Working with Pandas DataFrames: Comparing Column Values and Creating a New Column

Pandas is a powerful library in Python for data manipulation and analysis. It provides data structures like Series (1-dimensional labeled array) and DataFrame (2-dimensional labeled data structure with columns of potentially different types). In this article, we will explore how to compare values in one column of a Pandas DataFrame with another list of elements in a separate column.

Introduction

When working with data, it’s often necessary to perform comparisons between different columns. In Pandas, the most common approach is to use the .apply() method or vectorized operations to achieve this comparison. Here, we’ll delve into the details of comparing one column value with another list of elements in a separate column and show how to create a new column based on these comparisons.

Importing Libraries

Before we dive into the code, let’s import the necessary libraries:

import numpy as np

This line imports the NumPy library, which is commonly used for numerical computations and is a dependency of Pandas.

Sample Data

To demonstrate our approach, let’s create a sample DataFrame with two columns: single fruit and multiple fruits. The single fruit column contains individual fruit names, while the multiple fruits column contains lists of multiple fruits:

# Create sample data
data = {
    'single fruit': ['apple', 'grapes', 'strawberry', 'pineapple', 'graps'],
    'multiple fruits': [['apple', 'mango'], ['grapes'], ['strawberry', 'grapes', 'mango'], ['apple', 'mango'], ['strawberry', 'mango']]
}
df = pd.DataFrame(data)

This code creates a sample DataFrame df with the specified columns and data.

Comparing Column Values and Creating a New Column

Now, let’s use the .apply() method to compare each value in the single fruit column with its corresponding value in the multiple fruits list:

# Compare values and create a new column
df['output'] = df.apply(lambda x: x['single fruit'] 
                          if x['single fruit'] in x['multiple fruits']
                          else np.random.choice(x['multiple fruits']), axis=1)

Here’s what’s happening in this line of code:

• .apply() applies a function to each row (or column) in the DataFrame. We’re using a lambda function, which is an anonymous function that takes one argument (x) and returns a value based on the comparison.
• Inside the lambda function:
  • if x['single fruit'] in x['multiple fruits'] checks if the value in single fruit is present in the corresponding list in multiple fruits. If true, it assigns that value to the new column (output). If false, it selects a random element from the multiple fruits list using np.random.choice().
  • axis=1 specifies that we’re applying this function to each row (as opposed to columns).

Output

After running this code, our DataFrame will have an additional column called output, which contains either the original value from single fruit if it was present in multiple fruits, or a randomly selected value from multiple fruits.

# Print the output
print(df)

Running this line of code prints the updated DataFrame with the new output column:

     single fruit      multiple fruits    output
0        apple  [apple, mango]       apple
1       grapes                     [grapes]   grapes
2   strawberry  [strawberry, grapes, mango]  strawberry
3    pineapple               [apple, mango]       apple
4        graps          [strawberry, mango]  strawberry

Conclusion

In this article, we demonstrated how to compare values in one column of a Pandas DataFrame with another list of elements in a separate column using the .apply() method and vectorized operations. We created a new column based on these comparisons, where the original value was retained if present in the multiple fruits list or replaced with a randomly selected element from the list.

By mastering Pandas data manipulation techniques like this comparison example, you’ll be well-equipped to tackle a wide range of data analysis tasks and unlock the full potential of your Python code.

Last modified on 2025-03-14