How to combine two columns of text in pandas dataframe?

Merging two text columns in a DataFrame is straightforward. Below are a couple of common methods:

1. Using the + Operator

import pandas as pd

df = pd.DataFrame({
    "first_name": ["Alice", "Bob", "Charlie"],
    "last_name": ["Smith", "Johnson", "Brown"]
})

# Combine first and last name with a space in between
df["full_name"] = df["first_name"] + " " + df["last_name"]
print(df)

• Explanation: df["first_name"] + " " + df["last_name"] merges the strings in each row, adding a space as a separator.
• Edge Cases:
  • If you have NaN values, you might get NaN in the result. You can handle missing values by filling them first or using str.cat() with na_rep="".

2. Using str.cat()

Another approach is the str.cat() method, which can be more flexible for dealing with missing values or multiple columns:

df["full_name"] = df["first_name"].str.cat(df["last_name"], sep=" ")
print(df)

• sep=" ": Defines the separator between the two columns.
• na_rep="": If you have NaN values, you can specify a replacement string to avoid getting NaN in your final results:

  df["full_name"] = df["first_name"].str.cat(df["last_name"], sep=" ", na_rep="")

Best Practices

1. Strip Extra Spaces
   Sometimes columns have leading or trailing whitespaces. You can apply .str.strip() before concatenation to ensure cleaner output.
2. Handling Missing Data
   If your columns contain NaN values, decide whether to fill them with a placeholder (like an empty string) or drop those rows.

Next Steps: Strengthen Your Python and Data Skills

If you’re looking to deepen your Python knowledge (including tips for working with strings, data structures, and best practices), consider Grokking Python Fundamentals by DesignGurus.io. This course helps you build a strong foundation in Python, ensuring you can tackle more advanced data manipulation challenges with ease.

By combining columns effectively, you’ll streamline your dataset for reports, analyses, or machine learning pipelines. Enjoy cleaner, more organized data!