How Can Pandas Efficiently Parse Excel-Style Dates?

Parsing Excel Style Dates with Pandas

When dealing with data sets, it's common to encounter dates formatted in the Excel style, where a floating-point number represents the number of days since a specific epoch date. Pandas provides a convenient way to convert these numbers into regular datetime objects, enabling seamless data manipulation and analysis.

In the case outlined in the provided content, the goal is to parse an XML file containing dates in Excel style, such as 42580.3333333333. To achieve this, Pandas offers a straightforward solution using TimedeltaIndex:

import pandas as pd
import datetime as dt

df = pd.DataFrame({'date': [42580.3333333333, 10023]})

df['real_date'] = pd.TimedeltaIndex(df['date'], unit='d') + dt.datetime(1900, 1, 1)

This code constructs a TimedeltaIndex from the float values and adds it to the scalar datetime for January 1, 1900, effectively converting the Excel dates to datetime objects.

However, it's important to note that Excel employs a slightly different epoch date than standard datetime objects, so the resulting dates may need to be adjusted accordingly. To account for this, the code can be modified as follows:

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

This ensures that Excel style dates are converted to the correct datetime values, enabling accurate data processing and analysis within the Pandas framework.

The above is the detailed content of How Can Pandas Efficiently Parse Excel-Style Dates?. For more information, please follow other related articles on the PHP Chinese website!