Newbie Help with Datetime formats and numPy

[xlr8shentmx]

I am just getting started on my journey of learning Python and have hit a wall trying to replicate a scenario that I commonly achieve using SAS.

I am reading in a .csv file which contains "Start" and "Complete" dates for projects. Once read in, I need to simply need to calculate the number of business days between the two dates.

Output:
example csv file.
Title Start_Date Complete_Date
----- ---------- -------------
projTitle 1/19/20 1/26/20
projTitle2 2/11/20 2/15/20

Here is what I've done.

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#read list csv file parse_dates = ['Start_Date', 'Complete_Date'] df = pd.read_csv('sharepoint_requests_tst.csv', parse_dates=parse_dates)   #new column for bus day count df['TAT'] = np.busday_count(df['Start_Date'], df['Complete_Date'])   Error I get: TypeError: Iterator operand 0 dtype could not be cast from dtype('<M8[ns]') to dtype('<M8[D]') according to the rule 'safe'

What am I missing in this logic?

Thank you in advance for any help

Larz60+ write Dec-25-2020, 11:33 AM:
Please post all code, output and errors (it it's entirety) between their respective tags. Refer to BBCode help topic on how to post. Use the "Preview Post" button to make sure the code is presented as you expect before hitting the "Post Reply/Thread" button.
fixed for you this time, please use code tags on future posts.

[Mark17]

I'm new too and I ask much more than I answer at this point.

However, I was messing around with dates and datetimes today because my current project makes use of them.

Dates and datetimes are different. Whether you import datetime and/or date from datetime can make a difference. There's also timedelta, which may be imported (from datetime) and can be used to add/subtract dates to get resultant time intervals. You can change from datetime to integer by taking .day attribute of a datetime object, for example.

I would suggest playing around with all these things to find exactly what may suit your needs. I'm not sure parse_dates=parse_dates is enough to cover everything.

[palladium]

See this SO post:

https://stackoverflow.com/questions/3191...with-numba

Essentially pandas store all datetimes in datetime64[ns] format only (i.e. down to nanoseconds), but busday_count requires datetimes in datetime64[D] format.

One option is to convert the dates to datetime64[D] format and store it as a numpy array. Something like:

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import pandas as pd import numpy as np   df = pd.read_csv('test11.csv', parse_dates = [1,2]) start_dates = np.array(df['Start_Date'].values.astype('datetime64[D]')) complete_dates = np.array(df['Complete_Date'].values.astype('datetime64[D]')) df['TAT'] = np.busday_count(start_dates, complete_dates) print(df)

Output:
        Title Start_Date Complete_Date  TAT
0   projTitle 2020-01-19    2020-01-26    5
1  projTitle2 2020-02-11    2020-02-15    4