I need to calculate hour difference between two dates (format: year-month-dayTHH:MM:SS I could also potentially transform data format to (format: year-month-day HH:MM:SS) from huge excel file. What is the most efficient way to do it in Python? I have tried to use Datatime/Time object (TypeError: expected string or buffer), Timestamp (ValueError) and DataFrame (does not give hour result).
Excel File:
Order_Date Received_Customer Column3
2000-10-06T13:00:58 2000-11-06T13:00:58 1
2000-10-21T15:40:15 2000-12-27T10:09:29 2
2000-10-23T10:09:29 2000-10-26T10:09:29 3
..... ....
Datatime/Time object code (TypeError: expected string or buffer):
import pandas as pd
import time as t
data=pd.read_excel('/path/file.xlsx')
s1 = (data,['Order_Date'])
s2 = (data,['Received_Customer'])
s1Time = t.strptime(s1, "%Y:%m:%d:%H:%M:%S")
s2Time = t.strptime(s2, "%Y:%m:%d:%H:%M:%S")
deltaInHours = (t.mktime(s2Time) - t.mktime(s1Time))
print deltaInHours, "hours"
Timestamp (ValueError) code:
import pandas as pd
import datetime as dt
data=pd.read_excel('/path/file.xlsx')
df = pd.DataFrame(data,columns=['Order_Date','Received_Customer'])
df.to = [pd.Timestamp('Order_Date')]
df.fr = [pd.Timestamp('Received_Customer')]
(df.fr-df.to).astype('timedelta64[h]')
DataFrame (does not return the desired result)
import pandas as pd
data=pd.read_excel('/path/file.xlsx')
df = pd.DataFrame(data,columns=['Order_Date','Received_Customer'])
df['Order_Date'] = pd.to_datetime(df['Order_Date'])
df['Received_Customer'] = pd.to_datetime(df['Received_Customer'])
answer = df.dropna()['Order_Date'] - df.dropna()['Received_Customer']
answer.astype('timedelta64[h]')
print(answer)
Output:
0 24 days 16:38:07
1 0 days 00:00:00
2 20 days 12:39:52
dtype: timedelta64[ns]
Should be something like this:
0 592 hour
1 0 hour
2 492 hour
Is there another way to convert timedelta64[ns]
into hours than answer.astype('timedelta64[h]')
?