🐍Adding, updating, and deleting columns of data frame with pandas

In Pandas, DataFrames are like tables where columns hold specific attributes. Often, we need to add new columns, update existing ones, or delete unwanted columns. Let’s explore how to do these operations effectively.

🔹 Adding New Columns

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie", "David"],
    "Age": [25, 30, 35, 28],
    "Salary": [50000, 60000, 75000, 58000]
}
df = pd.DataFrame(data)

# Add a new column with fixed value
df["Country"] = "USA"

# Add a column based on calculation
df["Bonus"] = df["Salary"] * 0.1

print(df)
    

✅ You can add columns with either a fixed value or using existing columns.

🔹 Updating Columns

# Update all values in a column
df["Country"] = "UK"

# Update specific rows based on condition
df.loc[df["Age"] > 30, "Bonus"] = df["Salary"] * 0.2

print(df)
    

✅ Use .loc to conditionally update rows in a column.

🔹 Deleting Columns

# Delete a column permanently
df.drop("Bonus", axis=1, inplace=True)

# Delete multiple columns
df.drop(["Age", "Country"], axis=1, inplace=True)

print(df)
    

✅ Use axis=1 to delete columns. Setting inplace=True modifies the DataFrame directly.

⚠️ Common Mistakes

• Forgetting axis=1 when dropping columns (default is axis=0 which drops rows).
• Not using inplace=True or reassigning to df after drop (changes won’t be saved otherwise).
• Misspelling column names — Pandas is case-sensitive.
• Updating without .loc for conditional changes, which may give unexpected results.

Next Steps

This completes the basics of DataFrame operations. Next, we will learn about handling missing data in Pandas.

➡️ Next: Data cleaning and Handling Missing Data

⬅️ Back to filtering data with Pandas

🏠 Back to Pandas main page

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