How Can Pandas Efficiently Convert Excel-Style Dates to DateTime Objects?

Converting Excel Style Date with Pandas

Many data sources, including XML files, may store dates in the Excel style date format, represented as floating-point numbers. These numbers represent the number of days since a specified base date, typically either January 1, 1900, or December 30, 1899. Converting these numbers into regular datetime objects can be a common challenge.

Pandas Datetime Conversion

Pandas provides a powerful solution for converting Excel style dates. By utilizing the pandas.TimedeltaIndex and pandas.DataFrame methods, you can seamlessly transform these numbers into readable datetime values.

Implementation

The following code snippet demonstrates the conversion process:

import datetime as dt
import pandas as pd

# Create a DataFrame with an 'date' column containing Excel style dates
df = pd.DataFrame({'date': [42580.3333333333, 10023]})

# Construct a TimedeltaIndex from the dates and add it to a datetime object
df['real_date'] = pd.TimedeltaIndex(df['date'], unit='d') + dt.datetime(1900, 1, 1)

In this example, the TimedeltaIndex is constructed using the unit='d' parameter, indicating that the numbers represent days. The default base date is January 1, 1900.

Additional Considerations

Some Excel applications may use a different base date, such as December 30, 1899. In such cases, you can specify the desired base date in the datetime constructor.

# Specify base date as December 30, 1899
df['real_date'] = pd.TimedeltaIndex(df['date'], unit='d') + dt.datetime(1899, 12, 30)

By employing Pandas' date manipulation capabilities, you can efficiently and accurately convert Excel style dates into datetime objects for further data analysis and processing.

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