Creating new columns in Pandas - Try It Yourself

Creating new columns

📖 Scenario: You work in a small store that keeps track of products and their prices. You want to add new information to your data to help with sales.

🎯 Goal: Create a new column in a pandas DataFrame that shows the price after a 10% discount.

📋 What You'll Learn

Create a pandas DataFrame with product names and prices

Create a variable for the discount rate

Add a new column with discounted prices using the discount rate

Print the updated DataFrame

💡 Why This Matters

🌍 Real World

Stores often need to update prices with discounts or taxes to help customers and manage sales.

💼 Career

Data analysts and data scientists use pandas to prepare and analyze sales data, including creating new columns for calculations.

1

Create the initial DataFrame

Create a pandas DataFrame called products with two columns: 'Product' and 'Price'. The DataFrame should have these exact rows: 'Apple' with price 1.0, 'Banana' with price 0.5, and 'Cherry' with price 2.0.

Pandas

# Create the DataFrame called products with the given data
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of column values.

2

Set the discount rate

Create a variable called discount_rate and set it to 0.10 to represent a 10% discount.

Pandas

import pandas as pd

products = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Cherry'],
    'Price': [1.0, 0.5, 2.0]
})

# Create the discount_rate variable and set it to 0.10
# Your code here

Hint

Just assign 0.10 to the variable discount_rate.

3

Add a new column with discounted prices

Create a new column in the products DataFrame called 'Discounted Price'. Calculate it by subtracting the discount from the original 'Price' using the discount_rate variable.

Pandas

import pandas as pd

products = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Cherry'],
    'Price': [1.0, 0.5, 2.0]
})

discount_rate = 0.10

# Add the 'Discounted Price' column to products
# Your code here

Hint

Multiply the 'Price' column by (1 - discount_rate) to get the discounted price.

4

Print the updated DataFrame

Print the products DataFrame to show the new 'Discounted Price' column.

Pandas

import pandas as pd

products = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Cherry'],
    'Price': [1.0, 0.5, 2.0]
})

discount_rate = 0.10

products['Discounted Price'] = products['Price'] * (1 - discount_rate)

# Print the products DataFrame
# Your code here

Hint

Use print(products) to display the DataFrame.

Practice

1. What is the correct way to add a new column named age + 10 to a DataFrame df that adds 10 to the existing age column?

easy

A. df['age + 10'] == df['age'] + 10

B. df['age + 10'] = df['age'] + 10

C. df.add_column('age + 10', df['age'] + 10)

D. df.age + 10 = df.age + 10

Solution

1. Step 1: Understand how to create a new column in pandas

   In pandas, new columns are created by assigning a value to df['new_column_name'].

2. Step 2: Apply the addition operation to the existing column

   Adding 10 to the age column is done by df['age'] + 10. Assigning this to df['age + 10'] creates the new column.

3. Final Answer:

   df['age + 10'] = df['age'] + 10 -> Option B

4. Quick Check:

   New column assignment = df['age + 10'] = df['age'] + 10 [OK]

Hint: Use df['new_col'] = expression to add columns [OK]

Common Mistakes:

• Using double equals (==) instead of single equals (=) for assignment
• Trying to use a method like add_column which doesn't exist
• Using dot notation for new columns with spaces or special characters

2. Which of the following is the correct syntax to create a new column full_name by combining first_name and last_name columns with a space in between in DataFrame df?

easy

A. df['full_name'] = df['first_name'] + ' ' + df['last_name']

B. df['full_name'] = df['first_name'].append(' ').append(df['last_name'])

C. df['full_name'] = df['first_name'].join(' ', df['last_name'])

D. df['full_name'] = df['first_name'] & ' ' & df['last_name']

Solution

1. Step 1: Combine string columns with + operator

   In pandas, string columns can be combined using the + operator. To add a space, include ' ' between them.

2. Step 2: Assign the combined string to a new column

   Assign the result to df['full_name'] to create the new column.

3. Final Answer:

   df['full_name'] = df['first_name'] + ' ' + df['last_name'] -> Option A

4. Quick Check:

   String concat with + = df['full_name'] = df['first_name'] + ' ' + df['last_name'] [OK]

Hint: Use + to join string columns with spaces [OK]

Common Mistakes:

• Using append or join methods which don't work on pandas Series
• Using & operator which is for bitwise operations
• Forgetting to add space between names

3. Given the DataFrame df below:

   price  quantity
0    100         2
1    150         3
2    200         1

What will be the output of this code?

df['total'] = df['price'] * df['quantity']
df['total']

medium

A. [200, 450, 200]

B. [100, 150, 200]

C. [2, 3, 1]

D. [300, 450, 600]

Solution

1. Step 1: Multiply price and quantity element-wise

   Multiply each row's price by quantity: 100*2=200, 150*3=450, 200*1=200.

2. Step 2: Assign result to new column and output it

   The new column 'total' will have values [200, 450, 200]. Printing df['total'] shows this list.

3. Final Answer:

   [200, 450, 200] -> Option A

4. Quick Check:

   Element-wise multiply = [200, 450, 200] [OK]

Hint: Multiply columns element-wise for new column [OK]

Common Mistakes:

• Adding instead of multiplying columns
• Confusing row-wise with column-wise operations
• Expecting a scalar instead of a Series output

4. What is wrong with this code that tries to create a new column discounted_price by subtracting 10 from price in DataFrame df?

df.discounted_price = df['price'] - 10

medium

A. It will fail if the column name has spaces or special characters.

B. It will cause a syntax error because of dot notation.

C. It will create a new variable, not a DataFrame column.

D. It will create the column correctly.

Solution

1. Step 1: Understand dot notation for DataFrame columns

   Dot notation df.col = value works to create new columns only if 'col' is a valid Python identifier (no spaces, no special characters).

2. Step 2: Check the column name 'discounted_price'

   'discounted_price' consists of letters and underscores, which is valid. Therefore, the code creates the column correctly.

3. Final Answer:

   It will create the column correctly. -> Option D

4. Quick Check:

   Dot notation works for valid identifiers [OK]

Hint: Use df['col'] if column name has spaces or special chars [OK]

Common Mistakes:

• Thinking dot notation fails for valid column names
• Confusing DataFrame column assignment with local variable creation
• Believing underscores invalidate column names for dot notation

5. You have a DataFrame df with columns score and max_score. You want to create a new column percentage that shows the score as a percentage of max_score, but if max_score is zero, set percentage to None. Which code correctly does this?

hard

A. df['percentage'] = df['score'] / df['max_score'].replace(0, None) * 100

B. df.loc[df['max_score'] == 0, 'percentage'] = None
df['percentage'] = df['score'] / df['max_score'] * 100

C. df['percentage'] = df.apply(lambda row: row['score'] / row['max_score'] * 100 if row['max_score'] != 0 else None, axis=1)

D. df['percentage'] = df['score'] / df['max_score'] * 100
df['percentage'][df['max_score'] == 0] = None

Solution

1. Step 1: Use row-wise function to handle zero max_score

   Using df.apply with a lambda lets us check each row and apply the condition: if max_score is zero, return None; else calculate percentage.

2. Step 2: Assign the result to new column 'percentage'

   The lambda returns the correct value per row, and axis=1 ensures row-wise operation.

3. Final Answer:

   df['percentage'] = df.apply(lambda row: row['score'] / row['max_score'] * 100 if row['max_score'] != 0 else None, axis=1) -> Option C

4. Quick Check:

   Use apply with condition for safe division [OK]

Hint: Use apply with lambda for row-wise conditional columns [OK]

Common Mistakes:

• Replacing zero with None in Series causes errors
• Using chained indexing which raises warnings
• Not handling division by zero properly