Adding and removing columns in Data Analysis Python - Mini Project: Build & Apply

Adding and removing columns

📖 Scenario: You work in a small grocery store. You have a table of products with their prices and quantities. You want to add a new column for the total value of each product (price times quantity). Later, you decide to remove the quantity column to keep the table simple.

🎯 Goal: Create a data table using a dictionary, add a new column for total value, then remove the quantity column, and finally display the updated table.

📋 What You'll Learn

Create a dictionary called products with keys 'Name', 'Price', and 'Quantity' and the exact values specified.

Create a new column 'TotalValue' by multiplying Price and Quantity for each product.

Remove the 'Quantity' column from the dictionary.

Print the final products dictionary.

💡 Why This Matters

🌍 Real World

Stores and businesses often keep product data in tables. Adding and removing columns helps update the data as needed.

💼 Career

Data analysts and scientists frequently manipulate tables by adding calculated columns and cleaning data by removing unnecessary columns.

1

Create the initial products dictionary

Create a dictionary called products with these exact keys and values: 'Name' with the list ['Apple', 'Banana', 'Carrot'], 'Price' with the list [0.5, 0.3, 0.2], and 'Quantity' with the list [10, 20, 15].

Data Analysis Python

# Create the products dictionary with Name, Price, and Quantity
# Your code here

Hint

Use a dictionary with keys 'Name', 'Price', and 'Quantity'. Each key should have a list of values.

2

Add a new column for total value

Create a new key 'TotalValue' in the products dictionary. Set its value to a list where each element is the product of the corresponding Price and Quantity values.

Data Analysis Python

products = {
    'Name': ['Apple', 'Banana', 'Carrot'],
    'Price': [0.5, 0.3, 0.2],
    'Quantity': [10, 20, 15]
}
# Add the TotalValue column by multiplying Price and Quantity
# Your code here

Hint

Use a list comprehension with zip(products['Price'], products['Quantity']) to multiply each pair.

3

Remove the Quantity column

Remove the 'Quantity' key and its values from the products dictionary using the del statement.

Data Analysis Python

products = {
    'Name': ['Apple', 'Banana', 'Carrot'],
    'Price': [0.5, 0.3, 0.2],
    'Quantity': [10, 20, 15]
}
products['TotalValue'] = [p * q for p, q in zip(products['Price'], products['Quantity'])]
# Remove the Quantity column from products
# Your code here

Hint

Use del products['Quantity'] to remove the column.

4

Print the final products dictionary

Print the products dictionary to display the updated table with the 'Name', 'Price', and 'TotalValue' columns.

Data Analysis Python

products = {
    'Name': ['Apple', 'Banana', 'Carrot'],
    'Price': [0.5, 0.3, 0.2],
    'Quantity': [10, 20, 15]
}
products['TotalValue'] = [p * q for p, q in zip(products['Price'], products['Quantity'])]
del products['Quantity']
# Print the products dictionary
# Your code here

Hint

Use print(products) to show the final dictionary.

Practice

1. What is the correct way to add a new column named 'age' with all values set to 30 in a pandas DataFrame df?

easy

A. df.append('age', 30)

B. df.add_column('age', 30)

C. df['age'] = 30

D. df.insert_column('age', 30)

Solution

1. Step 1: Understand how to add columns in pandas

   In pandas, you add a new column by assigning a value or list to df['column_name'].

2. Step 2: Apply the correct syntax to add a column named 'age'

   Using df['age'] = 30 adds a new column 'age' with all values set to 30.

3. Final Answer:

   df['age'] = 30 -> Option C

4. Quick Check:

   Adding column = df['new_col'] = value [OK]

Hint: Use df['col_name'] = value to add columns fast [OK]

Common Mistakes:

• Using non-existent methods like add_column or insert_column
• Trying to use append which is for rows, not columns
• Forgetting to use square brackets for column name

2. Which of the following is the correct syntax to remove a column named 'salary' from a pandas DataFrame df?

easy

A. df.drop('salary', axis=1)

B. df.remove('salary')

C. df.delete_column('salary')

D. df.drop_column('salary')

Solution

1. Step 1: Recall pandas method to remove columns

   The pandas method to remove columns is drop() with axis=1 to specify columns.

2. Step 2: Use correct syntax to drop 'salary' column

   Using df.drop('salary', axis=1) removes the 'salary' column from the DataFrame.

3. Final Answer:

   df.drop('salary', axis=1) -> Option A

4. Quick Check:

   Remove column = df.drop('col', axis=1) [OK]

Hint: Use df.drop('col', axis=1) to remove columns [OK]

Common Mistakes:

• Using methods like remove or delete_column which don't exist
• Forgetting axis=1 to specify column removal
• Using drop without parentheses or wrong parameters

3. Given the DataFrame df below:

   name  score
0  Amy     90
1  Bob     80
2  Cara    85

What will be the output of this code?

df['passed'] = df['score'] >= 85
print(df)

medium

A. name score passed
0 Amy 90 False
1 Bob 80 True
2 Cara 85 False

B. name score passed
0 Amy 90 True
1 Bob 80 False
2 Cara 85 True

C. name score
0 Amy 90
1 Bob 80
2 Cara 85

D. name score passed
0 Amy 90 90
1 Bob 80 80
2 Cara 85 85

Solution

1. Step 1: Understand the condition applied to 'score'

   The expression df['score'] >= 85 creates a boolean series: True where score is 85 or more, False otherwise.

2. Step 2: Assign boolean series to new column 'passed'

   Adding df['passed'] = ... adds this boolean series as a new column.

3. Final Answer:

   DataFrame with 'passed' column showing True for scores >= 85 -> Option B

4. Quick Check:

   Boolean condition adds True/False column [OK]

Hint: Compare column with condition to add True/False column [OK]

Common Mistakes:

• Expecting numeric values instead of booleans in new column
• Not using >= operator correctly
• Printing df before adding the new column

4. What is wrong with this code snippet to remove the column 'height' from DataFrame df?

df.drop('height')

medium

A. Missing axis=1 to specify column removal

B. The column name should be in a list like ['height']

C. The method drop does not exist in pandas

D. You must assign the result back to df like df = df.drop('height')

Solution

1. Step 1: Check the drop method parameters

   By default, drop() removes rows. To remove columns, you must specify axis=1.

2. Step 2: Identify the missing axis parameter

   The code misses axis=1, so it tries to remove a row named 'height', causing an error if no such row exists.

3. Final Answer:

   Missing axis=1 to specify column removal -> Option A

4. Quick Check:

   drop columns = drop('col', axis=1) [OK]

Hint: Always add axis=1 to drop columns, not rows [OK]

Common Mistakes:

• Forgetting axis=1 causes wrong row removal or error
• Thinking drop modifies df inplace without assignment
• Using wrong data type for column name

5. You have a DataFrame df with columns ['A', 'B', 'C']. You want to create a new column 'D' which is the sum of 'A' and 'B', then remove column 'C'. Which code correctly does this in one step?

hard

A. df['D'] = df['A'] + df['B']
df.remove('C')

B. df['D'] = df['A'] + df['B']
df.drop('C', axis=1, inplace=True)

C. df['D'] = df['A'] + df['B']
df = df.drop('C', axis=1)

D. df = df.assign(D=df['A'] + df['B']).drop('C', axis=1)

Solution

1. Step 1: Create new column 'D' as sum of 'A' and 'B'

   Using assign() allows adding a new column in a chainable way without modifying df inplace.

2. Step 2: Remove column 'C' using drop() chained after assign()

   Chaining drop('C', axis=1) removes column 'C' and returns the modified DataFrame.

3. Step 3: Assign the result back to df

   Assigning the chained operations back to df updates the DataFrame correctly in one step.

4. Final Answer:

   df = df.assign(D=df['A'] + df['B']).drop('C', axis=1) -> Option D

5. Quick Check:

   Use assign + drop chained and assign back [OK]

Hint: Chain assign and drop, then assign back to df [OK]

Common Mistakes:

• Using drop without axis=1 or inplace causing no change
• Using non-existent remove method
• Not assigning chained result back to df