How do you instantiate a datetime.timedelta to a number of seconds in python? For example, 43880.6543
Edit (as per clarification by OP): I have an Excel sheet, where a cell shows 43880.6543 (when the format is Generic) instead of a date. How do I convert this value to a datetime in python?
Just use
value = timedelta(seconds=43880.6543)
You can check the value is ok with
value.total_seconds()
Edit: If, in an Excel spreadsheet, you have a date expressed as a number, you can use python to convert that number into a datetime by doing this:
value = datetime(1900, 1, 1) + timedelta(days=43880.6543)
# value will be February 2nd, 2020 in the afternoon
Related
I have a db with dates in this format: 44378.097351.
If I put that in excel and choose the date format is says it is 01/07/2021 2:20:11, which is correct.
The problem is that now I need to do a plot with python with these dates and I extract the data directly from the db but I don't know how to convert it to an understandable date format.
Any suggestions?
Thank you
From Date systems in Excel, we can find how such a number was converted to a datetime.
The 1900 date system
In the 1900 date system, dates are calculated by using January 1, 1900, as a starting point. When you enter a date, it is converted into a serial number that represents the number of days elapsed since January 1, 1900. For example, if you enter July 5, 2011, Excel converts the date to the serial number 40729. This is the default date system in Excel for Windows, Excel 2016 for Mac, and Excel for Mac 2011. If you choose to convert the pasted data, Excel adjusts the underlying values, and the pasted dates match the dates that you copied.
from datetime import timedelta, datetime
dt = datetime(1900, 1, 1) + timedelta(44378.097351 - 2)
This format corresponds to the number of days since 01/01/1900.
you should check out this or this post to find an adequate solution ;)
Good Afternoon,
I have a huge dataset where time informations are stored as a float64 (or integer) in one column of the dataframe in format 'ddmmyyyy' (ex. 20 January 2020 would be the float 20012020.0). I need to convert it into a datetime like 'dd-mm-yyyy'. I saw the function to_datetime, but i can't really manage to obtain what i want. Does someone know how to do it?
Massimo
You could try converting to string and after that, to date format, you want like this:
# The first step is to change the type of the column,
# in order to get rid of the .0 we will change first to int and then to
# string
df["date"] = df["date"].astype(int)
df["date"] = df["date"].astype(str)
for i, row in df.iterrows():
# In case that the date format is similar to 20012020
x = str(df["date"].iloc[i])
if len(x) == 8:
df.at[i,'date'] = "{}-{}-{}".format(x[:2], x[2:4], x[4:])
# In case that the format is similar to 1012020
else:
df.at[i,'date'] = "0{}-{}-{}".format(x[0], x[1:3], x[3:])
Edit:
As you said this solution only works if the month always comes in 2
digits.
Added missing variable in the loop
Added change column types before entering the loop.
Let me know if this helps!
I've a datetime (int64) column in my pandas dataframe.
I'm trying to convert its value of 201903250428 to a datetime value.
The value i have for the datetime (int64) column is only till minute level with 24 hours format.
I tried various methods like striptime, to_datetime methods but no luck.
pd.datetime.strptime('201903250428','%y%m%d%H%M')
I get this error when i use the above code.
ValueError: unconverted data remains: 0428
I wanted this value to be converted to like '25-03-2019 04:28:00'
Lower-case y means two-digit years only, so this is trying to parse "20" as the year, 1 as the month, 9 the day, and 03:25 as the time, leaving "0428" unconverted.
You need to use %Y which will work fine:
pd.datetime.strptime('201903250428','%Y%m%d%H%M')
http://strftime.org/ is a handy reference for time formatting/parsing parameters.
This question already has an answer here:
convert numerical representation of date (excel format) to python date and time, then split them into two seperate dataframe columns in pandas
(1 answer)
Closed 4 years ago.
I have seen that excel identifies dates with specific serial numbers. For example :
09/07/2018 = 43290
10/07/2018 = 43291
I know that we use the DATEVALUE , VALUE and the TEXT functions to convert between these types.
But what is the logic behind this conversion? why 43290 for 09/07/2018 ?
Also , if I have a list of these dates in the number format in a dataframe (Python), how can I convert this number to the date format?
Similarly with time, I see decimal values in place of a regular time format. What is the logic behind these time conversions?
The following question that has been given in the comments is informative, but does not answer my question of the logic behind the conversion between Date and Text format :
convert numerical representation of date (excel format) to python date and time, then split them into two seperate dataframe columns in pandas
It is simply the number of days (or fraction of days, if talking about date and time) since January 1st 1900:
The DATEVALUE function converts a date that is stored as text to a
serial number that Excel recognizes as a date. For example, the
formula =DATEVALUE("1/1/2008") returns 39448, the serial number of the
date 1/1/2008. Remember, though, that your computer's system date
setting may cause the results of a DATEVALUE function to vary from
this example
...
Excel stores dates as sequential serial numbers so that they can be used in calculations. By default, January 1, 1900 is serial number 1, and January 1, 2008 is serial number 39448 because it is 39,447 days after January 1, 1900.
from DATEVALUE docs
if I have a list of these dates in the number format in a dataframe
(Python), how can I convert this number to the date format?
Since we know this number represents the number of days since 1/1/1900 it can be easily converted to a date:
from datetime import datetime, timedelta
day_number = 43290
print(datetime(1900, 1, 1) + timedelta(days=day_number - 2))
# 2018-07-09 00:00:00 ^ subtracting 2 because 1/1/1900 is
# "day 1", not "day 0"
However pd.read_excel should be able to handle this automatically.
I have only year parameter as input in the following manner:
2014,2015,2016
I want to convert each element from my list into python's datetime format. Is it possible to do this kind of things if the only given parameter is the year ?
Just set month and day manually to 1
from datetime import date
YearLst = [2014,2015,2016]
map(lambda t: date(t, 1, 1),YearLst)