Скачать или смотреть How to Extract Company Names from a String in Python

Learn how to easily extract company names or capitalized names from strings in Python using regular expressions and pandas.
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Extracting Company Names from Strings in Python

In the world of programming, particularly when dealing with strings, it's common to face challenges that require specific solutions. One such problem is extracting substrings from a longer string, especially when the substring follows certain structural rules – for example, only containing capital letters at the end of the string. If you've ever needed to extract company names or similar identifiers from text, you're in the right place. Let’s tackle this together!

The Problem

Imagine you have several text strings that include transfer details, such as:

Transfer to account IBAN NL10FRGS000000 FAKE COMPANY LTD

Transfer to account IBAN NL10FRGS000000 FAKE-COMPANY 22 LTD

Transfer -1234 received IBAN 00000 JOHN SMITH

In these strings, the company name can always be found at the end. What makes it tricky is that the name can include spaces and hyphens, while also sometimes being a person's name written in all capital letters. Here’s a quick rundown of what we need to achieve:

Extract Capitalized Names: Which can either be company names ending in "LTD" or "CO," and these names may contain hyphens.

Ignore Other Formats: Like regular sentences that don’t fit the structure defined above.

The Solution

To tackle this problem, we'll rely on Python's re module for regular expressions. Here's how to break down the solution:

Step 1: Define Your Strings

First, we'll define the strings from which we want to extract information. We'll set up an example list for demonstration:

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

Step 2: Create a Regular Expression

Next, we need to craft a regex pattern to find our target substrings within the text. Here's the pattern we'll use:

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

Breaking it Down:

[A-Z]{2}: Matches two uppercase letters.

[0-9]{2}: Matches two digits.

[A-Z0-9]{1,30}: Matches one to thirty alphanumeric characters that follow (this mimics the IBAN structure).

\s(.*$): Captures everything after the IBAN until the end of the line.

Step 3: Use Regex to Extract the Company Names

You can extract the matching parts using Python's re library. Here's how:

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

When you run this code, the output will look like this:

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Note: The last entry did not match because there is no valid IBAN pattern in the string provided.

Using Pandas to Simplify the Process

If your transfers are located in a pandas DataFrame, you can streamline the extraction process without needing to loop through each string explicitly. Here's how:

Define your DataFrame:

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

Use regex for extraction directly on a DataFrame column:

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

The output would show:

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

Adding More Data

You can also modify the regex pattern to capture the IBAN along with the company name simultaneously:

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

This will yield an output like:

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

Conclusion

Extracting specific capitalized substrings from a string can be a powerful tool in data retrieval and processing. By using regular expressions in Python, you can quickly pinpoint what you need, whether it’s a company name or a person’s name. The examples shared above can be adapted to various scenarios, so don’t hesitate to explore further!

If you found this guide helpful, consider sharing it with fellow programmers or saving it for your future reference! Happy coding!