Скачать или смотреть How to Get a Word Frequency Count Grouped by a Second Variable in Python Using Pandas

Discover how to efficiently calculate a `word frequency count` grouped by categories in Python. Learn to use Pandas for effective data manipulation and analysis.
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How to Get a Word Frequency Count Grouped by a Second Variable in Python Using Pandas

When working with data in Python, particularly with text analysis, you may encounter the need to get a word frequency count for different categories. If you're using Pandas, this task can be done efficiently with the right approach. In this guide, we will explore how to calculate the most common words per category in a dataset that contains a property description alongside its category.

Understanding the Problem

Suppose you have a dataset containing two columns: Property and Description. Each record in the Description field consists of various attributes about the property. The goal is to find out how often each word appears in the descriptions, grouped by each type of property. Here's a sample of what your data might look like:

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The problem is straightforward but requires some manipulations to achieve the desired output. Your current approach successfully counts the words on a general basis (e.g., "Blue = 3"), but you need to differentiate that "Blue" appears twice for Car and once for House.

Solution Approach

To conquer this problem, we need to leverage the capabilities of Pandas to manipulate our dataset efficiently. Below are the steps you can follow to achieve the desired output using Chained Operations in Pandas.

Step 1: Create the Dataframe

First, we create the Pandas DataFrame from the data provided.

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Step 2: Split and Explode

In this step, we split the Description string into a list of words. Then we use the explode function to transform each item in the list into its own row, associated with the respective property. This will help in counting the words later.

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Step 3: Group and Count

Finally, we group the dataset by Property and count the occurrences of each word. Using the value_counts() function will help in easily summarizing the frequency of each word under its respective category.

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Expected Output

Running the above code will give you the following output, showing the frequency of each word categorized by properties:

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This output clearly indicates how often each word appears within each property category.

Conclusion

Using Pandas, we can efficiently analyze and manipulate data to derive insights such as word frequencies grouped by categories. By following the steps outlined above, you can adapt this solution to cater to other similar requirements in your data analysis tasks.

Don't hesitate to experiment with more complex datasets and enjoy discovering the potential of Python for data manipulation!