Working with Microsecond Resolution in pandas to_excel

In recent versions of the popular Python data science library, pandas, users have been able to store datetime objects with microsecond resolution. However, when writing these objects to an Excel file using the to_excel() method, the resulting Excel files do not display the microsecond resolution as expected. In this article, we will explore the reasons behind this behavior and provide a solution that allows us to write pandas dataframes with microsecond resolution to Excel files without explicit conversion.

Understanding Microsecond Resolution in pandas

pandas provides a convenient way to work with datetime objects in Python. When creating a datetime object, you can specify the timezone and the resolution of the time component (e.g., seconds, milliseconds, microseconds). The pd.to_datetime() function automatically infers the resolution based on the input data.

In the case of microsecond resolution, pandas stores the datetime objects as follows:

Seconds: integer value representing the number of seconds
Milliseconds: 4-bit unsigned integer value representing the number of milliseconds in the range [0-999] to avoid overflows
Microseconds: 10-bit unsigned integer value representing the number of microseconds

The resulting datetime64[ns] object is stored as a 64-bit signed integer, where the lower bits represent the microsecond resolution.

Issues with Writing to Excel

When writing pandas dataframes to an Excel file using the to_excel() method, pandas truncates the datetime values to millisecond resolution (i.e., four decimal places). This issue persists in both .xls and .xlsx formats.

The reason for this behavior is due to how Excel handles date and time formatting. When writing a datetime object to an Excel file, pandas converts it to an integer value representing the number of days since January 1st, 1900 (also known as the 1900-01-01 epoch). This value is then stored in the Excel file.

Using the `date_format` Parameter

In order to write pandas dataframes with microsecond resolution to Excel files without explicit conversion, we can use the date_format parameter when creating an ExcelWriter object. Specifically, we can specify the date format as ‘hh:mm:ss.000’ which will display the datetime values in hours, minutes, and seconds with microseconds.

Here is an example of how to create an ExcelWriter object using this parameter:

import pandas as pd
from datetime import datetime

df = pd.DataFrame([datetime(2014, 2, 1, 12, 30, 5, 60000)])

writer = pd.ExcelWriter("time.xlsx", date_format='hh:mm:ss.000')

df.to_excel(writer, "Sheet1")

writer.close()

The Impact of the Excel Date Format Limitations

As mentioned earlier, the Excel file format has limitations when it comes to displaying microsecond resolution. In particular, the maximum value that can be displayed in the Excel date field is 2^24 - 1 milliseconds (approximately 17.9 million years).

Therefore, when writing datetime objects with microsecond resolution to an Excel file, we may encounter issues if the resulting integer values exceed this limit.

Avoiding Explicit Conversion

While using the date_format parameter provides a convenient solution for displaying microsecond resolution in pandas dataframes written to Excel files, there are scenarios where explicit conversion is necessary. For example, when working with legacy systems that require specific date and time formats.

In such cases, we can use the strftime() function to convert the datetime values to strings in the desired format:

import pandas as pd
from datetime import datetime

df = pd.DataFrame([datetime(2014, 2, 1, 12, 30, 5, 60000)])

# Convert datetime values to string with microseconds
df['date'] = df['time'].dt.strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]  # Remove milliseconds

writer = pd.ExcelWriter("time.xlsx")

df.to_excel(writer, "Sheet1")

writer.close()

In this example, the strftime() function is used to convert the datetime values to strings in the format ‘YYYY-MM-DD HH:MM:SS.mmmmmm’, where ‘mmm’ represents microseconds. The resulting string value is then stored in a new column called ‘date’.

Best Practices for Writing Microsecond Resolution Dataframes

When working with pandas dataframes containing microsecond resolution datetime objects, there are several best practices to keep in mind:

Use the date_format parameter when creating an ExcelWriter object to display datetime values in hours, minutes, and seconds with microseconds.
Be aware of Excel’s limitations regarding date and time formatting, particularly when dealing with microsecond resolution.
Consider using explicit conversion (e.g., strftime()) in scenarios where precise control over the date and time format is required.

By following these guidelines and leveraging pandas’ built-in functionality, you can effectively write dataframes with microsecond resolution to Excel files without the need for explicit conversion.

Last modified on 2025-02-15