import datetime
def datetime_pytom(d,t):
'''
Input
d Date as an instance of type datetime.date
t Time as an instance of type datetime.time
Output
The fractional day count since 0-Jan-0000 (proleptic ISO calendar)
This is the 'datenum' datatype in matlab
Notes on day counting
matlab: day one is 1 Jan 0000
python: day one is 1 Jan 0001
hence an increase of 366 days, for year 0 AD was a leap year
'''
dd = d.toordinal() + 366
tt = datetime.timedelta(hours=t.hour,minutes=t.minute,
seconds=t.second)
tt = datetime.timedelta.total_seconds(tt) / 86400
return dd + tt
def datetime_mtopy(datenum):
'''
Input
The fractional day count according to datenum datatype in matlab
Output
The date and time as a instance of type datetime in python
Notes on day counting
matlab: day one is 1 Jan 0000
python: day one is 1 Jan 0001
hence a reduction of 366 days, for year 0 AD was a leap year
'''
ii = datetime.datetime.fromordinal(int(datenum) - 366)
ff = datetime.timedelta(days=datenum%1)
return ii + ff
Hope this helps and happy to be corrected.

Improving the result of conversion of datenum to datetime

I have to convert a MATLAB's datenum to Python's datetime (e.g.2010-11-04 00:03:50.209589).
The datenum is represented in milliseconds and the date must be from 2010-11-04 00:00:00 to 2011-06-11 00:00:00.
The following code is as below:
matlab_datenum = 6.365057116950260162e+10
python_datetime = datetime.datetime.fromtimestamp(matlab_datenum / 1e3)
print (python_datetime)
The result is : 1972-01-07 16:42:51.169503
The result is wrong because the date must be from 2010-11-04 to 2011-06-11.
Do you have any idea how to correct the result ?
Thank you for your help

The datenum page in the Matlab documentation states:
The datenum function creates a numeric array that represents each point in time as the number of days from January 0, 0000.
Python's datetime module page states the following for fromtimestamp:
Return the local date corresponding to the POSIX timestamp
which is 00:00:00 1 January 1970
The two functions are counting from different start points and using different units (days and seconds), hence the discrepancy between your two dates.

How to convert the integer date format into YYYYMMDD?

Python and Matlab quite often have integer date representations as follows:
733828.0
733829.0
733832.0
733833.0
733834.0
733835.0
733836.0
733839.0
733840.0
733841.0
these numbers correspond to some dates this year. Do you guys know which function can convert them back to YYYYMMDD format?
thanks a million!

The datetime.datetime class can help you here. The following works, if those values are treated as integer days (you don't specify what they are).
>>> from datetime import datetime
>>> dt = datetime.fromordinal(733828)
>>> dt
datetime.datetime(2010, 2, 25, 0, 0)
>>> dt.strftime('%Y%m%d')
'20100225'
You show the values as floats, and the above doesn't take floats. If you can give more detail about what the data is (and where it comes from) it will be possible to give a more complete answer.

Since Python example was already demonstrated, here is the matlab one:
>> datestr(733828, 'yyyymmdd')
ans =
20090224
Also, note that while looking similar these are actually different things in Matlab and Python:
Matlab
A serial date number represents the whole and fractional number of days
from a specific date and time, where datenum('Jan-1-0000 00:00:00') returns
the number 1. (The year 0000 is merely a reference point and is not intended
to be interpreted as a real year in time.)
Python, datetime.date.fromordinal
Return the date corresponding to the proleptic Gregorian ordinal, where January 1 of year 1 has ordinal 1.
So they would differ by 366 days, which is apparently the length of the year 0.

Dates like 733828.0 are Rata Die dates, counted from January 1, 1 A.D. (and decimal fraction of days). They may be UTC or by your timezone.
Julian Dates, used mostly by astronomers, count the days (and decimal fraction of days) since January 1, 4713 BC Greenwich noon. Julian date is frequently confused with Ordinal date, which is the date count from January 1 of the current year (Feb 2 = ordinal day 33).
So datetime is calling these things ordinal dates, but I think this only makes sense locally, in the world of python.

Is 733828.0 a timestamp? If so, you can do the following:
import datetime as dt
dt.date.fromtimestamp(733828.0).strftime('%Y%m%d')
PS
I think Peter Hansen is right :)
I am not a native English speaker. Just trying to help. I don't quite know the difference between a timestamp and an ordinal :(