Extracting Matching Keywords from Two Columns in a Pandas DataFrame

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In this article, we will explore the process of extracting matching keywords from two columns in a pandas DataFrame. We will dive into the details of how to achieve this using various methods, including the use of string manipulation techniques and applying functions to individual rows or the entire DataFrame.

Introduction

Pandas is a powerful library used for data manipulation and analysis in Python. One of its key features is the ability to easily manipulate and process data in DataFrames, which are two-dimensional labeled data structures with columns of potentially different types. In this article, we will focus on extracting matching keywords from two columns in a pandas DataFrame.

Background

When working with text data, it’s common to encounter words or phrases that appear multiple times within the same column or between different columns. Extracting these matching keywords can be useful for various applications, such as sentiment analysis, topic modeling, or simply highlighting common themes within a dataset.

Method 1: Using Apply

The first method we will explore is using the apply function to extract matching keywords from individual rows in the DataFrame.

import pandas as pd

# Create a sample DataFrame
data = {
    "column1": ["A girl is going to market", "A girl is going to school", "The sky is blue in color"],
    "column2": ["girl market school", "girl market school", "sky blue orange color"]
}
df = pd.DataFrame(data)

# Use apply to extract matching keywords
def extract_keywords(row):
    return " ".join(i for i in row["column2"].split() if i in row["column1"])

# Apply the function to each row
df["column3"] = df.apply(extract_keywords, axis=1)

How It Works

In this example, we define a function extract_keywords that takes a row as input and returns a string containing matching keywords. The function splits both columns into individual words using the split() method and then uses a list comprehension to create a new list containing only the words that appear in both columns.

We then apply this function to each row in the DataFrame using the apply function, which applies the function to each row individually. The resulting list of matching keywords is assigned to a new column called “column3”.

Method 2: Using Pandas’ Str.contains

Another approach is to use pandas’ built-in string manipulation functions, such as str.contains, to extract matching keywords.

import pandas as pd

# Create a sample DataFrame
data = {
    "column1": ["A girl is going to market", "A girl is going to school", "The sky is blue in color"],
    "column2": ["girl market school", "girl market school", "sky blue orange color"]
}
df = pd.DataFrame(data)

# Use str.contains to extract matching keywords
df["column3"] = df.apply(lambda x: ", ".join(x["column2"].str.contains(x["column1"]).index), axis=1)

How It Works

In this example, we use the str.contains function on both columns and then apply the resulting boolean mask to extract matching keywords. The apply function is used again to apply the join method to create a comma-separated list of matching keywords.

Method 3: Using List Comprehensions

List comprehensions can also be used to simplify the code and make it more efficient.

import pandas as pd

# Create a sample DataFrame
data = {
    "column1": ["A girl is going to market", "A girl is going to school", "The sky is blue in color"],
    "column2": ["girl market school", "girl market school", "sky blue orange color"]
}
df = pd.DataFrame(data)

# Use list comprehensions to extract matching keywords
df["column3"] = df.apply(lambda x: ", ".join([i for i in x["column2"].split() if i in x["column1"]]), axis=1)

How It Works

In this example, we use a list comprehension to create a new list containing only the words that appear in both columns. The resulting list is then joined into a comma-separated string using the join method.

Comparison of Methods

Conclusion

Extracting matching keywords from two columns in a pandas DataFrame can be achieved using various methods, including the use of string manipulation techniques and applying functions to individual rows or the entire DataFrame. In this article, we explored three different approaches: using apply, Pandas’ built-in string manipulation functions, and list comprehensions.

While each method has its own advantages and disadvantages, choosing the right approach depends on the specific requirements of your project and your personal preference. By understanding how to extract matching keywords from two columns in a pandas DataFrame, you can unlock new insights and analyses in your data.

Additional Tips

• When working with text data, it’s essential to consider the nuances of string manipulation, such as handling punctuation, capitalization, and typos.
• Pandas provides various functions for string manipulation, including str.contains, str.split, and str.lower. Take advantage of these functions to simplify your code and improve performance.
• List comprehensions can be a powerful tool for creating concise and efficient code. Use them whenever possible to reduce clutter and improve readability.

By mastering the art of extracting matching keywords from two columns in a pandas DataFrame, you’ll become more proficient in data manipulation and analysis, and unlock new insights and opportunities in your work.

Last modified on 2024-05-27