How to Time an Unexpected Bug to Rise on a Specific Month

In the world of software development, encountering bugs is an inevitable part of the process. Some bugs are predictable and can be tackled with routine testing, while others surface unexpectedly, causing delays and frustration. Interestingly, there are scenarios where specific bugs may arise due to the peculiarities of date and time handling in programming languages. In this blog post, we'll explore a real-life example involving the pandas library in Python, and how an unexpected bug related to date parsing was triggered by a specific month format.

The Setup

Imagine you're working on a cron job that parses futuristic dates around 2-3 months from today. For example, you're parsing data for upcoming events. Here’s how your code should look like:

import pandas as pd

df = pd.DataFrame([
  {"date": "May-16-2024", "event_type": "...", "event_name": "...", "event_description": "..."},
  {"date": "May-17-2024", "event_type": "...", "event_name": "...", "event_description": "..."},
  {"date": "May-24-2024", "event_type": "...", "event_name": "...", "event_description": "..."},
  {"date": "May-27-2024", "event_type": "...", "event_name": "...", "event_description": "..."},
  {"date": "Jun-05-2024", "event_type": "...", "event_name": "...", "event_description": "..."}
])

df["date"] = pd.to_datetime(df["date"])

The Unexpected Bug

When running the above code, you expect pandas to seamlessly convert your date strings into datetime objects. However, instead, you encounter an error:

ValueError: time data "Jun-05-2024" doesn't match format "%B-%d-%Y", at position 4. You might want to try:
    - passing `format` if your strings have a consistent format;
    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;
    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.

What just happened? The issue lies in how pandas infers the date format based on the first element of your data. The first element "May-16-2024" is interpreted as having a full month name ("%B-%d-%Y"), and pandas expects the same format for the subsequent dates. This works for "May-16-2024" to "May-27-2024" since "May" has the same abbreviated and full form. However, it fails when it encounters "Jun-05-2024" because "Jun" is an abbreviation.

Timing the Bug

We can strategically make this bug rise during the whole month of May. This is due to how pandas infers date formats. If the first date in the dataset falls within May, pandas interprets the format as full month names ("%B-%d-%Y"). Since "May" is the same in both full and abbreviated forms, it continues to parse subsequent May dates without issues. However, as soon as it hits a date with an abbreviated month (e.g., "Jun"), it raises an error.

Handling the Bug

To handle this issue, you can specify the format parameter in pd.to_datetime to correctly parse the dates. While pandas provides a workaround using format='mixed', being explicit about the format can save you from unexpected errors:

df["date"] = pd.to_datetime(df["date"], format='%b-%d-%Y')

By specifying '%b-%d-%Y', you indicate that the month names are abbreviated, ensuring pandas correctly parses the dates.

Conclusion

Timing an unexpected bug to rise on a specific month might sound like an odd goal, but understanding how and why certain bugs appear can help you better manage your data parsing tasks. In this case, knowing how pandas interprets date formats can save you from a lot of headaches.

Always be aware of the format of your date strings and specify the format explicitly when using pd.to_datetime to avoid unexpected errors. This practice will ensure your code runs smoothly regardless of the peculiarities of your dataset.

Happy coding!