Скачать или смотреть Efficiently Identify Non-Matching Names in Pandas Dataframes with str.contains()

Discover how to effectively use `str.contains()` in Pandas to filter DataFrames and identify names that don't match, helping you manage your datasets with ease.
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Efficiently Identify Non-Matching Names in Pandas Dataframes with str.contains()

Pandas is a powerful library in Python that allows data manipulation and analysis with ease. However, when working with different data sources, especially when those sources have inconsistencies or formatting differences, some common tasks can become quite challenging. One such task is identifying names in one DataFrame that do not match names in another DataFrame due to these inconsistencies. In this guide, we'll explore how to efficiently identify non-matching names using the str.contains() method and some additional set operations to simplify this process.

The Problem

Imagine you have two Pandas DataFrames: df1 containing full names and df2 containing names from a different source with potential formatting differences, such as dropping suffixes or using nicknames instead of formal names. For example:

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

In this scenario, you want to filter df1 to only include people whose names exist in df2. However, due to formatting discrepancies, you also wish to gather names from df2 that do not successfully match any names in df1. While you could achieve this using a looping technique, there is a more efficient and elegant way to complete this task.

The Solution

Step 1: Filtering Names Using str.contains()

To filter df1 based on the names present in df2, you can utilize the following line of code:

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

This effectively filters out rows from df1 where the names do not match any of those in df2, addressing the suffix and nickname issues. However, this doesn't yet help us with finding names that remain unmatched.

Step 2: Identifying Non-Matching Names

To gather names from df2 that did not match any names in df1, we can leverage the power of set operations along with the extractall() method as follows:

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

Breakdown of the Code:

set(df2['name']): Converts the names from df2 into a set for easy comparison.

df1['name'].str.extractall(f"({'|'.join(df2['name'])})"): Uses extractall() to extract all matching instances of names from df1 based on the patterns from df2.

.difference(...): Calculates the difference between the sets, thereby identifying the names in df2 that don’t have matches in df1.

Example Output

Implementing the code above would provide you with an output similar to this:

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

You can also view all matched terms by using the following code snippet:

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

This will give you an insight into which names from df2 were successfully matched with names from df1.

Conclusion

When working with datasets containing names that may have source-specific formatting disparities, leveraging Pandas' string methods can significantly reduce the complexity of your analysis. By using str.contains() for filtering and set operations to identify unmatched names, you can efficiently manage data between different DataFrames. This approach not only saves time but also enhances the elegance of your code, allowing you to focus on further data analysis and insights.

Feel free to implement these strategies in your own data analysis workflows and see how they can simplify your tasks!