Converting Python Datetime Values to Excel-Style Numbers: A Guide
In this article, we'll explore how to convert Python datetime values into the numerical format used by Excel. This technique is essential when working with data that needs to be seamlessly integrated between Python and Excel.
Understanding the Difference
Excel represents dates and times as a single numerical value, with the integer portion representing the number of days since January 1, 1900, and the decimal portion representing the fraction of the day. This means that "10/21/2023 2:00:49 PM" in Excel translates to the numerical value 45220.583900463.
However, Python datetime objects store dates and times as a tuple of integers representing year, month, day, hour, minute, second, and microsecond. This format is different from Excel's numerical representation.
Converting Python Datetime to Excel-Style Numbers
There are several ways to convert Python datetime values to Excel-style numbers:
1. Using xlrd library
This method is especially useful when reading Excel files directly.
import xlrd
# Sample datetime string
datetime_str = "10/21/2023 2:00:49 PM"
# Convert datetime string to Excel date number
excel_date_number = xlrd.xldate_as_tuple(xlrd.xldate.xldate_from_datetime_tuple(datetime.datetime.strptime(datetime_str, '%m/%d/%Y %H:%M:%S %p')), 0)[0]
print(excel_date_number)
This code uses xlrd.xldate.xldate_from_datetime_tuple to convert the datetime object to an Excel date number.
2. Using datetime and timedelta objects
This approach calculates the number of days between the reference date (January 1, 1900) and the target date.
import datetime
# Sample datetime string
datetime_str = "10/21/2023 2:00:49 PM"
# Convert datetime string to datetime object
datetime_obj = datetime.datetime.strptime(datetime_str, '%m/%d/%Y %H:%M:%S %p')
# Calculate the difference between the target date and the reference date
timedelta = datetime_obj - datetime.datetime(1900, 1, 1)
# Calculate Excel date number
excel_date_number = timedelta.days + timedelta.seconds / (24 * 60 * 60)
print(excel_date_number)
This code first converts the datetime string to a datetime object. Then, it calculates the difference between the target date and the reference date, expressed in days and seconds. Finally, it converts the seconds into a fraction of a day and adds it to the total number of days.
3. Using pandas library
The pandas library provides convenient methods for working with dates.
import pandas as pd
# Sample datetime string
datetime_str = "10/21/2023 2:00:49 PM"
# Convert datetime string to pandas datetime object
datetime_obj = pd.to_datetime(datetime_str)
# Calculate Excel date number
excel_date_number = datetime_obj.to_excel()
print(excel_date_number)
This code leverages pandas.to_datetime to create a pandas datetime object from the datetime string. Then, it uses the to_excel method to directly obtain the Excel date number.
Addressing the Code Example
The original code snippet you provided tries to use the .timestamp() method, which gives the number of seconds since the epoch (January 1, 1970). This is not compatible with Excel's date system.
The provided solutions above offer robust and accurate methods to convert Python datetime values to Excel-style numbers. These techniques can be easily integrated into your existing Python code, ensuring seamless data exchange between Python and Excel.
Note: The code snippets provided in this article assume that your Excel workbook is using the 1900 date system. If you are using the 1904 date system, you will need to adjust the reference date accordingly.
