Скачать или смотреть Filtering Correct Date and Time Values in a DataFrame using Regex

Learn how to filter and clean date and time values in a pandas DataFrame using regex and built-in functions to ensure data integrity.
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Filtering Correct Date and Time Values in a DataFrame using Regex

When working with data, one common challenge is dealing with improperly formatted dates and times. In particular, if your data comes in as strings, you may encounter invalid entries that need to be filtered out. This guide addresses how to cleanse such date and time values in a pandas DataFrame using regular expressions (regex) and data cleaning techniques.

The Problem

Imagine you have a DataFrame consisting of two columns: one for dates and another for times. The strings in these columns might look like this:

[[See Video to Reveal this Text or Code Snippet]]

As you can see, some of the entries do not conform to the expected formats:

Date: must match DDMMMYY

Time: must match HM (Hours and Minutes)

To solve the problem, we aim to exclude entries that violate these formats and also ensure that we filter out entries where:

The day exceeds 31

The hour exceeds 24

The minute exceeds 59

Solution Breakdown

Step 1: Extract Valid Date and Time Formats

First, we can use regex to filter out invalid formats for both the date and time:

[[See Video to Reveal this Text or Code Snippet]]

This code uses regex to capture valid formats:

\d{2}[a-zA-Z]{3}[0-9]{2} for dates (two digits, three letters, two digits)

^\d{4}$ for times (exactly four digits)

Step 2: Replace Invalid Values with NaN

Next, we need to check if the day and hour/minute constraints are not violated. If they are, we replace those invalid values with NaN:

[[See Video to Reveal this Text or Code Snippet]]

Result

After applying the above steps, the DataFrame should look like this:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

In this guide, we tackled how to filter and clean date and time strings in a pandas DataFrame using regex and pd.to_datetime. This not only helps in maintaining data integrity but also prepares our DataFrame for further analytical processing without causing errors due to invalid entries.

By following these steps, you can ensure that your date and time data remain accurate and usable for future data analysis tasks.

Remember, keeping your data clean is crucial for making informed decisions based on your analysis!